From 0ea83b436462b2d298f0754f3621575e0094c831 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Fri, 6 Mar 2026 14:55:45 +0800 Subject: [PATCH 001/196] feat(web): enable MCP market configuration and service management - Add market configuration API endpoints for creating, updating, and retrieving market configs - Add market MCP listing and detail endpoints with support for activated services - Implement MarketConfigModal component for configuring market connections with URL and API key - Implement McpServiceModal component for viewing and managing MCP services from markets - Add infinite scroll pagination for market sources and MCP services - Add market connection status indicators (connected/disconnected/connecting states) - Add i18n translations for market configuration UI (en and zh) - Update Market component to display market sources with connection management - Add MarketQuery type for market-specific API queries - Refactor market data structure to match backend API response format --- web/src/api/tools.ts | 42 +- web/src/i18n/en.ts | 15 +- web/src/i18n/zh.ts | 15 +- web/src/views/ToolManagement/Market.tsx | 535 ++++++++++++------ .../components/MarketConfigModal.tsx | 97 ++-- .../components/McpServiceModal.tsx | 8 +- web/src/views/ToolManagement/index.tsx | 4 +- web/src/views/ToolManagement/types.ts | 6 + 8 files changed, 515 insertions(+), 207 deletions(-) diff --git a/web/src/api/tools.ts b/web/src/api/tools.ts index b14905f8..2aed3f80 100644 --- a/web/src/api/tools.ts +++ b/web/src/api/tools.ts @@ -1,5 +1,5 @@ import { request } from '@/utils/request' -import type { Query, CustomToolItem, ExecuteData, MCPToolItem, InnerToolItem } from '@/views/ToolManagement/types' +import type { Query, MarketQuery, CustomToolItem, ExecuteData, MCPToolItem, InnerToolItem } from '@/views/ToolManagement/types' // 工具列表 export const getTools = (data: Query) => { @@ -33,4 +33,44 @@ export const getToolDetail = (tool_id: string) => { } export const getToolMethods = (tool_id: string) => { return request.get(`/tools/${tool_id}/methods`) +} + +// MCP市场列表 +export const getMarketTools = (data: Query) => { + return request.get('/mcp_markets/mcp_markets', data) +} +// 市场配置创建 +export const createMarketConfig = (values: { + mcp_market_id: string; + token: string; + status: number; +}) => { + return request.post('/mcp_market_configs/mcp_market_config', values) +} +// 市场配置更新 +export const updateMarketConfig = (values: { + mcp_market_config_id: string; + token: string; + status: number; +}) => { + return request.put(`/mcp_market_configs/${values.mcp_market_config_id}`, values) +} +// 市场根据id获取配置 +export const getMarketConfig = (mcp_market_id: string) => { + return request.get(`/mcp_market_configs/mcp_market_id/${mcp_market_id}`) +} +// 市场MCP列表 +export const getMarketMCPs = (data: MarketQuery) => { + return request.get('/mcp_market_configs/mcp_servers', data) +} +// 根据配置ID serverId 获取MCP服务详情 +export const getMarketMCPDetail = (data:{ + mcp_market_config_id: string; + server_id: string; +}) => { + return request.get(`/mcp_market_configs/mcp_server`,data) +} +// 市场已激活MCP列表 +export const getMarketMCPsActivated = (data: MarketQuery) => { + return request.get('/mcp_market_configs/operational_mcp_servers', data) } \ No newline at end of file diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index b9ab09b0..5b9504b4 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1948,7 +1948,20 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re path: 'Path', viewDetail: 'View Details', textLink: 'Test Connection', - noResult: 'Processing results will be displayed here' + noResult: 'Processing results will be displayed here', + + marketConfig: 'Configure {{name}}', + marketSaveAndConnect: 'Save & Connect', + marketUrl: 'Market URL', + marketUrlPlaceholder: 'Market URL', + marketCopy: 'Copy', + marketApiKeyOptional: 'Optional', + marketApiKeyExtra: 'Some markets require an API Key to access the full service list', + marketApiKeyPlaceholder: 'Enter API Key to access more services', + marketConnectionStatus: 'Connection Status', + marketConnected: '● Connected', + marketDisconnected: '○ Disconnected', + marketConnecting: 'Connecting to {{name}}...', }, workflow: { coreNode: 'Core Nodes', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 71a20207..6d836ce9 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1945,7 +1945,20 @@ export const zh = { path: '路径', viewDetail: '查看详情', textLink: '测试连接', - noResult: '处理结果将显示在这里' + noResult: '处理结果将显示在这里', + + marketConfig: '配置 {{name}}', + marketSaveAndConnect: '保存并连接', + marketUrl: '市场地址', + marketUrlPlaceholder: '市场地址', + marketCopy: '复制', + marketApiKeyOptional: '可选', + marketApiKeyExtra: '部分市场需要 API Key 才能获取完整的服务列表', + marketApiKeyPlaceholder: '输入 API Key 以获取更多服务', + marketConnectionStatus: '连接状态', + marketConnected: '● 已连接', + marketDisconnected: '○ 未连接', + marketConnecting: '正在连接 {{name}}...', }, workflow: { coreNode: '核心节点', diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index 59fbddcc..7a2df6df 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -1,122 +1,259 @@ -import React, { useState, useRef, type ReactNode } from 'react'; -import { Input, Button, Spin, App } from 'antd'; +import React, { useState, useRef, useEffect, useCallback, type ReactNode } from 'react'; +import { Input, Button, App, Card, Space, Skeleton, Tag } from 'antd'; import { SearchOutlined, SettingOutlined, GlobalOutlined, SyncOutlined } from '@ant-design/icons'; import { useTranslation } from 'react-i18next'; +import InfiniteScroll from 'react-infinite-scroll-component'; import MarketConfigModal, { type MarketConfigModalRef } from './components/MarketConfigModal'; - +import McpServiceModal from './components/McpServiceModal'; +import type { McpServiceModalRef } from './types'; +import { getMarketTools, getMarketConfig, getMarketMCPs, getMarketMCPDetail, getMarketMCPsActivated } from '@/api/tools'; interface MarketSource { id: string; name: string; category: string; - icon: string; + logo_url: string; url: string; - desc: string; - apiKey: string; + description: string; + api_key?: string; connected: boolean; - mcpCount: number; + mcp_count: number; + created_at?: number; + created_by?: string; } interface MarketMcp { id: string; name: string; - provider: string; - type: string; - desc: string; - downloads?: string; - stars?: string; - icon: string; - configTemplate: any; + chinese_name?: string; + description: string; + logo_url: string; + publisher: string; + categories?: string[]; + tags?: string[]; + view_count?: number; + activated?: boolean; + locales?: { + [lang: string]: { + name: string; + description: string; + }; + }; } interface MarketCategory { id: string; name: string; - icon: string; +} + +interface MarketApiResponse { + items: MarketSource[]; } const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => { - const { t } = useTranslation(); + const { t, i18n } = useTranslation(); const { message } = App.useApp(); + + const getLocaleField = (mcp: MarketMcp, field: 'name' | 'description') => { + const lang = i18n.language?.startsWith('zh') ? 'zh' : 'en'; + return mcp.locales?.[lang]?.[field] || mcp[field] || ''; + }; const [loading, setLoading] = useState(false); const [selectedSource, setSelectedSource] = useState(null); const marketConfigModalRef = useRef(null); - const [marketSources, setMarketSources] = useState([ - { id: 'smithery', name: 'Smithery', category: 'official', icon: '🔧', url: 'https://mcp.smithery.ai', desc: '官方 MCP 服务市场，提供丰富的 MCP 服务', apiKey: '', connected: false, mcpCount: 2847 }, - { id: 'mcpmarket', name: 'MCP Market', category: 'official', icon: '🏪', url: 'https://mcpmarket.com', desc: '综合性 MCP 市场平台', apiKey: '', connected: false, mcpCount: 1523 }, - { id: 'glama', name: 'Glama.ai MCP', category: 'official', icon: '✨', url: 'https://glama.ai/mcp', desc: 'Glama AI 提供的 MCP 服务集合', apiKey: '', connected: false, mcpCount: 892 }, - { id: 'github-mcp', name: 'modelcontextprotocol/servers', category: 'official', icon: '🐙', url: 'https://github.com/modelcontextprotocol/servers', desc: 'GitHub 官方 MCP 服务器仓库', apiKey: '', connected: true, mcpCount: 156 }, - { id: 'aliyun-bailian', name: '阿里云百炼 MCP', category: 'china-cloud', icon: '☁️', url: 'https://bailian.console.aliyun.com/mcp', desc: '阿里云百炼平台 MCP 市场', apiKey: '', connected: false, mcpCount: 423 }, - { id: 'modelscope', name: '魔搭社区 MCP', category: 'china-cloud', icon: '🎭', url: 'https://modelscope.cn/mcp', desc: '阿里达摩院魔搭社区 MCP 市场', apiKey: '', connected: false, mcpCount: 312 }, - ]); - - const [categories] = useState([ - { id: 'official', name: '官方/综合', icon: '🌐' }, - { id: 'china-cloud', name: '国内云', icon: '☁️' }, - { id: 'community', name: '社区/垂直', icon: '👥' } - ]); - - const [mcpCache, setMcpCache] = useState>({ - 'github-mcp': [ - { id: 'gh-1', name: 'Fetch', provider: 'modelcontextprotocol', type: 'Hosted', desc: '使用浏览器模拟大型语言模型检索和处理网页内容', downloads: '203.7m', stars: '308.2k', icon: '🌐', configTemplate: {} }, - { id: 'gh-2', name: 'Filesystem', provider: 'modelcontextprotocol', type: 'Local', desc: '安全的文件系统操作，支持读写文件和目录管理', downloads: '156.2m', stars: '245.1k', icon: '📁', configTemplate: {} }, - { id: 'gh-3', name: 'GitHub', provider: 'modelcontextprotocol', type: 'Hosted', desc: 'GitHub API 集成，支持仓库、Issue、PR 等操作', downloads: '89.4m', stars: '178.3k', icon: '🐙', configTemplate: {} }, - ] - }); - + const mcpServiceModalRef = useRef(null); + const [marketSources, setMarketSources] = useState([]); + const [categories, setCategories] = useState([]); + const [mcpCache, setMcpCache] = useState>({}); + const [mcpTotal, setMcpTotal] = useState(0); const [searchKeyword, setSearchKeyword] = useState(''); + const [configIdMap, setConfigIdMap] = useState>({}); + const [hasMore, setHasMore] = useState(false); + const [activatedMcps, setActivatedMcps] = useState([]); + const [currentPage, setCurrentPage] = useState(1); + const pageSize = 20; - const handleSelectSource = (sourceId: string) => { - setSelectedSource(sourceId); - }; - - const handleRefresh = (sourceId: string) => { - setLoading(true); - setTimeout(() => { - // 模拟刷新数据 - const source = marketSources.find(s => s.id === sourceId); - if (source) { - message.success(`${source.name} 列表已刷新`); + // 获取市场数据 + useEffect(() => { + const fetchMarketData = async () => { + setLoading(true); + try { + const response = await getMarketTools({}) as MarketApiResponse; + if (response?.items && Array.isArray(response.items)) { + setMarketSources(response.items); + + // 根据 category 字段分组 + const categoryMap = new Map(); + response.items.forEach(item => { + if (item.category && !categoryMap.has(item.category)) { + categoryMap.set(item.category, { + id: item.category, + name: item.category + }); + } + }); + + setCategories(Array.from(categoryMap.values())); + } + } catch (error) { + console.error('获取市场数据失败:', error); + message.error('获取市场数据失败'); + } finally { + setLoading(false); } + }; + + fetchMarketData(); + }, [message]); + + const fetchMcpList = async (sourceId: string, page = 1, append = false) => { + setLoading(true); + try { + let configId = configIdMap[sourceId]; + + // 如果没有缓存 configId，先获取配置 + if (!configId) { + const config: any = await getMarketConfig(sourceId); + if (config?.id) { + configId = config.id; + setConfigIdMap(prev => ({ ...prev, [sourceId]: configId })); + } else { + return; + } + } + + // 第一次加载时获取已激活列表 + let activatedIds: string[] = activatedMcps; + if (page === 1 && !append) { + const activatedRes: any = await getMarketMCPsActivated({ mcp_market_config_id: configId }); + if (activatedRes && Array.isArray(activatedRes)) { + activatedIds = activatedRes.map((item: any) => item.id); + setActivatedMcps(activatedIds); + } + } + + const res: any = await getMarketMCPs({ mcp_market_config_id: configId, page, pagesize: pageSize }); + if (res?.items && Array.isArray(res.items)) { + // 标记已激活的 MCP + const mcpsWithActivated = res.items.map((item: MarketMcp) => ({ + ...item, + activated: activatedIds.includes(item.id) + })); + + setMcpCache(prev => ({ + ...prev, + [sourceId]: append ? [...(prev[sourceId] || []), ...mcpsWithActivated] : mcpsWithActivated + })); + } + if (res?.page) { + setMcpTotal(res.page.total || 0); + setHasMore(!!res.page.has_next); + setCurrentPage(res.page.page || page); + } + } catch (error) { + console.error('获取 MCP 列表失败:', error); + } finally { setLoading(false); - }, 600); + } }; - const handleOpenConfig = (sourceId: string) => { + const loadMore = useCallback(() => { + if (!selectedSource || loading) return; + fetchMcpList(selectedSource, currentPage + 1, true); + }, [selectedSource, currentPage, loading]); + + const handleSelectSource = async (sourceId: string) => { + setSelectedSource(sourceId); + setSearchKeyword(''); + setCurrentPage(1); + setHasMore(false); + setMcpTotal(0); + + // 如果缓存中已有数据，直接使用 + if (mcpCache[sourceId]) return; + + await fetchMcpList(sourceId, 1); + }; + + const handleRefresh = async (sourceId: string) => { + // 清除缓存，重新从第一页加载 + setMcpCache(prev => { + const next = { ...prev }; + delete next[sourceId]; + return next; + }); + setCurrentPage(1); + await fetchMcpList(sourceId, 1); const source = marketSources.find(s => s.id === sourceId); if (source) { + message.success(`${source.name} 列表已刷新`); + } + }; + + const handleOpenConfig = async (sourceId: string) => { + const source = marketSources.find(s => s.id === sourceId); + if (!source) return; + try { + const config: any = await getMarketConfig(sourceId); + marketConfigModalRef.current?.handleOpen({ + ...source, + connected: config?.status === 1, + token: config?.token || '', + configId: config?.id || '', + }); + } catch { marketConfigModalRef.current?.handleOpen(source); } }; - const handleConnect = (sourceId: string, apiKey: string) => { - // 更新市场源状态 + const handleOpenMcpServiceModal = async (mcp: MarketMcp) => { + if (!selectedSource || !configIdMap[selectedSource]) return; + try { + const detail: any = await getMarketMCPDetail({ + mcp_market_config_id: configIdMap[selectedSource], + server_id: mcp.id, + }); + const toolItem = { + name: detail.name, + description: detail.description, + config_data: { + server_url: detail.servers?.[0]?.url || '', + connection_config: { + auth_type: 'none', + timeout: 30, + headers: {}, + }, + }, + }; + mcpServiceModalRef.current?.handleOpen(toolItem as any); + } catch (error) { + console.error('获取 MCP 服务详情失败:', error); + } + }; + + const handleConnect = async (sourceId: string, configId: string) => { + // 更新市场源状态，缓存 configId setMarketSources(prev => prev.map(source => { if (source.id === sourceId) { - return { - ...source, - apiKey, - connected: true - }; + return { ...source, connected: true }; } return source; })); + setConfigIdMap(prev => ({ ...prev, [sourceId]: configId })); - // 模拟获取MCP列表 - setTimeout(() => { - const source = marketSources.find(s => s.id === sourceId); - if (source && !mcpCache[sourceId]) { - // 生成模拟数据 - const mockData: MarketMcp[] = [ - { id: `${sourceId}-1`, name: `${source.name} 服务 1`, provider: source.name, type: 'Hosted', desc: `来自 ${source.name} 的 MCP 服务`, downloads: '10.2m', stars: '23.4k', icon: '🔧', configTemplate: {} }, - { id: `${sourceId}-2`, name: `${source.name} 服务 2`, provider: source.name, type: 'Local', desc: `来自 ${source.name} 的本地 MCP 服务`, downloads: '8.5m', stars: '18.7k', icon: '⚙️', configTemplate: {} } - ]; - setMcpCache(prev => ({ - ...prev, - [sourceId]: mockData - })); + // 用 configId 获取第一页 MCP 列表 + try { + const res: any = await getMarketMCPs({ mcp_market_config_id: configId, page: 1, pagesize: pageSize }); + if (res?.items && Array.isArray(res.items)) { + setMcpCache(prev => ({ ...prev, [sourceId]: res.items })); } - message.success(`已连接 ${source?.name}`); - }, 800); + if (res?.page) { + setMcpTotal(res.page.total || 0); + setHasMore(!!res.page.has_next); + setCurrentPage(1); + } + } catch (error) { + console.error('获取 MCP 列表失败:', error); + } }; const renderSourceDetail = () => { @@ -134,38 +271,45 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => if (!source) return null; const mcpList = mcpCache[selectedSource] || []; - const filteredList = mcpList.filter(mcp => - mcp.name.toLowerCase().includes(searchKeyword.toLowerCase()) || - mcp.desc.toLowerCase().includes(searchKeyword.toLowerCase()) - ); + const filteredList = mcpList.filter(mcp => { + const name = getLocaleField(mcp, 'name'); + const desc = getLocaleField(mcp, 'description'); + return name.toLowerCase().includes(searchKeyword.toLowerCase()) || + desc.toLowerCase().includes(searchKeyword.toLowerCase()); + }); return ( <> -
-
-
- {source.icon} +
+
+
+ {source.logo_url ? ( + {source.name} { + e.currentTarget.style.display = 'none'; + const parent = e.currentTarget.parentElement; + if (parent) { + parent.innerHTML = '🏪'; + parent.style.fontSize = '48px'; + } + }} + /> + ) : ( + 🏪 + )}
-
-

{source.name}

-

{source.desc}

+
+

{source.name}

+ 可用 MCP 服务 ({mcpTotal}) + {/*

{source.description}

*/}
-
- - -
-
-
-
-

- 可用 MCP 服务 ({mcpList.length}) -

+
{source.connected && (
+ +
+
+
{mcpList.length > 0 ? ( - -
- {filteredList.map(mcp => ( +
+ } + scrollableTarget="mcpScrollableDiv" + > +
+ {filteredList.map(mcp => (
-
- {mcp.icon} +
+ {mcp.logo_url ? ( + {getLocaleField(mcp, { + e.currentTarget.style.display = 'none'; + const parent = e.currentTarget.parentElement; + if (parent) { + parent.innerHTML = '🔧'; + parent.style.fontSize = '24px'; + } + }} + /> + ) : ( + 🔧 + )}
- - {mcp.type} - + {mcp.categories?.[0] && ( + + {mcp.categories[0]} + + )}
-

{mcp.name}

- {mcp.provider && ( +

{getLocaleField(mcp, 'name')}

+ {mcp.publisher && (
- @ {mcp.provider} + {mcp.publisher.startsWith('@') ? mcp.publisher : `@${mcp.publisher}`}
)} -

{mcp.desc}

+

{getLocaleField(mcp, 'description')}

- {mcp.downloads && ( + {mcp.view_count != null && ( - {mcp.downloads} - - )} - {mcp.stars && ( - - ⭐ {mcp.stars} + {mcp.view_count.toLocaleString()} )}
-
-
))} -
- +
+ +
) : (
{source.connected ? '📭' : '🔌'}
@@ -254,50 +425,76 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => }; return ( -
+
{/* 左侧市场源列表 */} -
-
- MCP 市场 -
- {categories.map(cat => ( -
-
- {cat.icon} - {cat.name} -
-
- {marketSources - .filter(s => s.category === cat.id) - .map(source => ( -
handleSelectSource(source.id)} - > - {source.icon} - - {source.name} - - - {source.mcpCount} - - {source.connected && ( - ● - )} -
- ))} -
-
- ))} +
+ + {categories.map(cat => ( + + {cat.name} +
+ } + classNames={{ + body: "rb:p-[10px]!", + header: "rb:bg-[#F6F8FC]!" + }} + > + + {marketSources + .filter(s => s.category === cat.id) + .map(source => ( +
handleSelectSource(source.id)} + > +
+ {source.logo_url ? ( + {source.name} { + e.currentTarget.style.display = 'none'; + const parent = e.currentTarget.parentElement; + if (parent) { + parent.innerHTML = '🏪'; + parent.style.fontSize = '16px'; + } + }} + /> + ) : ( + 🏪 + )} +
+ + {source.name} + + + {source.mcp_count} + + {source.connected && ( + ● + )} +
+ ))} + + + ))} +
{/* 右侧内容区 */} -
+
{renderSourceDetail()}
@@ -308,6 +505,10 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => ref={marketConfigModalRef} onConnect={handleConnect} /> + {}} + />
); }; diff --git a/web/src/views/ToolManagement/components/MarketConfigModal.tsx b/web/src/views/ToolManagement/components/MarketConfigModal.tsx index d1d87563..2b4496fa 100644 --- a/web/src/views/ToolManagement/components/MarketConfigModal.tsx +++ b/web/src/views/ToolManagement/components/MarketConfigModal.tsx @@ -2,6 +2,7 @@ import { forwardRef, useImperativeHandle, useState } from 'react'; import { Form, Input, Button, App, Space } from 'antd'; import { useTranslation } from 'react-i18next'; import { CopyOutlined, EyeInvisibleOutlined, EyeOutlined } from '@ant-design/icons'; +import { createMarketConfig,updateMarketConfig } from '@/api/tools'; import RbModal from '@/components/RbModal'; const FormItem = Form.Item; @@ -9,15 +10,16 @@ const FormItem = Form.Item; interface MarketSource { id: string; name: string; - icon: string; + logo_url: string; url: string; - desc: string; - apiKey: string; + description: string; + token?: string; connected: boolean; + configId?: string; } interface MarketConfigModalProps { - onConnect: (sourceId: string, apiKey: string) => void; + onConnect: (sourceId: string, configId: string) => void; } export interface MarketConfigModalRef { @@ -47,8 +49,7 @@ const MarketConfigModal = forwardRef { setCurrentSource(source); form.setFieldsValue({ - url: source.url, - apiKey: source.apiKey, + token: source.token || '', }); setVisible(true); }; @@ -56,18 +57,36 @@ const MarketConfigModal = forwardRef { form .validateFields() - .then((values) => { + .then(async (values) => { if (!currentSource) return; setLoading(true); - - // 模拟连接延迟 - setTimeout(() => { - onConnect(currentSource.id, values.apiKey || ''); - message.success(`正在连接 ${currentSource.name}...`); - setLoading(false); + try { + let res: any; + if (currentSource.configId) { + // 更新配置 + res = await updateMarketConfig({ + mcp_market_config_id: currentSource.configId, + token: values.token || '', + status: 1, + }); + message.success(t('tool.marketConfigUpdated', { name: currentSource.name })); + } else { + // 创建配置 + res = await createMarketConfig({ + mcp_market_id: currentSource.id || '', + token: values.token || '', + status: 1, + }); + message.success(t('tool.marketConnecting', { name: currentSource.name })); + } + onConnect(currentSource.id, res.id || currentSource.configId); handleClose(); - }, 500); + } catch (error) { + console.error('保存配置失败:', error); + } finally { + setLoading(false); + } }) .catch((err) => { console.log('表单验证失败:', err); @@ -91,10 +110,10 @@ const MarketConfigModal = forwardRef {/* 市场源信息头部 */}
-
- {currentSource.icon} +
+ {currentSource.logo_url ? ( + {currentSource.name} { + e.currentTarget.style.display = 'none'; + const parent = e.currentTarget.parentElement; + if (parent) { + parent.innerHTML = '🏪'; + parent.style.fontSize = '32px'; + } + }} + /> + ) : ( + 🏪 + )}

{currentSource.name}

-

{currentSource.desc}

+

{currentSource.description}

@@ -115,39 +150,34 @@ const MarketConfigModal = forwardRef - {/* 市场地址 */} - + - {/* API Key */} - API Key (可选) + API Key ({t('tool.marketApiKeyOptional')}) } - extra="部分市场需要 API Key 才能获取完整的服务列表" + extra={{t('tool.marketApiKeyExtra')}} >
); }; diff --git a/web/src/views/ToolManagement/types.ts b/web/src/views/ToolManagement/types.ts index aa97db66..98976e28 100644 --- a/web/src/views/ToolManagement/types.ts +++ b/web/src/views/ToolManagement/types.ts @@ -136,4 +136,10 @@ export interface ExecuteData { export interface CustomToolModalRef { handleOpen: (data?: ToolItem) => void; handleClose: () => void; +} + +export interface MarketQuery { + mcp_market_config_id?: string; + page?: number; + pagesize?: number; } \ No newline at end of file From 247db844a4885371b0e515da5623517b77838b74 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Fri, 6 Mar 2026 15:11:50 +0800 Subject: [PATCH 002/196] fix:market --- web/src/views/ToolManagement/index.tsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/web/src/views/ToolManagement/index.tsx b/web/src/views/ToolManagement/index.tsx index 12d40f46..e790c7ba 100644 --- a/web/src/views/ToolManagement/index.tsx +++ b/web/src/views/ToolManagement/index.tsx @@ -4,7 +4,7 @@ * @Author: yujiangping * @Date: 2026-01-05 17:22:23 * @LastEditors: yujiangping - * @LastEditTime: 2026-03-06 15:10:05 + * @LastEditTime: 2026-03-06 15:11:31 */ import React, { useState } from 'react'; import { Tabs } from 'antd'; @@ -16,7 +16,7 @@ import Custom from './Custom'; import Market from './Market'; import Tag from '@/components/Tag' -const tabKeys = ['mcp', 'inner', 'custom'] // , 'market' +const tabKeys = ['mcp', 'inner', 'custom', 'market'] // const ToolManagement: React.FC = () => { const { t } = useTranslation(); const [activeTab, setActiveTab] = useState('mcp'); From 2612abc9d07b6c152c12d9a3ec965af7cd80cd7a Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Sat, 7 Mar 2026 13:56:15 +0800 Subject: [PATCH 003/196] [add] Create a Celery task for checking the existence of the "implicit_emotions" data --- api/app/celery_app.py | 1 + .../memory_dashboard_controller.py | 11 + api/app/services/workspace_service.py | 237 +++++++++--------- api/app/tasks.py | 126 ++++++++++ api/docker-compose.yml | 14 ++ 5 files changed, 272 insertions(+), 117 deletions(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 0319e079..7ace1f9b 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -113,6 +113,7 @@ celery_app.conf.update( 'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'}, 'app.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'}, 'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'}, + 'app.tasks.init_implicit_emotions_for_users': {'queue': 'ondemand_tasks'}, }, ) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 1b5b45fb..1c82b636 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -149,6 +149,17 @@ async def get_workspace_end_users( return {uid: {"total": 0} for uid in end_user_ids} + # 触发按需初始化：为 implicit_emotions_storage 中没有记录的用户异步生成数据 + try: + from app.celery_app import celery_app as _celery_app + _celery_app.send_task( + "app.tasks.init_implicit_emotions_for_users", + kwargs={"end_user_ids": end_user_ids}, + ) + api_logger.info(f"已触发隐性记忆按需初始化任务，候选用户数: {len(end_user_ids)}") + except Exception as e: + api_logger.warning(f"触发隐性记忆按需初始化任务失败（不影响主流程）: {e}") + # 并发执行配置查询和记忆数量查询 memory_configs_map, memory_nums_map = await asyncio.gather( get_memory_configs(), diff --git a/api/app/services/workspace_service.py b/api/app/services/workspace_service.py index 74880410..7861ef62 100644 --- a/api/app/services/workspace_service.py +++ b/api/app/services/workspace_service.py @@ -130,6 +130,7 @@ def _create_workspace_only( business_logger.error(f"创建工作空间失败: {workspace.name} - {str(e)}") raise + def create_workspace( db: Session, workspace: WorkspaceCreate, user: User, language: str = "zh" ) -> Workspace: @@ -966,6 +967,125 @@ def update_workspace_models_configs( raise BusinessException(f"更新模型配置失败: {str(e)}", BizCode.INTERNAL_ERROR) +def _fill_workspace_configs_model_defaults( + db: Session, + workspace: Workspace +) -> None: + """Fill empty model fields for all memory configs in a workspace. + + Updates llm_id, embedding_id, rerank_id, reflection_model_id, and emotion_model_id + if they are None, using the corresponding workspace default models. + + Args: + db: Database session + workspace: The workspace containing default model settings + """ + from app.models.memory_config_model import MemoryConfig + + # Get all configs for this workspace + configs = db.query(MemoryConfig).filter( + MemoryConfig.workspace_id == workspace.id + ).all() + + if not configs: + return + + # Map of memory_config field -> workspace field + model_field_mappings = [ + ("llm_id", "llm"), + ("embedding_id", "embedding"), + ("rerank_id", "rerank"), + ("reflection_model_id", "llm"), # reflection uses LLM + ("emotion_model_id", "llm"), # emotion uses LLM + ] + + configs_updated = 0 + + for memory_config in configs: + updated_fields = [] + + for config_field, workspace_field in model_field_mappings: + config_value = getattr(memory_config, config_field, None) + workspace_value = getattr(workspace, workspace_field, None) + + if not config_value and workspace_value: + setattr(memory_config, config_field, workspace_value) + updated_fields.append(config_field) + + if updated_fields: + configs_updated += 1 + business_logger.debug( + f"Updated memory config {memory_config.config_id} fields: {updated_fields}" + ) + + if configs_updated > 0: + try: + db.commit() + business_logger.info( + f"Updated {configs_updated} memory configs in workspace {workspace.id} with default models" + ) + except Exception as e: + db.rollback() + business_logger.error( + f"Failed to update memory configs in workspace {workspace.id}: {str(e)}" + ) + + +def _create_default_memory_config( + db: Session, + workspace_id: uuid.UUID, + workspace_name: str, + llm_id: Optional[uuid.UUID] = None, + embedding_id: Optional[uuid.UUID] = None, + rerank_id: Optional[uuid.UUID] = None, + scene_id: Optional[uuid.UUID] = None, + pruning_scene_name: Optional[str] = None, +) -> None: + """Create a default memory config for a newly created workspace. + + Args: + db: Database session + workspace_id: The workspace ID + workspace_name: The workspace name (used for config naming) + llm_id: Optional LLM model ID + embedding_id: Optional embedding model ID + rerank_id: Optional rerank model ID + scene_id: Optional ontology scene ID (默认关联教育场景) + pruning_scene_name: Optional pruning scene name，取自 ontology_scene.scene_name + """ + from app.models.memory_config_model import MemoryConfig + + config_id = uuid.uuid4() + + default_config = MemoryConfig( + config_id=config_id, + config_name=f"{workspace_name} 默认配置", + config_desc="工作空间创建时自动生成的默认记忆配置", + workspace_id=workspace_id, + llm_id=str(llm_id) if llm_id else None, + embedding_id=str(embedding_id) if embedding_id else None, + rerank_id=str(rerank_id) if rerank_id else None, + scene_id=scene_id, # 关联本体场景ID（默认为"在线教育"场景） + pruning_scene=pruning_scene_name, # 语义剪枝场景直接使用 scene_name + state=True, # Active by default + is_default=True, # Mark as workspace default + ) + + db.add(default_config) + db.flush() # 使用 flush 而不是 commit，让调用者统一提交 + + business_logger.info( + "Created default memory config for workspace", + extra={ + "workspace_id": str(workspace_id), + "config_id": str(config_id), + "config_name": default_config.config_name, + "scene_id": str(scene_id) if scene_id else None, + } + ) + +# ==================== 检查配置相关服务 ==================== + def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: """Ensure a workspace has a default memory config, creating one if missing. @@ -1045,70 +1165,6 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: _fill_workspace_configs_model_defaults(db, workspace) -def _fill_workspace_configs_model_defaults( - db: Session, - workspace: Workspace -) -> None: - """Fill empty model fields for all memory configs in a workspace. - - Updates llm_id, embedding_id, rerank_id, reflection_model_id, and emotion_model_id - if they are None, using the corresponding workspace default models. - - Args: - db: Database session - workspace: The workspace containing default model settings - """ - from app.models.memory_config_model import MemoryConfig - - # Get all configs for this workspace - configs = db.query(MemoryConfig).filter( - MemoryConfig.workspace_id == workspace.id - ).all() - - if not configs: - return - - # Map of memory_config field -> workspace field - model_field_mappings = [ - ("llm_id", "llm"), - ("embedding_id", "embedding"), - ("rerank_id", "rerank"), - ("reflection_model_id", "llm"), # reflection uses LLM - ("emotion_model_id", "llm"), # emotion uses LLM - ] - - configs_updated = 0 - - for memory_config in configs: - updated_fields = [] - - for config_field, workspace_field in model_field_mappings: - config_value = getattr(memory_config, config_field, None) - workspace_value = getattr(workspace, workspace_field, None) - - if not config_value and workspace_value: - setattr(memory_config, config_field, workspace_value) - updated_fields.append(config_field) - - if updated_fields: - configs_updated += 1 - business_logger.debug( - f"Updated memory config {memory_config.config_id} fields: {updated_fields}" - ) - - if configs_updated > 0: - try: - db.commit() - business_logger.info( - f"Updated {configs_updated} memory configs in workspace {workspace.id} with default models" - ) - except Exception as e: - db.rollback() - business_logger.error( - f"Failed to update memory configs in workspace {workspace.id}: {str(e)}" - ) - - def _ensure_default_ontology_scenes(db: Session, workspace: Workspace) -> None: """Ensure a workspace has default ontology scenes, creating them if missing. @@ -1154,56 +1210,3 @@ def _ensure_default_ontology_scenes(db: Session, workspace: Workspace) -> None: f"为工作空间 {workspace.id} 补建默认本体场景异常: {str(e)}" ) - -def _create_default_memory_config( - db: Session, - workspace_id: uuid.UUID, - workspace_name: str, - llm_id: Optional[uuid.UUID] = None, - embedding_id: Optional[uuid.UUID] = None, - rerank_id: Optional[uuid.UUID] = None, - scene_id: Optional[uuid.UUID] = None, - pruning_scene_name: Optional[str] = None, -) -> None: - """Create a default memory config for a newly created workspace. - - Args: - db: Database session - workspace_id: The workspace ID - workspace_name: The workspace name (used for config naming) - llm_id: Optional LLM model ID - embedding_id: Optional embedding model ID - rerank_id: Optional rerank model ID - scene_id: Optional ontology scene ID (默认关联教育场景) - pruning_scene_name: Optional pruning scene name，取自 ontology_scene.scene_name - """ - from app.models.memory_config_model import MemoryConfig - - config_id = uuid.uuid4() - - default_config = MemoryConfig( - config_id=config_id, - config_name=f"{workspace_name} 默认配置", - config_desc="工作空间创建时自动生成的默认记忆配置", - workspace_id=workspace_id, - llm_id=str(llm_id) if llm_id else None, - embedding_id=str(embedding_id) if embedding_id else None, - rerank_id=str(rerank_id) if rerank_id else None, - scene_id=scene_id, # 关联本体场景ID（默认为"在线教育"场景） - pruning_scene=pruning_scene_name, # 语义剪枝场景直接使用 scene_name - state=True, # Active by default - is_default=True, # Mark as workspace default - ) - - db.add(default_config) - db.flush() # 使用 flush 而不是 commit，让调用者统一提交 - - business_logger.info( - "Created default memory config for workspace", - extra={ - "workspace_id": str(workspace_id), - "config_id": str(config_id), - "config_name": default_config.config_name, - "scene_id": str(scene_id) if scene_id else None, - } - ) diff --git a/api/app/tasks.py b/api/app/tasks.py index a6ebbb8e..82904b21 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2416,3 +2416,129 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: "elapsed_time": elapsed_time, "task_id": self.request.id } + + +# ============================================================================= + +@celery_app.task( + name="app.tasks.init_implicit_emotions_for_users", + bind=True, + ignore_result=True, + max_retries=0, + acks_late=False, + time_limit=3600, + soft_time_limit=3300, +) +def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]: + """按需初始化：为指定用户列表中尚未生成隐性记忆/情绪数据的用户执行首次生成。 + + 由 /dashboard/end_users 接口触发，仅处理 implicit_emotions_storage 表中不存在记录的用户。 + + Args: + end_user_ids: 需要检查并初始化的用户ID列表 + + Returns: + 包含任务执行结果的字典 + """ + start_time = time.time() + + async def _run() -> Dict[str, Any]: + from app.core.logging_config import get_logger + from app.repositories.implicit_emotions_storage_repository import ImplicitEmotionsStorageRepository + from app.services.implicit_memory_service import ImplicitMemoryService + from app.services.emotion_analytics_service import EmotionAnalyticsService + + logger = get_logger(__name__) + logger.info(f"开始按需初始化隐性记忆/情绪数据，候选用户数: {len(end_user_ids)}") + + initialized = 0 + failed = 0 + skipped = 0 + + with get_db_context() as db: + repo = ImplicitEmotionsStorageRepository(db) + + for end_user_id in end_user_ids: + # 幂等检查：已有记录则跳过 + existing = repo.get_by_end_user_id(end_user_id) + if existing is not None: + skipped += 1 + continue + + logger.info(f"用户 {end_user_id} 无隐性记忆数据，开始初始化") + implicit_ok = False + emotion_ok = False + + try: + try: + implicit_service = ImplicitMemoryService(db=db, end_user_id=end_user_id) + profile_data = await implicit_service.generate_complete_profile(user_id=end_user_id) + await implicit_service.save_profile_cache( + end_user_id=end_user_id, + profile_data=profile_data, + db=db + ) + implicit_ok = True + except Exception as e: + logger.error(f"用户 {end_user_id} 隐性记忆初始化失败: {e}") + + try: + emotion_service = EmotionAnalyticsService() + suggestions_data = await emotion_service.generate_emotion_suggestions( + end_user_id=end_user_id, + db=db, + language="zh" + ) + await emotion_service.save_suggestions_cache( + end_user_id=end_user_id, + suggestions_data=suggestions_data, + db=db + ) + emotion_ok = True + except Exception as e: + logger.error(f"用户 {end_user_id} 情绪建议初始化失败: {e}") + + if implicit_ok or emotion_ok: + initialized += 1 + else: + failed += 1 + + except Exception as e: + failed += 1 + logger.error(f"用户 {end_user_id} 初始化异常: {e}") + + logger.info(f"按需初始化完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}") + return { + "status": "SUCCESS", + "initialized": initialized, + "skipped": skipped, + "failed": failed, + } + + try: + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) + result["elapsed_time"] = time.time() - start_time + result["task_id"] = self.request.id + return result + except Exception as e: + return { + "status": "FAILURE", + "error": str(e), + "elapsed_time": time.time() - start_time, + "task_id": self.request.id, + } diff --git a/api/docker-compose.yml b/api/docker-compose.yml index 69763de2..1fcfc977 100644 --- a/api/docker-compose.yml +++ b/api/docker-compose.yml @@ -63,6 +63,20 @@ services: networks: - celery + # On-demand worker - API-triggered tasks (e.g. implicit emotions init) + worker-ondemand: + image: redbear-mem-open:latest + container_name: worker-ondemand + env_file: + - .env + volumes: + - ./files:/files + - /etc/localtime:/etc/localtime:ro + command: celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=prefork --concurrency=4 --queues=ondemand_tasks --max-tasks-per-child=50 -n ondemand_worker@%h + restart: unless-stopped + networks: + - celery + # Celery Beat - scheduler beat: image: redbear-mem-open:latest From d19fec2155122884efeece7950af8347051491d4 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Sat, 7 Mar 2026 14:40:43 +0800 Subject: [PATCH 004/196] fix(web): add notes node; jinja2 editor bugfix --- .../Workflow/components/Editor/plugin/InitialValuePlugin.tsx | 3 ++- web/src/views/Workflow/constant.ts | 4 ++++ web/src/views/Workflow/hooks/useWorkflowGraph.ts | 4 ++-- 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx b/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx index 4021a9ee..481c61c2 100644 --- a/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx +++ b/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx @@ -25,6 +25,7 @@ const InitialValuePlugin: React.FC = ({ value, options const textContent = root.getTextContent(); if (textContent !== prevValueRef.current) { isUserInputRef.current = true; + prevValueRef.current = textContent; } }); }); @@ -33,7 +34,7 @@ const InitialValuePlugin: React.FC = ({ value, options }, [editor]); useEffect(() => { - if ((value !== prevValueRef.current || enableLineNumbers !== prevEnableLineNumbersRef.current) && !isUserInputRef.current) { + if (value !== prevValueRef.current || enableLineNumbers !== prevEnableLineNumbersRef.current) { queueMicrotask(() => { editor.update(() => { const root = $getRoot(); diff --git a/web/src/views/Workflow/constant.ts b/web/src/views/Workflow/constant.ts index e7d2177a..0b2ec5ce 100644 --- a/web/src/views/Workflow/constant.ts +++ b/web/src/views/Workflow/constant.ts @@ -529,6 +529,10 @@ export const unknownNode = { type: 'unknown', icon: unknownIcon } +export const noteNode = { + type: 'notes', + icon: unknownIcon +} export const nodeWidth = 240; /** diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts index 2d8d1939..b262e47e 100644 --- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts +++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts @@ -12,7 +12,7 @@ import { Graph, Node, MiniMap, Snapline, Clipboard, Keyboard, type Edge } from ' import { register } from '@antv/x6-react-shape'; import type { PortMetadata } from '@antv/x6/lib/model/port'; -import { nodeRegisterLibrary, graphNodeLibrary, nodeLibrary, portMarkup, portAttrs, edgeAttrs, edge_color, edge_selected_color, portTextAttrs, defaultAbsolutePortGroups, nodeWidth, unknownNode } from '../constant'; +import { nodeRegisterLibrary, graphNodeLibrary, nodeLibrary, portMarkup, portAttrs, edgeAttrs, edge_color, edge_selected_color, portTextAttrs, defaultAbsolutePortGroups, nodeWidth, unknownNode, noteNode } from '../constant'; import type { WorkflowConfig, NodeProperties, ChatVariable } from '../types'; import { getWorkflowConfig, saveWorkflowConfig } from '@/api/application' @@ -128,7 +128,7 @@ export const useWorkflowGraph = ({ if (nodes.length) { const nodeList = nodes.map(node => { const { id, type, name, position, config = {} } = node - let nodeLibraryConfig = [...nodeLibrary, { nodes: [unknownNode] }] + let nodeLibraryConfig = [...nodeLibrary, { nodes: [unknownNode, noteNode] }] .flatMap(category => category.nodes) .find(n => n.type === type) nodeLibraryConfig = JSON.parse(JSON.stringify({ config: {}, ...nodeLibraryConfig })) as NodeProperties From f01185a7fce9b4e808a368f3fd7c60c61534f9e1 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Sat, 7 Mar 2026 14:41:12 +0800 Subject: [PATCH 005/196] fix(workflow): fix compatibility issues when importing workflows from dify --- api/app/core/workflow/adapters/dify/converter.py | 8 ++++++-- api/app/core/workflow/adapters/dify/dify_adapter.py | 11 ++++++----- api/app/core/workflow/engine/graph_builder.py | 2 ++ api/app/core/workflow/nodes/enums.py | 1 + api/app/core/workflow/nodes/knowledge/node.py | 2 ++ api/app/core/workflow/validator.py | 2 +- 6 files changed, 18 insertions(+), 8 deletions(-) diff --git a/api/app/core/workflow/adapters/dify/converter.py b/api/app/core/workflow/adapters/dify/converter.py index 3c9348c7..32d420b5 100644 --- a/api/app/core/workflow/adapters/dify/converter.py +++ b/api/app/core/workflow/adapters/dify/converter.py @@ -185,6 +185,9 @@ class DifyConverter(BaseConverter): "not empty": ComparisonOperator.NOT_EMPTY, "start with": ComparisonOperator.START_WITH, "end with": ComparisonOperator.END_WITH, + "not contains": ComparisonOperator.NOT_CONTAINS, + "exists": ComparisonOperator.NOT_EMPTY, + "not exists": ComparisonOperator.EMPTY } return operator_map.get(operator, operator) @@ -364,7 +367,7 @@ class DifyConverter(BaseConverter): node_data = node["data"] cases = [] for case in node_data["cases"]: - case_id = case["id"] + case_id = case.get("id") or case.get("case_id") logical_operator = case["logical_operator"] conditions = [] for condition in case["conditions"]: @@ -540,7 +543,8 @@ class DifyConverter(BaseConverter): ] = self.trans_variable_format(content["value"]) else: if node_data["body"]["data"]: - body_content = node_data["body"]["data"][0]["value"] + body_content = (node_data["body"]["data"][0].get("value") or + self._process_list_variable_litearl(node_data["body"]["data"][0].get("file"))) else: body_content = "" diff --git a/api/app/core/workflow/adapters/dify/dify_adapter.py b/api/app/core/workflow/adapters/dify/dify_adapter.py index 5b506d16..895b3d37 100644 --- a/api/app/core/workflow/adapters/dify/dify_adapter.py +++ b/api/app/core/workflow/adapters/dify/dify_adapter.py @@ -44,7 +44,8 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): "parameter-extractor": NodeType.PARAMETER_EXTRACTOR, "question-classifier": NodeType.QUESTION_CLASSIFIER, "variable-aggregator": NodeType.VAR_AGGREGATOR, - "tool": NodeType.TOOL + "tool": NodeType.TOOL, + "": NodeType.NOTES } def __init__(self, config: dict[str, Any]): @@ -165,7 +166,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): return NodeDefinition( id=node["id"], type=self.map_node_type(node_data["type"]), - name=node_data.get("title"), + name=node_data.get("title") or "notes", cycle=node.get("parentId"), description=None, config=self._convert_node_config(node), @@ -209,16 +210,15 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): source = edge["source"] target = edge["target"] - edge_id = edge["id"] label = None if source in self.branch_node_cache: - case_id = "-".join(edge_id.split("-")[1:-2]) + case_id = edge["sourceHandle"] if case_id == "false": label = f'CASE{len(self.branch_node_cache[source])+1}' else: label = f'CASE{self.branch_node_cache[source].index(case_id) + 1}' if source in self.error_branch_node_cache: - case_id = "-".join(edge_id.split("-")[1:-2]) + case_id = edge["sourceHandle"] if case_id == "source": label = "SUCCESS" else: @@ -243,6 +243,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): name=variable["name"], default=variable["value"], type=self.variable_type_map(variable["value_type"]), + description=variable.get("description") ) except Exception as e: self.errors.append(ExceptionDefineition( diff --git a/api/app/core/workflow/engine/graph_builder.py b/api/app/core/workflow/engine/graph_builder.py index 5e4569ad..90668ad9 100644 --- a/api/app/core/workflow/engine/graph_builder.py +++ b/api/app/core/workflow/engine/graph_builder.py @@ -292,6 +292,8 @@ class GraphBuilder: """ for node in self.nodes: node_type = node.get("type") + if node_type == NodeType.NOTES: + continue node_id = node.get("id") cycle_node = node.get("cycle") if cycle_node: diff --git a/api/app/core/workflow/nodes/enums.py b/api/app/core/workflow/nodes/enums.py index ae9b81ff..43ab593b 100644 --- a/api/app/core/workflow/nodes/enums.py +++ b/api/app/core/workflow/nodes/enums.py @@ -25,6 +25,7 @@ class NodeType(StrEnum): MEMORY_WRITE = "memory-write" UNKNOWN = "unknown" + NOTES = "notes" BRANCH_NODES = [NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER] diff --git a/api/app/core/workflow/nodes/knowledge/node.py b/api/app/core/workflow/nodes/knowledge/node.py index 17f55319..696298eb 100644 --- a/api/app/core/workflow/nodes/knowledge/node.py +++ b/api/app/core/workflow/nodes/knowledge/node.py @@ -180,6 +180,8 @@ class KnowledgeRetrievalNode(BaseNode): RuntimeError: If no valid knowledge base is found or access is denied. """ self.typed_config = KnowledgeRetrievalNodeConfig(**self.config) + if not self.typed_config.knowledge_bases: + return [] query = self._render_template(self.typed_config.query, variable_pool) with get_db_read() as db: knowledge_bases = self.typed_config.knowledge_bases diff --git a/api/app/core/workflow/validator.py b/api/app/core/workflow/validator.py index 47256b75..3b6e9036 100644 --- a/api/app/core/workflow/validator.py +++ b/api/app/core/workflow/validator.py @@ -138,7 +138,7 @@ class WorkflowValidator: errors.append("工作流必须至少有一个 end 节点") # 3. 验证节点 ID 唯一性 - node_ids = [n.get("id") for n in nodes] + node_ids = [n.get("id") for n in nodes if n.get("type") != NodeType.NOTES] if len(node_ids) != len(set(node_ids)): duplicates = [nid for nid in node_ids if node_ids.count(nid) > 1] errors.append(f"节点 ID 必须唯一，重复的 ID: {set(duplicates)}") From 7373f681725d34f6a35bab32cfc8979e5af6c2a8 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Sat, 7 Mar 2026 14:52:00 +0800 Subject: [PATCH 006/196] fix(web): jinja2 editor bugfix --- .../components/Editor/plugin/InitialValuePlugin.tsx | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx b/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx index 481c61c2..b263120a 100644 --- a/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx +++ b/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx @@ -35,6 +35,12 @@ const InitialValuePlugin: React.FC = ({ value, options useEffect(() => { if (value !== prevValueRef.current || enableLineNumbers !== prevEnableLineNumbersRef.current) { + // Skip reset if the change was triggered by user input (avoid cursor jump) + if (isUserInputRef.current && enableLineNumbers === prevEnableLineNumbersRef.current) { + prevValueRef.current = value; + isUserInputRef.current = false; + return; + } queueMicrotask(() => { editor.update(() => { const root = $getRoot(); From 819d205166c4ae9595b5bd8a1e40e313e8080833 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Sat, 7 Mar 2026 15:23:56 +0800 Subject: [PATCH 007/196] fix(web): change mousewheel factor --- web/src/views/Workflow/hooks/useWorkflowGraph.ts | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts index b262e47e..971d591a 100644 --- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts +++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts @@ -2,7 +2,7 @@ * @Author: ZhaoYing * @Date: 2026-02-03 15:17:48 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-02-28 17:59:34 + * @Last Modified time: 2026-03-07 15:23:39 */ import { useRef, useEffect, useState } from 'react'; import { useParams } from 'react-router-dom'; @@ -715,6 +715,8 @@ export const useWorkflowGraph = ({ panning: isHandMode, mousewheel: { enabled: true, + factor: 0.1, + modifiers: null, }, connecting: { connector: { From c14f067afb5a50c26940b59d9f2d8ab78720042c Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Sat, 7 Mar 2026 16:23:59 +0800 Subject: [PATCH 008/196] [add] The "update-implicit-emotions-storage" task uses the timeline to filter the updated data users. --- .../implicit_emotions_storage_repository.py | 51 +++++++++ api/app/tasks.py | 107 ++++++++++-------- 2 files changed, 109 insertions(+), 49 deletions(-) diff --git a/api/app/repositories/implicit_emotions_storage_repository.py b/api/app/repositories/implicit_emotions_storage_repository.py index 97405ab6..d1edf0ec 100644 --- a/api/app/repositories/implicit_emotions_storage_repository.py +++ b/api/app/repositories/implicit_emotions_storage_repository.py @@ -111,6 +111,57 @@ class ImplicitEmotionsStorageRepository: logger.error(f"分批获取用户ID失败: offset={offset}, error={e}") break + def get_users_needing_refresh(self, redis_client, batch_size: int = 100) -> Generator[str, None, None]: + """分批次获取需要刷新隐性记忆/情绪数据的存量用户ID。 + + 筛选逻辑： + - 查询 implicit_emotions_storage 中所有用户的 end_user_id 和 updated_at + - 从 Redis 读取 write_message:last_done:{end_user_id} 的时间戳 + - 若 Redis 中无记录（该用户从未写入过记忆），跳过 + - 若 last_done > updated_at，说明上次刷新后又有新记忆写入，需要刷新 + - 若 last_done <= updated_at，说明已是最新，跳过 + + Args: + redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB） + batch_size: 每批次加载的数量 + + Yields: + 需要刷新的用户ID字符串 + """ + from datetime import timezone + offset = 0 + while True: + try: + stmt = ( + select(ImplicitEmotionsStorage.end_user_id, ImplicitEmotionsStorage.updated_at) + .order_by(ImplicitEmotionsStorage.end_user_id) + .limit(batch_size) + .offset(offset) + ) + batch = self.db.execute(stmt).all() + if not batch: + break + + for end_user_id, updated_at in batch: + raw = redis_client.get(f"write_message:last_done:{end_user_id}") + if raw is None: + # 该用户从未有过 write_message 成功记录，跳过 + continue + try: + last_done = datetime.fromisoformat(raw) + # 统一去掉时区信息做 naive 比较 + if last_done.tzinfo is not None: + last_done = last_done.astimezone(timezone.utc).replace(tzinfo=None) + if updated_at is None or last_done > updated_at: + yield end_user_id + except Exception as e: + logger.warning(f"解析 last_done 时间戳失败: end_user_id={end_user_id}, raw={raw}, error={e}") + + offset += batch_size + except Exception as e: + logger.error(f"get_users_needing_refresh 分批查询失败: offset={offset}, error={e}") + break + def get_new_user_ids_today(self, batch_size: int = 100) -> Generator[str, None, None]: """分批次获取当天新增的、尚未初始化隐性记忆和情绪建议数据的用户ID diff --git a/api/app/tasks.py b/api/app/tasks.py index 82904b21..d4afcc68 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -1090,6 +1090,25 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s logger.info( f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}") + # 记录该用户最后一次 write_message 成功的时间，供 init_implicit_emotions_for_users 做时间轴筛选 + try: + import redis as _redis + from urllib.parse import quote as _quote + _r = _redis.StrictRedis( + host=settings.REDIS_HOST, + port=settings.REDIS_PORT, + db=settings.REDIS_DB_CELERY_BACKEND, + password=settings.REDIS_PASSWORD, + decode_responses=True, + ) + _r.set( + f"write_message:last_done:{end_user_id}", + datetime.utcnow().isoformat(), + ex=86400 * 30, # 30天过期 + ) + except Exception as _e: + logger.warning(f"[CELERY WRITE] 写入 last_done 时间戳失败（不影响主流程）: {_e}") + return { "status": "SUCCESS", "result": result, @@ -2167,18 +2186,27 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: with get_db_context() as db: try: - # 获取所有已存储数据的用户ID（分批次处理） repo = ImplicitEmotionsStorageRepository(db) - + # 先统计总数用于日志 from sqlalchemy import func total_users = db.execute( select(func.count()).select_from(ImplicitEmotionsStorage) ).scalar() or 0 - logger.info(f"找到 {total_users} 个需要更新的用户") + logger.info(f"表中存量用户总数: {total_users}，开始时间轴筛选") - # 遍历每个用户并更新数据（分批次，避免一次性加载所有ID） - for end_user_id in repo.get_all_user_ids(batch_size=100): + # 构建 Redis 同步客户端，用于时间轴筛选 + import redis as _redis + _redis_client = _redis.StrictRedis( + host=settings.REDIS_HOST, + port=settings.REDIS_PORT, + db=settings.REDIS_DB_CELERY_BACKEND, + password=settings.REDIS_PASSWORD, + decode_responses=True, + ) + + # 只处理 last_done > updated_at 的用户（有新记忆写入的用户） + for end_user_id in repo.get_users_needing_refresh(_redis_client, batch_size=100): logger.info(f"开始处理用户: {end_user_id}") user_start_time = time.time() @@ -2264,10 +2292,10 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: user_results.append(error_info) logger.error(f"处理用户 {end_user_id} 时出错: {str(e)}") - # ---- 处理增量用户（当天新增、尚未初始化的用户）---- + # ---- 当天新增用户兜底初始化 ---- new_users_initialized = 0 new_users_failed = 0 - logger.info("开始处理当天新增的增量用户初始化") + logger.info("开始处理当天新增用户的兜底初始化") for end_user_id in repo.get_new_user_ids_today(batch_size=100): logger.info(f"开始初始化新用户: {end_user_id}") @@ -2281,35 +2309,27 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: implicit_service = ImplicitMemoryService(db=db, end_user_id=end_user_id) profile_data = await implicit_service.generate_complete_profile(user_id=end_user_id) await implicit_service.save_profile_cache( - end_user_id=end_user_id, - profile_data=profile_data, - db=db + end_user_id=end_user_id, profile_data=profile_data, db=db ) implicit_success = True logger.info(f"成功初始化新用户 {end_user_id} 的隐性记忆画像") except Exception as e: - error_msg = f"隐性记忆初始化失败: {str(e)}" - errors.append(error_msg) - logger.error(f"新用户 {end_user_id} {error_msg}") + errors.append(f"隐性记忆初始化失败: {str(e)}") + logger.error(f"新用户 {end_user_id} 隐性记忆初始化失败: {e}") try: emotion_service = EmotionAnalyticsService() suggestions_data = await emotion_service.generate_emotion_suggestions( - end_user_id=end_user_id, - db=db, - language="zh" + end_user_id=end_user_id, db=db, language="zh" ) await emotion_service.save_suggestions_cache( - end_user_id=end_user_id, - suggestions_data=suggestions_data, - db=db + end_user_id=end_user_id, suggestions_data=suggestions_data, db=db ) emotion_success = True logger.info(f"成功初始化新用户 {end_user_id} 的情绪建议") except Exception as e: - error_msg = f"情绪建议初始化失败: {str(e)}" - errors.append(error_msg) - logger.error(f"新用户 {end_user_id} {error_msg}") + errors.append(f"情绪建议初始化失败: {str(e)}") + logger.error(f"新用户 {end_user_id} 情绪建议初始化失败: {e}") if implicit_success or emotion_success: new_users_initialized += 1 @@ -2319,7 +2339,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: user_elapsed = time.time() - user_start_time user_results.append({ "end_user_id": end_user_id, - "type": "init", + "type": "new_user_init", "implicit_success": implicit_success, "emotion_success": emotion_success, "errors": errors, @@ -2331,7 +2351,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: user_elapsed = time.time() - user_start_time user_results.append({ "end_user_id": end_user_id, - "type": "init", + "type": "new_user_init", "implicit_success": False, "emotion_success": False, "errors": [str(e)], @@ -2339,27 +2359,24 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: }) logger.error(f"初始化新用户 {end_user_id} 时出错: {str(e)}") - logger.info( - f"增量用户初始化完成: 成功={new_users_initialized}, 失败={new_users_failed}" - ) - # ---- 增量用户处理结束 ---- + logger.info(f"当天新增用户兜底初始化完成: 成功={new_users_initialized}, 失败={new_users_failed}") + # ---- 新增用户兜底初始化结束 ---- - # 记录总体统计信息 logger.info( f"隐性记忆和情绪数据更新定时任务完成: " f"存量用户总数={total_users}, " f"隐性记忆成功={successful_implicit}, " f"情绪建议成功={successful_emotion}, " f"存量失败={failed}, " - f"增量初始化成功={new_users_initialized}, " - f"增量初始化失败={new_users_failed}" + f"新增用户初始化成功={new_users_initialized}, " + f"新增用户初始化失败={new_users_failed}" ) return { "status": "SUCCESS", "message": ( f"存量用户 {total_users} 个，隐性记忆 {successful_implicit} 个成功，情绪建议 {successful_emotion} 个成功；" - f"增量新用户初始化 {new_users_initialized} 个成功，{new_users_failed} 个失败" + f"当天新增用户初始化 {new_users_initialized} 个成功，{new_users_failed} 个失败" ), "total_users": total_users, "successful_implicit": successful_implicit, @@ -2367,7 +2384,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: "failed": failed, "new_users_initialized": new_users_initialized, "new_users_failed": new_users_failed, - "user_results": user_results[:50] # 只保留前50个用户的详细结果 + "user_results": user_results[:50] } except Exception as e: @@ -2430,12 +2447,13 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: soft_time_limit=3300, ) def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]: - """按需初始化：为指定用户列表中尚未生成隐性记忆/情绪数据的用户执行首次生成。 + """事件触发任务：对指定用户列表做存在性检查，无记录则执行首次初始化。 - 由 /dashboard/end_users 接口触发，仅处理 implicit_emotions_storage 表中不存在记录的用户。 + 由 /dashboard/end_users 接口触发，已有数据的用户直接跳过。 + 存量用户的数据刷新由定时任务 update_implicit_emotions_storage 负责。 Args: - end_user_ids: 需要检查并初始化的用户ID列表 + end_user_ids: 需要检查的用户ID列表 Returns: 包含任务执行结果的字典 @@ -2459,24 +2477,20 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, repo = ImplicitEmotionsStorageRepository(db) for end_user_id in end_user_ids: - # 幂等检查：已有记录则跳过 existing = repo.get_by_end_user_id(end_user_id) if existing is not None: skipped += 1 continue - logger.info(f"用户 {end_user_id} 无隐性记忆数据，开始初始化") + logger.info(f"用户 {end_user_id} 无记录，开始初始化") implicit_ok = False emotion_ok = False - try: try: implicit_service = ImplicitMemoryService(db=db, end_user_id=end_user_id) profile_data = await implicit_service.generate_complete_profile(user_id=end_user_id) await implicit_service.save_profile_cache( - end_user_id=end_user_id, - profile_data=profile_data, - db=db + end_user_id=end_user_id, profile_data=profile_data, db=db ) implicit_ok = True except Exception as e: @@ -2485,14 +2499,10 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, try: emotion_service = EmotionAnalyticsService() suggestions_data = await emotion_service.generate_emotion_suggestions( - end_user_id=end_user_id, - db=db, - language="zh" + end_user_id=end_user_id, db=db, language="zh" ) await emotion_service.save_suggestions_cache( - end_user_id=end_user_id, - suggestions_data=suggestions_data, - db=db + end_user_id=end_user_id, suggestions_data=suggestions_data, db=db ) emotion_ok = True except Exception as e: @@ -2502,7 +2512,6 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, initialized += 1 else: failed += 1 - except Exception as e: failed += 1 logger.error(f"用户 {end_user_id} 初始化异常: {e}") From cef14cda9e3819cd485458f4148a37f7582cb76b Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Sat, 7 Mar 2026 16:36:24 +0800 Subject: [PATCH 009/196] [add] Standardize time zones; Reuse a single Redis client; Use "mget" for batch writing requests --- .../implicit_emotions_storage_repository.py | 29 +++++++--- api/app/tasks.py | 58 +++++++++++-------- 2 files changed, 57 insertions(+), 30 deletions(-) diff --git a/api/app/repositories/implicit_emotions_storage_repository.py b/api/app/repositories/implicit_emotions_storage_repository.py index d1edf0ec..dfc7061b 100644 --- a/api/app/repositories/implicit_emotions_storage_repository.py +++ b/api/app/repositories/implicit_emotions_storage_repository.py @@ -142,17 +142,32 @@ class ImplicitEmotionsStorageRepository: if not batch: break - for end_user_id, updated_at in batch: - raw = redis_client.get(f"write_message:last_done:{end_user_id}") + # 批量获取当前批次所有用户的 last_done 时间戳（一次网络往返） + keys = [f"write_message:last_done:{end_user_id}" for end_user_id, _ in batch] + raw_values = redis_client.mget(keys) + + for (end_user_id, updated_at), raw in zip(batch, raw_values): if raw is None: - # 该用户从未有过 write_message 成功记录，跳过 continue try: + CST = timezone(timedelta(hours=8)) last_done = datetime.fromisoformat(raw) - # 统一去掉时区信息做 naive 比较 - if last_done.tzinfo is not None: - last_done = last_done.astimezone(timezone.utc).replace(tzinfo=None) - if updated_at is None or last_done > updated_at: + # 统一转为 CST naive 时间做比较 + if last_done.tzinfo is None: + last_done = last_done.replace(tzinfo=timezone.utc).astimezone(CST).replace(tzinfo=None) + else: + last_done = last_done.astimezone(CST).replace(tzinfo=None) + + if updated_at is None: + yield end_user_id + continue + # updated_at 同样转为 CST naive + if updated_at.tzinfo is None: + updated_at_cst = updated_at.replace(tzinfo=timezone.utc).astimezone(CST).replace(tzinfo=None) + else: + updated_at_cst = updated_at.astimezone(CST).replace(tzinfo=None) + + if last_done > updated_at_cst: yield end_user_id except Exception as e: logger.warning(f"解析 last_done 时间戳失败: end_user_id={end_user_id}, raw={raw}, error={e}") diff --git a/api/app/tasks.py b/api/app/tasks.py index d4afcc68..0c0fd01e 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -15,6 +15,29 @@ from uuid import UUID import redis import requests +# 模块级同步 Redis 客户端单例，供 Celery 任务共享使用（避免每次任务新建连接） +# 连接 CELERY_BACKEND DB，与 write_message:last_done 时间戳写入保持一致 +def _build_sync_redis_client(): + try: + return redis.StrictRedis( + host=settings.REDIS_HOST, + port=settings.REDIS_PORT, + db=settings.REDIS_DB_CELERY_BACKEND, + password=settings.REDIS_PASSWORD, + decode_responses=True, + ) + except Exception: + return None + +_sync_redis_client: redis.StrictRedis = None + +def get_sync_redis_client() -> redis.StrictRedis: + """获取模块级同步 Redis 客户端（懒初始化单例）""" + global _sync_redis_client + if _sync_redis_client is None: + _sync_redis_client = _build_sync_redis_client() + return _sync_redis_client + # Import a unified Celery instance from app.celery_app import celery_app from app.core.config import settings @@ -1090,22 +1113,18 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s logger.info( f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}") - # 记录该用户最后一次 write_message 成功的时间，供 init_implicit_emotions_for_users 做时间轴筛选 + # 记录该用户最后一次 write_message 成功的时间，供时间轴筛选使用 try: - import redis as _redis - from urllib.parse import quote as _quote - _r = _redis.StrictRedis( - host=settings.REDIS_HOST, - port=settings.REDIS_PORT, - db=settings.REDIS_DB_CELERY_BACKEND, - password=settings.REDIS_PASSWORD, - decode_responses=True, - ) - _r.set( - f"write_message:last_done:{end_user_id}", - datetime.utcnow().isoformat(), - ex=86400 * 30, # 30天过期 - ) + _r = get_sync_redis_client() + if _r is not None: + from datetime import timezone as _tz, timedelta as _td + _CST = _tz(timedelta(hours=8)) + _now_cst = datetime.now(_CST).replace(tzinfo=None).isoformat() + _r.set( + f"write_message:last_done:{end_user_id}", + _now_cst, + ex=86400 * 30, + ) except Exception as _e: logger.warning(f"[CELERY WRITE] 写入 last_done 时间戳失败（不影响主流程）: {_e}") @@ -2196,14 +2215,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: logger.info(f"表中存量用户总数: {total_users}，开始时间轴筛选") # 构建 Redis 同步客户端，用于时间轴筛选 - import redis as _redis - _redis_client = _redis.StrictRedis( - host=settings.REDIS_HOST, - port=settings.REDIS_PORT, - db=settings.REDIS_DB_CELERY_BACKEND, - password=settings.REDIS_PASSWORD, - decode_responses=True, - ) + _redis_client = get_sync_redis_client() # 只处理 last_done > updated_at 的用户（有新记忆写入的用户） for end_user_id in repo.get_users_needing_refresh(_redis_client, batch_size=100): From 8429279eeaa5796da0a108fc1716887531503b64 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Sat, 7 Mar 2026 16:55:06 +0800 Subject: [PATCH 010/196] [add] Verification of the existence of interest distribution --- api/app/celery_app.py | 1 + .../memory_dashboard_controller.py | 8 +- api/app/tasks.py | 107 ++++++++++++++++++ 3 files changed, 114 insertions(+), 2 deletions(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 7ace1f9b..02b7df77 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -114,6 +114,7 @@ celery_app.conf.update( 'app.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'}, 'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'}, 'app.tasks.init_implicit_emotions_for_users': {'queue': 'ondemand_tasks'}, + 'app.tasks.init_interest_distribution_for_users': {'queue': 'ondemand_tasks'}, }, ) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 1c82b636..50e8ec8f 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -156,9 +156,13 @@ async def get_workspace_end_users( "app.tasks.init_implicit_emotions_for_users", kwargs={"end_user_ids": end_user_ids}, ) - api_logger.info(f"已触发隐性记忆按需初始化任务，候选用户数: {len(end_user_ids)}") + _celery_app.send_task( + "app.tasks.init_interest_distribution_for_users", + kwargs={"end_user_ids": end_user_ids}, + ) + api_logger.info(f"已触发按需初始化任务，候选用户数: {len(end_user_ids)}") except Exception as e: - api_logger.warning(f"触发隐性记忆按需初始化任务失败（不影响主流程）: {e}") + api_logger.warning(f"触发按需初始化任务失败（不影响主流程）: {e}") # 并发执行配置查询和记忆数量查询 memory_configs_map, memory_nums_map = await asyncio.gather( diff --git a/api/app/tasks.py b/api/app/tasks.py index 0c0fd01e..2387a9e4 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2563,3 +2563,110 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, "elapsed_time": time.time() - start_time, "task_id": self.request.id, } + + +# ============================================================================= + +@celery_app.task( + name="app.tasks.init_interest_distribution_for_users", + bind=True, + ignore_result=True, + max_retries=0, + acks_late=False, + time_limit=3600, + soft_time_limit=3300, +) +def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]: + """事件触发任务：检查指定用户列表的兴趣分布缓存，无缓存则生成并写入 Redis。 + + 由 /dashboard/end_users 接口触发，已有缓存的用户直接跳过。 + 默认生成中文（zh）兴趣分布数据。 + + Args: + end_user_ids: 需要检查的用户ID列表 + + Returns: + 包含任务执行结果的字典 + """ + start_time = time.time() + + async def _run() -> Dict[str, Any]: + from app.core.logging_config import get_logger + from app.cache.memory.interest_memory import InterestMemoryCache, INTEREST_CACHE_EXPIRE + from app.services.memory_agent_service import MemoryAgentService + + logger = get_logger(__name__) + logger.info(f"开始按需初始化兴趣分布缓存，候选用户数: {len(end_user_ids)}") + + initialized = 0 + failed = 0 + skipped = 0 + language = "zh" + + service = MemoryAgentService() + + with get_db_context() as db: + for end_user_id in end_user_ids: + # 存在性检查：缓存有数据则跳过 + cached = await InterestMemoryCache.get_interest_distribution( + end_user_id=end_user_id, + language=language, + ) + if cached is not None: + skipped += 1 + continue + + logger.info(f"用户 {end_user_id} 无兴趣分布缓存，开始生成") + try: + result = await service.get_interest_distribution_by_user( + end_user_id=end_user_id, + limit=5, + language=language, + ) + await InterestMemoryCache.set_interest_distribution( + end_user_id=end_user_id, + language=language, + data=result, + expire=INTEREST_CACHE_EXPIRE, + ) + initialized += 1 + logger.info(f"用户 {end_user_id} 兴趣分布缓存生成成功") + except Exception as e: + failed += 1 + logger.error(f"用户 {end_user_id} 兴趣分布缓存生成失败: {e}") + + logger.info(f"兴趣分布按需初始化完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}") + return { + "status": "SUCCESS", + "initialized": initialized, + "skipped": skipped, + "failed": failed, + } + + try: + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) + result["elapsed_time"] = time.time() - start_time + result["task_id"] = self.request.id + return result + except Exception as e: + return { + "status": "FAILURE", + "error": str(e), + "elapsed_time": time.time() - start_time, + "task_id": self.request.id, + } From 94a40e49a05f83dcf37468509cca625847976333 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Sat, 7 Mar 2026 17:07:38 +0800 Subject: [PATCH 011/196] [add] Throw out explicit error messages; Using the CST time zone --- .../implicit_emotions_storage_repository.py | 15 +++++++++------ api/app/tasks.py | 11 +++++++++-- 2 files changed, 18 insertions(+), 8 deletions(-) diff --git a/api/app/repositories/implicit_emotions_storage_repository.py b/api/app/repositories/implicit_emotions_storage_repository.py index dfc7061b..bb869934 100644 --- a/api/app/repositories/implicit_emotions_storage_repository.py +++ b/api/app/repositories/implicit_emotions_storage_repository.py @@ -122,12 +122,17 @@ class ImplicitEmotionsStorageRepository: - 若 last_done <= updated_at，说明已是最新，跳过 Args: - redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB） + redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB），为 None 时抛出 RuntimeError batch_size: 每批次加载的数量 + Raises: + RuntimeError: redis_client 为 None 时，调用方可捕获并回退到 get_all_user_ids + Yields: 需要刷新的用户ID字符串 """ + if redis_client is None: + raise RuntimeError("get_users_needing_refresh: redis_client 不可用，无法执行时间轴筛选") from datetime import timezone offset = 0 while True: @@ -152,16 +157,14 @@ class ImplicitEmotionsStorageRepository: try: CST = timezone(timedelta(hours=8)) last_done = datetime.fromisoformat(raw) - # 统一转为 CST naive 时间做比较 - if last_done.tzinfo is None: - last_done = last_done.replace(tzinfo=timezone.utc).astimezone(CST).replace(tzinfo=None) - else: + # last_done 写入时已是 CST naive，直接使用，无需转换 + if last_done.tzinfo is not None: last_done = last_done.astimezone(CST).replace(tzinfo=None) if updated_at is None: yield end_user_id continue - # updated_at 同样转为 CST naive + # updated_at 数据库存的是 UTC naive，转为 CST naive 再比较 if updated_at.tzinfo is None: updated_at_cst = updated_at.replace(tzinfo=timezone.utc).astimezone(CST).replace(tzinfo=None) else: diff --git a/api/app/tasks.py b/api/app/tasks.py index 2387a9e4..c5e8a105 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -1117,7 +1117,7 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s try: _r = get_sync_redis_client() if _r is not None: - from datetime import timezone as _tz, timedelta as _td + from datetime import timezone as _tz _CST = _tz(timedelta(hours=8)) _now_cst = datetime.now(_CST).replace(tzinfo=None).isoformat() _r.set( @@ -2218,7 +2218,14 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: _redis_client = get_sync_redis_client() # 只处理 last_done > updated_at 的用户（有新记忆写入的用户） - for end_user_id in repo.get_users_needing_refresh(_redis_client, batch_size=100): + # Redis 不可用时回退到全量处理 + try: + refresh_iter = repo.get_users_needing_refresh(_redis_client, batch_size=100) + except RuntimeError as e: + logger.warning(f"时间轴筛选不可用，回退到全量刷新: {e}") + refresh_iter = repo.get_all_user_ids(batch_size=100) + + for end_user_id in refresh_iter: logger.info(f"开始处理用户: {end_user_id}") user_start_time = time.time() From 8f789d47a2a932866723cf307c5b3a2e58f8c108 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 9 Mar 2026 02:38:35 +0800 Subject: [PATCH 012/196] feat(workflow): add support for notes nodes --- .../core/workflow/adapters/dify/converter.py | 71 ++++++++++++++----- .../workflow/adapters/dify/dify_adapter.py | 2 +- api/app/core/workflow/nodes/configs.py | 4 +- api/app/core/workflow/nodes/notes/__init__.py | 0 api/app/core/workflow/nodes/notes/config.py | 8 +++ 5 files changed, 65 insertions(+), 20 deletions(-) create mode 100644 api/app/core/workflow/nodes/notes/__init__.py create mode 100644 api/app/core/workflow/nodes/notes/config.py diff --git a/api/app/core/workflow/adapters/dify/converter.py b/api/app/core/workflow/adapters/dify/converter.py index 32d420b5..c7539ea2 100644 --- a/api/app/core/workflow/adapters/dify/converter.py +++ b/api/app/core/workflow/adapters/dify/converter.py @@ -8,34 +8,59 @@ from typing import Any from urllib.parse import quote from app.core.workflow.adapters.base_converter import BaseConverter -from app.core.workflow.adapters.errors import UnsupportVariableType, UnknowModelWarning, ExceptionDefineition, \ +from app.core.workflow.adapters.errors import ( + UnsupportVariableType, + UnknowModelWarning, + ExceptionDefineition, ExceptionType -from app.core.workflow.nodes.assigner import AssignerNodeConfig +) from app.core.workflow.nodes.assigner.config import AssignmentItem from app.core.workflow.nodes.base_config import VariableDefinition, BaseNodeConfig -from app.core.workflow.nodes.code import CodeNodeConfig from app.core.workflow.nodes.code.config import InputVariable, OutputVariable -from app.core.workflow.nodes.configs import StartNodeConfig, LLMNodeConfig -from app.core.workflow.nodes.cycle_graph import LoopNodeConfig, IterationNodeConfig -from app.core.workflow.nodes.cycle_graph.config import ConditionDetail as LoopConditionDetail, ConditionsConfig, \ +from app.core.workflow.nodes.configs import ( + StartNodeConfig, + LLMNodeConfig, + AssignerNodeConfig, + CodeNodeConfig, + LoopNodeConfig, + IterationNodeConfig, + EndNodeConfig, + HttpRequestNodeConfig, + IfElseNodeConfig, + JinjaRenderNodeConfig, + KnowledgeRetrievalNodeConfig, + NoteNodeConfig, + ParameterExtractorNodeConfig, + QuestionClassifierNodeConfig, + VariableAggregatorNodeConfig +) +from app.core.workflow.nodes.cycle_graph.config import ( + ConditionDetail as LoopConditionDetail, + ConditionsConfig, CycleVariable -from app.core.workflow.nodes.end import EndNodeConfig -from app.core.workflow.nodes.enums import ValueInputType, ComparisonOperator, AssignmentOperator, HttpAuthType, \ - HttpContentType, HttpErrorHandle -from app.core.workflow.nodes.http_request import HttpRequestNodeConfig -from app.core.workflow.nodes.http_request.config import HttpAuthConfig, HttpContentTypeConfig, HttpFormData, \ - HttpTimeOutConfig, HttpRetryConfig, HttpErrorDefaultTamplete, HttpErrorHandleConfig -from app.core.workflow.nodes.if_else import IfElseNodeConfig +) +from app.core.workflow.nodes.enums import ( + ValueInputType, + ComparisonOperator, + AssignmentOperator, + HttpAuthType, + HttpContentType, + HttpErrorHandle +) +from app.core.workflow.nodes.http_request.config import ( + HttpAuthConfig, + HttpContentTypeConfig, + HttpFormData, + HttpTimeOutConfig, + HttpRetryConfig, + HttpErrorDefaultTamplete, + HttpErrorHandleConfig +) from app.core.workflow.nodes.if_else.config import ConditionDetail, ConditionBranchConfig -from app.core.workflow.nodes.jinja_render import JinjaRenderNodeConfig from app.core.workflow.nodes.jinja_render.config import VariablesMappingConfig -from app.core.workflow.nodes.knowledge import KnowledgeRetrievalNodeConfig from app.core.workflow.nodes.llm.config import MemoryWindowSetting, MessageConfig -from app.core.workflow.nodes.parameter_extractor import ParameterExtractorNodeConfig from app.core.workflow.nodes.parameter_extractor.config import ParamsConfig -from app.core.workflow.nodes.question_classifier import QuestionClassifierNodeConfig from app.core.workflow.nodes.question_classifier.config import ClassifierConfig -from app.core.workflow.nodes.variable_aggregator import VariableAggregatorNodeConfig from app.core.workflow.variable.base_variable import VariableType, DEFAULT_VALUE @@ -63,6 +88,7 @@ class DifyConverter(BaseConverter): "question-classifier": self.convert_question_classifier_node_config, "variable-aggregator": self.convert_variable_aggregator_node_config, "tool": self.convert_tool_node_config, + "": self.convert_notes_config, "loop-start": lambda x: {}, "iteration-start": lambda x: {}, "loop-end": lambda x: {}, @@ -732,3 +758,12 @@ class DifyConverter(BaseConverter): detail=f"Please reconfigure the tool node.", )) return {} + + @staticmethod + def convert_notes_config(node: dict): + node_data = node["data"] + result = NoteNodeConfig.model_construct( + author=node_data.get("author", ""), + text=node_data.get("text", "") + ).model_dump() + return result diff --git a/api/app/core/workflow/adapters/dify/dify_adapter.py b/api/app/core/workflow/adapters/dify/dify_adapter.py index 895b3d37..a190aadd 100644 --- a/api/app/core/workflow/adapters/dify/dify_adapter.py +++ b/api/app/core/workflow/adapters/dify/dify_adapter.py @@ -193,7 +193,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): type=ExceptionType.NODE, node_id=node["id"], node_name=node["data"]["title"], - detail=f"node type {node_type if node_type else 'notes'} is unsupported", + detail=f"node type {node_type} is unsupported", )) return converter(node) except Exception as e: diff --git a/api/app/core/workflow/nodes/configs.py b/api/app/core/workflow/nodes/configs.py index e4e418fe..31dadc38 100644 --- a/api/app/core/workflow/nodes/configs.py +++ b/api/app/core/workflow/nodes/configs.py @@ -23,6 +23,7 @@ from app.core.workflow.nodes.question_classifier.config import QuestionClassifie from app.core.workflow.nodes.start.config import StartNodeConfig from app.core.workflow.nodes.tool.config import ToolNodeConfig from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig +from app.core.workflow.nodes.notes.config import NoteNodeConfig __all__ = [ # 基础类 @@ -47,5 +48,6 @@ __all__ = [ "ToolNodeConfig", "MemoryReadNodeConfig", "MemoryWriteNodeConfig", - "CodeNodeConfig" + "CodeNodeConfig", + "NoteNodeConfig" ] diff --git a/api/app/core/workflow/nodes/notes/__init__.py b/api/app/core/workflow/nodes/notes/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/api/app/core/workflow/nodes/notes/config.py b/api/app/core/workflow/nodes/notes/config.py new file mode 100644 index 00000000..d7bfa383 --- /dev/null +++ b/api/app/core/workflow/nodes/notes/config.py @@ -0,0 +1,8 @@ +from pydantic import Field + +from app.core.workflow.nodes.base_config import BaseNodeConfig + + +class NoteNodeConfig(BaseNodeConfig): + author: str = Field(..., description="author") + text: str = Field(..., description="note context") From 966bd8528d4dafcb90c0d40618e2a97e8df291ae Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 9 Mar 2026 03:08:44 +0800 Subject: [PATCH 013/196] feat(workflow): simplify node converter registry --- .../core/workflow/adapters/dify/converter.py | 40 +++++++++---------- .../workflow/adapters/dify/dify_adapter.py | 22 +++++----- 2 files changed, 30 insertions(+), 32 deletions(-) diff --git a/api/app/core/workflow/adapters/dify/converter.py b/api/app/core/workflow/adapters/dify/converter.py index c7539ea2..023091a3 100644 --- a/api/app/core/workflow/adapters/dify/converter.py +++ b/api/app/core/workflow/adapters/dify/converter.py @@ -45,7 +45,8 @@ from app.core.workflow.nodes.enums import ( AssignmentOperator, HttpAuthType, HttpContentType, - HttpErrorHandle + HttpErrorHandle, + NodeType ) from app.core.workflow.nodes.http_request.config import ( HttpAuthConfig, @@ -73,25 +74,24 @@ class DifyConverter(BaseConverter): def __init__(self): self.CONFIG_CONVERT_MAP = { - "start": self.convert_start_node_config, - "llm": self.convert_llm_node_config, - "answer": self.convert_end_node_config, - "if-else": self.convert_if_else_node_config, - "loop": self.convert_loop_node_config, - "iteration": self.convert_iteration_node_config, - "assigner": self.convert_assigner_node_config, - "code": self.convert_code_node_config, - "http-request": self.convert_http_node_config, - "template-transform": self.convert_jinja_render_node_config, - "knowledge-retrieval": self.convert_knowledge_node_config, - "parameter-extractor": self.convert_parameter_extractor_node_config, - "question-classifier": self.convert_question_classifier_node_config, - "variable-aggregator": self.convert_variable_aggregator_node_config, - "tool": self.convert_tool_node_config, - "": self.convert_notes_config, - "loop-start": lambda x: {}, - "iteration-start": lambda x: {}, - "loop-end": lambda x: {}, + NodeType.START: self.convert_start_node_config, + NodeType.LLM: self.convert_llm_node_config, + NodeType.END: self.convert_end_node_config, + NodeType.IF_ELSE: self.convert_if_else_node_config, + NodeType.LOOP: self.convert_loop_node_config, + NodeType.ITERATION: self.convert_iteration_node_config, + NodeType.ASSIGNER: self.convert_assigner_node_config, + NodeType.CODE: self.convert_code_node_config, + NodeType.HTTP_REQUEST: self.convert_http_node_config, + NodeType.JINJARENDER: self.convert_jinja_render_node_config, + NodeType.KNOWLEDGE_RETRIEVAL: self.convert_knowledge_node_config, + NodeType.PARAMETER_EXTRACTOR: self.convert_parameter_extractor_node_config, + NodeType.QUESTION_CLASSIFIER: self.convert_question_classifier_node_config, + NodeType.VAR_AGGREGATOR: self.convert_variable_aggregator_node_config, + NodeType.TOOL: self.convert_tool_node_config, + NodeType.NOTES: self.convert_notes_config, + NodeType.CYCLE_START: lambda x: {}, + NodeType.BREAK: lambda x: {}, } def get_node_convert(self, node_type): diff --git a/api/app/core/workflow/adapters/dify/dify_adapter.py b/api/app/core/workflow/adapters/dify/dify_adapter.py index a190aadd..c9aa7ca9 100644 --- a/api/app/core/workflow/adapters/dify/dify_adapter.py +++ b/api/app/core/workflow/adapters/dify/dify_adapter.py @@ -50,7 +50,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): def __init__(self, config: dict[str, Any]): DifyConverter.__init__(self) - BasePlatformAdapter.__init__(self, config) + BasePlatformAdapter.__init__(self, config) def get_metadata(self) -> PlatformMetadata: return PlatformMetadata( @@ -84,7 +84,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): require_fields = frozenset({'app', 'kind', 'version', 'workflow'}) if not all(field in self.config for field in require_fields): return False - if self.config.get("app",{}).get("mode") == "workflow": + if self.config.get("app", {}).get("mode") == "workflow": self.errors.append(ExceptionDefineition( type=ExceptionType.PLATFORM, detail="workflow mode is not supported" @@ -163,13 +163,14 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): def _convert_node(self, node: dict[str, Any]) -> NodeDefinition | None: node_data = node["data"] try: + node_type = self.map_node_type(node_data["type"]) return NodeDefinition( id=node["id"], - type=self.map_node_type(node_data["type"]), + type=node_type, name=node_data.get("title") or "notes", cycle=node.get("parentId"), description=None, - config=self._convert_node_config(node), + config=self._convert_node_config(node_type, node), position={ "x": node["position"]["x"], "y": node["position"]["y"] @@ -183,17 +184,16 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): except Exception as e: logger.debug(f"convert node error - {e}", exc_info=True) - def _convert_node_config(self, node: dict): - node_data = node["data"] - node_type = node_data["type"] + def _convert_node_config(self, node_type: str, node: dict): try: + node_data = node["data"] converter = self.get_node_convert(node_type) - if node_type not in self.CONFIG_CONVERT_MAP: + if node_type == NodeType.UNKNOWN: self.errors.append(ExceptionDefineition( type=ExceptionType.NODE, node_id=node["id"], node_name=node["data"]["title"], - detail=f"node type {node_type} is unsupported", + detail=f"node type {node_data.get('type')} is unsupported", )) return converter(node) except Exception as e: @@ -214,7 +214,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): if source in self.branch_node_cache: case_id = edge["sourceHandle"] if case_id == "false": - label = f'CASE{len(self.branch_node_cache[source])+1}' + label = f'CASE{len(self.branch_node_cache[source]) + 1}' else: label = f'CASE{self.branch_node_cache[source].index(case_id) + 1}' if source in self.error_branch_node_cache: @@ -257,5 +257,3 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): def _convert_execution(self, execution: dict[str, Any]) -> ExecutionConfig: return ExecutionConfig() - - From 389dd8d4026b089408e0d50ca49988fd096ec929 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 9 Mar 2026 03:18:33 +0800 Subject: [PATCH 014/196] feat(workflow): support resizing comment nodes, add theme and author display toggle --- api/app/core/workflow/adapters/dify/converter.py | 6 +++++- api/app/core/workflow/adapters/dify/dify_adapter.py | 4 ++-- api/app/core/workflow/nodes/notes/config.py | 8 ++++++-- 3 files changed, 13 insertions(+), 5 deletions(-) diff --git a/api/app/core/workflow/adapters/dify/converter.py b/api/app/core/workflow/adapters/dify/converter.py index 023091a3..467beb07 100644 --- a/api/app/core/workflow/adapters/dify/converter.py +++ b/api/app/core/workflow/adapters/dify/converter.py @@ -764,6 +764,10 @@ class DifyConverter(BaseConverter): node_data = node["data"] result = NoteNodeConfig.model_construct( author=node_data.get("author", ""), - text=node_data.get("text", "") + text=node_data.get("text", ""), + width=node_data.get("width", 80), + height=node_data.get("height", 80), + theme=node_data.get("theme", "blue"), + show_author=node_data.get("showAuthor", True) ).model_dump() return result diff --git a/api/app/core/workflow/adapters/dify/dify_adapter.py b/api/app/core/workflow/adapters/dify/dify_adapter.py index c9aa7ca9..10397ad0 100644 --- a/api/app/core/workflow/adapters/dify/dify_adapter.py +++ b/api/app/core/workflow/adapters/dify/dify_adapter.py @@ -59,7 +59,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): support_node_types=list(self.NODE_TYPE_MAPPING.keys()) ) - def map_node_type(self, platform_node_type) -> str: + def map_node_type(self, platform_node_type) -> NodeType: return self.NODE_TYPE_MAPPING.get(platform_node_type, NodeType.UNKNOWN) @property @@ -184,7 +184,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter): except Exception as e: logger.debug(f"convert node error - {e}", exc_info=True) - def _convert_node_config(self, node_type: str, node: dict): + def _convert_node_config(self, node_type: NodeType, node: dict): try: node_data = node["data"] converter = self.get_node_convert(node_type) diff --git a/api/app/core/workflow/nodes/notes/config.py b/api/app/core/workflow/nodes/notes/config.py index d7bfa383..42b4a1ab 100644 --- a/api/app/core/workflow/nodes/notes/config.py +++ b/api/app/core/workflow/nodes/notes/config.py @@ -4,5 +4,9 @@ from app.core.workflow.nodes.base_config import BaseNodeConfig class NoteNodeConfig(BaseNodeConfig): - author: str = Field(..., description="author") - text: str = Field(..., description="note context") + author: str = Field(default="", description="author") + text: str = Field(default="", description="note content") + width: int = Field(default=80) + height: int = Field(default=80) + theme: str = Field(default="blue") + show_author: bool = Field(default=True) From 349d46e043eb1698f547d564829a5fc2cd444bf8 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Mon, 9 Mar 2026 11:26:54 +0800 Subject: [PATCH 015/196] [changes] Add restriction words to avoid the "implicit" and "emotional" content from being mistakenly pruned. --- .../data_preprocessing/data_pruning.py | 10 ++++++++++ .../data_preprocessing/scene_config.py | 18 ++++++++++++++++++ 2 files changed, 28 insertions(+) diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py index 904b238f..ecbe0411 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py @@ -252,6 +252,16 @@ class SemanticPruner: if re.fullmatch(r"[。！？,.!?…·\s]+", t): return True + # 安全防线：包含情绪词或兴趣词的消息，无论多短都不视为填充 + # 避免"我好开心呀"、"好喜欢打羽毛球呀"等被误删 + _emotion_interest_guard = re.compile( + r"开心|高兴|快乐|幸福|感动|难过|悲伤|伤心|委屈|失落|沮丧|郁闷|" + r"生气|愤怒|烦躁|焦虑|害怕|担心|压力|兴奋|期待|" + r"喜欢|热爱|爱好|兴趣|擅长|享受|沉迷|着迷|讨厌|厌恶" + ) + if _emotion_interest_guard.search(t): + return False + return False async def _batch_evaluate_importance_with_llm( diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py index ed9592af..a79ebea5 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py @@ -51,6 +51,22 @@ class SceneConfigRegistry: (r"今天|明天|后天|昨天|前天", 3), # 相对时间（提高权重） (r"下周|下月|下年|上周|上月|上年|本周|本月|本年", 3), (r"今年|去年|明年", 3), + # ---- 情绪内容（所有场景通用，用于情绪提取） ---- + (r"开心|高兴|快乐|兴奋|愉快|幸福|满足|喜悦|欣喜", 4), + (r"难过|悲伤|伤心|痛苦|委屈|失落|沮丧|郁闷|忧郁|绝望", 4), + (r"生气|愤怒|烦躁|焦虑|紧张|害怕|恐惧|担心|担忧|压力", 4), + (r"感动|温暖|感激|感谢|惊喜|期待|憧憬|向往", 3), + (r"无聊|无奈|尴尬|后悔|遗憾|羞愧|惭愧", 3), + (r"好[开高快]心|很[开高快]心|超[开高快]心|非常[开高快]心", 4), + (r"好难过|好伤心|好悲伤|好委屈|好痛苦", 4), + (r"好开心|好高兴|好快乐|好幸福|好感动", 4), + # ---- 兴趣/爱好内容（所有场景通用，用于兴趣提取） ---- + (r"喜欢|热爱|爱好|兴趣|擅长|享受|沉迷|着迷|痴迷", 4), + (r"不喜欢|讨厌|厌恶|反感|排斥", 3), + (r"羽毛球|篮球|足球|排球|乒乓球|网球|棒球|高尔夫", 4), + (r"游泳|跑步|健身|瑜伽|舞蹈|武术|骑行|登山|徒步", 4), + (r"音乐|唱歌|吉他|钢琴|绘画|摄影|书法|手工|烹饪", 4), + (r"游戏|电影|动漫|小说|阅读|旅游|美食|宠物", 3), ] BASE_LOW_PRIORITY = [ @@ -58,6 +74,8 @@ class SceneConfigRegistry: (r"\d{1,2}点\d{0,2}分?", 2), # 时间点 X点Y分 或 X点 (r"上午|下午|中午|晚上|早上|傍晚|凌晨", 2), # 时段（提高权重并扩充） (r"AM|PM|am|pm", 1), + # ---- 情绪程度副词（辅助情绪识别） ---- + (r"特别|非常|超级|极其|十分|很|好[开高快]|太.*了", 1), ] BASE_FILLERS = { From 476632294fe69899eea0a30a464cc7874d1c0f60 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Mon, 9 Mar 2026 14:02:23 +0800 Subject: [PATCH 016/196] [changes] Remove the "worker-ondemand" queue --- api/app/celery_app.py | 2 +- api/app/tasks.py | 2 ++ api/docker-compose.yml | 16 +--------------- 3 files changed, 4 insertions(+), 16 deletions(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 7ace1f9b..e6b239dd 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -113,7 +113,7 @@ celery_app.conf.update( 'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'}, 'app.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'}, 'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'}, - 'app.tasks.init_implicit_emotions_for_users': {'queue': 'ondemand_tasks'}, + 'app.tasks.init_implicit_emotions_for_users': {'queue': 'periodic_tasks'}, }, ) diff --git a/api/app/tasks.py b/api/app/tasks.py index 0c0fd01e..65a0a091 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2457,6 +2457,8 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: acks_late=False, time_limit=3600, soft_time_limit=3300, + # 触发型任务标识，区别于 periodic_tasks 队列中的定时任务 + triggered=True, ) def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]: """事件触发任务：对指定用户列表做存在性检查，无记录则执行首次初始化。 diff --git a/api/docker-compose.yml b/api/docker-compose.yml index 1fcfc977..5d358f2c 100644 --- a/api/docker-compose.yml +++ b/api/docker-compose.yml @@ -49,7 +49,7 @@ services: networks: - celery - # Periodic worker - Scheduled/beat tasks (prefork, low concurrency) + # Periodic worker - Scheduled/beat tasks + API-triggered tasks (prefork, low concurrency) worker-periodic: image: redbear-mem-open:latest container_name: worker-periodic @@ -63,20 +63,6 @@ services: networks: - celery - # On-demand worker - API-triggered tasks (e.g. implicit emotions init) - worker-ondemand: - image: redbear-mem-open:latest - container_name: worker-ondemand - env_file: - - .env - volumes: - - ./files:/files - - /etc/localtime:/etc/localtime:ro - command: celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=prefork --concurrency=4 --queues=ondemand_tasks --max-tasks-per-child=50 -n ondemand_worker@%h - restart: unless-stopped - networks: - - celery - # Celery Beat - scheduler beat: image: redbear-mem-open:latest From c8065b0c602242cb4233c85d67dd255b744170cc Mon Sep 17 00:00:00 2001 From: Ke Sun Date: Mon, 9 Mar 2026 14:12:53 +0800 Subject: [PATCH 017/196] feat(implicit-emotions): add Redis resilience and connection pooling - Replace single Redis client with connection pool for better concurrency and auto-reconnection - Add graceful degradation when Redis is unavailable (None handling in get_users_needing_refresh) - Add RedisError exception handling with fallback to process all users on mget failures - Add type hints (Optional[redis.StrictRedis]) to Redis client parameters - Add health check and socket timeout configuration to connection pool - Add logging for Redis connection failures and degradation events - Reorganize imports alphabetically for consistency across both files - Update get_sync_redis_client to validate connection with ping() before returning --- .../implicit_emotions_storage_repository.py | 45 +++++++-- api/app/tasks.py | 92 +++++++++++++------ 2 files changed, 102 insertions(+), 35 deletions(-) diff --git a/api/app/repositories/implicit_emotions_storage_repository.py b/api/app/repositories/implicit_emotions_storage_repository.py index dfc7061b..58e98dfd 100644 --- a/api/app/repositories/implicit_emotions_storage_repository.py +++ b/api/app/repositories/implicit_emotions_storage_repository.py @@ -5,13 +5,15 @@ Implicit Emotions Storage Repository 事务由调用方控制，仓储层只使用 flush/refresh """ import logging -from datetime import datetime, date, timezone, timedelta -from typing import Optional, Generator -from sqlalchemy.orm import Session -from sqlalchemy import select, not_, exists +from datetime import date, datetime, timedelta, timezone +from typing import Generator, Optional + +import redis +from sqlalchemy import exists, not_, select +from sqlalchemy.orm import Session -from app.models.implicit_emotions_storage_model import ImplicitEmotionsStorage from app.models.end_user_model import EndUser +from app.models.implicit_emotions_storage_model import ImplicitEmotionsStorage logger = logging.getLogger(__name__) @@ -111,7 +113,7 @@ class ImplicitEmotionsStorageRepository: logger.error(f"分批获取用户ID失败: offset={offset}, error={e}") break - def get_users_needing_refresh(self, redis_client, batch_size: int = 100) -> Generator[str, None, None]: + def get_users_needing_refresh(self, redis_client: Optional[redis.StrictRedis], batch_size: int = 100) -> Generator[str, None, None]: """分批次获取需要刷新隐性记忆/情绪数据的存量用户ID。 筛选逻辑： @@ -120,15 +122,28 @@ class ImplicitEmotionsStorageRepository: - 若 Redis 中无记录（该用户从未写入过记忆），跳过 - 若 last_done > updated_at，说明上次刷新后又有新记忆写入，需要刷新 - 若 last_done <= updated_at，说明已是最新，跳过 + + 如果 redis_client 为 None，则降级为返回所有用户（禁用时间过滤）。 Args: - redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB） + redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB），如果为 None 则禁用时间过滤 batch_size: 每批次加载的数量 Yields: 需要刷新的用户ID字符串 """ from datetime import timezone + + from redis.exceptions import RedisError + + # 如果 Redis 不可用，降级为处理所有用户 + if redis_client is None: + logger.warning( + "Redis 客户端不可用，时间过滤已禁用，将处理所有存量用户" + ) + yield from self.get_all_user_ids(batch_size) + return + offset = 0 while True: try: @@ -144,7 +159,18 @@ class ImplicitEmotionsStorageRepository: # 批量获取当前批次所有用户的 last_done 时间戳（一次网络往返） keys = [f"write_message:last_done:{end_user_id}" for end_user_id, _ in batch] - raw_values = redis_client.mget(keys) + + try: + raw_values = redis_client.mget(keys) + except RedisError as e: + logger.error( + f"Redis mget 操作失败: {e}，当前批次降级为处理所有用户", + extra={"offset": offset, "batch_size": len(batch)} + ) + # Redis 操作失败，降级为返回当前批次所有用户 + yield from (end_user_id for end_user_id, _ in batch) + offset += batch_size + continue for (end_user_id, updated_at), raw in zip(batch, raw_values): if raw is None: @@ -190,7 +216,8 @@ class ImplicitEmotionsStorageRepository: Yields: 用户ID字符串 """ - from sqlalchemy import cast, String as SAString + from sqlalchemy import String as SAString + from sqlalchemy import cast CST = timezone(timedelta(hours=8)) now_cst = datetime.now(CST) today_start = now_cst.replace(hour=0, minute=0, second=0, microsecond=0).astimezone(timezone.utc).replace(tzinfo=None) diff --git a/api/app/tasks.py b/api/app/tasks.py index 65a0a091..5958d77d 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -1,5 +1,6 @@ import asyncio import json +import logging import os import re import shutil @@ -14,29 +15,62 @@ from uuid import UUID import redis import requests +from redis.exceptions import RedisError -# 模块级同步 Redis 客户端单例，供 Celery 任务共享使用（避免每次任务新建连接） +logger = logging.getLogger(__name__) + +# 模块级同步 Redis 连接池，供 Celery 任务共享使用 # 连接 CELERY_BACKEND DB，与 write_message:last_done 时间戳写入保持一致 -def _build_sync_redis_client(): +# 使用连接池而非单例客户端，提供更好的并发性能和自动重连 +_sync_redis_pool: redis.ConnectionPool = None + +def _get_or_create_redis_pool() -> redis.ConnectionPool: + """获取或创建 Redis 连接池（懒初始化）""" + global _sync_redis_pool + if _sync_redis_pool is None: + try: + _sync_redis_pool = redis.ConnectionPool( + host=settings.REDIS_HOST, + port=settings.REDIS_PORT, + db=settings.REDIS_DB_CELERY_BACKEND, + password=settings.REDIS_PASSWORD, + decode_responses=True, + max_connections=10, + socket_connect_timeout=5, + socket_timeout=5, + retry_on_timeout=True, + health_check_interval=30, + ) + logger.info("Redis connection pool created for Celery tasks") + except Exception as e: + logger.error(f"Failed to create Redis connection pool: {e}", exc_info=True) + return None + return _sync_redis_pool + +def get_sync_redis_client() -> Optional[redis.StrictRedis]: + """获取同步 Redis 客户端（使用连接池） + + 使用连接池提供的客户端，支持自动重连和健康检查。 + 如果 Redis 不可用，返回 None，调用方应优雅降级。 + + Returns: + redis.StrictRedis: Redis 客户端实例，如果连接失败则返回 None + """ try: - return redis.StrictRedis( - host=settings.REDIS_HOST, - port=settings.REDIS_PORT, - db=settings.REDIS_DB_CELERY_BACKEND, - password=settings.REDIS_PASSWORD, - decode_responses=True, - ) - except Exception: + pool = _get_or_create_redis_pool() + if pool is None: + return None + + client = redis.StrictRedis(connection_pool=pool) + # 验证连接可用性 + client.ping() + return client + except RedisError as e: + logger.error(f"Redis connection failed: {e}", exc_info=True) + return None + except Exception as e: + logger.error(f"Unexpected error getting Redis client: {e}", exc_info=True) return None - -_sync_redis_client: redis.StrictRedis = None - -def get_sync_redis_client() -> redis.StrictRedis: - """获取模块级同步 Redis 客户端（懒初始化单例）""" - global _sync_redis_client - if _sync_redis_client is None: - _sync_redis_client = _build_sync_redis_client() - return _sync_redis_client # Import a unified Celery instance from app.celery_app import celery_app @@ -1117,8 +1151,9 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s try: _r = get_sync_redis_client() if _r is not None: - from datetime import timezone as _tz, timedelta as _td - _CST = _tz(timedelta(hours=8)) + from datetime import timedelta as _td + from datetime import timezone as _tz + _CST = _tz(_td(hours=8)) _now_cst = datetime.now(_CST).replace(tzinfo=None).isoformat() _r.set( f"write_message:last_done:{end_user_id}", @@ -2187,12 +2222,15 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: start_time = time.time() async def _run() -> Dict[str, Any]: + from sqlalchemy import func, select + from app.core.logging_config import get_logger - from app.repositories.implicit_emotions_storage_repository import ImplicitEmotionsStorageRepository from app.models.implicit_emotions_storage_model import ImplicitEmotionsStorage - from sqlalchemy import select, func - from app.services.implicit_memory_service import ImplicitMemoryService + from app.repositories.implicit_emotions_storage_repository import ( + ImplicitEmotionsStorageRepository, + ) from app.services.emotion_analytics_service import EmotionAnalyticsService + from app.services.implicit_memory_service import ImplicitMemoryService logger = get_logger(__name__) logger.info("开始执行隐性记忆和情绪数据更新定时任务") @@ -2476,9 +2514,11 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, async def _run() -> Dict[str, Any]: from app.core.logging_config import get_logger - from app.repositories.implicit_emotions_storage_repository import ImplicitEmotionsStorageRepository - from app.services.implicit_memory_service import ImplicitMemoryService + from app.repositories.implicit_emotions_storage_repository import ( + ImplicitEmotionsStorageRepository, + ) from app.services.emotion_analytics_service import EmotionAnalyticsService + from app.services.implicit_memory_service import ImplicitMemoryService logger = get_logger(__name__) logger.info(f"开始按需初始化隐性记忆/情绪数据，候选用户数: {len(end_user_ids)}") From 50466124c820e469de7cc1b73f88d94fd2004b10 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Sat, 7 Mar 2026 16:55:06 +0800 Subject: [PATCH 018/196] [add] Verification of the existence of interest distribution --- api/app/celery_app.py | 1 + .../memory_dashboard_controller.py | 8 +- api/app/tasks.py | 107 ++++++++++++++++++ 3 files changed, 114 insertions(+), 2 deletions(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index e6b239dd..cac4eff1 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -114,6 +114,7 @@ celery_app.conf.update( 'app.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'}, 'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'}, 'app.tasks.init_implicit_emotions_for_users': {'queue': 'periodic_tasks'}, + 'app.tasks.init_interest_distribution_for_users': {'queue': 'periodic_tasks'}, }, ) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 1c82b636..50e8ec8f 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -156,9 +156,13 @@ async def get_workspace_end_users( "app.tasks.init_implicit_emotions_for_users", kwargs={"end_user_ids": end_user_ids}, ) - api_logger.info(f"已触发隐性记忆按需初始化任务，候选用户数: {len(end_user_ids)}") + _celery_app.send_task( + "app.tasks.init_interest_distribution_for_users", + kwargs={"end_user_ids": end_user_ids}, + ) + api_logger.info(f"已触发按需初始化任务，候选用户数: {len(end_user_ids)}") except Exception as e: - api_logger.warning(f"触发隐性记忆按需初始化任务失败（不影响主流程）: {e}") + api_logger.warning(f"触发按需初始化任务失败（不影响主流程）: {e}") # 并发执行配置查询和记忆数量查询 memory_configs_map, memory_nums_map = await asyncio.gather( diff --git a/api/app/tasks.py b/api/app/tasks.py index 5958d77d..05bc022d 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2605,3 +2605,110 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, "elapsed_time": time.time() - start_time, "task_id": self.request.id, } + + +# ============================================================================= + +@celery_app.task( + name="app.tasks.init_interest_distribution_for_users", + bind=True, + ignore_result=True, + max_retries=0, + acks_late=False, + time_limit=3600, + soft_time_limit=3300, +) +def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]: + """事件触发任务：检查指定用户列表的兴趣分布缓存，无缓存则生成并写入 Redis。 + + 由 /dashboard/end_users 接口触发，已有缓存的用户直接跳过。 + 默认生成中文（zh）兴趣分布数据。 + + Args: + end_user_ids: 需要检查的用户ID列表 + + Returns: + 包含任务执行结果的字典 + """ + start_time = time.time() + + async def _run() -> Dict[str, Any]: + from app.core.logging_config import get_logger + from app.cache.memory.interest_memory import InterestMemoryCache, INTEREST_CACHE_EXPIRE + from app.services.memory_agent_service import MemoryAgentService + + logger = get_logger(__name__) + logger.info(f"开始按需初始化兴趣分布缓存，候选用户数: {len(end_user_ids)}") + + initialized = 0 + failed = 0 + skipped = 0 + language = "zh" + + service = MemoryAgentService() + + with get_db_context() as db: + for end_user_id in end_user_ids: + # 存在性检查：缓存有数据则跳过 + cached = await InterestMemoryCache.get_interest_distribution( + end_user_id=end_user_id, + language=language, + ) + if cached is not None: + skipped += 1 + continue + + logger.info(f"用户 {end_user_id} 无兴趣分布缓存，开始生成") + try: + result = await service.get_interest_distribution_by_user( + end_user_id=end_user_id, + limit=5, + language=language, + ) + await InterestMemoryCache.set_interest_distribution( + end_user_id=end_user_id, + language=language, + data=result, + expire=INTEREST_CACHE_EXPIRE, + ) + initialized += 1 + logger.info(f"用户 {end_user_id} 兴趣分布缓存生成成功") + except Exception as e: + failed += 1 + logger.error(f"用户 {end_user_id} 兴趣分布缓存生成失败: {e}") + + logger.info(f"兴趣分布按需初始化完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}") + return { + "status": "SUCCESS", + "initialized": initialized, + "skipped": skipped, + "failed": failed, + } + + try: + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) + result["elapsed_time"] = time.time() - start_time + result["task_id"] = self.request.id + return result + except Exception as e: + return { + "status": "FAILURE", + "error": str(e), + "elapsed_time": time.time() - start_time, + "task_id": self.request.id, + } From 21ae448ed74f00d3d7bc0bcc41fd869c0c6d4f08 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Sat, 7 Mar 2026 17:07:38 +0800 Subject: [PATCH 019/196] [add] Throw out explicit error messages; Using the CST time zone --- .../implicit_emotions_storage_repository.py | 15 +++++++++------ api/app/tasks.py | 9 ++++++++- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/api/app/repositories/implicit_emotions_storage_repository.py b/api/app/repositories/implicit_emotions_storage_repository.py index 58e98dfd..aa62e07d 100644 --- a/api/app/repositories/implicit_emotions_storage_repository.py +++ b/api/app/repositories/implicit_emotions_storage_repository.py @@ -126,12 +126,17 @@ class ImplicitEmotionsStorageRepository: 如果 redis_client 为 None，则降级为返回所有用户（禁用时间过滤）。 Args: - redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB），如果为 None 则禁用时间过滤 + redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB），为 None 时抛出 RuntimeError batch_size: 每批次加载的数量 + Raises: + RuntimeError: redis_client 为 None 时，调用方可捕获并回退到 get_all_user_ids + Yields: 需要刷新的用户ID字符串 """ + if redis_client is None: + raise RuntimeError("get_users_needing_refresh: redis_client 不可用，无法执行时间轴筛选") from datetime import timezone from redis.exceptions import RedisError @@ -178,16 +183,14 @@ class ImplicitEmotionsStorageRepository: try: CST = timezone(timedelta(hours=8)) last_done = datetime.fromisoformat(raw) - # 统一转为 CST naive 时间做比较 - if last_done.tzinfo is None: - last_done = last_done.replace(tzinfo=timezone.utc).astimezone(CST).replace(tzinfo=None) - else: + # last_done 写入时已是 CST naive，直接使用，无需转换 + if last_done.tzinfo is not None: last_done = last_done.astimezone(CST).replace(tzinfo=None) if updated_at is None: yield end_user_id continue - # updated_at 同样转为 CST naive + # updated_at 数据库存的是 UTC naive，转为 CST naive 再比较 if updated_at.tzinfo is None: updated_at_cst = updated_at.replace(tzinfo=timezone.utc).astimezone(CST).replace(tzinfo=None) else: diff --git a/api/app/tasks.py b/api/app/tasks.py index 05bc022d..101c66a5 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2256,7 +2256,14 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: _redis_client = get_sync_redis_client() # 只处理 last_done > updated_at 的用户（有新记忆写入的用户） - for end_user_id in repo.get_users_needing_refresh(_redis_client, batch_size=100): + # Redis 不可用时回退到全量处理 + try: + refresh_iter = repo.get_users_needing_refresh(_redis_client, batch_size=100) + except RuntimeError as e: + logger.warning(f"时间轴筛选不可用，回退到全量刷新: {e}") + refresh_iter = repo.get_all_user_ids(batch_size=100) + + for end_user_id in refresh_iter: logger.info(f"开始处理用户: {end_user_id}") user_start_time = time.time() From 4c2b31f31f8aaf7cd98e99c29acc8179dd7c351f Mon Sep 17 00:00:00 2001 From: yujiangping Date: Mon, 9 Mar 2026 15:36:49 +0800 Subject: [PATCH 020/196] feat(web): add MCP market database tracking and refresh status messages MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add i18n translations for refresh success and failure messages in English and Chinese - Track MCP tools already stored in database with inDatabase flag in Market component - Display "已入库" (In Database) tag alongside activation status for MCPs - Import getTools API to fetch full tool list for database status comparison - Add market metadata fields (source_channel, market_id, market_config_id, mcp_service_id) to tool items when adding from market - Preserve market source information through McpServiceModal when saving tools - Update ToolItem type to include market tracking fields in config_data - Improve MCP card layout to properly display multiple status tags --- web/src/i18n/en.ts | 2 + web/src/i18n/zh.ts | 2 + web/src/views/ToolManagement/Market.tsx | 39 +++++++++++++++---- web/src/views/ToolManagement/Mcp.tsx | 1 - .../components/McpServiceModal.tsx | 13 +++++++ web/src/views/ToolManagement/types.ts | 4 ++ 6 files changed, 52 insertions(+), 9 deletions(-) diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 19498811..3904b88b 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1806,6 +1806,8 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re error_desc: 'API is configured but connection error', testConnectionSuccess: 'Test Connection Successful', + refreshSuccess: 'Refresh Successful', + refreshFailed: 'Refresh Failed', serviceEndpoint: 'Service Endpoint URL', serviceEndpointPlaceholder: 'URL of the service endpoint', serviceEndpointExtra: 'Complete access address of the MCP service', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 664795fe..24e56d4e 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1803,6 +1803,8 @@ export const zh = { error_desc: 'API 已配置但链接异常', testConnectionSuccess: '测试连接成功', + refreshSuccess: '刷新成功', + refreshFailed: '刷新失败', serviceEndpoint: '服务端点 URL', serviceEndpointPlaceholder: '服务端点的 URL', serviceEndpointExtra: 'MCP服务的完整访问地址', diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index 7a2df6df..00dea715 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -6,7 +6,7 @@ import InfiniteScroll from 'react-infinite-scroll-component'; import MarketConfigModal, { type MarketConfigModalRef } from './components/MarketConfigModal'; import McpServiceModal from './components/McpServiceModal'; import type { McpServiceModalRef } from './types'; -import { getMarketTools, getMarketConfig, getMarketMCPs, getMarketMCPDetail, getMarketMCPsActivated } from '@/api/tools'; +import { getMarketTools, getMarketConfig, getMarketMCPs, getMarketMCPDetail, getMarketMCPsActivated, getTools } from '@/api/tools'; interface MarketSource { id: string; name: string; @@ -32,6 +32,7 @@ interface MarketMcp { tags?: string[]; view_count?: number; activated?: boolean; + inDatabase?: boolean; locales?: { [lang: string]: { name: string; @@ -131,13 +132,27 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => } } + // 获取全量工具列表，用于标记已入库的 MCP + const allTools: any = await getTools({ tool_type: 'mcp' }); + const toolsList = Array.isArray(allTools) ? allTools : []; + const res: any = await getMarketMCPs({ mcp_market_config_id: configId, page, pagesize: pageSize }); if (res?.items && Array.isArray(res.items)) { - // 标记已激活的 MCP - const mcpsWithActivated = res.items.map((item: MarketMcp) => ({ - ...item, - activated: activatedIds.includes(item.id) - })); + // 标记已激活和已入库的 MCP + const mcpsWithActivated = res.items.map((item: MarketMcp) => { + // 检查是否已入库：market_id = sourceId, market_config_id = configId, mcp_service_id = item.id + const isInDatabase = toolsList.some((tool: any) => + tool.config_data?.market_id === sourceId && + tool.config_data?.market_config_id === configId && + tool.config_data?.mcp_service_id === item.id + ); + + return { + ...item, + activated: activatedIds.includes(item.id), + inDatabase: isInDatabase + }; + }); setMcpCache(prev => ({ ...prev, @@ -212,9 +227,14 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => mcp_market_config_id: configIdMap[selectedSource], server_id: mcp.id, }); + const source = marketSources.find(s => s.id === selectedSource); const toolItem = { name: detail.name, description: detail.description, + source_channel: source?.name || '', + market_id: selectedSource, + market_config_id: configIdMap[selectedSource], + mcp_service_id: mcp.id, config_data: { server_url: detail.servers?.[0]?.url || '', connection_config: { @@ -392,8 +412,11 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => )}
-
- {mcp.activated && 已激活} +
+
+ {mcp.activated && 已激活} + {mcp.inDatabase && 已入库} +
diff --git a/web/src/views/ToolManagement/Mcp.tsx b/web/src/views/ToolManagement/Mcp.tsx index 55cb73f5..90883b36 100644 --- a/web/src/views/ToolManagement/Mcp.tsx +++ b/web/src/views/ToolManagement/Mcp.tsx @@ -61,7 +61,6 @@ const Mcp: React.FC<{ getStatusTag: (status: string) => ReactNode }> = ({ getSta getData() }) }; - // 删除服务 const handleDeleteService = (item: ToolItem) => { if (!item.id) { diff --git a/web/src/views/ToolManagement/components/McpServiceModal.tsx b/web/src/views/ToolManagement/components/McpServiceModal.tsx index 0c9f6759..8bd44ccf 100644 --- a/web/src/views/ToolManagement/components/McpServiceModal.tsx +++ b/web/src/views/ToolManagement/components/McpServiceModal.tsx @@ -87,6 +87,10 @@ const McpServiceModal = forwardRef(({ name, description, icon, ...(config_data ? { config: { ...config_data } } : {}) }) + // 如果是从 Market 组件传来的数据（包含 market_id），保存完整的 data 用于后续提交 + if ((data as any).market_id) { + setEditVo(data) + } } else { form.resetFields(); } @@ -116,6 +120,15 @@ const McpServiceModal = forwardRef(({ } } } + + // 如果是从 Market 组件传来的数据，添加市场相关字段 + if ((editVo as any)?.market_id) { + (newService.config as any).source_channel = (editVo as any).source_channel; + (newService.config as any).market_id = (editVo as any).market_id; + (newService.config as any).market_config_id = (editVo as any).market_config_id; + (newService.config as any).mcp_service_id = (editVo as any).mcp_service_id; + } + const request = editVo?.id ? updateTool(editVo.id, newService) : addTool(newService) request.then((res: any) => { message.success(t('common.saveSuccess')); diff --git a/web/src/views/ToolManagement/types.ts b/web/src/views/ToolManagement/types.ts index 98976e28..5508b200 100644 --- a/web/src/views/ToolManagement/types.ts +++ b/web/src/views/ToolManagement/types.ts @@ -75,6 +75,10 @@ export interface ToolItem { tool_class: string; schema_content: string; + source_channel?: string; + market_id?: string; + market_config_id?: string; + mcp_service_id?: string; }; status: 'available' | 'unavailable'; tags: string[]; From 5438d35f1710b077b69f2b58b8b46a8e9529400c Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Mon, 9 Mar 2026 16:19:55 +0800 Subject: [PATCH 021/196] [add] Specify the error types and clearly define the downgrade conditions --- .../implicit_emotions_storage_repository.py | 26 ++++++++----------- api/app/tasks.py | 3 ++- 2 files changed, 13 insertions(+), 16 deletions(-) diff --git a/api/app/repositories/implicit_emotions_storage_repository.py b/api/app/repositories/implicit_emotions_storage_repository.py index aa62e07d..f0871b4b 100644 --- a/api/app/repositories/implicit_emotions_storage_repository.py +++ b/api/app/repositories/implicit_emotions_storage_repository.py @@ -8,6 +8,13 @@ import logging from datetime import date, datetime, timedelta, timezone from typing import Generator, Optional + +class TimeFilterUnavailableError(Exception): + """redis_client 不可用，无法执行时间轴筛选。 + + 调用方捕获此异常后可选择回退到 get_all_user_ids 进行全量处理。 + """ + import redis from sqlalchemy import exists, not_, select from sqlalchemy.orm import Session @@ -113,7 +120,7 @@ class ImplicitEmotionsStorageRepository: logger.error(f"分批获取用户ID失败: offset={offset}, error={e}") break - def get_users_needing_refresh(self, redis_client: Optional[redis.StrictRedis], batch_size: int = 100) -> Generator[str, None, None]: + def get_users_needing_refresh(self, redis_client: redis.StrictRedis, batch_size: int = 100) -> Generator[str, None, None]: """分批次获取需要刷新隐性记忆/情绪数据的存量用户ID。 筛选逻辑： @@ -123,32 +130,21 @@ class ImplicitEmotionsStorageRepository: - 若 last_done > updated_at，说明上次刷新后又有新记忆写入，需要刷新 - 若 last_done <= updated_at，说明已是最新，跳过 - 如果 redis_client 为 None，则降级为返回所有用户（禁用时间过滤）。 - Args: - redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB），为 None 时抛出 RuntimeError + redis_client: 同步 redis.StrictRedis 实例（连接 CELERY_BACKEND DB） batch_size: 每批次加载的数量 Raises: - RuntimeError: redis_client 为 None 时，调用方可捕获并回退到 get_all_user_ids + TimeFilterUnavailableError: redis_client 为 None 时抛出，调用方可捕获并回退到 get_all_user_ids Yields: 需要刷新的用户ID字符串 """ if redis_client is None: - raise RuntimeError("get_users_needing_refresh: redis_client 不可用，无法执行时间轴筛选") - from datetime import timezone + raise TimeFilterUnavailableError("redis_client 不可用，无法执行时间轴筛选") from redis.exceptions import RedisError - # 如果 Redis 不可用，降级为处理所有用户 - if redis_client is None: - logger.warning( - "Redis 客户端不可用，时间过滤已禁用，将处理所有存量用户" - ) - yield from self.get_all_user_ids(batch_size) - return - offset = 0 while True: try: diff --git a/api/app/tasks.py b/api/app/tasks.py index 101c66a5..6fd9c954 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2228,6 +2228,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: from app.models.implicit_emotions_storage_model import ImplicitEmotionsStorage from app.repositories.implicit_emotions_storage_repository import ( ImplicitEmotionsStorageRepository, + TimeFilterUnavailableError, ) from app.services.emotion_analytics_service import EmotionAnalyticsService from app.services.implicit_memory_service import ImplicitMemoryService @@ -2259,7 +2260,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: # Redis 不可用时回退到全量处理 try: refresh_iter = repo.get_users_needing_refresh(_redis_client, batch_size=100) - except RuntimeError as e: + except TimeFilterUnavailableError as e: logger.warning(f"时间轴筛选不可用，回退到全量刷新: {e}") refresh_iter = repo.get_all_user_ids(batch_size=100) From e1939ef47256ecf982def5717effd372b52abb94 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Mon, 9 Mar 2026 16:31:45 +0800 Subject: [PATCH 022/196] feat(web): internationalize MCP market UI strings - Add 19 new i18n keys for market-related UI text in English and Chinese - Replace hardcoded Chinese strings with i18n translations in Market.tsx - Update market refresh success message to use i18n key - Internationalize market selection, configuration, and service browsing UI - Support multi-language display for market status tags and action buttons --- web/src/i18n/en.ts | 17 ++++++++++++++++ web/src/i18n/zh.ts | 17 ++++++++++++++++ web/src/views/ToolManagement/Market.tsx | 26 ++++++++++++------------- 3 files changed, 47 insertions(+), 13 deletions(-) diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index cf14b37d..11be15b4 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1809,6 +1809,23 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re testConnectionSuccess: 'Test Connection Successful', refreshSuccess: 'Refresh Successful', refreshFailed: 'Refresh Failed', + + // Market related + marketSelectTitle: 'Select an MCP Market', + marketSelectDesc: 'Choose a market source from the left, configure the connection to browse MCP services', + marketRefreshSuccess: 'List refreshed', + marketActivated: 'Activated', + marketInDatabase: 'In Database', + marketAdd: 'Add', + marketRefresh: 'Refresh', + marketConfig: 'Configure', + marketConfigConnection: 'Configure Connection', + marketNoServices: 'No MCP Services Available', + marketNotConnected: 'Not Connected to This Market', + marketNoServicesDesc: 'This market currently has no available services', + marketNotConnectedDesc: 'Click the "Configure" button in the upper right corner to set connection information', + marketSearchPlaceholder: 'Search services...', + marketVisit: 'Visit Market', serviceEndpoint: 'Service Endpoint URL', serviceEndpointPlaceholder: 'URL of the service endpoint', serviceEndpointExtra: 'Complete access address of the MCP service', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 8b598abe..94c145eb 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1805,6 +1805,23 @@ export const zh = { testConnectionSuccess: '测试连接成功', refreshSuccess: '刷新成功', refreshFailed: '刷新失败', + + // Market 相关 + marketSelectTitle: '选择一个 MCP 市场', + marketSelectDesc: '从左侧选择一个市场源，配置连接后即可浏览该市场的 MCP 服务', + marketRefreshSuccess: '列表已刷新', + marketActivated: '已激活', + marketInDatabase: '已入库', + marketAdd: '添加', + marketRefresh: '刷新', + marketConfig: '配置', + marketConfigConnection: '配置连接', + marketNoServices: '暂无可用的 MCP 服务', + marketNotConnected: '尚未连接此市场', + marketNoServicesDesc: '该市场暂时没有可用的服务', + marketNotConnectedDesc: '点击右上角"配置"按钮设置连接信息', + marketSearchPlaceholder: '搜索服务...', + marketVisit: '前往市场', serviceEndpoint: '服务端点 URL', serviceEndpointPlaceholder: '服务端点的 URL', serviceEndpointExtra: 'MCP服务的完整访问地址', diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index 00dea715..f6af8404 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -200,7 +200,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => await fetchMcpList(sourceId, 1); const source = marketSources.find(s => s.id === sourceId); if (source) { - message.success(`${source.name} 列表已刷新`); + message.success(`${source.name} ${t('tool.marketRefreshSuccess')}`); } }; @@ -281,8 +281,8 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => return (
🏪
-

选择一个 MCP 市场

-

从左侧选择一个市场源，配置连接后即可浏览该市场的 MCP 服务

+

{t('tool.marketSelectTitle')}

+

{t('tool.marketSelectDesc')}

); } @@ -333,13 +333,13 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () =>
{source.connected && ( )} {mcpList.length > 0 && ( } - placeholder="搜索服务..." + placeholder={t('tool.marketSearchPlaceholder')} value={searchKeyword} onChange={(e) => setSearchKeyword(e.target.value)} style={{ width: 200 }} @@ -347,10 +347,10 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => )}
@@ -414,11 +414,11 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () =>
- {mcp.activated && 已激活} - {mcp.inDatabase && 已入库} + {mcp.activated && {t('tool.marketActivated')}} + {mcp.inDatabase && {t('tool.marketInDatabase')}}
@@ -430,14 +430,14 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () =>
{source.connected ? '📭' : '🔌'}

- {source.connected ? '暂无可用的 MCP 服务' : '尚未连接此市场'} + {source.connected ? t('tool.marketNoServices') : t('tool.marketNotConnected')}

- {source.connected ? '该市场暂时没有可用的服务' : '点击右上角"配置"按钮设置连接信息'} + {source.connected ? t('tool.marketNoServicesDesc') : t('tool.marketNotConnectedDesc')}

{!source.connected && ( )}
From 0f221b7ee6196c07d035d9dbe3f50f7ccb326232 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Mon, 9 Mar 2026 16:45:48 +0800 Subject: [PATCH 023/196] fix:loading --- .../KnowledgeBase/components/ShareModal.tsx | 27 +++++++----- .../components/ShareSpaceModal.tsx | 44 ++++++++++--------- 2 files changed, 40 insertions(+), 31 deletions(-) diff --git a/web/src/views/KnowledgeBase/components/ShareModal.tsx b/web/src/views/KnowledgeBase/components/ShareModal.tsx index dc4a732a..2f2cbb7a 100644 --- a/web/src/views/KnowledgeBase/components/ShareModal.tsx +++ b/web/src/views/KnowledgeBase/components/ShareModal.tsx @@ -4,7 +4,7 @@ * @Author: yujiangping * @Date: 2025-11-10 18:52:55 * @LastEditors: yujiangping - * @LastEditTime: 2026-03-03 14:46:08 + * @LastEditTime: 2026-03-09 16:39:07 */ import { forwardRef, useImperativeHandle, useState, useRef } from 'react'; import { Switch } from 'antd'; @@ -58,16 +58,21 @@ const ShareModal = forwardRef(({ handleShare: } const handleShare = async() => { - const workspaceIds = spaceList - .map(item => item.target_kb?.workspace_id) - .filter(Boolean) - .join(','); - - console.log('Workspace IDs:', workspaceIds); - shareSpaceModalRef?.current?.handleOpen(kbId,knowledgeBase,workspaceIds); - - // Close modal after sharing - handleClose(); + setLoading(true); + try { + const workspaceIds = spaceList + .map(item => item.target_kb?.workspace_id) + .filter(Boolean) + .join(','); + + console.log('Workspace IDs:', workspaceIds); + shareSpaceModalRef?.current?.handleOpen(kbId,knowledgeBase,workspaceIds); + + // Close modal after sharing + handleClose(); + } finally { + setLoading(false); + } } const handleChange = (checked: boolean, item: any) => { // Toggle shared knowledge base status diff --git a/web/src/views/KnowledgeBase/components/ShareSpaceModal.tsx b/web/src/views/KnowledgeBase/components/ShareSpaceModal.tsx index db26fd93..f75a47d6 100644 --- a/web/src/views/KnowledgeBase/components/ShareSpaceModal.tsx +++ b/web/src/views/KnowledgeBase/components/ShareSpaceModal.tsx @@ -4,7 +4,7 @@ * @Author: yujiangping * @Date: 2025-11-10 18:52:55 * @LastEditors: yujiangping - * @LastEditTime: 2025-12-03 18:44:58 + * @LastEditTime: 2026-03-09 16:34:51 */ import { forwardRef, useImperativeHandle, useState } from 'react'; import { Switch } from 'antd'; @@ -50,34 +50,38 @@ const ShareModal = forwardRef(({ handleShare: setSpaceList(filteredItems as SpaceItem[]); } const handleShare = async() => { - // Get all data with checked = true const checkedItems = spaceList.filter(item => item.is_active); - debugger // Get currently selected item (corresponding to curIndex) const selectedItem = curIndex !== -1 ? spaceList[curIndex] : null; if(!selectedItem){ messageApi.error(t('knowledgeBase.selectSpace')); return; } - const payload = { - source_kb_id: kbId ?? '', - target_workspace_id: selectedItem?.id ?? '', - } - const respose = await shareKnowledgeBase(payload) - if(respose){ - messageApi.success(t('knowledgeBase.shareSuccess')); - }else{ - messageApi.error(t('knowledgeBase.shareFailed')); - } - // Call parent component's callback function with selected data - onShare?.({ - checkedItems, - selectedItem - }); - // Close modal after sharing - handleClose(); + setLoading(true); + try { + const payload = { + source_kb_id: kbId ?? '', + target_workspace_id: selectedItem?.id ?? '', + } + const respose = await shareKnowledgeBase(payload) + if(respose){ + messageApi.success(t('knowledgeBase.shareSuccess')); + }else{ + messageApi.error(t('knowledgeBase.shareFailed')); + } + // Call parent component's callback function with selected data + onShare?.({ + checkedItems, + selectedItem + }); + + // Close modal after sharing + handleClose(); + } finally { + setLoading(false); + } } const handleClick = (index: number, checked: boolean) => { if (!checked) return; From 33d12c43b27c4308ae04734b0f9f967298fb7387 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 9 Mar 2026 17:30:43 +0800 Subject: [PATCH 024/196] =?UTF-8?q?feat(web):=20=E6=B3=A8=E9=87=8A?= =?UTF-8?q?=E8=8A=82=E7=82=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- web/src/i18n/en.ts | 7 + web/src/i18n/zh.ts | 7 + .../Workflow/components/CanvasToolbar.tsx | 10 +- .../NoteNode/NoteEditor/NoteFormatPlugin.tsx | 108 ++++++++++ .../NoteNode/NoteEditor/NoteLinkPopovers.tsx | 74 +++++++ .../Nodes/NoteNode/NoteEditor/index.tsx | 184 ++++++++++++++++++ .../Nodes/NoteNode/NoteNodeToolbar.tsx | 163 ++++++++++++++++ .../components/Nodes/NoteNode/index.tsx | 155 +++++++++++++++ web/src/views/Workflow/constant.ts | 81 +++++++- .../views/Workflow/hooks/useWorkflowGraph.ts | 77 +++++++- web/src/views/Workflow/index.tsx | 4 +- 11 files changed, 857 insertions(+), 13 deletions(-) create mode 100644 web/src/views/Workflow/components/Nodes/NoteNode/NoteEditor/NoteFormatPlugin.tsx create mode 100644 web/src/views/Workflow/components/Nodes/NoteNode/NoteEditor/NoteLinkPopovers.tsx create mode 100644 web/src/views/Workflow/components/Nodes/NoteNode/NoteEditor/index.tsx create mode 100644 web/src/views/Workflow/components/Nodes/NoteNode/NoteNodeToolbar.tsx create mode 100644 web/src/views/Workflow/components/Nodes/NoteNode/index.tsx diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index ad9680d3..6e9239d6 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -2008,6 +2008,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re self_optimization: 'Self Optimization', process_evolution: 'Process Evolution', unknown: 'Unknown Node', + notes: 'Sticky Note', clickToConfigure: 'Click to configure node parameters', nodeProperties: 'Node Properties', @@ -2195,6 +2196,12 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re output_variables: 'Output Variables', refreshTip: 'Sync function signature to code', }, + notes: { + showAuth: 'Show Author', + enterLink: 'Enter Link URL', + placeholder: 'Enter note...', + removeLink: 'Remove Link', + }, name: 'Key', type: 'Type', value: 'Value', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index c4d2df71..45825a09 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -2004,6 +2004,7 @@ export const zh = { self_optimization: '自我优化', process_evolution: '流程演化', unknown: '未知节点', + notes: '便签', clickToConfigure: '点击配置节点参数', nodeProperties: '节点属性', @@ -2194,6 +2195,12 @@ export const zh = { unknown: { replaceNodeType: '替换节点' }, + notes: { + showAuth: '显示作者', + enterLink: '输入链接 URL', + placeholder: '输入注释...', + removeLink: '取消链接', + }, name: '键', type: '类型', value: '值', diff --git a/web/src/views/Workflow/components/CanvasToolbar.tsx b/web/src/views/Workflow/components/CanvasToolbar.tsx index 8ca272e1..8bf2c641 100644 --- a/web/src/views/Workflow/components/CanvasToolbar.tsx +++ b/web/src/views/Workflow/components/CanvasToolbar.tsx @@ -1,8 +1,8 @@ import type { FC } from 'react'; -import { Select } from 'antd'; +import { Select, Divider } from 'antd'; // import { Node } from '@antv/x6'; import type { GraphRef } from '../types' -import { PlusOutlined, MinusOutlined } from '@ant-design/icons' +import { PlusOutlined, MinusOutlined, FileAddOutlined } from '@ant-design/icons' interface CanvasToolbarProps { miniMapRef: React.RefObject; @@ -14,6 +14,7 @@ interface CanvasToolbarProps { canRedo: boolean; onUndo: () => void; onRedo: () => void; + addNotes: () => void; } const CanvasToolbar: FC = ({ @@ -26,6 +27,7 @@ const CanvasToolbar: FC = ({ // canRedo, // onUndo, // onRedo, + addNotes, }) => { // 整理布局函数 /* @@ -152,7 +154,7 @@ const CanvasToolbar: FC = ({ {/* 小地图 */}
{/* 缩放控制按钮 */} -
+
graphRef.current?.zoom(-0.1)} /> setUrl(e.target.value)} + onKeyDown={e => e.stopPropagation()} + onPressEnter={confirm} + autoFocus + /> + + +
, + document.body + ); +}; diff --git a/web/src/views/Workflow/components/Nodes/NoteNode/NoteEditor/index.tsx b/web/src/views/Workflow/components/Nodes/NoteNode/NoteEditor/index.tsx new file mode 100644 index 00000000..06984d35 --- /dev/null +++ b/web/src/views/Workflow/components/Nodes/NoteNode/NoteEditor/index.tsx @@ -0,0 +1,184 @@ +import { type FC, useState, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { LexicalComposer } from '@lexical/react/LexicalComposer'; +import { RichTextPlugin } from '@lexical/react/LexicalRichTextPlugin'; +import { ContentEditable } from '@lexical/react/LexicalContentEditable'; +import { HistoryPlugin } from '@lexical/react/LexicalHistoryPlugin'; +import { LexicalErrorBoundary } from '@lexical/react/LexicalErrorBoundary'; +import { ListPlugin } from '@lexical/react/LexicalListPlugin'; +import { LinkPlugin } from '@lexical/react/LexicalLinkPlugin'; +import { ListNode, ListItemNode } from '@lexical/list'; +import { LinkNode } from '@lexical/link'; +import { OnChangePlugin } from '@lexical/react/LexicalOnChangePlugin'; +import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext'; +import { useEffect, useRef } from 'react'; +import NoteFormatPlugin from './NoteFormatPlugin'; +import type { FormatState } from './NoteFormatPlugin'; +import { LinkPopover, EditLinkPopover } from './NoteLinkPopovers'; + +const theme = { + paragraph: 'editor-paragraph', + text: { + bold: 'editor-text-bold', + italic: 'editor-text-italic', + strikethrough: 'note-text-strikethrough', + }, + list: { ul: 'note-list-ul', listitem: 'note-list-item' }, + link: 'note-link', +}; + +const NOTE_NODES = [ListNode, ListItemNode, LinkNode]; + +const NOTE_STYLES = ` + .editor-text-bold { font-weight: bold; } + .editor-text-italic { font-style: italic; } + .note-text-strikethrough { text-decoration: line-through; } + .note-list-ul { list-style-type: disc; padding-left: 1.2em; margin: 0; } + .note-list-item { margin: 2px 0; } + .note-link { color: #2563eb; text-decoration: underline; cursor: pointer; } +`; + +const NoteInitPlugin: FC<{ value: string }> = ({ value }) => { + const [editor] = useLexicalComposerContext(); + const initialized = useRef(false); + useEffect(() => { + if (initialized.current || !value) return; + initialized.current = true; + try { + const parsed = JSON.parse(value); + if (parsed?.root) { + const state = editor.parseEditorState(JSON.stringify(parsed)); + editor.setEditorState(state); + return; + } + } catch {} + }, [editor, value]); + return null; +}; + + +interface NoteEditorProps { + nodeId: string; + value: string; + fontSize?: number; + onChange: (val: string) => void; + onFormatChange?: (state: FormatState) => void; +} + +const NoteEditor: FC = ({ nodeId, value, fontSize = 12, onChange, onFormatChange }) => { + const { t } = useTranslation(); + const [linkState, setLinkState] = useState<{ url: string; rect: DOMRect } | null>(null); + const [editLinkRect, setEditLinkRect] = useState<{ url: string; rect: DOMRect } | null>(null); + const removingLink = useRef(false); + + useEffect(() => { + if (!linkState) return; + const handler = () => setLinkState(null); + window.addEventListener('mousedown', handler); + return () => window.removeEventListener('mousedown', handler); + }, [!!linkState]); + + useEffect(() => { + const handler = (e: Event) => { + const { id, url, rect: passedRect } = (e as CustomEvent).detail; + if (id !== nodeId) return; + if (passedRect) { + setEditLinkRect({ url: url || '', rect: passedRect }); + return; + } + const sel = window.getSelection(); + if (sel && sel.rangeCount > 0) { + const r = sel.getRangeAt(0).getBoundingClientRect(); + if (r.width > 0 || r.height > 0) { setEditLinkRect({ url: url || '', rect: r }); return; } + } + const linkEl = document.querySelector(`[data-note-id="${nodeId}"] a.note-link`) as HTMLElement; + const rect = linkEl?.getBoundingClientRect() ?? new DOMRect(window.innerWidth / 2, 200, 0, 0); + setEditLinkRect({ url: url || '', rect }); + }; + window.addEventListener('note:edit-link', handler); + return () => window.removeEventListener('note:edit-link', handler); + }, [nodeId]); + + const handleFormatChange = useCallback((state: FormatState) => { + onFormatChange?.(state); + if (state.linkUrl) { + requestAnimationFrame(() => { + if (removingLink.current) { removingLink.current = false; return; } + const sel = window.getSelection(); + if (sel && sel.rangeCount > 0) { + const rect = sel.getRangeAt(0).getBoundingClientRect(); + if (rect.width > 0 || rect.height > 0) { + setLinkState({ url: state.linkUrl!, rect }); + return; + } + } + // fallback: find the link element in the correct editor + const editorEl = document.querySelector(`[data-note-id="${nodeId}"] a.note-link`) as HTMLElement; + if (editorEl) { + setLinkState({ url: state.linkUrl!, rect: editorEl.getBoundingClientRect() }); + } + }); + } else { + setLinkState(null); + } + }, [onFormatChange]); + + return ( + <> + + +
+ + } + placeholder={ +
+ {t('workflow.config.notes.placeholder')} +
+ } + ErrorBoundary={LexicalErrorBoundary} + /> + + + + onChange(JSON.stringify(editorState.toJSON()))} /> + + + {editLinkRect && ( + { + removingLink.current = true; + window.dispatchEvent(new CustomEvent('note:format', { detail: { id: nodeId, format: 'link', value: url || null } })); + setEditLinkRect(null); + }} + /> + )} + {linkState && ( + { + removingLink.current = true; + const { rect, url } = linkState; + setLinkState(null); + setEditLinkRect({ url, rect }); + }} + onRemove={() => { + removingLink.current = true; + setLinkState(null); + window.dispatchEvent(new CustomEvent('note:format', { detail: { id: nodeId, format: 'link', value: null } })); + }} + /> + )} +
+ + + ); +}; + +export default NoteEditor; diff --git a/web/src/views/Workflow/components/Nodes/NoteNode/NoteNodeToolbar.tsx b/web/src/views/Workflow/components/Nodes/NoteNode/NoteNodeToolbar.tsx new file mode 100644 index 00000000..52c0eb22 --- /dev/null +++ b/web/src/views/Workflow/components/Nodes/NoteNode/NoteNodeToolbar.tsx @@ -0,0 +1,163 @@ +import { type FC } from 'react'; +import { Flex, Dropdown, type MenuProps, Switch, Button, Divider } from 'antd'; +import { UnorderedListOutlined, BoldOutlined, ItalicOutlined, StrikethroughOutlined, LinkOutlined, DashOutlined } from '@ant-design/icons'; +import { Node } from '@antv/x6'; +import { useTranslation } from 'react-i18next' + +import { THEME_MAP } from '../../../constant'; +const FONT_SIZES = [ + { label: '小', value: 12 }, + { label: '中', value: 14 }, + { label: '大', value: 16 }, +]; + +interface NoteNodeToolbarProps { + node: Node; + onFormat: (type: string, value?: unknown) => void; + toolConfig: Record; + nodeId: string; +} + +const NoteNodeToolbar: FC = ({ node, onFormat, toolConfig, nodeId }) => { + const data = node?.getData() || {}; + const { t } = useTranslation(); + + const colorItems: MenuProps['items'] = Object.entries(THEME_MAP).map(([key, theme]) => ({ + key, + label: ( +
onFormat('color', key)} + /> + ), + })); + + const fontSizeItems: MenuProps['items'] = FONT_SIZES.map(({ label, value }) => ({ + key: value, + label: onFormat('fontSize', value)}>{label}, + })); + + const currentFontSize = FONT_SIZES.find(f => f.value === toolConfig.fontSize)?.label ?? '小'; + + const handleClick: MenuProps['onClick'] = (e) => { + switch (e.key) { + case 'delete': + node.remove() + break; + case 'copy': + break; + } + } + const handleChange = (type: string) => { + let show_author = data.config.show_author.defaultValue + if(type === 'showAuth'){ + show_author = !show_author + } + node.setData({ + ...data, + config: { + ...data.config, + show_author: { + ...data.config.show_author, + defaultValue: show_author + } + } + }) + } + + return ( + e.stopPropagation()} + > + {/* Color picker */} + +
+ + + + + {/* Font size */} + + + Aa + {currentFontSize} + + + + + + {/* Bold */} +
From ac86bbd60c780f2c3ebc1d1a4e6be78297043b59 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 9 Mar 2026 17:35:56 +0800 Subject: [PATCH 025/196] =?UTF-8?q?feat(web):=20=E8=B0=83=E6=95=B4?= =?UTF-8?q?=E4=BE=BF=E7=AD=BE=E8=8A=82=E7=82=B9=E4=BD=8D=E7=BD=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- web/src/views/Workflow/hooks/useWorkflowGraph.ts | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts index e654e4e9..db792c59 100644 --- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts +++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts @@ -1152,12 +1152,15 @@ export const useWorkflowGraph = ({ name: t('workflow.notes'), ...nodeConfig, }; - const area = graphRef.current.getGraphArea(); - const pos = graphRef.current.graphToLocal(area.center.x, area.bottom); + const container = graphRef.current.container; + const nodeW = graphNodeLibrary.notes?.width || nodeWidth; + const nodeH = graphNodeLibrary.notes?.height || 100; + const rect = container.getBoundingClientRect(); + const center = graphRef.current.clientToLocal(rect.left + rect.width / 2, rect.top + rect.height / 2); graphRef.current.addNode({ ...(graphNodeLibrary.notes || graphNodeLibrary.default), - x: pos.x - nodeWidth, - y: pos.y, + x: center.x - nodeW / 2, + y: center.y - nodeH / 2, id: cleanNodeData.id, data: { ...cleanNodeData }, }); From e4aaa18f6108189256b0a0281b433b5b2d29aa73 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Mon, 9 Mar 2026 18:50:32 +0800 Subject: [PATCH 026/196] [changes] User summaries stored in RAG, generation of memory insights --- api/app/core/rag_utils/chunk_insight.py | 42 +++++++++++++++----- api/app/core/rag_utils/chunk_summary.py | 32 ++++++++++++--- api/app/core/rag_utils/chunk_tags.py | 36 +++++++++++++---- api/app/services/memory_dashboard_service.py | 8 ++-- 4 files changed, 89 insertions(+), 29 deletions(-) diff --git a/api/app/core/rag_utils/chunk_insight.py b/api/app/core/rag_utils/chunk_insight.py index e904e53d..9fbdbbb2 100644 --- a/api/app/core/rag_utils/chunk_insight.py +++ b/api/app/core/rag_utils/chunk_insight.py @@ -5,8 +5,9 @@ This module provides functionality to analyze chunk content and generate insight """ import asyncio +import os from collections import Counter -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional from app.core.logging_config import get_business_logger from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -15,12 +16,31 @@ from pydantic import BaseModel, Field business_logger = get_business_logger() +DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus") -def _get_llm_client(): - """Get LLM client using db context.""" + +def _get_llm_client(end_user_id: Optional[str] = None): + """Get LLM client, preferring user-connected config with fallback to default.""" with get_db_context() as db: + try: + if end_user_id: + from app.services.memory_agent_service import get_end_user_connected_config + from app.services.memory_config_service import MemoryConfigService + connected_config = get_end_user_connected_config(end_user_id, db) + config_id = connected_config.get("memory_config_id") + workspace_id = connected_config.get("workspace_id") + if config_id or workspace_id: + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + workspace_id=workspace_id + ) + factory = MemoryClientFactory(db) + return factory.get_llm_client(memory_config.llm_model_id) + except Exception as e: + business_logger.warning(f"Failed to get user connected config, using default LLM: {e}") factory = MemoryClientFactory(db) - return factory.get_llm_client(None) # Uses default LLM + return factory.get_llm_client(DEFAULT_LLM_ID) class ChunkInsight(BaseModel): @@ -37,7 +57,7 @@ class DomainClassification(BaseModel): ) -async def classify_chunk_domain(chunk: str) -> str: +async def classify_chunk_domain(chunk: str, end_user_id: Optional[str] = None) -> str: """ Classify a chunk into a specific domain. @@ -48,7 +68,7 @@ async def classify_chunk_domain(chunk: str) -> str: Domain name """ try: - llm_client = _get_llm_client() + llm_client = _get_llm_client(end_user_id) prompt = f"""请将以下文本内容归类到最合适的领域中。 @@ -82,7 +102,7 @@ async def classify_chunk_domain(chunk: str) -> str: return "其他" -async def analyze_domain_distribution(chunks: List[str], max_chunks: int = 20) -> Dict[str, float]: +async def analyze_domain_distribution(chunks: List[str], max_chunks: int = 20, end_user_id: Optional[str] = None) -> Dict[str, float]: """ Analyze the domain distribution of chunks. @@ -103,7 +123,7 @@ async def analyze_domain_distribution(chunks: List[str], max_chunks: int = 20) - # 为每个chunk分类 domain_counts = Counter() for chunk in chunks_to_analyze: - domain = await classify_chunk_domain(chunk) + domain = await classify_chunk_domain(chunk, end_user_id) domain_counts[domain] += 1 # 计算百分比 @@ -121,7 +141,7 @@ async def analyze_domain_distribution(chunks: List[str], max_chunks: int = 20) - return {} -async def generate_chunk_insight(chunks: List[str], max_chunks: int = 15) -> str: +async def generate_chunk_insight(chunks: List[str], max_chunks: int = 15, end_user_id: Optional[str] = None) -> str: """ Generate insights from the given chunks. @@ -138,7 +158,7 @@ async def generate_chunk_insight(chunks: List[str], max_chunks: int = 15) -> str try: # 1. 分析领域分布 - domain_dist = await analyze_domain_distribution(chunks, max_chunks=max_chunks) + domain_dist = await analyze_domain_distribution(chunks, max_chunks=max_chunks, end_user_id=end_user_id) # 2. 统计基本信息 total_chunks = len(chunks) @@ -185,7 +205,7 @@ async def generate_chunk_insight(chunks: List[str], max_chunks: int = 15) -> str ] # 调用LLM生成洞察 - llm_client = _get_llm_client() + llm_client = _get_llm_client(end_user_id) response = await llm_client.chat(messages=messages) insight = response.content.strip() diff --git a/api/app/core/rag_utils/chunk_summary.py b/api/app/core/rag_utils/chunk_summary.py index 7f69af88..53df2ab3 100644 --- a/api/app/core/rag_utils/chunk_summary.py +++ b/api/app/core/rag_utils/chunk_summary.py @@ -5,7 +5,8 @@ This module provides functionality to summarize chunk content using LLM. """ import asyncio -from typing import Any, Dict, List +import os +from typing import Any, Dict, List, Optional from app.core.logging_config import get_business_logger from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -14,12 +15,31 @@ from pydantic import BaseModel, Field business_logger = get_business_logger() +DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus") -def _get_llm_client(): - """Get LLM client using db context.""" + +def _get_llm_client(end_user_id: Optional[str] = None): + """Get LLM client, preferring user-connected config with fallback to default.""" with get_db_context() as db: + try: + if end_user_id: + from app.services.memory_agent_service import get_end_user_connected_config + from app.services.memory_config_service import MemoryConfigService + connected_config = get_end_user_connected_config(end_user_id, db) + config_id = connected_config.get("memory_config_id") + workspace_id = connected_config.get("workspace_id") + if config_id or workspace_id: + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + workspace_id=workspace_id + ) + factory = MemoryClientFactory(db) + return factory.get_llm_client(memory_config.llm_model_id) + except Exception as e: + business_logger.warning(f"Failed to get user connected config, using default LLM: {e}") factory = MemoryClientFactory(db) - return factory.get_llm_client(None) # Uses default LLM + return factory.get_llm_client(DEFAULT_LLM_ID) class ChunkSummary(BaseModel): @@ -27,7 +47,7 @@ class ChunkSummary(BaseModel): summary: str = Field(..., description="简洁的chunk内容摘要") -async def generate_chunk_summary(chunks: List[str], max_chunks: int = 10) -> str: +async def generate_chunk_summary(chunks: List[str], max_chunks: int = 10, end_user_id: Optional[str] = None) -> str: """ Generate a summary for the given chunks. @@ -67,7 +87,7 @@ async def generate_chunk_summary(chunks: List[str], max_chunks: int = 10) -> str ] # 调用LLM生成摘要 - llm_client = _get_llm_client() + llm_client = _get_llm_client(end_user_id) response = await llm_client.chat(messages=messages) summary = response.content.strip() diff --git a/api/app/core/rag_utils/chunk_tags.py b/api/app/core/rag_utils/chunk_tags.py index 2057f8ac..98ab4a33 100644 --- a/api/app/core/rag_utils/chunk_tags.py +++ b/api/app/core/rag_utils/chunk_tags.py @@ -5,8 +5,9 @@ This module provides functionality to extract meaningful tags from chunk content """ import asyncio +import os from collections import Counter -from typing import List, Tuple +from typing import List, Optional, Tuple from app.core.logging_config import get_business_logger from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -15,12 +16,31 @@ from pydantic import BaseModel, Field business_logger = get_business_logger() +DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus") -def _get_llm_client(): - """Get LLM client using db context.""" + +def _get_llm_client(end_user_id: Optional[str] = None): + """Get LLM client, preferring user-connected config with fallback to default.""" with get_db_context() as db: + try: + if end_user_id: + from app.services.memory_agent_service import get_end_user_connected_config + from app.services.memory_config_service import MemoryConfigService + connected_config = get_end_user_connected_config(end_user_id, db) + config_id = connected_config.get("memory_config_id") + workspace_id = connected_config.get("workspace_id") + if config_id or workspace_id: + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + workspace_id=workspace_id + ) + factory = MemoryClientFactory(db) + return factory.get_llm_client(memory_config.llm_model_id) + except Exception as e: + business_logger.warning(f"Failed to get user connected config, using default LLM: {e}") factory = MemoryClientFactory(db) - return factory.get_llm_client(None) # Uses default LLM + return factory.get_llm_client(DEFAULT_LLM_ID) class ExtractedTags(BaseModel): @@ -33,7 +53,7 @@ class ExtractedPersona(BaseModel): personas: List[str] = Field(..., description="从文本中提取的人物形象列表，如'产品设计师'、'旅行爱好者'等") -async def extract_chunk_tags(chunks: List[str], max_tags: int = 10, max_chunks: int = 10) -> List[Tuple[str, int]]: +async def extract_chunk_tags(chunks: List[str], max_tags: int = 10, max_chunks: int = 10, end_user_id: Optional[str] = None) -> List[Tuple[str, int]]: """ Extract meaningful tags from the given chunks. @@ -64,7 +84,7 @@ async def extract_chunk_tags(chunks: List[str], max_tags: int = 10, max_chunks: "标签应该是名词或名词短语，能够准确概括文本的核心内容。" ) - llm_client = _get_llm_client() + llm_client = _get_llm_client(end_user_id) # 为每个chunk单独提取标签，然后统计频率 all_tags = [] @@ -116,7 +136,7 @@ async def extract_chunk_tags_with_frequency(chunks: List[str], max_tags: int = 1 return await extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=len(chunks)) -async def extract_chunk_persona(chunks: List[str], max_personas: int = 5, max_chunks: int = 20) -> List[str]: +async def extract_chunk_persona(chunks: List[str], max_personas: int = 5, max_chunks: int = 20, end_user_id: Optional[str] = None) -> List[str]: """ Extract persona (人物形象) from the given chunks. @@ -159,7 +179,7 @@ async def extract_chunk_persona(chunks: List[str], max_personas: int = 5, max_ch ] # 调用LLM提取人物形象 - llm_client = _get_llm_client() + llm_client = _get_llm_client(end_user_id) structured_response = await llm_client.response_structured( messages=messages, response_model=ExtractedPersona diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index 05aed57e..63a9c361 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -678,9 +678,9 @@ async def get_chunk_summary_and_tags( # 3. 并发生成摘要、提取标签和人物形象 import asyncio - summary_task = generate_chunk_summary(chunks, max_chunks=limit) - tags_task = extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit) - personas_task = extract_chunk_persona(chunks, max_personas=5, max_chunks=limit) + summary_task = generate_chunk_summary(chunks, max_chunks=limit, end_user_id=end_user_id) + tags_task = extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit, end_user_id=end_user_id) + personas_task = extract_chunk_persona(chunks, max_personas=5, max_chunks=limit, end_user_id=end_user_id) summary, tags_with_freq, personas = await asyncio.gather(summary_task, tags_task, personas_task) @@ -736,7 +736,7 @@ async def get_chunk_insight( from app.core.rag_utils import generate_chunk_insight # 3. 生成洞察 - insight = await generate_chunk_insight(chunks, max_chunks=limit) + insight = await generate_chunk_insight(chunks, max_chunks=limit, end_user_id=end_user_id) result = { "insight": insight From 9fe47e2fb25b4efaafa6c2d25b36655d06ab6250 Mon Sep 17 00:00:00 2001 From: Ke Sun Date: Mon, 9 Mar 2026 19:07:09 +0800 Subject: [PATCH 027/196] fix(memory_agent): handle draft run without current release - Add TODO comment to verify end_user sources (chat, draft, apikey) - Comment out release validation check to support draft run mode - Add TODO note explaining temporary fix for draft execution - Handle null current_release_id in result by returning None instead of failing - Improve import formatting for MemoryConfig model import statement - Allow configuration retrieval when app has no published release --- api/app/services/memory_agent_service.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index f272c541..a20b968a 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -1165,6 +1165,7 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An logger.info(f"Getting connected config for end_user: {end_user_id}") + # TODO: check sources for enduserid, should be one of these three: chat, draft, apikey # 1. 获取 end_user 及其 app_id end_user = db.query(EndUser).filter(EndUser.id == end_user_id).first() if not end_user: @@ -1179,10 +1180,10 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An if not app: logger.warning(f"App not found: {app_id}") raise ValueError(f"应用不存在: {app_id}") - - if not app.current_release_id: - logger.warning(f"No current release for app: {app_id}") - raise ValueError(f"应用未发布: {app_id}") + # TODO: temp fix for draft run + # if not app.current_release_id: + # logger.warning(f"No current release for app: {app_id}") + # raise ValueError(f"应用未发布: {app_id}") # 3. 兼容旧数据：如果 memory_config_id 为空，从 AppRelease.config 获取并回填 memory_config_id_to_use = end_user.memory_config_id @@ -1223,7 +1224,9 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An if legacy_config_id: # 验证提取的 config_id 是否存在于数据库中 - from app.models.memory_config_model import MemoryConfig as MemoryConfigModel + from app.models.memory_config_model import ( + MemoryConfig as MemoryConfigModel, + ) existing_config = db.get(MemoryConfigModel, legacy_config_id) if existing_config: @@ -1257,7 +1260,7 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An result = { "end_user_id": str(end_user_id), "app_id": str(app_id), - "release_id": str(app.current_release_id), + "release_id": str(app.current_release_id) if app.current_release_id else None, "memory_config_id": memory_config_id, "workspace_id": str(app.workspace_id) } From f405ac4d843cb3bd7016b2a73ba8a82c451aacea Mon Sep 17 00:00:00 2001 From: yujiangping Date: Mon, 9 Mar 2026 19:10:39 +0800 Subject: [PATCH 028/196] fix:next button --- .../[knowledgeBaseId]/CreateDataset.tsx | 18 ++++++++-- web/src/views/ToolManagement/Market.tsx | 33 ++++++++----------- 2 files changed, 29 insertions(+), 22 deletions(-) diff --git a/web/src/views/KnowledgeBase/[knowledgeBaseId]/CreateDataset.tsx b/web/src/views/KnowledgeBase/[knowledgeBaseId]/CreateDataset.tsx index 760c4292..b336fe04 100644 --- a/web/src/views/KnowledgeBase/[knowledgeBaseId]/CreateDataset.tsx +++ b/web/src/views/KnowledgeBase/[knowledgeBaseId]/CreateDataset.tsx @@ -82,6 +82,7 @@ const CreateDataset = () => { const [form] = Form.useForm(); const [data, setData] = useState([]); const [rechunkFileIds, setRechunkFileIds] = useState(initialFileIds); + const [textFormValid, setTextFormValid] = useState(false); const [pollingLoading, setPollingLoading] = useState(false); const pollingTimerRef = useRef | null>(null); @@ -624,7 +625,16 @@ const CreateDataset = () => { )} {source && source === 'text' && (
-
+ { + // 检查表单字段是否都已填写 + const values = form.getFieldsValue(); + const isValid = !!(values.title?.trim() && values.content?.trim()); + setTextFormValid(isValid); + }} + > { diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index f6af8404..5297903e 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -6,7 +6,10 @@ import InfiniteScroll from 'react-infinite-scroll-component'; import MarketConfigModal, { type MarketConfigModalRef } from './components/MarketConfigModal'; import McpServiceModal from './components/McpServiceModal'; import type { McpServiceModalRef } from './types'; +import pageEmptyIcon from '@/assets/images/empty/pageEmpty.png' +import Empty from '@/components/Empty/index' import { getMarketTools, getMarketConfig, getMarketMCPs, getMarketMCPDetail, getMarketMCPsActivated, getTools } from '@/api/tools'; +import BodyWrapper from '@/components/Empty/BodyWrapper'; interface MarketSource { id: string; name: string; @@ -280,9 +283,14 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => if (!selectedSource) { return (
-
🏪
-

{t('tool.marketSelectTitle')}

-

{t('tool.marketSelectDesc')}

+ +
); } @@ -356,7 +364,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () =>
- {mcpList.length > 0 ? ( +
ReactNode }> = () =>
- ) : ( -
-
{source.connected ? '📭' : '🔌'}
-

- {source.connected ? t('tool.marketNoServices') : t('tool.marketNotConnected')} -

-

- {source.connected ? t('tool.marketNoServicesDesc') : t('tool.marketNotConnectedDesc')} -

- {!source.connected && ( - - )} -
- )} +
); From 7cdbbefc64f6d070bb61459a12748b751d45a854 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 10 Mar 2026 10:44:24 +0800 Subject: [PATCH 029/196] feat(workspace, app, agent): add duplicate name validation and restrict model/memory config on agent publish --- api/app/core/config.py | 3 +- api/app/repositories/app_repository.py | 23 +- api/app/repositories/workspace_repository.py | 111 ++++++--- api/app/services/app_service.py | 15 +- api/app/services/workspace_service.py | 241 ++++++++++--------- 5 files changed, 233 insertions(+), 160 deletions(-) diff --git a/api/app/core/config.py b/api/app/core/config.py index bbe327b6..3bc77376 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -1,7 +1,6 @@ -import json import os from pathlib import Path -from typing import Annotated, Any, Dict, Optional +from typing import Annotated, Optional from dotenv import load_dotenv from pydantic import Field, TypeAdapter diff --git a/api/app/repositories/app_repository.py b/api/app/repositories/app_repository.py index 0c7ba6a4..75a91fd6 100644 --- a/api/app/repositories/app_repository.py +++ b/api/app/repositories/app_repository.py @@ -1,10 +1,11 @@ -from sqlalchemy.orm import Session -from typing import List, Optional import uuid +from typing import List -from app.models.app_model import App +from sqlalchemy import select +from sqlalchemy.orm import Session from app.core.logging_config import get_db_logger +from app.models.app_model import App # 获取数据库专用日志器 db_logger = get_db_logger() @@ -35,11 +36,27 @@ class AppRepository: except Exception as e: raise + def get_apps_by_name(self, app_name: str, app_type: str, workspace_id: uuid.UUID) -> List[App]: + try: + stmt = select(App).where( + App.name == app_name, + App.workspace_id == workspace_id, + App.type == app_type, + App.is_active.is_(True), + ) + apps = self.db.execute(stmt).scalars().all() + return list(apps) + except Exception as e: + db_logger.error(f"查询名称 {app_name} 应用异常: {str(e)}") + raise + + def get_apps_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> List[App]: """根据工作空间ID查询应用""" repo = AppRepository(db) return repo.get_apps_by_workspace_id(workspace_id) + def get_apps_by_id(db: Session, app_id: uuid.UUID) -> App: """根据工作空间ID查询应用""" repo = AppRepository(db) diff --git a/api/app/repositories/workspace_repository.py b/api/app/repositories/workspace_repository.py index 87b0e20f..68dbf13c 100644 --- a/api/app/repositories/workspace_repository.py +++ b/api/app/repositories/workspace_repository.py @@ -1,10 +1,13 @@ -from sqlalchemy.orm import Session, joinedload -from app.models.user_model import User -from typing import List, Optional import uuid -from app.models.workspace_model import Workspace, WorkspaceMember, WorkspaceRole -from app.schemas.workspace_schema import WorkspaceCreate, WorkspaceUpdate +from typing import List, Optional + +from sqlalchemy.orm import Session, joinedload +from sqlalchemy import select + from app.core.logging_config import get_db_logger +from app.models.user_model import User +from app.models.workspace_model import Workspace, WorkspaceMember, WorkspaceRole +from app.schemas.workspace_schema import WorkspaceCreate # 获取数据库专用日志器 db_logger = get_db_logger() @@ -19,7 +22,7 @@ class WorkspaceRepository: def create_workspace(self, workspace_data: WorkspaceCreate, tenant_id: uuid.UUID) -> Workspace: """创建工作空间""" db_logger.debug(f"创建工作空间记录: name={workspace_data.name}, tenant_id={tenant_id}") - + try: db_workspace = Workspace( name=workspace_data.name, @@ -34,7 +37,8 @@ class WorkspaceRepository: ) self.db.add(db_workspace) self.db.flush() - db_logger.info(f"工作空间记录创建成功: {workspace_data.name} (ID: {db_workspace.id}), storage_type: {workspace_data.storage_type}") + db_logger.info( + f"工作空间记录创建成功: {workspace_data.name} (ID: {db_workspace.id}), storage_type: {workspace_data.storage_type}") return db_workspace except Exception as e: db_logger.error(f"创建工作空间记录失败: name={workspace_data.name} - {str(e)}") @@ -43,7 +47,7 @@ class WorkspaceRepository: def get_workspace_by_id(self, workspace_id: uuid.UUID) -> Optional[Workspace]: """根据ID获取工作空间""" db_logger.debug(f"根据ID查询工作空间: workspace_id={workspace_id}") - + try: workspace = self.db.query(Workspace).filter(Workspace.id == workspace_id).first() if workspace: @@ -65,7 +69,7 @@ class WorkspaceRepository: 包含 llm, embedding, rerank 的字典，如果工作空间不存在则返回 None """ db_logger.debug(f"查询工作空间模型配置: workspace_id={workspace_id}") - + try: workspace = self.db.query(Workspace).filter(Workspace.id == workspace_id).first() if workspace: @@ -89,7 +93,7 @@ class WorkspaceRepository: def get_workspaces_by_user(self, user_id: uuid.UUID) -> List[Workspace]: """获取用户参与的所有工作空间（包括用户创建的和作为成员的）""" db_logger.debug(f"查询用户参与的工作空间: user_id={user_id}") - + try: # 首先获取用户信息以获取 tenant_id from app.models.user_model import User @@ -97,7 +101,7 @@ class WorkspaceRepository: if not user: db_logger.warning(f"用户不存在: user_id={user_id}") return [] - + if user.is_superuser: # 超级用户获取对应tenantid所有工作空间 workspaces = ( @@ -109,7 +113,7 @@ class WorkspaceRepository: ) db_logger.debug(f"超用户查询所有工作空间: user_id={user_id}, 数量={len(workspaces)}") return workspaces - + # 获取用户作为成员的工作空间 member_workspaces = ( self.db.query(Workspace) @@ -120,7 +124,7 @@ class WorkspaceRepository: .order_by(Workspace.updated_at.desc()) .all() ) - + db_logger.debug(f"用户工作空间查询成功: user_id={user_id}, 数量={len(member_workspaces)}") return member_workspaces except Exception as e: @@ -130,7 +134,7 @@ class WorkspaceRepository: def get_workspaces_by_tenant(self, tenant_id: uuid.UUID) -> List[Workspace]: """获取租户的所有工作空间""" db_logger.debug(f"查询租户的工作空间: tenant_id={tenant_id}") - + try: workspaces = ( self.db.query(Workspace) @@ -144,14 +148,32 @@ class WorkspaceRepository: db_logger.error(f"查询租户工作空间失败: tenant_id={tenant_id} - {str(e)}") raise - def add_member(self, workspace_id: uuid.UUID, user_id: uuid.UUID, role: WorkspaceRole = WorkspaceRole.member) -> WorkspaceMember: + def get_workspaces_by_name(self, tenant_id: uuid.UUID, workspace_name: str) -> List[Workspace]: + try: + stmt = ( + select(Workspace) + .where( + Workspace.tenant_id == tenant_id, + Workspace.name == workspace_name, + Workspace.is_active.is_(True) + ) + ) + + workspaces = self.db.execute(stmt).scalars().all() + return list(workspaces) + except Exception as e: + db_logger.error(f"查询工作空间失败: workspace_name={workspace_name} - {str(e)}") + raise + + def add_member(self, workspace_id: uuid.UUID, user_id: uuid.UUID, + role: WorkspaceRole = WorkspaceRole.member) -> WorkspaceMember: """添加工作空间成员""" db_logger.debug(f"添加工作空间成员: user_id={user_id}, workspace_id={workspace_id}, role={role}") - + try: db_member = WorkspaceMember( - user_id=user_id, - workspace_id=workspace_id, + user_id=user_id, + workspace_id=workspace_id, role=role ) self.db.add(db_member) @@ -165,7 +187,7 @@ class WorkspaceRepository: def get_member(self, user_id: uuid.UUID, workspace_id: uuid.UUID) -> Optional[WorkspaceMember]: """获取工作空间成员""" db_logger.debug(f"查询工作空间成员: user_id={user_id}, workspace_id={workspace_id}") - + try: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.user_id == user_id, @@ -173,7 +195,8 @@ class WorkspaceRepository: WorkspaceMember.is_active.is_(True), ).first() if member: - db_logger.debug(f"工作空间成员查询成功: user_id={user_id}, workspace_id={workspace_id}, role={member.role}") + db_logger.debug( + f"工作空间成员查询成功: user_id={user_id}, workspace_id={workspace_id}, role={member.role}") else: db_logger.debug(f"工作空间成员不存在: user_id={user_id}, workspace_id={workspace_id}") return member @@ -199,7 +222,7 @@ class WorkspaceRepository: except Exception as e: db_logger.error(f"查询成员列表失败: workspace_id={workspace_id} - {str(e)}") raise - + def get_member_by_id(self, member_id: uuid.UUID) -> WorkspaceMember: """按成员ID获取工作空间成员，并预加载 user 与 workspace 关系""" db_logger.debug(f"查询成员的工作空间: member_id={member_id}") @@ -214,7 +237,8 @@ class WorkspaceRepository: .first() ) if member: - db_logger.debug(f"成员查询成功: member_id={member_id}, workspace_id={member.workspace_id}, role={member.role}") + db_logger.debug( + f"成员查询成功: member_id={member_id}, workspace_id={member.workspace_id}, role={member.role}") else: db_logger.debug(f"成员不存在: member_id={member_id}") return member @@ -222,7 +246,8 @@ class WorkspaceRepository: db_logger.error(f"查询成员列表失败: member_id={member_id} - {str(e)}") raise - def update_member_role(self, workspace_id: uuid.UUID, user_id: uuid.UUID, role: WorkspaceRole) -> Optional[WorkspaceMember]: + def update_member_role(self, workspace_id: uuid.UUID, user_id: uuid.UUID, role: WorkspaceRole) -> Optional[ + WorkspaceMember]: try: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.workspace_id == workspace_id, @@ -255,7 +280,7 @@ class WorkspaceRepository: except Exception as e: db_logger.error(f"删除成员失败: workspace_id={workspace_id}, user_id={user_id} - {str(e)}") raise - + def delete_member_by_id(self, member_id: uuid.UUID) -> Optional[WorkspaceMember]: try: member = self.db.query(WorkspaceMember).filter( @@ -271,7 +296,7 @@ class WorkspaceRepository: except Exception as e: db_logger.error(f"删除成员失败: id={member_id} - {str(e)}") raise - + def update_member_role_by_id(self, id: uuid.UUID, role: WorkspaceRole) -> Optional[WorkspaceMember]: try: member = self.db.query(WorkspaceMember).filter( @@ -288,12 +313,18 @@ class WorkspaceRepository: db_logger.error(f"更新成员角色失败: id={id} - {str(e)}") raise + # 保持向后兼容的函数 def get_workspace_by_id(db: Session, workspace_id: uuid.UUID) -> Workspace | None: repo = WorkspaceRepository(db) return repo.get_workspace_by_id(workspace_id) +def get_workspaces_by_name(db: Session, tenant_id: uuid.UUID, name: str) -> List[Workspace]: + repo = WorkspaceRepository(db) + return repo.get_workspaces_by_name(tenant_id, name) + + def get_workspaces_by_user(db: Session, user_id: uuid.UUID) -> List[Workspace]: repo = WorkspaceRepository(db) return repo.get_workspaces_by_user(user_id) @@ -315,7 +346,7 @@ def create_workspace(db: Session, workspace: WorkspaceCreate, tenant_id: uuid.UU def add_member_to_workspace( - db: Session, user_id: uuid.UUID, workspace_id: uuid.UUID, role: WorkspaceRole + db: Session, user_id: uuid.UUID, workspace_id: uuid.UUID, role: WorkspaceRole ) -> WorkspaceMember: repo = WorkspaceRepository(db) return repo.add_member(workspace_id, user_id, role) @@ -325,39 +356,43 @@ def get_members_by_workspace(db: Session, workspace_id: uuid.UUID) -> List[Works repo = WorkspaceRepository(db) return repo.get_members_by_workspace(workspace_id) + def get_member_by_id(db: Session, member_id: uuid.UUID) -> WorkspaceMember | None: repo = WorkspaceRepository(db) return repo.get_member_by_id(member_id) + def update_member_role_in_workspace( - db: Session, - user_id: uuid.UUID, - workspace_id: uuid.UUID, - role: WorkspaceRole, + db: Session, + user_id: uuid.UUID, + workspace_id: uuid.UUID, + role: WorkspaceRole, ) -> Optional[WorkspaceMember]: repo = WorkspaceRepository(db) return repo.update_member_role(workspace_id, user_id, role) + def remove_member_from_workspace( - db: Session, - user_id: uuid.UUID, - workspace_id: uuid.UUID, + db: Session, + user_id: uuid.UUID, + workspace_id: uuid.UUID, ) -> Optional[WorkspaceMember]: repo = WorkspaceRepository(db) return repo.deactivate_member(workspace_id, user_id) + def remove_member_from_workspace_by_id( - db: Session, - member_id: uuid.UUID, + db: Session, + member_id: uuid.UUID, ) -> Optional[WorkspaceMember]: repo = WorkspaceRepository(db) return repo.delete_member_by_id(member_id) def update_member_role_by_id( - db: Session, - id: uuid.UUID, - role: WorkspaceRole, + db: Session, + id: uuid.UUID, + role: WorkspaceRole, ) -> Optional[WorkspaceMember]: repo = WorkspaceRepository(db) return repo.update_member_role_by_id(id, role) diff --git a/api/app/services/app_service.py b/api/app/services/app_service.py index 5a799937..88582cee 100644 --- a/api/app/services/app_service.py +++ b/api/app/services/app_service.py @@ -33,7 +33,7 @@ from app.models import ( Workspace, ) from app.models.app_model import AppStatus, AppType -from app.repositories.app_repository import get_apps_by_id +from app.repositories.app_repository import get_apps_by_id, AppRepository from app.repositories.workflow_repository import WorkflowConfigRepository from app.schemas import app_schema from app.schemas.workflow_schema import WorkflowConfigUpdate @@ -59,6 +59,7 @@ class AppService: db: 数据库会话 """ self.db = db + self.app_repo = AppRepository(self.db) # ==================== 私有辅助方法 ==================== @@ -521,6 +522,9 @@ class AppService: "创建应用", extra={"app_name": data.name, "type": data.type, "workspace_id": str(workspace_id)} ) + apps = self.app_repo.get_apps_by_name(data.name, data.type, workspace_id) + if apps: + raise BusinessException(message="已存在同名应用", code=BizCode.RESOURCE_ALREADY_EXISTS) try: now = datetime.datetime.now() @@ -1368,6 +1372,15 @@ class AppService: if not agent_cfg: raise BusinessException("Agent 应用缺少配置，无法发布", BizCode.AGENT_CONFIG_MISSING) + miss_params = [] + if agent_cfg.default_model_config_id is None: + miss_params.append("model config") + + if agent_cfg.memory.get("enabled") and not agent_cfg.memory.get("memory_config_id"): + miss_params.append("memory config") + if miss_params: + raise BusinessException(f"{', '.join(miss_params)} is required") + config = { "system_prompt": agent_cfg.system_prompt, "model_parameters": model_parameters_to_dict(agent_cfg.model_parameters), diff --git a/api/app/services/workspace_service.py b/api/app/services/workspace_service.py index 7861ef62..cefb8380 100644 --- a/api/app/services/workspace_service.py +++ b/api/app/services/workspace_service.py @@ -2,11 +2,11 @@ import datetime import hashlib import secrets import uuid -from os import getenv from typing import List, Optional from sqlalchemy.orm import Session +from app.config.default_ontology_initializer import DefaultOntologyInitializer from app.core.config import settings from app.core.error_codes import BizCode from app.core.exceptions import BusinessException, PermissionDeniedException @@ -30,17 +30,15 @@ from app.schemas.workspace_schema import ( WorkspaceModelsUpdate, WorkspaceUpdate, ) -from app.config.default_ontology_initializer import DefaultOntologyInitializer # 获取业务逻辑专用日志器 business_logger = get_business_logger() -from dotenv import load_dotenv -load_dotenv() + def switch_workspace( - db: Session, - workspace_id: uuid.UUID, - user: User, + db: Session, + workspace_id: uuid.UUID, + user: User, ): """切换工作空间""" business_logger.debug(f"用户 {user.username} 请求切换工作空间为 {workspace_id}") @@ -60,31 +58,32 @@ def switch_workspace( raise BusinessException(f"切换工作空间失败: {str(e)}", BizCode.INTERNAL_ERROR) -def delete_workspace_member( - db: Session, - workspace_id: uuid.UUID, - member_id: uuid.UUID, - user: User, - ): - """删除工作空间成员""" - business_logger.debug(f"用户 {user.username} 请求删除工作空间 {workspace_id} 的成员 {member_id}") - _check_workspace_admin_permission(db, workspace_id, user) - workspace_member = workspace_repository.get_member_by_id(db=db, member_id=member_id) - if not workspace_member: - raise BusinessException(f"工作空间成员 {member_id} 不存在", BizCode.WORKSPACE_NOT_FOUND) +def delete_workspace_member( + db: Session, + workspace_id: uuid.UUID, + member_id: uuid.UUID, + user: User, +): + """删除工作空间成员""" + business_logger.debug(f"用户 {user.username} 请求删除工作空间 {workspace_id} 的成员 {member_id}") + _check_workspace_admin_permission(db, workspace_id, user) + workspace_member = workspace_repository.get_member_by_id(db=db, member_id=member_id) + if not workspace_member: + raise BusinessException(f"工作空间成员 {member_id} 不存在", BizCode.WORKSPACE_NOT_FOUND) - if workspace_member.workspace_id != workspace_id: - raise BusinessException(f"工作空间成员 {member_id} 不存在于工作空间 {workspace_id}", BizCode.WORKSPACE_NOT_FOUND) + if workspace_member.workspace_id != workspace_id: + raise BusinessException(f"工作空间成员 {member_id} 不存在于工作空间 {workspace_id}", + BizCode.WORKSPACE_NOT_FOUND) - try: - workspace_member.is_active = False - workspace_member.user.current_workspace_id = None - db.commit() - business_logger.info(f"用户 {user.username} 成功删除工作空间 {workspace_id} 的成员 {member_id}") - except Exception as e: - db.rollback() - business_logger.error(f"删除工作空间成员失败 - 工作空间: {workspace_id}, 成员: {member_id}, 错误: {str(e)}") - raise BusinessException(f"删除工作空间成员失败: {str(e)}", BizCode.INTERNAL_ERROR) + try: + workspace_member.is_active = False + workspace_member.user.current_workspace_id = None + db.commit() + business_logger.info(f"用户 {user.username} 成功删除工作空间 {workspace_id} 的成员 {member_id}") + except Exception as e: + db.rollback() + business_logger.error(f"删除工作空间成员失败 - 工作空间: {workspace_id}, 成员: {member_id}, 错误: {str(e)}") + raise BusinessException(f"删除工作空间成员失败: {str(e)}", BizCode.INTERNAL_ERROR) def get_user_workspaces(db: Session, user: User) -> List[Workspace]: @@ -102,19 +101,19 @@ def get_user_workspaces(db: Session, user: User) -> List[Workspace]: """ business_logger.debug(f"获取用户工作空间列表: {user.username} (ID: {user.id})") workspaces = workspace_repository.get_workspaces_by_user(db=db, user_id=user.id) - + # Ensure each neo4j workspace has a default memory config for workspace in workspaces: if workspace.storage_type == 'neo4j': _ensure_default_memory_config(db, workspace) _ensure_default_ontology_scenes(db, workspace) - + business_logger.info(f"用户 {user.username} 的工作空间数量: {len(workspaces)}") return workspaces def _create_workspace_only( - db: Session, workspace: WorkspaceCreate, owner: User + db: Session, workspace: WorkspaceCreate, owner: User ) -> Workspace: business_logger.debug(f"创建工作空间: {workspace.name}, 创建者: {owner.username}") @@ -138,9 +137,14 @@ def create_workspace( f"创建工作空间: {workspace.name}, 创建者: {user.username}, " f"storage_type: {workspace.storage_type}" ) - llm=workspace.llm - embedding=workspace.embedding - rerank=workspace.rerank + if workspace_repository.get_workspaces_by_name(db=db, name=workspace.name, tenant_id=user.tenant_id): + raise BusinessException( + message="同名工作空间已存在", + code=BizCode.RESOURCE_ALREADY_EXISTS + ) + llm = workspace.llm + embedding = workspace.embedding + rerank = workspace.rerank try: # Create the workspace without adding any members business_logger.debug(f"创建工作空间: {workspace.name}") @@ -159,26 +163,26 @@ def create_workspace( success, error_msg = initializer.initialize_default_scenes( db_workspace.id, language=language ) - + if success: business_logger.info( f"为工作空间 {db_workspace.id} 创建默认本体场景成功 (language={language})" ) - - # 获取默认场景ID，优先使用"在线教育"场景，如果不存在则使用"情感陪伴"场景 + + # 获取默认场景ID，优先使用"在线教育"场景，如果不存在则使用"情感陪伴"场景 from app.repositories.ontology_scene_repository import OntologySceneRepository from app.config.default_ontology_config import ( - ONLINE_EDUCATION_SCENE, + ONLINE_EDUCATION_SCENE, EMOTIONAL_COMPANION_SCENE, get_scene_name ) - + scene_repo = OntologySceneRepository(db) - + # 优先尝试获取教育场景 education_scene_name = get_scene_name(ONLINE_EDUCATION_SCENE, language) education_scene = scene_repo.get_by_name(education_scene_name, db_workspace.id) - + if education_scene: default_scene_id = education_scene.scene_id default_scene_name = education_scene.scene_name @@ -189,7 +193,7 @@ def create_workspace( # 如果教育场景不存在，尝试获取情感陪伴场景 companion_scene_name = get_scene_name(EMOTIONAL_COMPANION_SCENE, language) companion_scene = scene_repo.get_by_name(companion_scene_name, db_workspace.id) - + if companion_scene: default_scene_id = companion_scene.scene_id default_scene_name = companion_scene.scene_name @@ -256,10 +260,10 @@ def create_workspace( avatar='', type=KnowledgeType.General, permission_id=PermissionType.Memory, - embedding_id=uuid.UUID(getenv('KB_embedding_id')) if None else embedding, - reranker_id=uuid.UUID(getenv('KB_reranker_id')) if None else rerank, - llm_id=uuid.UUID(getenv('KB_llm_id')) if None else llm, - image2text_id=uuid.UUID(getenv('KB_llm_id')) if None else llm, + embedding_id=embedding, + reranker_id=rerank, + llm_id=llm, + image2text_id=llm, parser_config={ "layout_recognize": "DeepDOC", "chunk_token_num": 256, @@ -294,7 +298,7 @@ def create_workspace( business_logger.info( f"工作空间 {db_workspace.id} 及相关资源创建完成并已提交" ) - + return db_workspace except Exception as e: @@ -304,11 +308,11 @@ def create_workspace( def update_workspace( - db: Session, workspace_id: uuid.UUID, workspace_in: WorkspaceUpdate, user: User + db: Session, workspace_id: uuid.UUID, workspace_in: WorkspaceUpdate, user: User ) -> Workspace: business_logger.info(f"更新工作空间: workspace_id={workspace_id}, 操作者: {user.username}") - db_workspace = _check_workspace_admin_permission(db,workspace_id,user) + db_workspace = _check_workspace_admin_permission(db, workspace_id, user) try: # 更新工作空间 business_logger.debug(f"执行工作空间更新: {db_workspace.name} (ID: {workspace_id})") @@ -328,7 +332,7 @@ def update_workspace( def get_workspace_members( - db: Session, workspace_id: uuid.UUID, user: User + db: Session, workspace_id: uuid.UUID, user: User ) -> List[WorkspaceMember]: """获取某工作空间的成员列表（关系序列化由模型关系支持）""" business_logger.info(f"获取工作空间成员: workspace_id={workspace_id}, 操作者: {user.username}") @@ -372,7 +376,6 @@ def get_workspace_members( return members - # ==================== 邀请相关服务方法 ==================== def _generate_invite_token() -> tuple[str, str]: @@ -465,13 +468,14 @@ def _check_workspace_admin_permission(db: Session, workspace_id: uuid.UUID, user def create_workspace_invite( - db: Session, - workspace_id: uuid.UUID, - invite_data: WorkspaceInviteCreate, - user: User + db: Session, + workspace_id: uuid.UUID, + invite_data: WorkspaceInviteCreate, + user: User ) -> WorkspaceInviteResponse: """创建工作空间邀请""" - business_logger.info(f"创建工作空间邀请: workspace_id={workspace_id}, email={invite_data.email}, 创建者: {user.username}") + business_logger.info( + f"创建工作空间邀请: workspace_id={workspace_id}, email={invite_data.email}, 创建者: {user.username}") try: # 检查权限 @@ -534,17 +538,18 @@ def create_workspace_invite( except Exception as e: db.rollback() - business_logger.error(f"创建工作空间邀请失败: workspace_id={workspace_id}, email={invite_data.email} - {str(e)}") + business_logger.error( + f"创建工作空间邀请失败: workspace_id={workspace_id}, email={invite_data.email} - {str(e)}") raise def get_workspace_invites( - db: Session, - workspace_id: uuid.UUID, - user: User, - status: Optional[InviteStatus] = None, - limit: int = 50, - offset: int = 0 + db: Session, + workspace_id: uuid.UUID, + user: User, + status: Optional[InviteStatus] = None, + limit: int = 50, + offset: int = 0 ) -> List[WorkspaceInviteResponse]: """获取工作空间邀请列表""" business_logger.info(f"获取工作空间邀请列表: workspace_id={workspace_id}, 操作者: {user.username}") @@ -605,9 +610,9 @@ def validate_invite_token(db: Session, token: str) -> InviteValidateResponse: def accept_workspace_invite( - db: Session, - accept_request: InviteAcceptRequest, - user: User + db: Session, + accept_request: InviteAcceptRequest, + user: User ) -> dict: """接受工作空间邀请""" business_logger.info(f"接受工作空间邀请: 用户 {user.username}") @@ -695,7 +700,8 @@ def accept_workspace_invite( # 获取工作空间信息 workspace = workspace_repository.get_workspace_by_id(db=db, workspace_id=invite.workspace_id) - business_logger.info(f"用户成功加入工作空间: user={user.username}, workspace={workspace.name}, role={workspace_role}") + business_logger.info( + f"用户成功加入工作空间: user={user.username}, workspace={workspace.name}, role={workspace_role}") return { "message": "Successfully joined the workspace", @@ -710,13 +716,14 @@ def accept_workspace_invite( def revoke_workspace_invite( - db: Session, - workspace_id: uuid.UUID, - invite_id: uuid.UUID, - user: User + db: Session, + workspace_id: uuid.UUID, + invite_id: uuid.UUID, + user: User ) -> dict: """撤销工作空间邀请""" - business_logger.info(f"撤销工作空间邀请: workspace_id={workspace_id}, invite_id={invite_id}, 操作者: {user.username}") + business_logger.info( + f"撤销工作空间邀请: workspace_id={workspace_id}, invite_id={invite_id}, 操作者: {user.username}") try: # 检查权限 @@ -745,13 +752,14 @@ def revoke_workspace_invite( def update_workspace_member_roles( - db: Session, - workspace_id: uuid.UUID, - updates: List[WorkspaceMemberUpdate], - user: User, + db: Session, + workspace_id: uuid.UUID, + updates: List[WorkspaceMemberUpdate], + user: User, ) -> List[WorkspaceMember]: """更新工作空间成员角色""" - business_logger.info(f"更新工作空间成员角色: workspace_id={workspace_id}, 操作者: {user.username}, 更新数量: {len(updates)}") + business_logger.info( + f"更新工作空间成员角色: workspace_id={workspace_id}, 操作者: {user.username}, 更新数量: {len(updates)}") # 检查管理员权限 _check_workspace_admin_permission(db, workspace_id, user) @@ -765,7 +773,8 @@ def update_workspace_member_roles( for upd in updates: # 检查成员是否存在 if upd.id not in member_map: - raise BusinessException(f"成员 {upd.id} 不存在于工作空间 {workspace_id}", BizCode.WORKSPACE_MEMBER_NOT_FOUND) + raise BusinessException(f"成员 {upd.id} 不存在于工作空间 {workspace_id}", + BizCode.WORKSPACE_MEMBER_NOT_FOUND) member = member_map[upd.id] @@ -917,10 +926,10 @@ def get_workspace_models_configs( def update_workspace_models_configs( - db: Session, - workspace_id: uuid.UUID, - models_update: WorkspaceModelsUpdate, - user: User, + db: Session, + workspace_id: uuid.UUID, + models_update: WorkspaceModelsUpdate, + user: User, ) -> Workspace: """更新工作空间的模型配置（llm, embedding, rerank） @@ -968,8 +977,8 @@ def update_workspace_models_configs( def _fill_workspace_configs_model_defaults( - db: Session, - workspace: Workspace + db: Session, + workspace: Workspace ) -> None: """Fill empty model fields for all memory configs in a workspace. @@ -981,43 +990,43 @@ def _fill_workspace_configs_model_defaults( workspace: The workspace containing default model settings """ from app.models.memory_config_model import MemoryConfig - + # Get all configs for this workspace configs = db.query(MemoryConfig).filter( MemoryConfig.workspace_id == workspace.id ).all() - + if not configs: return - + # Map of memory_config field -> workspace field model_field_mappings = [ ("llm_id", "llm"), ("embedding_id", "embedding"), ("rerank_id", "rerank"), ("reflection_model_id", "llm"), # reflection uses LLM - ("emotion_model_id", "llm"), # emotion uses LLM + ("emotion_model_id", "llm"), # emotion uses LLM ] - + configs_updated = 0 - + for memory_config in configs: updated_fields = [] - + for config_field, workspace_field in model_field_mappings: config_value = getattr(memory_config, config_field, None) workspace_value = getattr(workspace, workspace_field, None) - + if not config_value and workspace_value: setattr(memory_config, config_field, workspace_value) updated_fields.append(config_field) - + if updated_fields: configs_updated += 1 business_logger.debug( f"Updated memory config {memory_config.config_id} fields: {updated_fields}" ) - + if configs_updated > 0: try: db.commit() @@ -1032,14 +1041,14 @@ def _fill_workspace_configs_model_defaults( def _create_default_memory_config( - db: Session, - workspace_id: uuid.UUID, - workspace_name: str, - llm_id: Optional[uuid.UUID] = None, - embedding_id: Optional[uuid.UUID] = None, - rerank_id: Optional[uuid.UUID] = None, - scene_id: Optional[uuid.UUID] = None, - pruning_scene_name: Optional[str] = None, + db: Session, + workspace_id: uuid.UUID, + workspace_name: str, + llm_id: Optional[uuid.UUID] = None, + embedding_id: Optional[uuid.UUID] = None, + rerank_id: Optional[uuid.UUID] = None, + scene_id: Optional[uuid.UUID] = None, + pruning_scene_name: Optional[str] = None, ) -> None: """Create a default memory config for a newly created workspace. @@ -1054,9 +1063,9 @@ def _create_default_memory_config( pruning_scene_name: Optional pruning scene name，取自 ontology_scene.scene_name """ from app.models.memory_config_model import MemoryConfig - + config_id = uuid.uuid4() - + default_config = MemoryConfig( config_id=config_id, config_name=f"{workspace_name} 默认配置", @@ -1070,10 +1079,10 @@ def _create_default_memory_config( state=True, # Active by default is_default=True, # Mark as workspace default ) - + db.add(default_config) db.flush() # 使用 flush 而不是 commit，让调用者统一提交 - + business_logger.info( "Created default memory config for workspace", extra={ @@ -1084,6 +1093,7 @@ def _create_default_memory_config( } ) + # ==================== 检查配置相关服务 ==================== def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: @@ -1096,19 +1106,19 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: workspace: The workspace to check """ from app.models.memory_config_model import MemoryConfig - + # Check if default config exists for this workspace existing_default = db.query(MemoryConfig).filter( MemoryConfig.workspace_id == workspace.id, MemoryConfig.is_default == True ).first() - + if not existing_default: # No default config exists, create one business_logger.info( f"Workspace {workspace.id} missing default memory config, creating one" ) - + # 尝试获取默认场景ID，优先教育场景，其次情感陪伴场景 default_scene_id = None try: @@ -1118,7 +1128,7 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: EMOTIONAL_COMPANION_SCENE, get_scene_name ) - + scene_repo = OntologySceneRepository(db) # 尝试中文和英文场景名称 for language in ["zh", "en"]: @@ -1131,7 +1141,7 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: f"找到教育场景用于默认记忆配置: scene_id={default_scene_id}, scene_name={education_scene_name}" ) break - + # 如果教育场景不存在，尝试情感陪伴场景 companion_scene_name = get_scene_name(EMOTIONAL_COMPANION_SCENE, language) companion_scene = scene_repo.get_by_name(companion_scene_name, workspace.id) @@ -1145,7 +1155,7 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: business_logger.warning( f"获取默认场景失败，将创建不关联场景的记忆配置: {str(scene_error)}" ) - + try: _create_default_memory_config( db=db, @@ -1160,7 +1170,7 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None: business_logger.error( f"Failed to create default memory config for workspace {workspace.id}: {str(e)}" ) - + # Fill empty model fields for ALL configs in this workspace _fill_workspace_configs_model_defaults(db, workspace) @@ -1209,4 +1219,3 @@ def _ensure_default_ontology_scenes(db: Session, workspace: Workspace) -> None: business_logger.error( f"为工作空间 {workspace.id} 补建默认本体场景异常: {str(e)}" ) - From beea826377fb602663487954c50dd5d5e1dcd52c Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Tue, 10 Mar 2026 11:17:52 +0800 Subject: [PATCH 030/196] feat(app): Application (agent, workflow) import/export --- api/app/controllers/app_controller.py | 57 ++++ api/app/services/app_dsl_service.py | 390 ++++++++++++++++++++++++++ 2 files changed, 447 insertions(+) create mode 100644 api/app/services/app_dsl_service.py diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index cdf94345..a4048bf4 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -1,10 +1,12 @@ import uuid +import io from typing import Optional, Annotated import yaml from fastapi import APIRouter, Depends, Path, Form, UploadFile, File from fastapi.responses import StreamingResponse from sqlalchemy.orm import Session +from urllib.parse import quote from app.core.error_codes import BizCode from app.core.logging_config import get_business_logger @@ -25,6 +27,7 @@ from app.services.app_service import AppService from app.services.app_statistics_service import AppStatisticsService from app.services.workflow_import_service import WorkflowImportService from app.services.workflow_service import WorkflowService, get_workflow_service +from app.services.app_dsl_service import AppDslService router = APIRouter(prefix="/apps", tags=["Apps"]) logger = get_business_logger() @@ -1010,3 +1013,57 @@ def get_workspace_api_statistics( ) return success(data=result) + + +@router.get("/{app_id}/export", summary="导出应用配置为 YAML 文件") +@cur_workspace_access_guard() +async def export_app( + app_id: uuid.UUID, + db: Annotated[Session, Depends(get_db)], + current_user: Annotated[User, Depends(get_current_user)], + release_version: Optional[str] = None +): + """导出 agent / multi_agent / workflow 应用配置为 YAML 文件流。 + release_version: 指定发布版本号，不传则导出当前草稿配置。 + """ + yaml_str, filename = AppDslService(db).export_dsl(app_id, release_version) + encoded = quote(filename, safe=".") + yaml_bytes = yaml_str.encode("utf-8") + file_stream = io.BytesIO(yaml_bytes) + file_stream.seek(0) + return StreamingResponse( + file_stream, + media_type="application/octet-stream; charset=utf-8", + headers={"Content-Disposition": f"attachment; filename={encoded}", + "Content-Length": str(len(yaml_bytes))} + ) + + +@router.post("/import", summary="从 YAML 文件导入应用") +@cur_workspace_access_guard() +async def import_app( + file: UploadFile = File(...), + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user) +): + """从 YAML 文件导入 agent / multi_agent / workflow 应用。 + 跨空间/跨租户导入时，模型/工具/知识库会按名称匹配，匹配不到则置空并返回 warnings。 + """ + if not file.filename.lower().endswith((".yaml", ".yml")): + return fail(msg="仅支持 YAML 文件", code=BizCode.BAD_REQUEST) + + raw = (await file.read()).decode("utf-8") + dsl = yaml.safe_load(raw) + if not dsl or "app" not in dsl: + return fail(msg="YAML 格式无效，缺少 app 字段", code=BizCode.BAD_REQUEST) + + new_app, warnings = AppDslService(db).import_dsl( + dsl=dsl, + workspace_id=current_user.current_workspace_id, + tenant_id=current_user.tenant_id, + user_id=current_user.id, + ) + return success( + data={"app": app_schema.App.model_validate(new_app), "warnings": warnings}, + msg="应用导入成功" + ("，但部分资源需手动配置" if warnings else "") + ) diff --git a/api/app/services/app_dsl_service.py b/api/app/services/app_dsl_service.py new file mode 100644 index 00000000..7258fc2b --- /dev/null +++ b/api/app/services/app_dsl_service.py @@ -0,0 +1,390 @@ +"""应用 DSL 导入导出服务""" +import uuid +import datetime +from typing import Optional + +import yaml +from sqlalchemy.orm import Session + +from app.core.config import settings +from app.core.error_codes import BizCode +from app.core.exceptions import BusinessException, ResourceNotFoundException +from app.models import AgentConfig, MultiAgentConfig +from app.models.app_model import App, AppType +from app.models.app_release_model import AppRelease +from app.models.knowledge_model import Knowledge +from app.models.models_model import ModelConfig +from app.models.tool_model import ToolConfig as ToolConfigModel +from app.models.workflow_model import WorkflowConfig +from app.services.workflow_service import WorkflowService + + +class AppDslService: + + def __init__(self, db: Session): + self.db = db + + # ==================== 导出 ==================== + + def export_dsl(self, app_id: uuid.UUID, release_version: Optional[str] = None) -> tuple[str, str]: + """构建应用 DSL yaml 字符串，返回 (yaml_str, filename)""" + app = self.db.query(App).filter(App.id == app_id, App.is_active.is_(True)).first() + if not app: + raise ResourceNotFoundException("应用", str(app_id)) + + meta = { + "version": settings.SYSTEM_VERSION, + "platform": "MemoryBear", + "exported_at": datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S"), + } + app_meta = { + "name": app.name, + "description": app.description, + "icon": app.icon, + "icon_type": app.icon_type, + "type": app.type, + "tags": app.tags or [], + } + + if release_version is not None: + return self._export_release(app, release_version, meta, app_meta) + + return self._export_draft(app, meta, app_meta) + + def _export_release(self, app: App, release_version: str, meta: dict, app_meta: dict) -> tuple[str, str]: + release = self.db.query(AppRelease).filter( + AppRelease.app_id == app.id, + AppRelease.id == release_version, + AppRelease.is_active.is_(True) + ).first() + if not release: + raise ResourceNotFoundException("版本", str(release_version)) + + meta["release_version"] = release.version + meta["release_name"] = release.version_name + app_meta["name"] = release.name + app_meta["description"] = release.description + config_key = { + AppType.AGENT: "agent_config", + AppType.MULTI_AGENT: "multi_agent_config", + AppType.WORKFLOW: "workflow" + }.get(app.type, "config") + config_data = self._enrich_release_config(app.type, release.config or {}) + dsl = {**meta, "app": app_meta, config_key: config_data} + return yaml.dump(dsl, default_flow_style=False, allow_unicode=True), f"{release.name}_v{release.version_name}.yaml" + + def _enrich_release_config(self, app_type: str, cfg: dict) -> dict: + if app_type == AppType.AGENT: + enriched = {**cfg} + if "default_model_config_id" in cfg: + enriched["default_model_config_ref"] = self._model_ref(cfg["default_model_config_id"]) + if "knowledge_retrieval" in cfg: + enriched["knowledge_retrieval"] = self._enrich_knowledge_retrieval(cfg["knowledge_retrieval"]) + if "tools" in cfg: + enriched["tools"] = self._enrich_tools(cfg["tools"]) + return enriched + if app_type == AppType.MULTI_AGENT: + enriched = {**cfg} + if "default_model_config_id" in cfg: + enriched["default_model_config_ref"] = self._model_ref(cfg["default_model_config_id"]) + if "master_agent_id" in cfg: + enriched["master_agent_ref"] = self._release_ref(cfg["master_agent_id"]) + if "sub_agents" in cfg: + enriched["sub_agents"] = self._enrich_sub_agents(cfg["sub_agents"]) + if "routing_rules" in cfg: + enriched["routing_rules"] = [ + {**r, "_ref": self._agent_ref(r.get("target_agent_id"))} for r in (cfg["routing_rules"] or []) + ] + return enriched + return cfg + + def _export_draft(self, app: App, meta: dict, app_meta: dict) -> tuple[str, str]: + if app.type == AppType.WORKFLOW: + config = self.db.query(WorkflowConfig).filter(WorkflowConfig.app_id == app.id).first() + config_data = { + "variables": config.variables if config else [], + "edges": config.edges if config else [], + "nodes": config.nodes if config else [], + "execution_config": config.execution_config if config else {}, + "triggers": config.triggers if config else [], + } if config else {} + dsl = {**meta, "app": app_meta, "workflow": config_data} + + elif app.type == AppType.AGENT: + config = self.db.query(AgentConfig).filter(AgentConfig.app_id == app.id).first() + config_data = { + "system_prompt": config.system_prompt if config else None, + "model_parameters": self._to_dict(config.model_parameters) if config else None, + "default_model_config_ref": self._model_ref(config.default_model_config_id) if config else None, + "knowledge_retrieval": self._enrich_knowledge_retrieval(config.knowledge_retrieval) if config else None, + "memory": config.memory if config else None, + "variables": config.variables if config else [], + "tools": self._enrich_tools(config.tools) if config else [], + "skills": config.skills if config else {}, + } if config else {} + dsl = {**meta, "app": app_meta, "agent_config": config_data} + + elif app.type == AppType.MULTI_AGENT: + config = self.db.query(MultiAgentConfig).filter(MultiAgentConfig.app_id == app.id).first() + config_data = { + "orchestration_mode": config.orchestration_mode if config else None, + "master_agent_name": config.master_agent_name if config else None, + "model_parameters": self._to_dict(config.model_parameters) if config else None, + "default_model_config_ref": self._model_ref(config.default_model_config_id) if config else None, + "master_agent_ref": self._release_ref(config.master_agent_id) if config else None, + "sub_agents": self._enrich_sub_agents(config.sub_agents) if config else [], + "routing_rules": [ + {**r, "_ref": self._agent_ref(r.get("target_agent_id"))} for r in (config.routing_rules or []) + ] if config else [], + + "execution_config": config.execution_config if config else {}, + "aggregation_strategy": config.aggregation_strategy if config else "merge", + } if config else {} + dsl = {**meta, "app": app_meta, "multi_agent_config": config_data} + + else: + raise BusinessException(f"不支持的应用类型: {app.type}", BizCode.BAD_REQUEST) + + return yaml.dump(dsl, default_flow_style=False, allow_unicode=True), f"{app.name}.yaml" + + def _to_dict(self, value): + """将 Pydantic 对象转为普通 dict，供 yaml.dump 安全序列化""" + if value is None: + return None + if hasattr(value, "model_dump"): + return value.model_dump() + return value + + def _model_ref(self, model_config_id) -> Optional[dict]: + if not model_config_id: + return None + m = self.db.query(ModelConfig).filter(ModelConfig.id == model_config_id).first() + return {"id": str(model_config_id), "name": m.name, "provider": m.provider, "type": m.type} if m else {"id": str(model_config_id)} + + def _kb_ref(self, kb_id) -> Optional[dict]: + if not kb_id: + return None + kb = self.db.query(Knowledge).filter(Knowledge.id == kb_id).first() + return {"id": str(kb_id), "name": kb.name} if kb else {"id": str(kb_id)} + + def _tool_ref(self, tool_id) -> Optional[dict]: + if not tool_id: + return None + t = self.db.query(ToolConfigModel).filter(ToolConfigModel.id == tool_id).first() + return {"id": str(tool_id), "name": t.name, "tool_type": t.tool_type} if t else {"id": str(tool_id)} + + def _enrich_knowledge_retrieval(self, kr: Optional[dict]) -> Optional[dict]: + if not kr: + return kr + kbs = [{**kb, "_ref": self._kb_ref(kb.get("kb_id"))} for kb in kr.get("knowledge_bases", [])] + return {**kr, "knowledge_bases": kbs} + + def _enrich_tools(self, tools: list) -> list: + return [{**t, "_ref": self._tool_ref(t.get("tool_id"))} for t in (tools or [])] + + def _agent_ref(self, agent_id) -> Optional[dict]: + if not agent_id: + return None + a = self.db.query(App).filter(App.id == agent_id).first() + return {"id": str(agent_id), "name": a.name} if a else {"id": str(agent_id)} + + def _release_ref(self, release_id) -> Optional[dict]: + if not release_id: + return None + r = self.db.query(AppRelease).filter(AppRelease.id == release_id).first() + return {"id": str(release_id), "name": r.name, "version": r.version, "app_id": str(r.app_id)} if r else {"id": str(release_id)} + + def _enrich_sub_agents(self, sub_agents: list) -> list: + return [{**s, "_ref": self._agent_ref(s.get("agent_id"))} for s in (sub_agents or [])] + + # ==================== 导入 ==================== + + def import_dsl( + self, + dsl: dict, + workspace_id: uuid.UUID, + tenant_id: uuid.UUID, + user_id: uuid.UUID, + ) -> tuple[App, list[str]]: + """解析 DSL，创建应用及配置，返回 (new_app, warnings)""" + app_meta = dsl.get("app", {}) + app_type = app_meta.get("type") + if app_type not in (AppType.AGENT, AppType.MULTI_AGENT, AppType.WORKFLOW): + raise BusinessException(f"不支持的应用类型: {app_type}", BizCode.BAD_REQUEST) + + warnings: list[str] = [] + now = datetime.datetime.now() + + new_app = App( + id=uuid.uuid4(), + workspace_id=workspace_id, + created_by=user_id, + name=app_meta.get("name", "导入应用"), + description=app_meta.get("description"), + icon=app_meta.get("icon"), + icon_type=app_meta.get("icon_type"), + type=app_type, + visibility="private", + status="draft", + tags=app_meta.get("tags", []), + is_active=True, + created_at=now, + updated_at=now, + ) + self.db.add(new_app) + self.db.flush() + + if app_type == AppType.AGENT: + cfg = dsl.get("agent_config") or {} + self.db.add(AgentConfig( + id=uuid.uuid4(), + app_id=new_app.id, + system_prompt=cfg.get("system_prompt"), + model_parameters=cfg.get("model_parameters"), + default_model_config_id=self._resolve_model(cfg.get("default_model_config_ref"), tenant_id, warnings), + knowledge_retrieval=self._resolve_knowledge_retrieval(cfg.get("knowledge_retrieval"), workspace_id, warnings), + memory=cfg.get("memory"), + variables=cfg.get("variables", []), + tools=self._resolve_tools(cfg.get("tools", []), tenant_id, warnings), + skills=cfg.get("skills", {}), + is_active=True, + created_at=now, + updated_at=now, + )) + + elif app_type == AppType.MULTI_AGENT: + cfg = dsl.get("multi_agent_config") or {} + self.db.add(MultiAgentConfig( + id=uuid.uuid4(), + app_id=new_app.id, + orchestration_mode=cfg.get("orchestration_mode", "collaboration"), + master_agent_name=cfg.get("master_agent_name"), + model_parameters=cfg.get("model_parameters"), + default_model_config_id=self._resolve_model(cfg.get("default_model_config_ref"), tenant_id, warnings), + master_agent_id=self._resolve_release(cfg.get("master_agent_ref"), warnings), + sub_agents=self._resolve_sub_agents(cfg.get("sub_agents", []), warnings), + routing_rules=self._resolve_routing_rules(cfg.get("routing_rules"), warnings), + execution_config=cfg.get("execution_config", {}), + aggregation_strategy=cfg.get("aggregation_strategy", "merge"), + is_active=True, + created_at=now, + updated_at=now, + )) + + elif app_type == AppType.WORKFLOW: + wf = dsl.get("workflow") or {} + WorkflowService(self.db).create_workflow_config( + app_id=new_app.id, + nodes=wf.get("nodes", []), + edges=wf.get("edges", []), + variables=wf.get("variables", []), + execution_config=wf.get("execution_config", {}), + triggers=wf.get("triggers", []), + validate=False, + ) + + self.db.commit() + self.db.refresh(new_app) + return new_app, warnings + + def _resolve_model(self, ref: Optional[dict], tenant_id: uuid.UUID, warnings: list) -> Optional[uuid.UUID]: + if not ref: + return None + q = self.db.query(ModelConfig).filter( + ModelConfig.tenant_id == tenant_id, + ModelConfig.name == ref.get("name"), + ModelConfig.is_active.is_(True) + ) + if ref.get("provider"): + q = q.filter(ModelConfig.provider == ref["provider"]) + if ref.get("type"): + q = q.filter(ModelConfig.type == ref["type"]) + m = q.first() + if not m: + warnings.append(f"模型 '{ref.get('name')}' 未匹配，已置空，请导入后手动配置") + return m.id if m else None + + def _resolve_kb(self, ref: Optional[dict], workspace_id: uuid.UUID, warnings: list) -> Optional[str]: + if not ref: + return None + kb = self.db.query(Knowledge).filter( + Knowledge.workspace_id == workspace_id, + Knowledge.name == ref.get("name") + ).first() + if not kb: + warnings.append(f"知识库 '{ref.get('name')}' 未匹配，已置空，请导入后手动配置") + return str(kb.id) if kb else None + + def _resolve_tool(self, ref: Optional[dict], tenant_id: uuid.UUID, warnings: list) -> Optional[str]: + if not ref: + return None + q = self.db.query(ToolConfigModel).filter( + ToolConfigModel.tenant_id == tenant_id, + ToolConfigModel.name == ref.get("name") + ) + if ref.get("tool_type"): + q = q.filter(ToolConfigModel.tool_type == ref["tool_type"]) + t = q.first() + if not t: + warnings.append(f"工具 '{ref.get('name')}' 未匹配，已置空，请导入后手动配置") + return str(t.id) if t else None + + def _resolve_release(self, ref: Optional[dict], warnings: list) -> Optional[uuid.UUID]: + if not ref: + return None + r = self.db.query(AppRelease).filter( + AppRelease.app_id == ref.get("app_id"), + AppRelease.version == ref.get("version"), + AppRelease.is_active.is_(True) + ).first() + if not r: + warnings.append(f"主 Agent 发布版本 '{ref.get('name')}' 未匹配，已置空，请导入后手动配置") + return r.id if r else None + + def _resolve_sub_agents(self, sub_agents: list, warnings: list) -> list: + result = [] + for s in (sub_agents or []): + ref = s.get("_ref") + entry = {k: v for k, v in s.items() if k != "_ref"} + if ref: + a = self.db.query(App).filter(App.name == ref.get("name"), App.is_active.is_(True)).first() + if not a: + warnings.append(f"子 Agent '{ref.get('name')}' 未匹配，已置空，请导入后手动配置") + entry["agent_id"] = str(a.id) if a else None + result.append(entry) + return result + + def _resolve_routing_rules(self, rules: Optional[list], warnings: list) -> Optional[list]: + if rules is None: + return None + result = [] + for r in rules: + ref = r.get("_ref") + entry = {k: v for k, v in r.items() if k != "_ref"} + if ref: + a = self.db.query(App).filter(App.name == ref.get("name"), App.is_active.is_(True)).first() + if not a: + warnings.append(f"路由目标 Agent '{ref.get('name')}' 未匹配，已置空，请导入后手动配置") + entry["target_agent_id"] = str(a.id) if a else None + result.append(entry) + return result + + def _resolve_knowledge_retrieval(self, kr: Optional[dict], workspace_id: uuid.UUID, warnings: list) -> Optional[dict]: + if not kr: + return kr + resolved_kbs = [] + for kb in kr.get("knowledge_bases", []): + ref = kb.get("_ref") or ({"name": kb.get("kb_id")} if kb.get("kb_id") else None) + entry = {k: v for k, v in kb.items() if k != "_ref"} + entry["kb_id"] = self._resolve_kb(ref, workspace_id, warnings) + resolved_kbs.append(entry) + return {k: v for k, v in kr.items() if k != "knowledge_bases"} | {"knowledge_bases": resolved_kbs} + + def _resolve_tools(self, tools: list, tenant_id: uuid.UUID, warnings: list) -> list: + result = [] + for t in (tools or []): + ref = t.get("_ref") or ({"name": t.get("tool_id")} if t.get("tool_id") else None) + entry = {k: v for k, v in t.items() if k != "_ref"} + entry["tool_id"] = self._resolve_tool(ref, tenant_id, warnings) + result.append(entry) + return result From 3237f4cd6eb7f527d2467baf63b49dca93f58937 Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Tue, 10 Mar 2026 11:27:28 +0800 Subject: [PATCH 031/196] feat(app): Application (agent, workflow) import/export --- api/app/controllers/app_controller.py | 4 ++-- api/app/services/app_dsl_service.py | 12 ++++++------ 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index a4048bf4..cadc7df8 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -1021,12 +1021,12 @@ async def export_app( app_id: uuid.UUID, db: Annotated[Session, Depends(get_db)], current_user: Annotated[User, Depends(get_current_user)], - release_version: Optional[str] = None + release_id: Optional[uuid.UUID] = None ): """导出 agent / multi_agent / workflow 应用配置为 YAML 文件流。 release_version: 指定发布版本号，不传则导出当前草稿配置。 """ - yaml_str, filename = AppDslService(db).export_dsl(app_id, release_version) + yaml_str, filename = AppDslService(db).export_dsl(app_id, release_id) encoded = quote(filename, safe=".") yaml_bytes = yaml_str.encode("utf-8") file_stream = io.BytesIO(yaml_bytes) diff --git a/api/app/services/app_dsl_service.py b/api/app/services/app_dsl_service.py index 7258fc2b..c120d98b 100644 --- a/api/app/services/app_dsl_service.py +++ b/api/app/services/app_dsl_service.py @@ -26,7 +26,7 @@ class AppDslService: # ==================== 导出 ==================== - def export_dsl(self, app_id: uuid.UUID, release_version: Optional[str] = None) -> tuple[str, str]: + def export_dsl(self, app_id: uuid.UUID, release_id: Optional[uuid.UUID] = None) -> tuple[str, str]: """构建应用 DSL yaml 字符串，返回 (yaml_str, filename)""" app = self.db.query(App).filter(App.id == app_id, App.is_active.is_(True)).first() if not app: @@ -46,19 +46,19 @@ class AppDslService: "tags": app.tags or [], } - if release_version is not None: - return self._export_release(app, release_version, meta, app_meta) + if release_id is not None: + return self._export_release(app, release_id, meta, app_meta) return self._export_draft(app, meta, app_meta) - def _export_release(self, app: App, release_version: str, meta: dict, app_meta: dict) -> tuple[str, str]: + def _export_release(self, app: App, release_id: uuid.UUID, meta: dict, app_meta: dict) -> tuple[str, str]: release = self.db.query(AppRelease).filter( AppRelease.app_id == app.id, - AppRelease.id == release_version, + AppRelease.id == release_id, AppRelease.is_active.is_(True) ).first() if not release: - raise ResourceNotFoundException("版本", str(release_version)) + raise ResourceNotFoundException("版本", str(release_id)) meta["release_version"] = release.version meta["release_name"] = release.version_name From 3e5a7adfe4ea5845a89dcd2248ba05ff571f399b Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Tue, 10 Mar 2026 11:28:52 +0800 Subject: [PATCH 032/196] feat(app): Application (agent, workflow) import/export --- api/app/controllers/app_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index cadc7df8..ee957f28 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -1024,7 +1024,7 @@ async def export_app( release_id: Optional[uuid.UUID] = None ): """导出 agent / multi_agent / workflow 应用配置为 YAML 文件流。 - release_version: 指定发布版本号，不传则导出当前草稿配置。 + release_id: 指定发布版本id，不传则导出当前草稿配置。 """ yaml_str, filename = AppDslService(db).export_dsl(app_id, release_id) encoded = quote(filename, safe=".") From e2f5fa87b165b6becd5e3b886fdb65487a3bce48 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 11:41:09 +0800 Subject: [PATCH 033/196] [add] Add cache to RAG storage --- api/app/models/end_user_model.py | 6 + api/app/repositories/end_user_repository.py | 84 ++++++++++++ api/app/services/memory_dashboard_service.py | 132 ++++++++++--------- 3 files changed, 159 insertions(+), 63 deletions(-) diff --git a/api/app/models/end_user_model.py b/api/app/models/end_user_model.py index 30b56fc5..048d42ea 100644 --- a/api/app/models/end_user_model.py +++ b/api/app/models/end_user_model.py @@ -51,6 +51,12 @@ class EndUser(Base): growth_trajectory = Column(Text, nullable=True, comment="成长轨迹") memory_insight_updated_at = Column(DateTime, nullable=True, comment="洞察报告最后更新时间") + # RAG存储模式专用字段 - RAG Storage Mode Fields + storage_type = Column(String, nullable=True, default="neo4j", comment="存储模式类型: neo4j / rag") + rag_tags = Column(Text, nullable=True, comment="RAG模式下提取的标签列表（JSON格式）") + rag_personas = Column(Text, nullable=True, comment="RAG模式下提取的人物形象列表（JSON格式）") + rag_summary_updated_at = Column(DateTime, nullable=True, comment="RAG摘要/标签/人物形象最后更新时间") + # 与 App 的反向关系 app = relationship( "App", diff --git a/api/app/repositories/end_user_repository.py b/api/app/repositories/end_user_repository.py index 48c9c4ec..0b828a8b 100644 --- a/api/app/repositories/end_user_repository.py +++ b/api/app/repositories/end_user_repository.py @@ -220,6 +220,90 @@ class EndUserRepository: db_logger.error(f"更新终端用户 {end_user_id} 的用户摘要缓存时出错: {str(e)}") raise + def update_rag_summary_tags( + self, + end_user_id: uuid.UUID, + user_summary: str, + rag_tags: str, + rag_personas: str, + ) -> bool: + """更新RAG模式下的用户摘要、标签和人物形象缓存 + + Args: + end_user_id: 终端用户ID + user_summary: 用户摘要文本 + rag_tags: 标签列表（JSON字符串） + rag_personas: 人物形象列表（JSON字符串） + + Returns: + bool: 更新成功返回True，否则返回False + """ + try: + updated_count = ( + self.db.query(EndUser) + .filter(EndUser.id == end_user_id) + .update( + { + EndUser.user_summary: user_summary, + EndUser.rag_tags: rag_tags, + EndUser.rag_personas: rag_personas, + EndUser.storage_type: "rag", + EndUser.rag_summary_updated_at: datetime.datetime.now(), + }, + synchronize_session=False + ) + ) + self.db.commit() + if updated_count > 0: + db_logger.info(f"成功更新终端用户 {end_user_id} 的RAG摘要/标签/人物形象缓存") + return True + else: + db_logger.warning(f"未找到终端用户 {end_user_id}，无法更新RAG摘要缓存") + return False + except Exception as e: + self.db.rollback() + db_logger.error(f"更新终端用户 {end_user_id} 的RAG摘要缓存时出错: {str(e)}") + raise + + def update_rag_insight( + self, + end_user_id: uuid.UUID, + memory_insight: str, + ) -> bool: + """更新RAG模式下的记忆洞察缓存 + + Args: + end_user_id: 终端用户ID + memory_insight: 洞察文本 + + Returns: + bool: 更新成功返回True，否则返回False + """ + try: + updated_count = ( + self.db.query(EndUser) + .filter(EndUser.id == end_user_id) + .update( + { + EndUser.memory_insight: memory_insight, + EndUser.storage_type: "rag", + EndUser.memory_insight_updated_at: datetime.datetime.now(), + }, + synchronize_session=False + ) + ) + self.db.commit() + if updated_count > 0: + db_logger.info(f"成功更新终端用户 {end_user_id} 的RAG洞察缓存") + return True + else: + db_logger.warning(f"未找到终端用户 {end_user_id}，无法更新RAG洞察缓存") + return False + except Exception as e: + self.db.rollback() + db_logger.error(f"更新终端用户 {end_user_id} 的RAG洞察缓存时出错: {str(e)}") + raise + def get_all_by_workspace(self, workspace_id: uuid.UUID) -> List[EndUser]: """获取工作空间的所有终端用户 diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index 63a9c361..6559ef2f 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -647,55 +647,63 @@ async def get_chunk_summary_and_tags( ) -> dict: """ 获取chunk的总结、标签和人物形象 - - Args: - end_user_id: 宿主ID - limit: 返回的chunk数量限制 - max_tags: 最大标签数量 - db: 数据库会话 - current_user: 当前用户 - - Returns: - 包含summary、tags和personas的字典 + 优先返回end_user表中的缓存，若无缓存则实时生成并写库 """ + import json + from app.repositories.end_user_repository import EndUserRepository + business_logger.info(f"获取chunk摘要、标签和人物形象: end_user_id={end_user_id}, limit={limit}, 操作者: {current_user.username}") - + try: - # 1. 获取chunk内容 + repo = EndUserRepository(db) + end_user = repo.get_by_id(uuid.UUID(end_user_id)) + + # 读缓存：user_summary / rag_tags / rag_personas 均有值时直接返回 + if ( + end_user + and end_user.user_summary + and end_user.rag_tags + and end_user.rag_personas + ): + business_logger.info(f"命中缓存，直接返回end_user {end_user_id} 的摘要/标签/人物形象") + return { + "summary": end_user.user_summary, + "tags": json.loads(end_user.rag_tags), + "personas": json.loads(end_user.rag_personas), + } + + # 无缓存：实时生成 rag_content = get_rag_content(end_user_id, limit, db, current_user) chunks = rag_content.get("contents", []) - + if not chunks: business_logger.warning(f"未找到chunk内容: end_user_id={end_user_id}") - return { - "summary": "暂无内容", - "tags": [], - "personas": [] - } - - # 2. 导入RAG工具函数 + return {"summary": "暂无内容", "tags": [], "personas": []} + from app.core.rag_utils import generate_chunk_summary, extract_chunk_tags, extract_chunk_persona - - # 3. 并发生成摘要、提取标签和人物形象 import asyncio - summary_task = generate_chunk_summary(chunks, max_chunks=limit, end_user_id=end_user_id) - tags_task = extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit, end_user_id=end_user_id) - personas_task = extract_chunk_persona(chunks, max_personas=5, max_chunks=limit, end_user_id=end_user_id) - - summary, tags_with_freq, personas = await asyncio.gather(summary_task, tags_task, personas_task) - - # 4. 格式化标签数据 + + summary, tags_with_freq, personas = await asyncio.gather( + generate_chunk_summary(chunks, max_chunks=limit, end_user_id=end_user_id), + extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit, end_user_id=end_user_id), + extract_chunk_persona(chunks, max_personas=5, max_chunks=limit, end_user_id=end_user_id), + ) + tags = [{"tag": tag, "frequency": freq} for tag, freq in tags_with_freq] - - result = { - "summary": summary, - "tags": tags, - "personas": personas - } - + + # 写库缓存 + if end_user: + repo.update_rag_summary_tags( + end_user_id=end_user.id, + user_summary=summary, + rag_tags=json.dumps(tags, ensure_ascii=False), + rag_personas=json.dumps(personas, ensure_ascii=False), + ) + + result = {"summary": summary, "tags": tags, "personas": personas} business_logger.info(f"成功获取chunk摘要、{len(tags)} 个标签和 {len(personas)} 个人物形象") return result - + except Exception as e: business_logger.error(f"获取chunk摘要、标签和人物形象失败: end_user_id={end_user_id} - {str(e)}") raise @@ -709,42 +717,40 @@ async def get_chunk_insight( ) -> dict: """ 获取chunk的洞察分析 - - Args: - end_user_id: 宿主ID - limit: 返回的chunk数量限制 - db: 数据库会话 - current_user: 当前用户 - - Returns: - 包含insight的字典 + 优先返回end_user表中的缓存，若无缓存则实时生成并写库 """ + from app.repositories.end_user_repository import EndUserRepository + business_logger.info(f"获取chunk洞察: end_user_id={end_user_id}, limit={limit}, 操作者: {current_user.username}") - + try: - # 1. 获取chunk内容 + repo = EndUserRepository(db) + end_user = repo.get_by_id(uuid.UUID(end_user_id)) + + # 读缓存 + if end_user and end_user.memory_insight: + business_logger.info(f"命中缓存，直接返回end_user {end_user_id} 的洞察") + return {"insight": end_user.memory_insight} + + # 无缓存：实时生成 rag_content = get_rag_content(end_user_id, limit, db, current_user) chunks = rag_content.get("contents", []) - + if not chunks: business_logger.warning(f"未找到chunk内容: end_user_id={end_user_id}") - return { - "insight": "暂无足够数据生成洞察报告" - } - - # 2. 导入RAG工具函数 + return {"insight": "暂无足够数据生成洞察报告"} + from app.core.rag_utils import generate_chunk_insight - - # 3. 生成洞察 + insight = await generate_chunk_insight(chunks, max_chunks=limit, end_user_id=end_user_id) - - result = { - "insight": insight - } - + + # 写库缓存 + if end_user: + repo.update_rag_insight(end_user_id=end_user.id, memory_insight=insight) + business_logger.info("成功获取chunk洞察") - return result - + return {"insight": insight} + except Exception as e: business_logger.error(f"获取chunk洞察失败: end_user_id={end_user_id} - {str(e)}") raise \ No newline at end of file From fd556f9b00b8fdbb2c0977abfa4f4d69a16de928 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 11:51:17 +0800 Subject: [PATCH 034/196] [add] Generate user summaries and memory insights using Jinja2 tags --- api/app/core/rag_utils/__init__.py | 3 +- api/app/core/rag_utils/chunk_insight.py | 310 +++++++++---------- api/app/core/rag_utils/chunk_summary.py | 150 ++++----- api/app/services/memory_dashboard_service.py | 22 +- 4 files changed, 242 insertions(+), 243 deletions(-) diff --git a/api/app/core/rag_utils/__init__.py b/api/app/core/rag_utils/__init__.py index d5a8ce1c..0efe1938 100644 --- a/api/app/core/rag_utils/__init__.py +++ b/api/app/core/rag_utils/__init__.py @@ -4,11 +4,12 @@ RAG chunk analysis utilities. from .chunk_summary import generate_chunk_summary from .chunk_tags import extract_chunk_tags, extract_chunk_persona -from .chunk_insight import generate_chunk_insight +from .chunk_insight import generate_chunk_insight, generate_chunk_insight_sections __all__ = [ "generate_chunk_summary", "extract_chunk_tags", "extract_chunk_persona", "generate_chunk_insight", + "generate_chunk_insight_sections", ] diff --git a/api/app/core/rag_utils/chunk_insight.py b/api/app/core/rag_utils/chunk_insight.py index 9fbdbbb2..b46ce3c9 100644 --- a/api/app/core/rag_utils/chunk_insight.py +++ b/api/app/core/rag_utils/chunk_insight.py @@ -1,24 +1,33 @@ """ -Generate insights from RAG chunks. +Generate memory insight report for RAG chunks using memory_insight.jinja2 prompt template. -This module provides functionality to analyze chunk content and generate insights using LLM. +The memory_insight.jinja2 template produces a four-section report: + 【总体概述】 → memory_insight + 【行为模式】 → behavior_pattern + 【关键发现】 → key_findings + 【成长轨迹】 → growth_trajectory + +generate_chunk_insight() returns the full raw text (stored in end_user.memory_insight). +generate_chunk_insight_sections() returns a dict with all four fields for richer storage. """ import asyncio import os +import re from collections import Counter -from typing import Any, Dict, List, Optional +from typing import Dict, List, Optional from app.core.logging_config import get_business_logger from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context -from pydantic import BaseModel, Field business_logger = get_business_logger() DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus") +# ── LLM client helper ──────────────────────────────────────────────────────── + def _get_llm_client(end_user_id: Optional[str] = None): """Get LLM client, preferring user-connected config with fallback to default.""" with get_db_context() as db: @@ -43,191 +52,156 @@ def _get_llm_client(end_user_id: Optional[str] = None): return factory.get_llm_client(DEFAULT_LLM_ID) -class ChunkInsight(BaseModel): - """Pydantic model for chunk insight.""" - insight: str = Field(..., description="对chunk内容的深度洞察分析") +# ── Domain analysis helpers (kept for building prompt inputs) ───────────────── +async def _classify_domain(chunk: str, llm_client) -> str: + """Classify a single chunk into a domain category.""" + from pydantic import BaseModel, Field -class DomainClassification(BaseModel): - """Pydantic model for domain classification.""" - domain: str = Field( - ..., - description="内容所属的领域分类", - examples=["技术", "商业", "教育", "生活", "娱乐", "健康", "其他"] - ) + class _Domain(BaseModel): + domain: str = Field(..., description="领域分类") - -async def classify_chunk_domain(chunk: str, end_user_id: Optional[str] = None) -> str: - """ - Classify a chunk into a specific domain. - - Args: - chunk: Chunk content string - - Returns: - Domain name - """ try: - llm_client = _get_llm_client(end_user_id) - - prompt = f"""请将以下文本内容归类到最合适的领域中。 - -可选领域及其关键词： -- 技术：编程、软件、硬件、算法、数据、网络、系统、开发、工程等 -- 商业：市场、销售、管理、财务、投资、创业、营销、战略等 -- 教育：学习、课程、培训、教学、知识、技能、考试、研究等 -- 生活：日常、家庭、饮食、购物、旅行、休闲、娱乐等 -- 娱乐：游戏、电影、音乐、体育、艺术、文化等 -- 健康：医疗、养生、运动、心理、保健、疾病等 -- 其他：无法归入以上类别的内容 - -文本内容: {chunk[:500]}... - -请直接返回最合适的领域名称。""" - - messages = [ - {"role": "system", "content": "你是一个专业的文本分类助手。请仔细分析文本内容，选择最合适的领域分类。"}, - {"role": "user", "content": prompt} - ] - - classification = await llm_client.response_structured( - messages=messages, - response_model=DomainClassification + prompt = ( + "请将以下文本归类到最合适的领域（技术/商业/教育/生活/娱乐/健康/其他）。\n\n" + f"文本: {chunk[:500]}\n\n直接返回领域名称。" ) - - return classification.domain if classification else "其他" - - except Exception as e: - business_logger.error(f"分类chunk领域失败: {str(e)}") + result = await llm_client.response_structured( + messages=[{"role": "user", "content": prompt}], + response_model=_Domain, + ) + return result.domain if result else "其他" + except Exception: return "其他" -async def analyze_domain_distribution(chunks: List[str], max_chunks: int = 20, end_user_id: Optional[str] = None) -> Dict[str, float]: +async def _build_insight_inputs( + chunks: List[str], + max_chunks: int, + end_user_id: Optional[str], +) -> Dict[str, str]: """ - Analyze the domain distribution of chunks. - - Args: - chunks: List of chunk content strings - max_chunks: Maximum number of chunks to analyze - - Returns: - Dictionary of domain -> percentage + Derive domain_distribution, active_periods, social_connections strings + to feed into the memory_insight.jinja2 template. """ - if not chunks: - return {} - - try: - # 限制分析的chunk数量 - chunks_to_analyze = chunks[:max_chunks] - - # 为每个chunk分类 - domain_counts = Counter() - for chunk in chunks_to_analyze: - domain = await classify_chunk_domain(chunk, end_user_id) - domain_counts[domain] += 1 - - # 计算百分比 - total = sum(domain_counts.values()) - domain_distribution = { - domain: count / total - for domain, count in domain_counts.items() - } - - # 按百分比降序排序 - return dict(sorted(domain_distribution.items(), key=lambda x: x[1], reverse=True)) - - except Exception as e: - business_logger.error(f"分析领域分布失败: {str(e)}") - return {} + llm_client = _get_llm_client(end_user_id) + chunks_sample = chunks[:max_chunks] + + # Domain distribution + domain_counts: Counter = Counter() + for chunk in chunks_sample: + domain = await _classify_domain(chunk, llm_client) + domain_counts[domain] += 1 + + total = sum(domain_counts.values()) or 1 + domain_distribution = ", ".join( + f"{d}({c / total:.0%})" for d, c in domain_counts.most_common(3) + ) + + return { + "domain_distribution": domain_distribution, + "active_periods": None, # RAG模式暂无时间维度数据 + "social_connections": None, # RAG模式暂无社交关联数据 + } -async def generate_chunk_insight(chunks: List[str], max_chunks: int = 15, end_user_id: Optional[str] = None) -> str: +# ── Section parser ──────────────────────────────────────────────────────────── + +_ZH_SECTIONS = { + "memory_insight": r"【总体概述】(.*?)(?=【|$)", + "behavior_pattern": r"【行为模式】(.*?)(?=【|$)", + "key_findings": r"【关键发现】(.*?)(?=【|$)", + "growth_trajectory": r"【成长轨迹】(.*?)(?=【|$)", +} + +_EN_SECTIONS = { + "memory_insight": r"【Overview】(.*?)(?=【|$)", + "behavior_pattern": r"【Behavior Pattern】(.*?)(?=【|$)", + "key_findings": r"【Key Findings】(.*?)(?=【|$)", + "growth_trajectory": r"【Growth Trajectory】(.*?)(?=【|$)", +} + + +def _parse_sections(text: str, language: str = "zh") -> Dict[str, str]: + """Extract the four sections from the LLM output.""" + patterns = _ZH_SECTIONS if language == "zh" else _EN_SECTIONS + result = {} + for key, pattern in patterns.items(): + match = re.search(pattern, text, re.DOTALL) + result[key] = match.group(1).strip() if match else "" + return result + + +# ── Public API ──────────────────────────────────────────────────────────────── + +async def generate_chunk_insight( + chunks: List[str], + max_chunks: int = 15, + end_user_id: Optional[str] = None, + language: str = "zh", +) -> str: """ - Generate insights from the given chunks. - - Args: - chunks: List of chunk content strings - max_chunks: Maximum number of chunks to analyze - - Returns: - A comprehensive insight report + Generate a memory insight report from RAG chunks. + + Returns the full raw report text (suitable for end_user.memory_insight). + Use generate_chunk_insight_sections() when you need all four dimensions. + """ + sections = await generate_chunk_insight_sections( + chunks=chunks, + max_chunks=max_chunks, + end_user_id=end_user_id, + language=language, + ) + return sections.get("memory_insight") or sections.get("_raw", "洞察生成失败") + + +async def generate_chunk_insight_sections( + chunks: List[str], + max_chunks: int = 15, + end_user_id: Optional[str] = None, + language: str = "zh", +) -> Dict[str, str]: + """ + Generate a four-section memory insight report from RAG chunks. + + Returns a dict with keys: + memory_insight, behavior_pattern, key_findings, growth_trajectory + (plus '_raw' containing the full LLM output for debugging) """ if not chunks: business_logger.warning("没有提供chunk内容用于生成洞察") - return "暂无足够数据生成洞察报告" - + empty = {k: "" for k in ("memory_insight", "behavior_pattern", "key_findings", "growth_trajectory")} + empty["_raw"] = "暂无足够数据生成洞察报告" + return empty + try: - # 1. 分析领域分布 - domain_dist = await analyze_domain_distribution(chunks, max_chunks=max_chunks, end_user_id=end_user_id) - - # 2. 统计基本信息 - total_chunks = len(chunks) - avg_length = sum(len(chunk) for chunk in chunks) / total_chunks if total_chunks > 0 else 0 - - # 3. 构建洞察prompt - prompt_parts = [] - - if domain_dist: - top_domains = ", ".join([f"{k}({v:.0%})" for k, v in list(domain_dist.items())[:3]]) - prompt_parts.append(f"- 内容领域分布: {top_domains}") - - prompt_parts.append(f"- 内容规模: 共{total_chunks}个知识片段，平均长度{avg_length:.0f}字") - - # 添加部分chunk内容作为参考 - sample_chunks = chunks[:5] - sample_content = "\n".join([f"示例{i+1}: {chunk[:200]}..." for i, chunk in enumerate(sample_chunks)]) - prompt_parts.append(f"\n内容示例:\n{sample_content}") - - system_prompt = """你是一位专业的知识内容分析师。你的任务是根据提供的信息，生成一段简洁、有洞察力的分析报告。 + from app.core.memory.utils.prompt.prompt_utils import render_memory_insight_prompt -重要规则： -1. 报告需要将所有要点流畅地串联成一个段落 -2. 语言风格要专业、客观，同时易于理解 -3. 不要添加任何额外的解释或标题，直接输出报告内容 -4. 基于提供的数据和示例内容进行分析，不要编造信息 -5. 重点关注内容的主题、特点和价值 -6. 报告长度控制在150-200字 + # Build template inputs from chunk analysis + inputs = await _build_insight_inputs(chunks, max_chunks, end_user_id) -例如，如果输入是： -- 内容领域分布: 技术(60%), 商业(25%), 教育(15%) -- 内容规模: 共50个知识片段，平均长度320字 -内容示例: [示例内容...] + rendered_prompt = await render_memory_insight_prompt( + domain_distribution=inputs["domain_distribution"], + active_periods=inputs["active_periods"], + social_connections=inputs["social_connections"], + language=language, + ) -你的输出应该类似： -"该知识库主要聚焦于技术领域(60%)，涵盖商业(25%)和教育(15%)相关内容。共包含50个知识片段，平均每个片段约320字，内容详实。从示例来看，内容涉及[具体主题]，体现了[特点]，对[目标用户]具有较高的参考价值。" -""" - - user_prompt = "\n".join(prompt_parts) - - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt} - ] - - # 调用LLM生成洞察 + messages = [{"role": "user", "content": rendered_prompt}] llm_client = _get_llm_client(end_user_id) response = await llm_client.chat(messages=messages) - - insight = response.content.strip() - business_logger.info(f"成功生成chunk洞察，分析了 {min(len(chunks), max_chunks)} 个片段") - - return insight - + raw_text = response.content.strip() if response and response.content else "" + + sections = _parse_sections(raw_text, language=language) + sections["_raw"] = raw_text + + business_logger.info( + f"成功生成chunk洞察四维度，分析了 {min(len(chunks), max_chunks)} 个片段" + ) + return sections + except Exception as e: business_logger.error(f"生成chunk洞察失败: {str(e)}") - return "洞察生成失败" - - -if __name__ == "__main__": - # 测试代码 - test_chunks = [ - "Python是一种高级编程语言，以其简洁的语法和强大的功能而闻名。它广泛应用于Web开发、数据分析、人工智能等领域。", - "机器学习算法可以从数据中自动学习模式，无需显式编程。常见的算法包括决策树、随机森林、神经网络等。", - "深度学习是机器学习的一个分支，使用多层神经网络来学习数据的层次化表示。它在图像识别、语音识别等任务中表现出色。", - "自然语言处理技术使计算机能够理解和生成人类语言。应用包括机器翻译、情感分析、文本摘要等。", - "数据科学结合了统计学、计算机科学和领域知识，用于从数据中提取有价值的洞察。" - ] - - print("开始生成chunk洞察...") - insight = asyncio.run(generate_chunk_insight(test_chunks)) - print(f"\n生成的洞察：\n{insight}") + empty = {k: "" for k in ("memory_insight", "behavior_pattern", "key_findings", "growth_trajectory")} + empty["_raw"] = "洞察生成失败" + return empty diff --git a/api/app/core/rag_utils/chunk_summary.py b/api/app/core/rag_utils/chunk_summary.py index 53df2ab3..dd1c904e 100644 --- a/api/app/core/rag_utils/chunk_summary.py +++ b/api/app/core/rag_utils/chunk_summary.py @@ -1,12 +1,10 @@ """ -Generate summary for RAG chunks. - -This module provides functionality to summarize chunk content using LLM. +Generate summary for RAG chunks using memory_summary.jinja2 prompt template. """ import asyncio import os -from typing import Any, Dict, List, Optional +from typing import List, Optional from app.core.logging_config import get_business_logger from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -18,6 +16,29 @@ business_logger = get_business_logger() DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus") +# ── Schema ────────────────────────────────────────────────────────────────── + +class MemorySummaryStatement(BaseModel): + """Single labelled statement extracted by memory_summary.jinja2.""" + statement: str = Field(..., description="提取的陈述内容") + label: Optional[str] = Field(None, description="陈述标签") + + +class MemorySummaryResponse(BaseModel): + """ + Structured output expected from memory_summary.jinja2. + The template asks for a JSON array of labelled statements; + we wrap it in an object so response_structured can parse it. + """ + statements: List[MemorySummaryStatement] = Field( + default_factory=list, + description="从chunk中提取的陈述列表" + ) + summary: Optional[str] = Field(None, description="整体摘要文本（可选）") + + +# ── LLM client helper ──────────────────────────────────────────────────────── + def _get_llm_client(end_user_id: Optional[str] = None): """Get LLM client, preferring user-connected config with fallback to default.""" with get_db_context() as db: @@ -42,86 +63,75 @@ def _get_llm_client(end_user_id: Optional[str] = None): return factory.get_llm_client(DEFAULT_LLM_ID) -class ChunkSummary(BaseModel): - """Pydantic model for chunk summary.""" - summary: str = Field(..., description="简洁的chunk内容摘要") +# ── Core function ───────────────────────────────────────────────────────────── - -async def generate_chunk_summary(chunks: List[str], max_chunks: int = 10, end_user_id: Optional[str] = None) -> str: +async def generate_chunk_summary( + chunks: List[str], + max_chunks: int = 10, + end_user_id: Optional[str] = None, + language: str = "zh", +) -> str: """ - Generate a summary for the given chunks. - + Generate a user summary from RAG chunks using the memory_summary.jinja2 template. + + The template extracts labelled statements from the chunks; we then join them + into a coherent summary string that can be stored in end_user.user_summary. + Args: chunks: List of chunk content strings - max_chunks: Maximum number of chunks to process (default: 10) - + max_chunks: Maximum number of chunks to process + end_user_id: Optional end-user ID for model selection + language: Output language ("zh" or "en") + Returns: - A concise summary of the chunks + Summary string (joined statements or fallback text) """ if not chunks: business_logger.warning("没有提供chunk内容用于生成摘要") return "暂无内容" - + try: - # 限制处理的chunk数量，避免token过多 + from app.core.memory.utils.prompt.prompt_utils import render_memory_summary_prompt + chunks_to_process = chunks[:max_chunks] - - # 合并chunk内容 - combined_content = "\n\n".join([f"片段{i+1}: {chunk}" for i, chunk in enumerate(chunks_to_process)]) - - # 构建prompt - system_prompt = ( - "你是一位专业的文本摘要助手。请基于提供的文本片段，生成简洁的摘要。要求：\n" - "- 摘要长度控制在100-150字；\n" - "- 提取核心信息和关键要点；\n" - "- 使用客观、清晰的语言；\n" - "- 避免冗余和重复；\n" - "- 如果内容涉及多个主题，按重要性排序呈现。" + chunk_texts = "\n\n".join( + [f"片段{i + 1}: {chunk}" for i, chunk in enumerate(chunks_to_process)] ) - - user_prompt = f"请为以下文本片段生成摘要：\n\n{combined_content}" - - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ] - - # 调用LLM生成摘要 + + json_schema = MemorySummaryResponse.model_json_schema() + + rendered_prompt = await render_memory_summary_prompt( + chunk_texts=chunk_texts, + json_schema=json_schema, + max_words=200, + language=language, + ) + + messages = [{"role": "user", "content": rendered_prompt}] + llm_client = _get_llm_client(end_user_id) - response = await llm_client.chat(messages=messages) - - summary = response.content.strip() - business_logger.info(f"成功生成chunk摘要，处理了 {len(chunks_to_process)} 个片段") - + + # Try structured output first; fall back to plain chat if unsupported + try: + response: MemorySummaryResponse = await llm_client.response_structured( + messages=messages, + response_model=MemorySummaryResponse, + ) + if response.summary: + summary = response.summary.strip() + elif response.statements: + summary = "；".join(s.statement for s in response.statements) + else: + summary = "暂无内容" + except Exception: + raw = await llm_client.chat(messages=messages) + summary = raw.content.strip() if raw and raw.content else "暂无内容" + + business_logger.info( + f"成功生成chunk摘要，处理了 {len(chunks_to_process)} 个片段" + ) return summary - + except Exception as e: business_logger.error(f"生成chunk摘要失败: {str(e)}") return "摘要生成失败" - - -async def generate_chunk_summary_batch(chunks_list: List[List[str]]) -> List[str]: - """ - Generate summaries for multiple chunk lists in batch. - - Args: - chunks_list: List of chunk lists - - Returns: - List of summaries - """ - tasks = [generate_chunk_summary(chunks) for chunks in chunks_list] - return await asyncio.gather(*tasks) - - -if __name__ == "__main__": - # 测试代码 - test_chunks = [ - "这是第一段测试内容，讲述了关于机器学习的基础知识。", - "第二段内容介绍了深度学习的应用场景和发展历史。", - "第三段讨论了自然语言处理技术的最新进展。" - ] - - print("开始生成chunk摘要...") - summary = asyncio.run(generate_chunk_summary(test_chunks)) - print(f"\n生成的摘要：\n{summary}") diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index 6559ef2f..cbe0bd80 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -740,13 +740,27 @@ async def get_chunk_insight( business_logger.warning(f"未找到chunk内容: end_user_id={end_user_id}") return {"insight": "暂无足够数据生成洞察报告"} - from app.core.rag_utils import generate_chunk_insight + from app.core.rag_utils import generate_chunk_insight_sections - insight = await generate_chunk_insight(chunks, max_chunks=limit, end_user_id=end_user_id) + sections = await generate_chunk_insight_sections(chunks, max_chunks=limit, end_user_id=end_user_id) + insight = sections.get("memory_insight") or sections.get("_raw", "") - # 写库缓存 + # 写库缓存（四维度全部入库） if end_user: - repo.update_rag_insight(end_user_id=end_user.id, memory_insight=insight) + from app.repositories.end_user_repository import EndUserRepository as _Repo + _repo = _Repo(db) + _repo.update_memory_insight( + end_user_id=end_user.id, + memory_insight=insight, + behavior_pattern=sections.get("behavior_pattern", ""), + key_findings=sections.get("key_findings", ""), + growth_trajectory=sections.get("growth_trajectory", ""), + ) + # 同时标记 storage_type 为 rag + db.query(end_user.__class__).filter( + end_user.__class__.id == end_user.id + ).update({"storage_type": "rag"}, synchronize_session=False) + db.commit() business_logger.info("成功获取chunk洞察") return {"insight": insight} From 424d2033ea7b88199c382c5224b8d12a8bfbf705 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 12:11:13 +0800 Subject: [PATCH 035/196] [add] Added an interface for refreshing RAG storage image data --- .../memory_dashboard_controller.py | 74 ++++--- api/app/services/memory_dashboard_service.py | 190 +++++++++--------- 2 files changed, 143 insertions(+), 121 deletions(-) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 50e8ec8f..1582a042 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -422,26 +422,18 @@ async def get_chunk_summary_tag( current_user: User = Depends(get_current_user), ): """ - 获取chunk总结、提取的标签和人物形象 - + 读取RAG摘要、标签和人物形象（纯读库，不触发生成）。 + 返回格式： { - "summary": "chunk内容的总结", - "tags": [ - {"tag": "标签1", "frequency": 5}, - {"tag": "标签2", "frequency": 3}, - ... - ], - "personas": [ - "产品设计师", - "旅行爱好者", - "摄影发烧友", - ... - ] + "summary": "用户摘要", + "tags": [{"tag": "标签1", "frequency": 5}, ...], + "personas": ["产品设计师", ...], + "generated": true/false // false表示尚未生产，请调用 /generate_rag_profile } """ - api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk摘要、标签和人物形象") - + api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG摘要/标签/人物形象") + data = await memory_dashboard_service.get_chunk_summary_and_tags( end_user_id=end_user_id, limit=limit, @@ -449,9 +441,8 @@ async def get_chunk_summary_tag( db=db, current_user=current_user ) - - api_logger.info(f"成功获取chunk摘要、{len(data.get('tags', []))} 个标签和 {len(data.get('personas', []))} 个人物形象") - return success(data=data, msg="chunk摘要、标签和人物形象获取成功") + + return success(data=data, msg="获取成功") @router.get("/chunk_insight", response_model=ApiResponse) @@ -462,24 +453,53 @@ async def get_chunk_insight( current_user: User = Depends(get_current_user), ): """ - 获取chunk的洞察内容 - + 读取RAG洞察报告（纯读库，不触发生成）。 + 返回格式： { - "insight": "对chunk内容的深度洞察分析" + "insight": "总体概述", + "behavior_pattern": "行为模式", + "key_findings": "关键发现", + "growth_trajectory": "成长轨迹", + "generated": true/false // false表示尚未生产，请调用 /generate_rag_profile } """ - api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk洞察") - + api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG洞察") + data = await memory_dashboard_service.get_chunk_insight( end_user_id=end_user_id, limit=limit, db=db, current_user=current_user ) - - api_logger.info("成功获取chunk洞察") - return success(data=data, msg="chunk洞察获取成功") + + return success(data=data, msg="获取成功") + + +@router.post("/generate_rag_profile", response_model=ApiResponse) +async def generate_rag_profile( + end_user_id: str = Query(..., description="宿主ID"), + limit: int = Query(15, description="参与生成的chunk数量上限"), + max_tags: int = Query(10, description="最大标签数量"), + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user), +): + """ + 生产接口：为RAG存储模式的宿主全量重新生成完整画像并持久化到end_user表。 + 每次请求都会重新生成，覆盖已有数据。 + """ + api_logger.info(f"用户 {current_user.username} 触发RAG画像生产: end_user_id={end_user_id}") + + data = await memory_dashboard_service.generate_rag_profile( + end_user_id=end_user_id, + limit=limit, + max_tags=max_tags, + db=db, + current_user=current_user, + ) + + api_logger.info(f"RAG画像生产完成: {data}") + return success(data=data, msg="RAG画像生产完成") @router.get("/dashboard_data", response_model=ApiResponse) diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index cbe0bd80..db49c50a 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -646,67 +646,26 @@ async def get_chunk_summary_and_tags( current_user: User ) -> dict: """ - 获取chunk的总结、标签和人物形象 - 优先返回end_user表中的缓存，若无缓存则实时生成并写库 + 纯读库：从end_user表返回RAG摘要、标签和人物形象缓存。 + 无数据时返回空结构，不触发LLM生成。 """ import json from app.repositories.end_user_repository import EndUserRepository - business_logger.info(f"获取chunk摘要、标签和人物形象: end_user_id={end_user_id}, limit={limit}, 操作者: {current_user.username}") + business_logger.info(f"读取chunk摘要/标签/人物形象缓存: end_user_id={end_user_id}") - try: - repo = EndUserRepository(db) - end_user = repo.get_by_id(uuid.UUID(end_user_id)) + repo = EndUserRepository(db) + end_user = repo.get_by_id(uuid.UUID(end_user_id)) - # 读缓存：user_summary / rag_tags / rag_personas 均有值时直接返回 - if ( - end_user - and end_user.user_summary - and end_user.rag_tags - and end_user.rag_personas - ): - business_logger.info(f"命中缓存，直接返回end_user {end_user_id} 的摘要/标签/人物形象") - return { - "summary": end_user.user_summary, - "tags": json.loads(end_user.rag_tags), - "personas": json.loads(end_user.rag_personas), - } + if not end_user: + return {"summary": "", "tags": [], "personas": [], "generated": False} - # 无缓存：实时生成 - rag_content = get_rag_content(end_user_id, limit, db, current_user) - chunks = rag_content.get("contents", []) - - if not chunks: - business_logger.warning(f"未找到chunk内容: end_user_id={end_user_id}") - return {"summary": "暂无内容", "tags": [], "personas": []} - - from app.core.rag_utils import generate_chunk_summary, extract_chunk_tags, extract_chunk_persona - import asyncio - - summary, tags_with_freq, personas = await asyncio.gather( - generate_chunk_summary(chunks, max_chunks=limit, end_user_id=end_user_id), - extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit, end_user_id=end_user_id), - extract_chunk_persona(chunks, max_personas=5, max_chunks=limit, end_user_id=end_user_id), - ) - - tags = [{"tag": tag, "frequency": freq} for tag, freq in tags_with_freq] - - # 写库缓存 - if end_user: - repo.update_rag_summary_tags( - end_user_id=end_user.id, - user_summary=summary, - rag_tags=json.dumps(tags, ensure_ascii=False), - rag_personas=json.dumps(personas, ensure_ascii=False), - ) - - result = {"summary": summary, "tags": tags, "personas": personas} - business_logger.info(f"成功获取chunk摘要、{len(tags)} 个标签和 {len(personas)} 个人物形象") - return result - - except Exception as e: - business_logger.error(f"获取chunk摘要、标签和人物形象失败: end_user_id={end_user_id} - {str(e)}") - raise + return { + "summary": end_user.user_summary or "", + "tags": json.loads(end_user.rag_tags) if end_user.rag_tags else [], + "personas": json.loads(end_user.rag_personas) if end_user.rag_personas else [], + "generated": bool(end_user.user_summary), + } async def get_chunk_insight( @@ -716,55 +675,98 @@ async def get_chunk_insight( current_user: User ) -> dict: """ - 获取chunk的洞察分析 - 优先返回end_user表中的缓存，若无缓存则实时生成并写库 + 纯读库：从end_user表返回RAG洞察缓存。 + 无数据时返回空结构，不触发LLM生成。 """ from app.repositories.end_user_repository import EndUserRepository - business_logger.info(f"获取chunk洞察: end_user_id={end_user_id}, limit={limit}, 操作者: {current_user.username}") + business_logger.info(f"读取chunk洞察缓存: end_user_id={end_user_id}") - try: - repo = EndUserRepository(db) - end_user = repo.get_by_id(uuid.UUID(end_user_id)) + repo = EndUserRepository(db) + end_user = repo.get_by_id(uuid.UUID(end_user_id)) - # 读缓存 - if end_user and end_user.memory_insight: - business_logger.info(f"命中缓存，直接返回end_user {end_user_id} 的洞察") - return {"insight": end_user.memory_insight} + if not end_user: + return {"insight": "", "behavior_pattern": "", "key_findings": "", "growth_trajectory": "", "generated": False} - # 无缓存：实时生成 - rag_content = get_rag_content(end_user_id, limit, db, current_user) - chunks = rag_content.get("contents", []) + return { + "insight": end_user.memory_insight or "", + "behavior_pattern": end_user.behavior_pattern or "", + "key_findings": end_user.key_findings or "", + "growth_trajectory": end_user.growth_trajectory or "", + "generated": bool(end_user.memory_insight), + } - if not chunks: - business_logger.warning(f"未找到chunk内容: end_user_id={end_user_id}") - return {"insight": "暂无足够数据生成洞察报告"} - from app.core.rag_utils import generate_chunk_insight_sections +async def generate_rag_profile( + end_user_id: str, + limit: int, + max_tags: int, + db: Session, + current_user: User, +) -> dict: + """ + 生产接口：为RAG存储模式的end_user全量重新生成并持久化完整画像数据。 + 每次调用都会重新生成，覆盖已有数据。 - sections = await generate_chunk_insight_sections(chunks, max_chunks=limit, end_user_id=end_user_id) - insight = sections.get("memory_insight") or sections.get("_raw", "") + 生成内容： + - user_summary / rag_tags / rag_personas + - memory_insight / behavior_pattern / key_findings / growth_trajectory + """ + import json + import asyncio + from app.repositories.end_user_repository import EndUserRepository + from app.core.rag_utils import ( + generate_chunk_summary, + extract_chunk_tags, + extract_chunk_persona, + generate_chunk_insight_sections, + ) - # 写库缓存（四维度全部入库） - if end_user: - from app.repositories.end_user_repository import EndUserRepository as _Repo - _repo = _Repo(db) - _repo.update_memory_insight( - end_user_id=end_user.id, - memory_insight=insight, - behavior_pattern=sections.get("behavior_pattern", ""), - key_findings=sections.get("key_findings", ""), - growth_trajectory=sections.get("growth_trajectory", ""), - ) - # 同时标记 storage_type 为 rag - db.query(end_user.__class__).filter( - end_user.__class__.id == end_user.id - ).update({"storage_type": "rag"}, synchronize_session=False) - db.commit() + business_logger.info(f"开始生产RAG画像: end_user_id={end_user_id}, 操作者: {current_user.username}") - business_logger.info("成功获取chunk洞察") - return {"insight": insight} + repo = EndUserRepository(db) + end_user = repo.get_by_id(uuid.UUID(end_user_id)) - except Exception as e: - business_logger.error(f"获取chunk洞察失败: end_user_id={end_user_id} - {str(e)}") - raise \ No newline at end of file + if not end_user: + raise ValueError(f"end_user {end_user_id} 不存在") + + rag_content = get_rag_content(end_user_id, limit, db, current_user) + chunks = rag_content.get("contents", []) + + if not chunks: + business_logger.warning(f"未找到chunk内容，无法生产RAG画像: end_user_id={end_user_id}") + raise ValueError("暂无chunk内容，无法生成画像") + + summary, tags_with_freq, personas, insight_sections = await asyncio.gather( + generate_chunk_summary(chunks, max_chunks=limit, end_user_id=end_user_id), + extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit, end_user_id=end_user_id), + extract_chunk_persona(chunks, max_personas=5, max_chunks=limit, end_user_id=end_user_id), + generate_chunk_insight_sections(chunks, max_chunks=limit, end_user_id=end_user_id), + ) + + tags = [{"tag": tag, "frequency": freq} for tag, freq in tags_with_freq] + + repo.update_rag_summary_tags( + end_user_id=end_user.id, + user_summary=summary, + rag_tags=json.dumps(tags, ensure_ascii=False), + rag_personas=json.dumps(personas, ensure_ascii=False), + ) + + repo.update_memory_insight( + end_user_id=end_user.id, + memory_insight=insight_sections.get("memory_insight", ""), + behavior_pattern=insight_sections.get("behavior_pattern", ""), + key_findings=insight_sections.get("key_findings", ""), + growth_trajectory=insight_sections.get("growth_trajectory", ""), + ) + + business_logger.info(f"RAG画像生产完成: end_user_id={end_user_id}, tags={len(tags)}, personas={len(personas)}") + + return { + "end_user_id": end_user_id, + "summary_length": len(summary), + "tags_count": len(tags), + "personas_count": len(personas), + "insight_generated": bool(insight_sections.get("memory_insight")), + } \ No newline at end of file From b4e81615b101bc98107adf24c84e6ae44ab0b45b Mon Sep 17 00:00:00 2001 From: zhaoying Date: Tue, 10 Mar 2026 13:35:52 +0800 Subject: [PATCH 036/196] feat(web): rag user memery add refresh --- web/src/api/memory.ts | 4 +++ web/src/views/UserMemoryDetail/Rag.tsx | 39 ++++++++++++++++++++++---- 2 files changed, 38 insertions(+), 5 deletions(-) diff --git a/web/src/api/memory.ts b/web/src/api/memory.ts index 2c840c9a..491f78ea 100644 --- a/web/src/api/memory.ts +++ b/web/src/api/memory.ts @@ -224,6 +224,10 @@ export const getConversationDetail = (end_user_id: string, conversation_id: stri export const forgetTrigger = (data: { max_merge_batch_size: number; min_days_since_access: number; end_user_id: string;}) => { return request.post(`/memory/forget-memory/trigger`, data) } +// RAG type - Refresh RAG user summary and memory insight +export const generateRagProfile = (end_user_id: string) => { + return request.post(`/dashboard/generate_rag_profile`, { end_user_id }) +} /*************** end User Memory APIs ******************************/ /****************** Memory Management APIs *******************************/ diff --git a/web/src/views/UserMemoryDetail/Rag.tsx b/web/src/views/UserMemoryDetail/Rag.tsx index 2d3443ce..0f82ee05 100644 --- a/web/src/views/UserMemoryDetail/Rag.tsx +++ b/web/src/views/UserMemoryDetail/Rag.tsx @@ -13,7 +13,8 @@ import { type FC, useEffect, useState } from 'react' import { useTranslation } from 'react-i18next' import clsx from 'clsx' -import { Row, Col, Skeleton } from 'antd' +import { Row, Col, Skeleton, Spin, Flex, Tooltip } from 'antd' +import { LoadingOutlined } from '@ant-design/icons'; import { useParams } from 'react-router-dom' import aboutUs from '@/assets/images/userMemory/aboutUs.svg' @@ -26,6 +27,7 @@ import { getUserProfile, getTotalRagMemoryCountByUser, getChunkInsight, + generateRagProfile } from '@/api/memory' import Empty from '@/components/Empty' import ConversationMemory from './components/ConversationMemory' @@ -133,16 +135,43 @@ const Rag: FC = () => { }) } const name = loading.detail ? '' : data?.name && data?.name !== '' ? data.name : id + + const [refreshLoading, setRefreshLoading] = useState(false) + const handleRefresh = () => { + if (refreshLoading || !id) return + setRefreshLoading(true) + generateRagProfile(id as string) + .then(() => { + getSummary() + getInsightReport() + }) + .finally(() => { + setRefreshLoading(false) + }) + } return (
{name?.[0]}
-
- {name}
-
{personas?.join(' | ')}
-
+ +
+ {name}
+
{personas?.join(' | ')}
+
+ + {refreshLoading + ? } /> + : ( +
+ ) + } + +
From 58aa60ca0e8b3b45c81306e11efa174d161d14d7 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 13:39:24 +0800 Subject: [PATCH 037/196] [add] Change to "Body - json" format and pass as parameters --- .../memory_dashboard_controller.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 1582a042..3bbb5cf7 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -1,4 +1,5 @@ from fastapi import APIRouter, Depends, HTTPException, status, Query +from pydantic import BaseModel, Field from sqlalchemy.orm import Session from typing import Optional from app.core.response_utils import success @@ -476,11 +477,15 @@ async def get_chunk_insight( return success(data=data, msg="获取成功") +class GenerateRagProfileRequest(BaseModel): + end_user_id: str = Field(..., description="宿主ID") + limit: int = Field(15, description="参与生成的chunk数量上限") + max_tags: int = Field(10, description="最大标签数量") + + @router.post("/generate_rag_profile", response_model=ApiResponse) async def generate_rag_profile( - end_user_id: str = Query(..., description="宿主ID"), - limit: int = Query(15, description="参与生成的chunk数量上限"), - max_tags: int = Query(10, description="最大标签数量"), + body: GenerateRagProfileRequest, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ): @@ -488,12 +493,12 @@ async def generate_rag_profile( 生产接口：为RAG存储模式的宿主全量重新生成完整画像并持久化到end_user表。 每次请求都会重新生成，覆盖已有数据。 """ - api_logger.info(f"用户 {current_user.username} 触发RAG画像生产: end_user_id={end_user_id}") + api_logger.info(f"用户 {current_user.username} 触发RAG画像生产: end_user_id={body.end_user_id}") data = await memory_dashboard_service.generate_rag_profile( - end_user_id=end_user_id, - limit=limit, - max_tags=max_tags, + end_user_id=body.end_user_id, + limit=body.limit, + max_tags=body.max_tags, db=db, current_user=current_user, ) From 10560fb34c7fcb012aa1deff71302ea748e1e6a4 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 13:55:53 +0800 Subject: [PATCH 038/196] [changes] Clearly stipulated, the conditions for raising an error --- api/app/core/rag_utils/chunk_insight.py | 2 +- api/app/core/rag_utils/chunk_summary.py | 18 ++++++++++++++---- 2 files changed, 15 insertions(+), 5 deletions(-) diff --git a/api/app/core/rag_utils/chunk_insight.py b/api/app/core/rag_utils/chunk_insight.py index b46ce3c9..935b1449 100644 --- a/api/app/core/rag_utils/chunk_insight.py +++ b/api/app/core/rag_utils/chunk_insight.py @@ -79,7 +79,7 @@ async def _build_insight_inputs( chunks: List[str], max_chunks: int, end_user_id: Optional[str], -) -> Dict[str, str]: +) -> Dict[str, Optional[str]]: """ Derive domain_distribution, active_periods, social_connections strings to feed into the memory_insight.jinja2 template. diff --git a/api/app/core/rag_utils/chunk_summary.py b/api/app/core/rag_utils/chunk_summary.py index dd1c904e..1b0f4395 100644 --- a/api/app/core/rag_utils/chunk_summary.py +++ b/api/app/core/rag_utils/chunk_summary.py @@ -111,7 +111,9 @@ async def generate_chunk_summary( llm_client = _get_llm_client(end_user_id) - # Try structured output first; fall back to plain chat if unsupported + # Try structured output; fall back to plain chat only for LLMClientException + # (indicates the model/provider doesn't support structured output). + # All other exceptions are re-raised so config/schema errors stay visible. try: response: MemorySummaryResponse = await llm_client.response_structured( messages=messages, @@ -123,9 +125,17 @@ async def generate_chunk_summary( summary = "；".join(s.statement for s in response.statements) else: summary = "暂无内容" - except Exception: - raw = await llm_client.chat(messages=messages) - summary = raw.content.strip() if raw and raw.content else "暂无内容" + except Exception as e: + from app.core.memory.llm_tools.llm_client import LLMClientException + if isinstance(e, LLMClientException): + business_logger.warning( + f"结构化输出不可用，降级为普通对话: end_user_id={end_user_id}, reason={e}" + ) + raw = await llm_client.chat(messages=messages) + summary = raw.content.strip() if raw and raw.content else "暂无内容" + else: + business_logger.error(f"生成摘要时发生非预期异常: {e}") + raise business_logger.info( f"成功生成chunk摘要，处理了 {len(chunks_to_process)} 个片段" From 16e2c959651b5d43ff8ec48ee7093916978d8ec3 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Tue, 10 Mar 2026 14:22:15 +0800 Subject: [PATCH 039/196] feat(web): app add export & import --- web/src/api/application.ts | 8 + web/src/utils/request.ts | 17 ++ .../views/ApplicationConfig/ReleasePage.tsx | 7 +- .../components/ConfigHeader.tsx | 11 +- .../components/UploadModal.tsx | 256 ++++++++++++++++++ web/src/views/ApplicationManagement/index.tsx | 9 + web/src/views/ApplicationManagement/types.ts | 8 + 7 files changed, 308 insertions(+), 8 deletions(-) create mode 100644 web/src/views/ApplicationManagement/components/UploadModal.tsx diff --git a/web/src/api/application.ts b/web/src/api/application.ts index c769dd91..71048454 100644 --- a/web/src/api/application.ts +++ b/web/src/api/application.ts @@ -135,4 +135,12 @@ export const getExperienceConfig = (share_token: string) => { 'Authorization': `Bearer ${localStorage.getItem(`shareToken_${share_token}`)}` } }) +} +// Export application +export const appExport = (app_id: string, appName: string, data?: { release_version: string }) => { + return request.getDownloadFile(`/apps/${app_id}/export`, `${appName}.yml`, data) +} +// Import application +export const appImport = (formData: FormData) => { + return request.uploadFile(`/apps/import`, formData) } \ No newline at end of file diff --git a/web/src/utils/request.ts b/web/src/utils/request.ts index 3f81d4ab..80c12f85 100644 --- a/web/src/utils/request.ts +++ b/web/src/utils/request.ts @@ -347,6 +347,23 @@ export const request = { document.body.removeChild(link); callback?.() }); + }, + getDownloadFile(url: string, fileName: string, data?: unknown, callback?: () => void) { + service.get(url, { + params: paramFilter(data as Record), + responseType: "blob", + }) + .then(res => { + const link = document.createElement("a"); + const blob = new Blob([res as unknown as BlobPart]); + link.style.display = "none"; + link.href = URL.createObjectURL(blob); + link.setAttribute("download", decodeURI(fileName || fileName)); + document.body.appendChild(link); + link.click(); + document.body.removeChild(link); + callback?.() + }); } }; diff --git a/web/src/views/ApplicationConfig/ReleasePage.tsx b/web/src/views/ApplicationConfig/ReleasePage.tsx index 63f6df71..b44252d6 100644 --- a/web/src/views/ApplicationConfig/ReleasePage.tsx +++ b/web/src/views/ApplicationConfig/ReleasePage.tsx @@ -11,7 +11,7 @@ import { Button, Space, Input, Form, App } from 'antd'; import Tag, { type TagProps } from './components/Tag' import RbCard from '@/components/RbCard/Card' -import { getReleaseList, rollbackRelease } from '@/api/application' +import { getReleaseList, rollbackRelease, appExport } from '@/api/application' import ReleaseModal from './components/ReleaseModal' import ReleaseShareModal from './components/ReleaseShareModal' import type { Release, ReleaseModalRef, ReleaseShareModalRef } from './types' @@ -67,6 +67,9 @@ const ReleasePage: FC<{data: Application; refresh: () => void}> = ({data, refres message.success(t('common.operateSuccess')) }) } + const handleExport = () => { + appExport(data.id, data.name) + } return (
@@ -123,7 +126,7 @@ const ReleasePage: FC<{data: Application; refresh: () => void}> = ({data, refres {selectedVersion && <> - {/* */} + {data?.type !== 'multi_agent' && } {data.current_release_id !== selectedVersion.id && } } diff --git a/web/src/views/ApplicationConfig/components/ConfigHeader.tsx b/web/src/views/ApplicationConfig/components/ConfigHeader.tsx index 42031d85..8131a819 100644 --- a/web/src/views/ApplicationConfig/components/ConfigHeader.tsx +++ b/web/src/views/ApplicationConfig/components/ConfigHeader.tsx @@ -19,9 +19,8 @@ import deleteIcon from '@/assets/images/delete_hover.svg' import type { Application, ApplicationModalRef } from '@/views/ApplicationManagement/types'; import ApplicationModal from '@/views/ApplicationManagement/components/ApplicationModal' import type { CopyModalRef, AgentRef, ClusterRef, WorkflowRef } from '../types' -import { deleteApplication } from '@/api/application' +import { deleteApplication, appExport } from '@/api/application' import CopyModal from './CopyModal' -import { exportToYaml } from '@/utils/yamlExport'; const { Header } = Layout; @@ -85,16 +84,16 @@ const ConfigHeader: FC = ({ * Handle menu item click */ const handleClick: MenuProps['onClick'] = ({ key }) => { + if (!application) return switch (key) { case 'edit': - applicationModalRef.current?.handleOpen(application as Application) + applicationModalRef.current?.handleOpen(application) break; case 'copy': copyModalRef.current?.handleOpen() break; case 'export': - console.log('export', workflowRef?.current?.config) - exportToYaml(workflowRef?.current?.config, application?.name ?`${application?.name}.yml`: undefined) + appExport(application.id, application.name) break; case 'delete': handleDelete() @@ -153,7 +152,7 @@ const ConfigHeader: FC = ({ * Format dropdown menu items */ const formatMenuItems = useMemo(() => { - const items = (application?.type === 'workflow' ? ['edit', 'copy', 'export', 'delete'] : ['edit', 'copy', 'delete']).map(key => ({ + const items = (application?.type !== 'multi_agent' ? ['edit', 'copy', 'export', 'delete'] : ['edit', 'copy', 'delete']).map(key => ({ key, icon: , label: t(`common.${key}`), diff --git a/web/src/views/ApplicationManagement/components/UploadModal.tsx b/web/src/views/ApplicationManagement/components/UploadModal.tsx new file mode 100644 index 00000000..f354c145 --- /dev/null +++ b/web/src/views/ApplicationManagement/components/UploadModal.tsx @@ -0,0 +1,256 @@ +/* + * @Author: ZhaoYing + * @Date: 2026-02-28 14:08:14 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-03-06 12:05:46 + */ +/** + * UploadModal Component + * + * This component provides a modal for uploading workflow files with a multi-step process: + * 1. Upload - Select platform and file + * 2. Complex - Show warnings and errors if any + * 3. SureInfo - Confirm and edit workflow information + * 4. Completed - Show success message and options + */ +import { forwardRef, useImperativeHandle, useState, useMemo } from 'react'; +import { Form, Steps, Flex, Alert, Button, Result, message } from 'antd'; +import { useTranslation } from 'react-i18next'; + +import type { Application, UploadModalRef } from '../types' +import RbModal from '@/components/RbModal' +import UploadFiles from '@/components/Upload/UploadFiles' +import { appImport } from '@/api/application' + +/** + * Props for UploadModal component + */ +interface UploadModalProps { + /** Function to refresh the parent component after workflow import */ + refresh: () => void; +} + + +/** + * Steps definition for the upload process + */ +const steps = [ + 'upload', // Step 1: File upload + 'complex', // Step 2: Error/warning display + 'completed' // Step 4: Success message +] +/** + * UploadModal component + * + * @param {UploadModalProps} props - Component props + * @param {React.Ref} ref - Ref for imperative methods + */ +const UploadModal = forwardRef(({ + refresh +}, ref) => { + const { t } = useTranslation(); + + // State management + const [visible, setVisible] = useState(false); // Modal visibility + const [form] = Form.useForm<{ file: File[] }>(); // Form instance + const [loading, setLoading] = useState(false); // Loading state + const [current, setCurrent] = useState(0); // Current step + const [appId, setAppId] = useState(null); // Imported application ID + const [warnings, setWarnings] = useState([]) + + /** + * Handle modal close + * Resets all states and form fields + */ + const handleClose = () => { + setVisible(false); + form.resetFields(); + setCurrent(0); + setAppId(null); + setLoading(false); + setWarnings([]) + }; + + /** + * Handle modal open + * Resets form fields and shows modal + */ + const handleOpen = () => { + form.resetFields(); + setVisible(true); + }; + + /** + * Handle save/submit action + * Processes different logic based on current step + */ + const handleSave = () => { + const values = form.getFieldsValue(); + + switch(current) { + case 0: // Step 1: Upload file + if (!values.file || values.file.length === 0) { + message.warning(t('application.pleaseUploadFile')); + return; + } + const formData = new FormData(); + formData.append('file', values.file[0]); + + setLoading(true) + // Call import API + appImport(formData) + .then(res => { + const { warnings, app } = res as { warnings: string[]; app: Application }; + + setAppId(app?.id) + if (warnings.length) { + setCurrent(1) + setWarnings(warnings) + } else { + setCurrent(2) + } + }) + .finally(() => setLoading(false)); + break; + case 2: + break; + } + }; + + // Expose methods to parent component via ref + useImperativeHandle(ref, () => ({ + handleOpen, + handleClose + })); + + /** + * Handle navigation after successful import + * @param {string} type - Navigation type ('detail' or 'list') + */ + const handleJump = (type: string) => { + handleClose(); + refresh(); + setTimeout(() => { + switch (type) { + case 'detail': + // Open application detail page in new tab + window.open(`/#/application/config/${appId}`, '_blank'); + break; + } + }, 100) + }; + + /** + * Generate modal footer based on current step + */ + const getFooter = useMemo(() => { + switch (current) { + case 0: // Step 1: Upload + return [ + , + + ]; + case 1: + return [ + , + + ] + default: + return null; + } + }, [current, loading]); + return ( + + {/* Steps indicator */} +
+ ({ title: t(`application.${key}`) }))} + /> +
+ {current === 0 && + + + + + + } + {/* Step 2: Error/warning display */} + {current === 1 && + + {warnings.map((vo, index) => ( + {vo}
} + type="warning" + showIcon + /> + ))} + + } + {current === 2 && + handleJump('list')}> + {t('application.gotoList')} + , + + ]} + /> + } + + ); +}); + +export default UploadModal; \ No newline at end of file diff --git a/web/src/views/ApplicationManagement/index.tsx b/web/src/views/ApplicationManagement/index.tsx index 055c0c8f..62c02574 100644 --- a/web/src/views/ApplicationManagement/index.tsx +++ b/web/src/views/ApplicationManagement/index.tsx @@ -25,6 +25,7 @@ import { getApplicationListUrl, deleteApplication } from '@/api/application' import PageScrollList, { type PageScrollListRef } from '@/components/PageScrollList' import { formatDateTime } from '@/utils/format'; import UploadWorkflowModal from './components/UploadWorkflowModal' +import UploadModal from './components/UploadModal' /** * Application management main component @@ -37,6 +38,7 @@ const ApplicationManagement: React.FC = () => { const applicationModalRef = useRef(null); const scrollListRef = useRef(null) const uploadWorkflowModalRef = useRef(null); + const uploadModalRef = useRef(null); useEffect(() => { // Convert URLSearchParams to a plain object for easier access @@ -91,6 +93,8 @@ const ApplicationManagement: React.FC = () => { case 'thirdParty': handleImport() break; + case 'import': + uploadModalRef.current?.handleOpen() } } return ( @@ -121,6 +125,7 @@ const ApplicationManagement: React.FC = () => { @@ -186,6 +191,10 @@ const ApplicationManagement: React.FC = () => { ref={uploadWorkflowModalRef} refresh={refresh} /> + ); }; diff --git a/web/src/views/ApplicationManagement/types.ts b/web/src/views/ApplicationManagement/types.ts index 696b828a..f9e17c17 100644 --- a/web/src/views/ApplicationManagement/types.ts +++ b/web/src/views/ApplicationManagement/types.ts @@ -233,4 +233,12 @@ export interface UploadData extends WorkflowConfig { export interface UploadWorkflowModalRef { /** Open the upload workflow modal */ handleOpen: () => void; +} + +/** + * Upload app modal ref interface + */ +export interface UploadModalRef { + /** Open the upload workflow modal */ + handleOpen: () => void; } \ No newline at end of file From e81faebf69bf25117b2965da323a1c52c95ef994 Mon Sep 17 00:00:00 2001 From: Mark Date: Tue, 10 Mar 2026 14:51:48 +0800 Subject: [PATCH 040/196] [add] migration script --- api/app/models/end_user_model.py | 2 +- .../versions/fb834419b18f_202603101453.py | 34 +++++++++++++++++++ 2 files changed, 35 insertions(+), 1 deletion(-) create mode 100644 api/migrations/versions/fb834419b18f_202603101453.py diff --git a/api/app/models/end_user_model.py b/api/app/models/end_user_model.py index 048d42ea..28a44f1f 100644 --- a/api/app/models/end_user_model.py +++ b/api/app/models/end_user_model.py @@ -52,7 +52,7 @@ class EndUser(Base): memory_insight_updated_at = Column(DateTime, nullable=True, comment="洞察报告最后更新时间") # RAG存储模式专用字段 - RAG Storage Mode Fields - storage_type = Column(String, nullable=True, default="neo4j", comment="存储模式类型: neo4j / rag") + # storage_type = Column(String, nullable=True, default="neo4j", comment="存储模式类型: neo4j / rag") rag_tags = Column(Text, nullable=True, comment="RAG模式下提取的标签列表（JSON格式）") rag_personas = Column(Text, nullable=True, comment="RAG模式下提取的人物形象列表（JSON格式）") rag_summary_updated_at = Column(DateTime, nullable=True, comment="RAG摘要/标签/人物形象最后更新时间") diff --git a/api/migrations/versions/fb834419b18f_202603101453.py b/api/migrations/versions/fb834419b18f_202603101453.py new file mode 100644 index 00000000..8b17e2e0 --- /dev/null +++ b/api/migrations/versions/fb834419b18f_202603101453.py @@ -0,0 +1,34 @@ +"""202603101453 + +Revision ID: fb834419b18f +Revises: 1ac07dc7366f +Create Date: 2026-03-10 14:46:48.038643 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = 'fb834419b18f' +down_revision: Union[str, None] = '1ac07dc7366f' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.add_column('end_users', sa.Column('rag_tags', sa.Text(), nullable=True, comment='RAG模式下提取的标签列表（JSON格式）')) + op.add_column('end_users', sa.Column('rag_personas', sa.Text(), nullable=True, comment='RAG模式下提取的人物形象列表（JSON格式）')) + op.add_column('end_users', sa.Column('rag_summary_updated_at', sa.DateTime(), nullable=True, comment='RAG摘要/标签/人物形象最后更新时间')) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('end_users', 'rag_summary_updated_at') + op.drop_column('end_users', 'rag_personas') + op.drop_column('end_users', 'rag_tags') + # ### end Alembic commands ### From 3f7083c5b3f38204c95d308a7cb007d3539c2a3f Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 16:16:05 +0800 Subject: [PATCH 041/196] [add] Modify reserved words to avoid being affected by the threshold. --- .../data_preprocessing/data_pruning.py | 139 +++++++++++------- .../prompt/prompts/extracat_Pruning.jinja2 | 77 +++++++++- 2 files changed, 151 insertions(+), 65 deletions(-) diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py index ecbe0411..9e8bc05e 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py @@ -33,6 +33,7 @@ class DialogExtractionResponse(BaseModel): - is_related：对话与场景的相关性判定。 - times / ids / amounts / contacts / addresses / keywords：重要信息片段，用来在不相关对话中保留关键消息。 + - preserve_keywords：情绪/兴趣/爱好/个人观点相关词，包含这些词的消息必须强制保留。 """ is_related: bool = Field(...) times: List[str] = Field(default_factory=list) @@ -41,6 +42,7 @@ class DialogExtractionResponse(BaseModel): contacts: List[str] = Field(default_factory=list) addresses: List[str] = Field(default_factory=list) keywords: List[str] = Field(default_factory=list) + preserve_keywords: List[str] = Field(default_factory=list, description="情绪/兴趣/爱好/个人观点相关词，包含这些词的消息强制保留") class MessageImportanceResponse(BaseModel): @@ -198,26 +200,37 @@ class SemanticPruner: return min(score, 10) # 最高10分 + # 情绪/兴趣/爱好安全防线正则（类级别，避免重复编译） + _EMOTION_INTEREST_GUARD = re.compile( + r"开心|高兴|快乐|幸福|感动|难过|悲伤|伤心|委屈|失落|沮丧|郁闷|" + r"生气|愤怒|烦躁|焦虑|害怕|担心|压力|兴奋|期待|惊喜|惊讶|" + r"喜欢|热爱|爱好|兴趣|擅长|享受|沉迷|着迷|讨厌|厌恶|" + r"happy|sad|angry|excited|anxious|love|hate|enjoy|like|dislike" + ) + def _is_filler_message(self, message: ConversationMessage) -> bool: """检测典型寒暄/口头禅/确认类短消息。 - 改进版：更严格的填充消息判断，避免误删场景相关内容 - 满足以下之一视为填充消息： - - 纯标点或空白 - - 在场景特定填充词库中（精确匹配） - - 纯表情符号 - - 常见寒暄（精确匹配短语） - - 注意：不再使用长度判断，避免误删短但重要的消息 + 判断顺序： + 1. 情绪/兴趣安全防线（最高优先级）：包含情绪词或兴趣词的消息，无论多短都不视为填充 + 2. 空消息 + 3. 场景特定填充词库精确匹配 + 4. 常见寒暄精确匹配 + 5. 纯表情/标点 """ t = message.msg.strip() if not t: return True - + + # ── 最高优先级：情绪/兴趣安全防线 ── + # "我好开心呀"、"好喜欢打羽毛球呀"、"我好难过" 等一律不视为填充 + if self._EMOTION_INTEREST_GUARD.search(t): + return False + # 检查是否在场景特定填充词库中（精确匹配） if t in self.scene_config.filler_phrases: return True - + # 常见寒暄和问候（精确匹配，避免误删） common_greetings = { "在吗", "在不在", "在呢", "在的", @@ -229,39 +242,29 @@ class SemanticPruner: } if t in common_greetings: return True - + # 检查是否为纯表情符号（方括号包裹） if re.fullmatch(r"(\[[^\]]+\])+", t): return True - + # 检查是否为纯emoji（Unicode表情） emoji_pattern = re.compile( "[" - "\U0001F600-\U0001F64F" # 表情符号 - "\U0001F300-\U0001F5FF" # 符号和象形文字 - "\U0001F680-\U0001F6FF" # 交通和地图符号 - "\U0001F1E0-\U0001F1FF" # 旗帜 + "\U0001F600-\U0001F64F" + "\U0001F300-\U0001F5FF" + "\U0001F680-\U0001F6FF" + "\U0001F1E0-\U0001F1FF" "\U00002702-\U000027B0" "\U000024C2-\U0001F251" "]+", flags=re.UNICODE ) if emoji_pattern.fullmatch(t): return True - + # 纯标点符号 if re.fullmatch(r"[。！？,.!?…·\s]+", t): return True - - # 安全防线：包含情绪词或兴趣词的消息，无论多短都不视为填充 - # 避免"我好开心呀"、"好喜欢打羽毛球呀"等被误删 - _emotion_interest_guard = re.compile( - r"开心|高兴|快乐|幸福|感动|难过|悲伤|伤心|委屈|失落|沮丧|郁闷|" - r"生气|愤怒|烦躁|焦虑|害怕|担心|压力|兴奋|期待|" - r"喜欢|热爱|爱好|兴趣|擅长|享受|沉迷|着迷|讨厌|厌恶" - ) - if _emotion_interest_guard.search(t): - return False - + return False async def _batch_evaluate_importance_with_llm( @@ -604,44 +607,63 @@ class SemanticPruner: result: List[DialogData] = [] total_original_msgs = 0 total_deleted_msgs = 0 - - for d_idx, dd in enumerate(dialogs): + + # 并发执行所有对话的 LLM 抽取（获取 preserve_keywords 等保护信息） + semaphore = asyncio.Semaphore(self.max_concurrent) + + async def extract_with_semaphore(dd: DialogData) -> DialogExtractionResponse: + async with semaphore: + try: + return await self._extract_dialog_important(dd.content) + except Exception as e: + self._log(f"[剪枝-LLM] 对话抽取失败，使用降级策略: {str(e)[:100]}") + return DialogExtractionResponse(is_related=True) + + extraction_tasks = [extract_with_semaphore(dd) for dd in dialogs] + extraction_results: List[DialogExtractionResponse] = await asyncio.gather(*extraction_tasks) + + for d_idx, (dd, extraction) in enumerate(zip(dialogs, extraction_results)): msgs = dd.context.msgs original_count = len(msgs) total_original_msgs += original_count - - # ========== 问答对保护（已注释，暂不启用，留作观察） ========== - # qa_pairs = self._identify_qa_pairs(msgs) - # protected_indices = self._get_protected_indices(msgs, qa_pairs, window_size=0) - # ======================================================== - - # 消息级分类：每条消息独立判断 - important_msgs = [] # 重要消息（保留） - unimportant_msgs = [] # 不重要消息（可删除） - filler_msgs = [] # 填充消息（优先删除） - - # 判断是否需要详细日志（仅对前N条消息记录） + + # 从 LLM 抽取结果中获取所有需要保留的 token + preserve_tokens = ( + extraction.times + extraction.ids + extraction.amounts + + extraction.contacts + extraction.addresses + extraction.keywords + + extraction.preserve_keywords # 情绪/兴趣/爱好关键词 + ) + + # 判断是否需要详细日志 should_log_details = self._detailed_prune_logging and original_count <= self._max_debug_msgs_per_dialog if self._detailed_prune_logging and original_count > self._max_debug_msgs_per_dialog: self._log(f" 对话[{d_idx}]消息数={original_count}，仅采样前{self._max_debug_msgs_per_dialog}条进行详细日志") - + + if extraction.preserve_keywords: + self._log(f" 对话[{d_idx}] LLM抽取到情绪/兴趣保护词: {extraction.preserve_keywords}") + + # 消息级分类：每条消息独立判断 + llm_protected_msgs = [] # LLM 保护消息（情绪/兴趣/重要token）：绝对不可删除 + rule_important_msgs = [] # 规则层重要消息（场景规则）：配额不足时可少量删除 + unimportant_msgs = [] # 不重要消息（可删除） + filler_msgs = [] # 填充消息（优先删除） + for idx, m in enumerate(msgs): msg_text = m.msg.strip() - - # ========== 问答对保护判断（已注释） ========== - # if idx in protected_indices: - # important_msgs.append((idx, m)) - # self._log(f" [{idx}] '{msg_text[:30]}...' → 重要（问答对保护）") - # ========================================== - + + # LLM 保护：消息包含 preserve_keywords（情绪/兴趣词）或其他重要 token → 绝对不可删除 + if self._msg_matches_tokens(m, preserve_tokens): + llm_protected_msgs.append((idx, m)) + if should_log_details or idx < self._max_debug_msgs_per_dialog: + self._log(f" [{idx}] '{msg_text[:30]}...' → 重要（LLM保护，不可删）") # 填充消息（寒暄、表情等） - if self._is_filler_message(m): + elif self._is_filler_message(m): filler_msgs.append((idx, m)) if should_log_details or idx < self._max_debug_msgs_per_dialog: self._log(f" [{idx}] '{msg_text[:30]}...' → 填充") - # 重要信息（学号、成绩、时间、金额等） + # 规则层重要信息（学号、成绩、时间、金额等） elif self._is_important_message(m): - important_msgs.append((idx, m)) + rule_important_msgs.append((idx, m)) if should_log_details or idx < self._max_debug_msgs_per_dialog: self._log(f" [{idx}] '{msg_text[:30]}...' → 重要（场景规则）") # 其他消息 @@ -649,6 +671,9 @@ class SemanticPruner: unimportant_msgs.append((idx, m)) if should_log_details or idx < self._max_debug_msgs_per_dialog: self._log(f" [{idx}] '{msg_text[:30]}...' → 不重要") + + # important_msgs 仅用于日志统计（兼容下方日志输出） + important_msgs = llm_protected_msgs + rule_important_msgs # 计算删除配额 delete_target = int(original_count * proportion) @@ -679,17 +704,17 @@ class SemanticPruner: to_delete_indices.add(idx) deleted_details.append(f"[{idx}] 不重要: '{msg.msg[:50]}'") - # 第三步：如果还需要删除，按重要性分数删除重要消息 + # 第三步：如果还需要删除，按重要性分数删除规则层重要消息（LLM保护消息绝对不删） remaining_quota = delete_target - len(to_delete_indices) - if remaining_quota > 0 and important_msgs: + if remaining_quota > 0 and rule_important_msgs: # 按重要性分数排序（分数低的优先删除） - imp_sorted = sorted(important_msgs, key=lambda x: self._importance_score(x[1])) + imp_sorted = sorted(rule_important_msgs, key=lambda x: self._importance_score(x[1])) imp_to_delete = min(len(imp_sorted), remaining_quota) for i in range(imp_to_delete): idx, msg = imp_sorted[i] to_delete_indices.add(idx) score = self._importance_score(msg) - deleted_details.append(f"[{idx}] 重要(分数{score}): '{msg.msg[:50]}'") + deleted_details.append(f"[{idx}] 规则重要(分数{score}): '{msg.msg[:50]}'") # 执行删除 kept_msgs = [] diff --git a/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 b/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 index 6b620df9..47b3badb 100644 --- a/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 +++ b/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 @@ -9,10 +9,11 @@ - contacts: [string]，联系方式（电话/手机号/邮箱/微信/QQ等） - addresses: [string]，地址/地点相关文本 - keywords: [string]，其它有助于保留的重要关键词（与场景强相关的术语） + - preserve_keywords: [string]，必须保留的情绪/兴趣/爱好/个人偏好相关词或短语片段 要求： - 必须只输出上述 JSON，且键名一致；不得输出解释、前后缀；不得包含注释。 - - times/ids/amounts/contacts/addresses/keywords 仅抽取原文片段或规范化后的简单字符串。 + - times/ids/amounts/contacts/addresses/keywords/preserve_keywords 仅抽取原文片段或规范化后的简单字符串。 - 仅输出上述键；避免多余解释或字段。 #} @@ -34,13 +35,11 @@ {# ── 确定最终使用的场景说明 ── #} {% if is_builtin_scene %} - {# 内置专门场景：使用固定说明 #} {% set scene_key = pruning_scene %} {% if scene_key not in builtin_scene_instructions %}{% set scene_key = 'education' %}{% endif %} {% set instruction = builtin_scene_instructions[scene_key][language] if language in ['zh', 'en'] else builtin_scene_instructions[scene_key]['zh'] %} {% set custom_types_str = '' %} {% else %} - {# 自定义场景：使用场景名称 + 本体类型列表构建说明 #} {% if ontology_classes and ontology_classes | length > 0 %} {% if language == 'en' %} {% set custom_types_str = ontology_classes | join(', ') %} @@ -50,7 +49,6 @@ {% set instruction = '自定义场景「' ~ pruning_scene ~ '」：对话涉及以下任意实体类型时视为相关：' ~ custom_types_str ~ '。' %} {% endif %} {% else %} - {# 无本体类型时退化为通用说明 #} {% if language == 'en' %} {% set instruction = 'Custom scene "' ~ pruning_scene ~ '": Determine whether the dialogue content is relevant to this scene based on overall context.' %} {% else %} @@ -61,12 +59,43 @@ {% endif %} {% if language == "zh" %} -请在下方对话全文基础上，按该场景进行一次性抽取并判定相关性： +你是一个对话内容分析助手。请对下方对话全文进行一次性分析，完成两项任务： +1. 判断对话是否与指定场景相关； +2. 从对话中抽取所有需要保留的重要信息片段。 + 场景说明：{{ instruction }} {% if not is_builtin_scene and custom_types_str %} 重要提示：只要对话中出现与上述实体类型（{{ custom_types_str }}）相关的内容，即判定为相关（is_related=true）。 {% endif %} +--- +【必须保留的内容（不可删除）】 +以下类型的内容无论是否与场景直接相关，都必须保留，请将其关键词/短语抽取到对应字段： +- 时间信息：日期、时间点、时间段、有效期 → times 字段 +- 编号信息：学号、工号、订单号、申请号、账号、ID → ids 字段 +- 金额信息：价格、费用、金额（含货币符号或单位） → amounts 字段 +- 联系方式：电话、手机号、邮箱、微信、QQ → contacts 字段 +- 地址信息：地点、地址、位置 → addresses 字段 +- 场景关键词：与场景强相关的专业术语、事件名称 → keywords 字段 +- **情绪与情感**：喜悦、悲伤、愤怒、焦虑、开心、难过、委屈、兴奋、害怕、担心、压力、感动等情绪表达 → preserve_keywords 字段 +- **兴趣与爱好**：喜欢、热爱、爱好、擅长、享受、沉迷、着迷、讨厌某事物等个人偏好表达 → preserve_keywords 字段 +- **个人观点与态度**：对某事物的明确看法、评价、立场 → preserve_keywords 字段 + +【可以删除的内容】 +以下类型的内容属于低价值信息，可以在剪枝时删除： +- 纯寒暄问候：如"你好"、"在吗"、"拜拜"、"嗯"、"好的"、"哦"等无实质内容的短语 +- 纯表情/符号：如"[微笑]"、"😊"、"哈哈"等 +- 重复确认：如"对对对"、"是的是的"、"嗯嗯嗯"等无新增信息的重复 +- 无意义填充：如"啊"、"呢"、"嘛"等语气词单独成句 + +**注意：即使消息很短，只要包含情绪、兴趣、爱好、个人观点等有价值信息，就必须保留，不得删除。** +例如： +- "我好开心呀" → 包含情绪（开心），必须保留，preserve_keywords 中加入"开心" +- "好喜欢打羽毛球呀" → 包含兴趣爱好（喜欢打羽毛球），必须保留，preserve_keywords 中加入"喜欢打羽毛球" +- "我好难过" → 包含情绪（难过），必须保留，preserve_keywords 中加入"难过" +- "太好啦！看到你开心，我也跟着心情亮起来" → 包含情绪，必须保留，preserve_keywords 中加入"开心" + +--- 对话全文： """ {{ dialog_text }} @@ -80,15 +109,46 @@ "amounts": [...], "contacts": [...], "addresses": [...], - "keywords": [...] + "keywords": [...], + "preserve_keywords": [...] } {% else %} -Based on the full dialogue below, perform one-time extraction and relevance determination according to this scenario: +You are a dialogue content analysis assistant. Please analyze the full dialogue below in one pass and complete two tasks: +1. Determine whether the dialogue is relevant to the specified scene; +2. Extract all important information fragments that must be preserved. + Scenario Description: {{ instruction }} {% if not is_builtin_scene and custom_types_str %} Important: If the dialogue contains content related to any of the entity types above ({{ custom_types_str }}), mark it as relevant (is_related=true). {% endif %} +--- +[MUST PRESERVE (cannot be deleted)] +The following types of content must always be preserved regardless of scene relevance. Extract their keywords/phrases into the corresponding fields: +- Time information: dates, time points, durations, expiry dates → times field +- ID information: student IDs, employee IDs, order numbers, application numbers, account IDs → ids field +- Amount information: prices, fees, amounts (with currency symbols or units) → amounts field +- Contact information: phone numbers, emails, WeChat, QQ → contacts field +- Address information: locations, addresses, places → addresses field +- Scene keywords: professional terms and event names strongly related to the scene → keywords field +- **Emotions and feelings**: joy, sadness, anger, anxiety, happiness, sadness, excitement, fear, worry, stress, being moved, etc. → preserve_keywords field +- **Interests and hobbies**: likes, loves, hobbies, good at, enjoys, obsessed with, hates something, personal preferences → preserve_keywords field +- **Personal opinions and attitudes**: clear views, evaluations, or stances on something → preserve_keywords field + +[CAN BE DELETED] +The following types of content are low-value and can be removed during pruning: +- Pure greetings: e.g., "hello", "are you there", "bye", "ok", "yeah" — short phrases with no substantive content +- Pure emojis/symbols: e.g., "[smile]", "😊", "haha" +- Repetitive confirmations: e.g., "yes yes yes", "right right", "uh huh" — repetitions with no new information +- Meaningless fillers: standalone interjections like "ah", "well", "hmm" + +**Note: Even if a message is short, if it contains emotions, interests, hobbies, or personal opinions, it MUST be preserved.** +Examples: +- "I'm so happy!" → contains emotion (happy), must preserve; add "happy" to preserve_keywords +- "I love playing badminton!" → contains interest (love playing badminton), must preserve; add "love playing badminton" to preserve_keywords +- "I feel so sad" → contains emotion (sad), must preserve; add "sad" to preserve_keywords + +--- Full Dialogue: """ {{ dialog_text }} @@ -102,6 +162,7 @@ Output strict JSON only (fixed keys, order doesn't matter): "amounts": [...], "contacts": [...], "addresses": [...], - "keywords": [...] + "keywords": [...], + "preserve_keywords": [...] } {% endif %} From f9415413040ffbf10bcdd8b4ba480385e17cc44b Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Tue, 10 Mar 2026 16:18:22 +0800 Subject: [PATCH 042/196] fix(app): Workflow import verification --- .../memory_bear/memory_bear_adapter.py | 117 +++++++++++++++--- .../memory_bear/memory_bear_converter.py | 85 +++++++++++++ api/app/services/app_dsl_service.py | 43 ++++++- api/env.example | 2 +- 4 files changed, 221 insertions(+), 26 deletions(-) create mode 100644 api/app/core/workflow/adapters/memory_bear/memory_bear_converter.py diff --git a/api/app/core/workflow/adapters/memory_bear/memory_bear_adapter.py b/api/app/core/workflow/adapters/memory_bear/memory_bear_adapter.py index 0e3f459f..3516cb58 100644 --- a/api/app/core/workflow/adapters/memory_bear/memory_bear_adapter.py +++ b/api/app/core/workflow/adapters/memory_bear/memory_bear_adapter.py @@ -4,65 +4,145 @@ # @Time : 2026/2/25 14:11 from typing import Any +from app.core.logging_config import get_logger from app.core.workflow.adapters.base_adapter import ( PlatformMetadata, PlatformType, BasePlatformAdapter, WorkflowParserResult ) -from app.schemas.workflow_schema import ExecutionConfig +from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType, UnsupportNodeType +from app.core.workflow.adapters.memory_bear.memory_bear_converter import MemoryBearConverter +from app.core.workflow.nodes.enums import NodeType +from app.schemas.workflow_schema import ExecutionConfig, NodeDefinition, EdgeDefinition, VariableDefinition + +logger = get_logger() + +VALID_NODE_TYPES = frozenset(t.value for t in NodeType if t != NodeType.UNKNOWN) -class MemoryBearAdapter(BasePlatformAdapter): - NODE_TYPE_MAPPING = {} +class MemoryBearAdapter(BasePlatformAdapter, MemoryBearConverter): + NODE_TYPE_MAPPING = {t.value: t for t in NodeType} + + def __init__(self, config: dict[str, Any]): + MemoryBearConverter.__init__(self) + BasePlatformAdapter.__init__(self, config) @property def origin_nodes(self): - return self.config.get("workflow").get("nodes") + return self.config.get("workflow").get("nodes") or [] @property def origin_edges(self): - return self.config.get("workflow").get("edges") + return self.config.get("workflow").get("edges") or [] @property def origin_variables(self): - return self.config.get("workflow").get("variables") + return self.config.get("workflow").get("variables") or [] def get_metadata(self) -> PlatformMetadata: return PlatformMetadata( platform_name=PlatformType.MEMORY_BEAR, version="0.2.5", - support_node_types=list(self.NODE_TYPE_MAPPING.keys()) + support_node_types=list(VALID_NODE_TYPES) ) - def map_node_type(self, platform_node_type) -> str: - return platform_node_type + def map_node_type(self, platform_node_type: str) -> NodeType: + return self.NODE_TYPE_MAPPING.get(platform_node_type, NodeType.UNKNOWN) @staticmethod - def _valid_nodes(node: dict[str, Any]): - if "type" not in node["data"]: - return False + def _valid_node(node: dict[str, Any]) -> bool: if "id" not in node or "type" not in node: return False + if not isinstance(node.get("config"), dict): + return False return True def validate_config(self) -> bool: require_fields = frozenset({'app', 'workflow'}) if not all(field in self.config for field in require_fields): return False - for node in self.origin_nodes: - if not self._valid_nodes(node): + if not self._valid_node(node): return False return True + def _convert_node(self, node: dict[str, Any]) -> NodeDefinition | None: + node_id = node.get("id") + node_name = node.get("name") + try: + node_type = self.map_node_type(node["type"]) + if node_type == NodeType.UNKNOWN: + self.errors.append(UnsupportNodeType( + node_id=node_id, + node_type=node["type"] + )) + return None + + config = node.get("config") or {} + converter = self.get_node_convert(node_type) + converter(node_id, node_name, config) # validates and appends errors if invalid + + return NodeDefinition(**node) + except Exception as e: + self.errors.append(ExceptionDefineition( + type=ExceptionType.NODE, + node_id=node_id, + node_name=node_name, + detail=f"convert node error - {e}" + )) + logger.debug(f"MemoryBear convert node error - {e}", exc_info=True) + return None + + def _convert_edge(self, edge: dict[str, Any], valid_node_ids: set) -> EdgeDefinition | None: + try: + if edge.get("source") not in valid_node_ids or edge.get("target") not in valid_node_ids: + self.warnings.append(ExceptionDefineition( + type=ExceptionType.EDGE, + detail=f"edge {edge.get('id')} skipped: source or target node not found" + )) + return None + return EdgeDefinition(**edge) + except Exception as e: + self.errors.append(ExceptionDefineition( + type=ExceptionType.EDGE, + detail=f"convert edge error - {e}" + )) + logger.debug(f"MemoryBear convert edge error - {e}", exc_info=True) + return None + + def _convert_variable(self, variable: dict[str, Any]) -> VariableDefinition | None: + try: + return VariableDefinition(**variable) + except Exception as e: + self.warnings.append(ExceptionDefineition( + type=ExceptionType.VARIABLE, + name=variable.get("name"), + detail=f"convert variable error - {e}" + )) + logger.debug(f"MemoryBear convert variable error - {e}", exc_info=True) + return None + def parse_workflow(self) -> WorkflowParserResult: - self.nodes = self.origin_nodes - self.edges = self.origin_edges - self.conv_variables = self.origin_variables + for node in self.origin_nodes: + converted = self._convert_node(node) + if converted: + self.nodes.append(converted) + + valid_node_ids = {n.id for n in self.nodes} + + for edge in self.origin_edges: + converted = self._convert_edge(edge, valid_node_ids) + if converted: + self.edges.append(converted) + + for variable in self.origin_variables: + converted = self._convert_variable(variable) + if converted: + self.conv_variables.append(converted) return WorkflowParserResult( - success=True, + success=not self.errors and not self.warnings, platform=self.get_metadata(), execution_config=ExecutionConfig(), origin_config=self.config, @@ -72,5 +152,4 @@ class MemoryBearAdapter(BasePlatformAdapter): variables=self.conv_variables, warnings=self.warnings, errors=self.errors, - ) diff --git a/api/app/core/workflow/adapters/memory_bear/memory_bear_converter.py b/api/app/core/workflow/adapters/memory_bear/memory_bear_converter.py new file mode 100644 index 00000000..031c7025 --- /dev/null +++ b/api/app/core/workflow/adapters/memory_bear/memory_bear_converter.py @@ -0,0 +1,85 @@ +# -*- coding: UTF-8 -*- +from app.core.workflow.adapters.base_converter import BaseConverter +from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType +from app.core.workflow.nodes.base_config import BaseNodeConfig +from app.core.workflow.nodes.configs import ( + StartNodeConfig, + EndNodeConfig, + LLMNodeConfig, + AgentNodeConfig, + IfElseNodeConfig, + KnowledgeRetrievalNodeConfig, + AssignerNodeConfig, + CodeNodeConfig, + HttpRequestNodeConfig, + JinjaRenderNodeConfig, + VariableAggregatorNodeConfig, + ParameterExtractorNodeConfig, + LoopNodeConfig, + IterationNodeConfig, + QuestionClassifierNodeConfig, + ToolNodeConfig, + MemoryReadNodeConfig, + MemoryWriteNodeConfig, + NoteNodeConfig, +) +from app.core.workflow.nodes.enums import NodeType + + +class MemoryBearConverter(BaseConverter): + errors: list + warnings: list + + CONFIG_CLASS_MAP: dict[NodeType, type[BaseNodeConfig]] = { + NodeType.START: StartNodeConfig, + NodeType.END: EndNodeConfig, + NodeType.ANSWER: EndNodeConfig, + NodeType.LLM: LLMNodeConfig, + NodeType.AGENT: AgentNodeConfig, + NodeType.IF_ELSE: IfElseNodeConfig, + NodeType.KNOWLEDGE_RETRIEVAL: KnowledgeRetrievalNodeConfig, + NodeType.ASSIGNER: AssignerNodeConfig, + NodeType.CODE: CodeNodeConfig, + NodeType.HTTP_REQUEST: HttpRequestNodeConfig, + NodeType.JINJARENDER: JinjaRenderNodeConfig, + NodeType.VAR_AGGREGATOR: VariableAggregatorNodeConfig, + NodeType.PARAMETER_EXTRACTOR: ParameterExtractorNodeConfig, + NodeType.LOOP: LoopNodeConfig, + NodeType.ITERATION: IterationNodeConfig, + NodeType.QUESTION_CLASSIFIER: QuestionClassifierNodeConfig, + NodeType.TOOL: ToolNodeConfig, + NodeType.MEMORY_READ: MemoryReadNodeConfig, + NodeType.MEMORY_WRITE: MemoryWriteNodeConfig, + NodeType.NOTES: NoteNodeConfig, + } + + @staticmethod + def _convert_file(var): + return None + + @staticmethod + def _convert_array_file(var): + return [] + + def config_validate(self, node_id: str, node_name: str, config_cls: type[BaseNodeConfig], value: dict): + try: + return config_cls.model_validate(value) + except Exception as e: + self.errors.append(ExceptionDefineition( + type=ExceptionType.CONFIG, + node_id=node_id, + node_name=node_name, + detail=str(e) + )) + return None + + def get_node_convert(self, node_type: NodeType): + config_cls = self.CONFIG_CLASS_MAP.get(node_type) + if not config_cls: + return lambda node_id, node_name, config: config + + def validate(node_id: str, node_name: str, config: dict): + self.config_validate(node_id, node_name, config_cls, config) + return config + + return validate diff --git a/api/app/services/app_dsl_service.py b/api/app/services/app_dsl_service.py index c120d98b..fc071177 100644 --- a/api/app/services/app_dsl_service.py +++ b/api/app/services/app_dsl_service.py @@ -17,6 +17,7 @@ from app.models.models_model import ModelConfig from app.models.tool_model import ToolConfig as ToolConfigModel from app.models.workflow_model import WorkflowConfig from app.services.workflow_service import WorkflowService +from app.core.workflow.adapters.memory_bear.memory_bear_adapter import MemoryBearAdapter class AppDslService: @@ -243,7 +244,7 @@ class AppDslService: model_parameters=cfg.get("model_parameters"), default_model_config_id=self._resolve_model(cfg.get("default_model_config_ref"), tenant_id, warnings), knowledge_retrieval=self._resolve_knowledge_retrieval(cfg.get("knowledge_retrieval"), workspace_id, warnings), - memory=cfg.get("memory"), + memory=self._resolve_memory(cfg.get("memory"), workspace_id, warnings), variables=cfg.get("variables", []), tools=self._resolve_tools(cfg.get("tools", []), tenant_id, warnings), skills=cfg.get("skills", {}), @@ -272,12 +273,20 @@ class AppDslService: )) elif app_type == AppType.WORKFLOW: + adapter = MemoryBearAdapter(dsl) + if not adapter.validate_config(): + raise BusinessException("工作流配置格式无效", BizCode.BAD_REQUEST) + result = adapter.parse_workflow() + for e in result.errors: + warnings.append(f"[节点错误] {e.node_name or e.node_id}: {e.detail}") + for w in result.warnings: + warnings.append(f"[节点警告] {w.node_name or w.node_id}: {w.detail}") wf = dsl.get("workflow") or {} WorkflowService(self.db).create_workflow_config( app_id=new_app.id, - nodes=wf.get("nodes", []), - edges=wf.get("edges", []), - variables=wf.get("variables", []), + nodes=[n.model_dump() for n in result.nodes], + edges=[e.model_dump() for e in result.edges], + variables=[v.model_dump() for v in result.variables], execution_config=wf.get("execution_config", {}), triggers=wf.get("triggers", []), validate=False, @@ -376,15 +385,37 @@ class AppDslService: for kb in kr.get("knowledge_bases", []): ref = kb.get("_ref") or ({"name": kb.get("kb_id")} if kb.get("kb_id") else None) entry = {k: v for k, v in kb.items() if k != "_ref"} - entry["kb_id"] = self._resolve_kb(ref, workspace_id, warnings) + resolved_id = self._resolve_kb(ref, workspace_id, warnings) + if resolved_id is None: + continue + entry["kb_id"] = resolved_id resolved_kbs.append(entry) return {k: v for k, v in kr.items() if k != "knowledge_bases"} | {"knowledge_bases": resolved_kbs} + def _resolve_memory(self, memory: Optional[dict], workspace_id: uuid.UUID, warnings: list) -> Optional[dict]: + if not memory: + return memory + config_id = memory.get("memory_config_id") or memory.get("memory_content") + if not config_id: + return memory + from app.models.memory_config_model import MemoryConfig as MemoryConfigModel + exists = self.db.query(MemoryConfigModel).filter( + MemoryConfigModel.config_id == config_id, + MemoryConfigModel.workspace_id == workspace_id + ).first() + if not exists: + warnings.append(f"记忆配置 '{config_id}' 未匹配，已置空，请导入后手动配置") + return {**memory, "memory_config_id": None, "enabled": False} + return memory + def _resolve_tools(self, tools: list, tenant_id: uuid.UUID, warnings: list) -> list: result = [] for t in (tools or []): ref = t.get("_ref") or ({"name": t.get("tool_id")} if t.get("tool_id") else None) entry = {k: v for k, v in t.items() if k != "_ref"} - entry["tool_id"] = self._resolve_tool(ref, tenant_id, warnings) + resolved_id = self._resolve_tool(ref, tenant_id, warnings) + if resolved_id is None: + continue + entry["tool_id"] = resolved_id result.append(entry) return result diff --git a/api/env.example b/api/env.example index bd7f3dae..e324d1e5 100644 --- a/api/env.example +++ b/api/env.example @@ -75,7 +75,7 @@ REFRESH_TOKEN_EXPIRE_DAYS=7 ENABLE_SINGLE_SESSION= # File Upload -MAX_FILE_SIZE=52428800 # 50MB:10 * 1024 * 1024 +MAX_FILE_SIZE=52428800 # 50MB:50 * 1024 * 1024 FILE_PATH=/files FILE_LOCAL_SERVER_URL="http://localhost:8000/api" From 5b431400be9959442dca774ddd95dc95719fd343 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 17:06:43 +0800 Subject: [PATCH 043/196] [add] Create community nodes --- .../clustering_engine/__init__.py | 3 + .../clustering_engine/label_propagation.py | 311 ++++++++++++++++++ .../neo4j/community_repository.py | 129 ++++++++ api/app/repositories/neo4j/cypher_queries.py | 93 +++++- api/app/repositories/neo4j/graph_saver.py | 34 ++ 5 files changed, 569 insertions(+), 1 deletion(-) create mode 100644 api/app/core/memory/storage_services/clustering_engine/__init__.py create mode 100644 api/app/core/memory/storage_services/clustering_engine/label_propagation.py create mode 100644 api/app/repositories/neo4j/community_repository.py diff --git a/api/app/core/memory/storage_services/clustering_engine/__init__.py b/api/app/core/memory/storage_services/clustering_engine/__init__.py new file mode 100644 index 00000000..992d8bff --- /dev/null +++ b/api/app/core/memory/storage_services/clustering_engine/__init__.py @@ -0,0 +1,3 @@ +from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine + +__all__ = ["LabelPropagationEngine"] diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py new file mode 100644 index 00000000..80e238fd --- /dev/null +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -0,0 +1,311 @@ +"""标签传播聚类引擎 + +基于 ZEP 论文的动态标签传播算法，对 Neo4j 中的 ExtractedEntity 节点进行社区聚类。 + +支持两种模式： +- 全量初始化（full_clustering）：首次运行，对所有实体做完整 LPA 迭代 +- 增量更新（incremental_update）：新实体到达时，只处理新实体及其邻居 +""" + +import logging +import uuid +from math import sqrt +from typing import Dict, List, Optional + +from app.repositories.neo4j.community_repository import CommunityRepository +from app.repositories.neo4j.neo4j_connector import Neo4jConnector + +logger = logging.getLogger(__name__) + +# 全量迭代最大轮数，防止不收敛 +MAX_ITERATIONS = 10 + + +def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float: + """计算两个向量的余弦相似度，任一为空则返回 0。""" + if not v1 or not v2 or len(v1) != len(v2): + return 0.0 + dot = sum(a * b for a, b in zip(v1, v2)) + norm1 = sqrt(sum(a * a for a in v1)) + norm2 = sqrt(sum(b * b for b in v2)) + if norm1 == 0 or norm2 == 0: + return 0.0 + return dot / (norm1 * norm2) + + +def _weighted_vote( + neighbors: List[Dict], + self_embedding: Optional[List[float]], +) -> Optional[str]: + """ + 加权多数投票，选出得票最高的社区。 + + 权重 = 语义相似度（name_embedding 余弦）* activation_value 加成 + 没有 community_id 的邻居不参与投票。 + """ + votes: Dict[str, float] = {} + for nb in neighbors: + cid = nb.get("community_id") + if not cid: + continue + sem = _cosine_similarity(self_embedding, nb.get("name_embedding")) + act = nb.get("activation_value") or 0.5 + # 语义相似度权重 0.6，激活值权重 0.4 + weight = 0.6 * sem + 0.4 * act + votes[cid] = votes.get(cid, 0.0) + weight + + if not votes: + return None + return max(votes, key=votes.__getitem__) + + +class LabelPropagationEngine: + """标签传播聚类引擎""" + + def __init__(self, connector: Neo4jConnector): + self.connector = connector + self.repo = CommunityRepository(connector) + + # ────────────────────────────────────────────────────────────────────────── + # 公开接口 + # ────────────────────────────────────────────────────────────────────────── + + async def run( + self, + end_user_id: str, + new_entity_ids: Optional[List[str]] = None, + ) -> None: + """ + 统一入口：自动判断全量还是增量。 + + - 若该用户尚无 Community 节点 → 全量初始化 + - 否则 → 增量更新（仅处理 new_entity_ids） + """ + has_communities = await self.repo.has_communities(end_user_id) + if not has_communities: + logger.info(f"[Clustering] 用户 {end_user_id} 首次聚类，执行全量初始化") + await self.full_clustering(end_user_id) + else: + if new_entity_ids: + logger.info( + f"[Clustering] 增量更新，新实体数: {len(new_entity_ids)}" + ) + await self.incremental_update(new_entity_ids, end_user_id) + + async def full_clustering(self, end_user_id: str) -> None: + """ + 全量标签传播初始化。 + + 1. 拉取所有实体，初始化每个实体为独立社区 + 2. 迭代：每轮对所有实体做邻居投票，更新社区标签 + 3. 直到标签不再变化或达到 MAX_ITERATIONS + 4. 将最终标签写入 Neo4j + """ + entities = await self.repo.get_all_entities(end_user_id) + if not entities: + logger.info(f"[Clustering] 用户 {end_user_id} 无实体，跳过全量聚类") + return + + # 初始化：每个实体持有自己 id 作为社区标签 + labels: Dict[str, str] = {e["id"]: e["id"] for e in entities} + embeddings: Dict[str, Optional[List[float]]] = { + e["id"]: e.get("name_embedding") for e in entities + } + + for iteration in range(MAX_ITERATIONS): + changed = 0 + # 随机顺序（Python dict 在 3.7+ 保持插入顺序，这里直接遍历） + for entity in entities: + eid = entity["id"] + neighbors = await self.repo.get_entity_neighbors(eid, end_user_id) + + # 将邻居的当前内存标签注入（覆盖 Neo4j 中的旧值） + enriched = [] + for nb in neighbors: + nb_copy = dict(nb) + nb_copy["community_id"] = labels.get(nb["id"], nb.get("community_id")) + enriched.append(nb_copy) + + new_label = _weighted_vote(enriched, embeddings.get(eid)) + if new_label and new_label != labels[eid]: + labels[eid] = new_label + changed += 1 + + logger.info( + f"[Clustering] 全量迭代 {iteration + 1}/{MAX_ITERATIONS}，" + f"标签变化数: {changed}" + ) + if changed == 0: + logger.info("[Clustering] 标签已收敛，提前结束迭代") + break + + # 将最终标签写入 Neo4j + await self._flush_labels(labels, end_user_id) + logger.info( + f"[Clustering] 全量聚类完成，共 {len(set(labels.values()))} 个社区，" + f"{len(labels)} 个实体" + ) + + async def incremental_update( + self, new_entity_ids: List[str], end_user_id: str + ) -> None: + """ + 增量更新：只处理新实体及其邻居，不重跑全图。 + + 1. 对每个新实体查询邻居 + 2. 加权多数投票决定社区归属 + 3. 若邻居无社区 → 创建新社区 + 4. 若邻居分属多个社区 → 评估是否合并 + """ + for entity_id in new_entity_ids: + await self._process_single_entity(entity_id, end_user_id) + + # ────────────────────────────────────────────────────────────────────────── + # 内部方法 + # ────────────────────────────────────────────────────────────────────────── + + async def _process_single_entity( + self, entity_id: str, end_user_id: str + ) -> None: + """处理单个新实体的社区分配。""" + neighbors = await self.repo.get_entity_neighbors(entity_id, end_user_id) + + # 查询自身 embedding（从邻居查询结果中无法获取，需单独查） + self_embedding = await self._get_entity_embedding(entity_id, end_user_id) + + if not neighbors: + # 孤立实体：创建单成员社区 + new_cid = self._new_community_id() + await self.repo.upsert_community(new_cid, end_user_id, member_count=1) + await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id) + logger.debug(f"[Clustering] 孤立实体 {entity_id} → 新社区 {new_cid}") + return + + # 统计邻居社区分布 + community_ids_in_neighbors = set( + nb["community_id"] for nb in neighbors if nb.get("community_id") + ) + + target_cid = _weighted_vote(neighbors, self_embedding) + + if target_cid is None: + # 邻居都没有社区，连同新实体一起创建新社区 + new_cid = self._new_community_id() + await self.repo.upsert_community(new_cid, end_user_id) + await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id) + for nb in neighbors: + await self.repo.assign_entity_to_community( + nb["id"], new_cid, end_user_id + ) + await self.repo.refresh_member_count(new_cid, end_user_id) + logger.debug( + f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}" + ) + else: + # 加入得票最多的社区 + await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id) + await self.repo.refresh_member_count(target_cid, end_user_id) + logger.debug(f"[Clustering] 新实体 {entity_id} → 社区 {target_cid}") + + # 若邻居分属多个社区，评估合并 + if len(community_ids_in_neighbors) > 1: + await self._evaluate_merge( + list(community_ids_in_neighbors), end_user_id + ) + + async def _evaluate_merge( + self, community_ids: List[str], end_user_id: str + ) -> None: + """ + 评估多个社区是否应合并。 + + 策略：计算各社区成员 embedding 的平均向量，若两两余弦相似度 > 0.75 则合并。 + 合并时保留成员数最多的社区，其余成员迁移过来。 + """ + MERGE_THRESHOLD = 0.75 + + community_embeddings: Dict[str, Optional[List[float]]] = {} + community_sizes: Dict[str, int] = {} + + for cid in community_ids: + members = await self.repo.get_community_members(cid, end_user_id) + community_sizes[cid] = len(members) + # 计算社区成员 embedding 的平均向量 + valid_embeddings = [ + m["name_embedding"] + for m in members + if m.get("name_embedding") + ] + if valid_embeddings: + dim = len(valid_embeddings[0]) + avg = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + community_embeddings[cid] = avg + else: + community_embeddings[cid] = None + + # 找出应合并的社区对 + to_merge: List[tuple] = [] + cids = list(community_ids) + for i in range(len(cids)): + for j in range(i + 1, len(cids)): + sim = _cosine_similarity( + community_embeddings[cids[i]], + community_embeddings[cids[j]], + ) + if sim > MERGE_THRESHOLD: + to_merge.append((cids[i], cids[j])) + + for c1, c2 in to_merge: + keep = c1 if community_sizes.get(c1, 0) >= community_sizes.get(c2, 0) else c2 + dissolve = c2 if keep == c1 else c1 + members = await self.repo.get_community_members(dissolve, end_user_id) + for m in members: + await self.repo.assign_entity_to_community( + m["id"], keep, end_user_id + ) + await self.repo.refresh_member_count(keep, end_user_id) + logger.info( + f"[Clustering] 社区合并: {dissolve} → {keep}，" + f"迁移 {len(members)} 个成员" + ) + + async def _flush_labels( + self, labels: Dict[str, str], end_user_id: str + ) -> None: + """将内存中的标签批量写入 Neo4j。""" + # 先创建所有唯一社区节点 + unique_communities = set(labels.values()) + for cid in unique_communities: + await self.repo.upsert_community(cid, end_user_id) + + # 再批量分配实体 + for entity_id, community_id in labels.items(): + await self.repo.assign_entity_to_community( + entity_id, community_id, end_user_id + ) + + # 刷新成员数 + for cid in unique_communities: + await self.repo.refresh_member_count(cid, end_user_id) + + async def _get_entity_embedding( + self, entity_id: str, end_user_id: str + ) -> Optional[List[float]]: + """查询单个实体的 name_embedding。""" + try: + result = await self.connector.execute_query( + "MATCH (e:ExtractedEntity {id: $eid, end_user_id: $uid}) " + "RETURN e.name_embedding AS name_embedding", + eid=entity_id, + uid=end_user_id, + ) + return result[0]["name_embedding"] if result else None + except Exception: + return None + + @staticmethod + def _new_community_id() -> str: + return str(uuid.uuid4()) diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py new file mode 100644 index 00000000..16e30a10 --- /dev/null +++ b/api/app/repositories/neo4j/community_repository.py @@ -0,0 +1,129 @@ +"""Community 节点仓库 + +管理 Neo4j 中 Community 节点及 BELONGS_TO_COMMUNITY 边的 CRUD 操作。 +""" + +import logging +from typing import Dict, List, Optional + +from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.repositories.neo4j.cypher_queries import ( + COMMUNITY_NODE_UPSERT, + ENTITY_JOIN_COMMUNITY, + ENTITY_LEAVE_ALL_COMMUNITIES, + GET_ENTITY_NEIGHBORS, + GET_ALL_ENTITIES_FOR_USER, + GET_COMMUNITY_MEMBERS, + CHECK_USER_HAS_COMMUNITIES, + UPDATE_COMMUNITY_MEMBER_COUNT, +) + +logger = logging.getLogger(__name__) + + +class CommunityRepository: + def __init__(self, connector: Neo4jConnector): + self.connector = connector + + async def upsert_community( + self, community_id: str, end_user_id: str, member_count: int = 0 + ) -> Optional[str]: + """创建或更新 Community 节点，返回 community_id。""" + try: + result = await self.connector.execute_query( + COMMUNITY_NODE_UPSERT, + community_id=community_id, + end_user_id=end_user_id, + member_count=member_count, + ) + return result[0]["community_id"] if result else None + except Exception as e: + logger.error(f"upsert_community failed: {e}") + return None + + async def assign_entity_to_community( + self, entity_id: str, community_id: str, end_user_id: str + ) -> bool: + """将实体关联到社区（先解除旧关联，再建立新关联）。""" + try: + await self.connector.execute_query( + ENTITY_LEAVE_ALL_COMMUNITIES, + entity_id=entity_id, + end_user_id=end_user_id, + ) + result = await self.connector.execute_query( + ENTITY_JOIN_COMMUNITY, + entity_id=entity_id, + community_id=community_id, + end_user_id=end_user_id, + ) + return bool(result) + except Exception as e: + logger.error(f"assign_entity_to_community failed: {e}") + return False + + async def get_entity_neighbors( + self, entity_id: str, end_user_id: str + ) -> List[Dict]: + """查询实体的直接邻居及其社区归属。""" + try: + return await self.connector.execute_query( + GET_ENTITY_NEIGHBORS, + entity_id=entity_id, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_entity_neighbors failed: {e}") + return [] + + async def get_all_entities(self, end_user_id: str) -> List[Dict]: + """拉取某用户下所有实体及其当前社区归属。""" + try: + return await self.connector.execute_query( + GET_ALL_ENTITIES_FOR_USER, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_all_entities failed: {e}") + return [] + + async def get_community_members( + self, community_id: str, end_user_id: str + ) -> List[Dict]: + """查询社区成员列表。""" + try: + return await self.connector.execute_query( + GET_COMMUNITY_MEMBERS, + community_id=community_id, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_community_members failed: {e}") + return [] + + async def has_communities(self, end_user_id: str) -> bool: + """检查该用户是否已有 Community 节点（用于判断全量 vs 增量）。""" + try: + result = await self.connector.execute_query( + CHECK_USER_HAS_COMMUNITIES, + end_user_id=end_user_id, + ) + return result[0]["community_count"] > 0 if result else False + except Exception as e: + logger.error(f"has_communities failed: {e}") + return False + + async def refresh_member_count( + self, community_id: str, end_user_id: str + ) -> int: + """重新统计并更新社区成员数，返回最新数量。""" + try: + result = await self.connector.execute_query( + UPDATE_COMMUNITY_MEMBER_COUNT, + community_id=community_id, + end_user_id=end_user_id, + ) + return result[0]["member_count"] if result else 0 + except Exception as e: + logger.error(f"refresh_member_count failed: {e}") + return 0 diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 651c513f..947097a2 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1058,4 +1058,95 @@ Graph_Node_query = """ 3 AS priority LIMIT $limit - """ \ No newline at end of file + """ + + +# ============================================================ +# Community 节点 & BELONGS_TO_COMMUNITY 边 +# ============================================================ + +COMMUNITY_NODE_SAVE = """ +MERGE (c:Community {community_id: $community_id}) +SET c.end_user_id = $end_user_id, + c.formed_at = $formed_at, + c.updated_at = datetime(), + c.status = $status, + c.member_count = $member_count +RETURN c.community_id AS community_id +""" + +COMMUNITY_ADD_MEMBER = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) +SET c.updated_at = datetime(), + c.member_count = $member_count +""" + + + +# ─── Community 聚类相关 Cypher 模板 ─────────────────────────────────────────── + +COMMUNITY_NODE_UPSERT = """ +MERGE (c:Community {community_id: $community_id}) +SET c.end_user_id = $end_user_id, + c.member_count = $member_count, + c.updated_at = datetime() +RETURN c.community_id AS community_id +""" + +ENTITY_JOIN_COMMUNITY = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) +SET c.updated_at = datetime() +RETURN e.id AS entity_id, c.community_id AS community_id +""" + +ENTITY_LEAVE_ALL_COMMUNITIES = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (e)-[r:BELONGS_TO_COMMUNITY]->(:Community) +DELETE r +""" + +GET_ENTITY_NEIGHBORS = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb:ExtractedEntity {end_user_id: $end_user_id}) +OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community) +RETURN DISTINCT + nb.id AS id, + nb.name AS name, + nb.name_embedding AS name_embedding, + nb.activation_value AS activation_value, + CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id +""" + +GET_ALL_ENTITIES_FOR_USER = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id}) +OPTIONAL MATCH (e)-[:BELONGS_TO_COMMUNITY]->(c:Community) +RETURN e.id AS id, + e.name AS name, + e.name_embedding AS name_embedding, + e.activation_value AS activation_value, + CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id +""" + +GET_COMMUNITY_MEMBERS = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id}) +RETURN e.id AS id, e.name AS name, e.entity_type AS entity_type, + e.importance_score AS importance_score, e.activation_value AS activation_value, + e.name_embedding AS name_embedding +ORDER BY coalesce(e.activation_value, 0) DESC +""" + +CHECK_USER_HAS_COMMUNITIES = """ +MATCH (c:Community {end_user_id: $end_user_id}) +RETURN count(c) AS community_count +""" + +UPDATE_COMMUNITY_MEMBER_COUNT = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id}) +WITH c, count(e) AS cnt +SET c.member_count = cnt +RETURN c.community_id AS community_id, cnt AS member_count +""" diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index 526d16ec..a94bc23b 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -1,3 +1,4 @@ +import asyncio from typing import List # 使用新的仓储层 @@ -288,6 +289,14 @@ async def save_dialog_and_statements_to_neo4j( } logger.info("Transaction completed. Summary: %s", summary) logger.debug("Full transaction results: %r", results) + + # 写入成功后，触发聚类 + if entity_nodes: + end_user_id = entity_nodes[0].end_user_id + new_entity_ids = [e.id for e in entity_nodes] + logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}") + await _trigger_clustering(new_entity_ids, end_user_id) + return True except Exception as e: @@ -295,3 +304,28 @@ async def save_dialog_and_statements_to_neo4j( print(f"Neo4j integration error: {e}") print("Continuing without database storage...") return False + + +async def _trigger_clustering( + new_entity_ids: List[str], + end_user_id: str, +) -> None: + """ + 聚类触发函数，自动判断全量初始化还是增量更新。 + """ + connector = None + try: + from app.core.memory.storage_services.clustering_engine import LabelPropagationEngine + logger.info(f"[Clustering] 开始聚类，end_user_id={end_user_id}, 实体数={len(new_entity_ids)}") + connector = Neo4jConnector() + engine = LabelPropagationEngine(connector) + await engine.run(end_user_id=end_user_id, new_entity_ids=new_entity_ids) + logger.info(f"[Clustering] 聚类完成，end_user_id={end_user_id}") + except Exception as e: + logger.error(f"[Clustering] 聚类触发失败: {e}", exc_info=True) + finally: + if connector: + try: + await connector.close() + except Exception: + pass From 7ff9dfee8cf0622ea1246a8a10b4c7fcd7283ff6 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 17:14:50 +0800 Subject: [PATCH 044/196] [changes] Remove hardcoded content --- .../data_preprocessing/data_pruning.py | 24 ------------------- 1 file changed, 24 deletions(-) diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py index ecbe0411..9389f1da 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py @@ -234,34 +234,10 @@ class SemanticPruner: if re.fullmatch(r"(\[[^\]]+\])+", t): return True - # 检查是否为纯emoji（Unicode表情） - emoji_pattern = re.compile( - "[" - "\U0001F600-\U0001F64F" # 表情符号 - "\U0001F300-\U0001F5FF" # 符号和象形文字 - "\U0001F680-\U0001F6FF" # 交通和地图符号 - "\U0001F1E0-\U0001F1FF" # 旗帜 - "\U00002702-\U000027B0" - "\U000024C2-\U0001F251" - "]+", flags=re.UNICODE - ) - if emoji_pattern.fullmatch(t): - return True - # 纯标点符号 if re.fullmatch(r"[。！？,.!?…·\s]+", t): return True - # 安全防线：包含情绪词或兴趣词的消息，无论多短都不视为填充 - # 避免"我好开心呀"、"好喜欢打羽毛球呀"等被误删 - _emotion_interest_guard = re.compile( - r"开心|高兴|快乐|幸福|感动|难过|悲伤|伤心|委屈|失落|沮丧|郁闷|" - r"生气|愤怒|烦躁|焦虑|害怕|担心|压力|兴奋|期待|" - r"喜欢|热爱|爱好|兴趣|擅长|享受|沉迷|着迷|讨厌|厌恶" - ) - if _emotion_interest_guard.search(t): - return False - return False async def _batch_evaluate_importance_with_llm( From 97eabc0c3697d9a33c1283187713ed4aa5b03cc7 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 17:25:32 +0800 Subject: [PATCH 045/196] [add] Remove hardcoding --- .../data_preprocessing/data_pruning.py | 22 +++++-------------- 1 file changed, 5 insertions(+), 17 deletions(-) diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py index 295dc560..567c0347 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py @@ -200,33 +200,21 @@ class SemanticPruner: return min(score, 10) # 最高10分 - # 情绪/兴趣/爱好安全防线正则（类级别，避免重复编译） - _EMOTION_INTEREST_GUARD = re.compile( - r"开心|高兴|快乐|幸福|感动|难过|悲伤|伤心|委屈|失落|沮丧|郁闷|" - r"生气|愤怒|烦躁|焦虑|害怕|担心|压力|兴奋|期待|惊喜|惊讶|" - r"喜欢|热爱|爱好|兴趣|擅长|享受|沉迷|着迷|讨厌|厌恶|" - r"happy|sad|angry|excited|anxious|love|hate|enjoy|like|dislike" - ) + # 情绪/兴趣/爱好安全防线正则已移除，改由 extracat_Pruning.jinja2 提示词中的 preserve_keywords 机制处理 def _is_filler_message(self, message: ConversationMessage) -> bool: """检测典型寒暄/口头禅/确认类短消息。 判断顺序： - 1. 情绪/兴趣安全防线（最高优先级）：包含情绪词或兴趣词的消息，无论多短都不视为填充 - 2. 空消息 - 3. 场景特定填充词库精确匹配 - 4. 常见寒暄精确匹配 - 5. 纯表情/标点 + 1. 空消息 + 2. 场景特定填充词库精确匹配 + 3. 常见寒暄精确匹配 + 4. 纯表情/标点 """ t = message.msg.strip() if not t: return True - # ── 最高优先级：情绪/兴趣安全防线 ── - # "我好开心呀"、"好喜欢打羽毛球呀"、"我好难过" 等一律不视为填充 - if self._EMOTION_INTEREST_GUARD.search(t): - return False - # 检查是否在场景特定填充词库中（精确匹配） if t in self.scene_config.filler_phrases: return True From 45d9e45346ebc0c4ef471bf304924c18f189ebe1 Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Tue, 10 Mar 2026 18:05:34 +0800 Subject: [PATCH 046/196] fix(file): S3 file storage resolves the issue of inconsistent end_point and region. --- api/app/core/config.py | 1 + api/app/core/storage/factory.py | 1 + api/app/core/storage/s3.py | 24 ++++++++++++++++++++++++ 3 files changed, 26 insertions(+) diff --git a/api/app/core/config.py b/api/app/core/config.py index 3bc77376..25713967 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -114,6 +114,7 @@ class Settings: S3_ACCESS_KEY_ID: str = os.getenv("S3_ACCESS_KEY_ID", "") S3_SECRET_ACCESS_KEY: str = os.getenv("S3_SECRET_ACCESS_KEY", "") S3_BUCKET_NAME: str = os.getenv("S3_BUCKET_NAME", "") + S3_ENDPOINT_URL: str = os.getenv("S3_ENDPOINT_URL", "") # VOLC ASR settings VOLC_APP_KEY: str = os.getenv("VOLC_APP_KEY", "") diff --git a/api/app/core/storage/factory.py b/api/app/core/storage/factory.py index 52749e3c..93fa847b 100644 --- a/api/app/core/storage/factory.py +++ b/api/app/core/storage/factory.py @@ -85,6 +85,7 @@ class StorageFactory: access_key_id=settings.S3_ACCESS_KEY_ID, secret_access_key=settings.S3_SECRET_ACCESS_KEY, bucket_name=settings.S3_BUCKET_NAME, + endpoint_url=settings.S3_ENDPOINT_URL, ) else: diff --git a/api/app/core/storage/s3.py b/api/app/core/storage/s3.py index e59da8d5..4ecaad4e 100644 --- a/api/app/core/storage/s3.py +++ b/api/app/core/storage/s3.py @@ -35,6 +35,19 @@ class S3Storage(StorageBackend): bucket_name: The name of the S3 bucket. region: The AWS region. """ + AMAZON_S3_ENDPOINT_MAP = { + "us-east-1": "https://s3.us-east-1.amazonaws.com", # 特殊：无地域后缀 + "us-east-2": "https://s3.us-east-2.amazonaws.com", + "us-west-1": "https://s3.us-west-1.amazonaws.com", + "us-west-2": "https://s3.us-west-2.amazonaws.com", + "ap-east-1": "https://s3.ap-east-1.amazonaws.com", # 香港 + "ap-southeast-1": "https://s3.ap-southeast-1.amazonaws.com", # 新加坡 + "ap-southeast-2": "https://s3.ap-southeast-2.amazonaws.com", # 悉尼 + "ap-northeast-1": "https://s3.ap-northeast-1.amazonaws.com", # 东京 + "eu-central-1": "https://s3.eu-central-1.amazonaws.com", # 法兰克福 + "eu-west-1": "https://s3.eu-west-1.amazonaws.com", # 爱尔兰 + # 可根据需要扩展其他地域 + } def __init__( self, @@ -42,6 +55,7 @@ class S3Storage(StorageBackend): access_key_id: str, secret_access_key: str, bucket_name: str, + endpoint_url: Optional[str] = None ): """ Initialize the S3Storage backend. @@ -51,6 +65,7 @@ class S3Storage(StorageBackend): access_key_id: The AWS access key ID. secret_access_key: The AWS secret access key. bucket_name: The name of the S3 bucket. + endpoint_url: The complete URL to use for the constructed client. Raises: StorageConfigError: If any required configuration is missing. @@ -69,10 +84,19 @@ class S3Storage(StorageBackend): self.region = region self.bucket_name = bucket_name + if not endpoint_url: + # 优先匹配内置映射表（解决特殊地域） + if region in self.AMAZON_S3_ENDPOINT_MAP: + endpoint_url = self.AMAZON_S3_ENDPOINT_MAP[region] + # 兜底：通用拼接（适配未配置的新地域） + else: + endpoint_url = f"https://s3.{region}.amazonaws.com" + try: self.client = boto3.client( "s3", region_name=region, + endpoint_url=endpoint_url, aws_access_key_id=access_key_id, aws_secret_access_key=secret_access_key, ) From 99e94b3567d27f1532da73cf9f40755c4516c979 Mon Sep 17 00:00:00 2001 From: Eternity <61316157+myhMARS@users.noreply.github.com> Date: Tue, 10 Mar 2026 18:28:16 +0800 Subject: [PATCH 047/196] feat(workflow,app): add MIME-based file handling and HTTP response files --- api/Dockerfile | 3 +- api/app/core/workflow/nodes/base_node.py | 20 +- .../workflow/nodes/http_request/config.py | 6 + .../core/workflow/nodes/http_request/node.py | 131 ++++++++- api/app/core/workflow/utils/file_processer.py | 56 ++++ .../core/workflow/variable/base_variable.py | 9 +- api/app/schemas/app_schema.py | 8 + api/app/services/multimodal_service.py | 252 +++++++++--------- api/app/services/workflow_service.py | 2 +- api/pyproject.toml | 2 + 10 files changed, 347 insertions(+), 142 deletions(-) create mode 100644 api/app/core/workflow/utils/file_processer.py diff --git a/api/Dockerfile b/api/Dockerfile index f6c082d2..a38739ca 100644 --- a/api/Dockerfile +++ b/api/Dockerfile @@ -45,7 +45,8 @@ RUN --mount=type=cache,id=mem_apt,target=/var/cache/apt,sharing=locked \ apt install -y libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev && \ apt install -y libjemalloc-dev && \ apt install -y python3-pip pipx nginx unzip curl wget git vim less && \ - apt install -y ghostscript + apt install -y ghostscript && \ + apt install -y libmagic1 RUN if [ "$NEED_MIRROR" == "1" ]; then \ pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \ diff --git a/api/app/core/workflow/nodes/base_node.py b/api/app/core/workflow/nodes/base_node.py index 496454ba..39c7887b 100644 --- a/api/app/core/workflow/nodes/base_node.py +++ b/api/app/core/workflow/nodes/base_node.py @@ -1,5 +1,6 @@ import asyncio import logging +import uuid from abc import ABC, abstractmethod from datetime import datetime from functools import cached_property @@ -643,15 +644,18 @@ class BaseNode(ABC): return content.content_cache[provider] with get_db_read() as db: multimodel_service = MultimodalService(db, provider, is_omni=is_omni) - message = await multimodel_service.process_files( - [FileInput.model_construct( - type=content.type, - url=content.url, - transfer_method=content.transfer_method, - file_type=content.origin_file_type, - upload_file_id=content.file_id - )] + file_obj = FileInput( + type=content.type, + url=content.url, + transfer_method=content.transfer_method, + origin_file_type=content.origin_file_type, + upload_file_id=uuid.UUID(content.file_id) if content.file_id else None, ) + file_obj.set_content(content.get_content()) + message = await multimodel_service.process_files( + [file_obj] + ) + content.set_content(file_obj.get_content()) if message: content.content_cache[provider] = message return message diff --git a/api/app/core/workflow/nodes/http_request/config.py b/api/app/core/workflow/nodes/http_request/config.py index 9b41d9f2..fe38fafb 100644 --- a/api/app/core/workflow/nodes/http_request/config.py +++ b/api/app/core/workflow/nodes/http_request/config.py @@ -4,6 +4,7 @@ from pydantic import Field, BaseModel, field_validator from app.core.workflow.nodes.base_config import BaseNodeConfig from app.core.workflow.nodes.enums import HttpRequestMethod, HttpAuthType, HttpContentType, HttpErrorHandle +from app.core.workflow.variable.base_variable import FileObject class HttpAuthConfig(BaseModel): @@ -260,6 +261,11 @@ class HttpRequestNodeOutput(BaseModel): description="Http response headers" ) + files: list[FileObject] = Field( + default_factory=list, + description="List of files", + ) + output: str = Field( default="SUCCESS", description="HTTP response body", diff --git a/api/app/core/workflow/nodes/http_request/node.py b/api/app/core/workflow/nodes/http_request/node.py index e6c00eff..23378c83 100644 --- a/api/app/core/workflow/nodes/http_request/node.py +++ b/api/app/core/workflow/nodes/http_request/node.py @@ -1,24 +1,146 @@ import asyncio import json import logging +import mimetypes import uuid +import imghdr +from email.message import Message from typing import Any, Callable, Coroutine import httpx -# import filetypes # TODO: File support (Feature) from httpx import AsyncClient, Response, Timeout +import magic from app.core.workflow.engine.state_manager import WorkflowState from app.core.workflow.engine.variable_pool import VariablePool from app.core.workflow.nodes.base_node import BaseNode from app.core.workflow.nodes.enums import HttpRequestMethod, HttpErrorHandle, HttpAuthType, HttpContentType from app.core.workflow.nodes.http_request.config import HttpRequestNodeConfig, HttpRequestNodeOutput -from app.core.workflow.variable.base_variable import VariableType +from app.core.workflow.utils.file_processer import mime_to_file_type +from app.core.workflow.variable.base_variable import VariableType, FileObject from app.core.workflow.variable.variable_objects import FileVariable, ArrayVariable +from app.schemas import FileType, TransferMethod logger = logging.getLogger(__file__) +class HttpResponse: + def __init__(self, response: httpx.Response): + self.response = response + self.headers = dict(response.headers) + + self._is_file: bool | None = None + + @property + def content_type(self) -> str: + return self.headers.get("content-type", "") + + @property + def content_disposition(self) -> Message | None: + content_disposition = self.headers.get("content-disposition", "") + if content_disposition: + msg = Message() + msg["content-disposition"] = content_disposition + return msg + return None + + @property + def is_file(self) -> bool: + if self._is_file is not None: + return self._is_file + content_type = self.content_type.split(";")[0].strip().lower() + + parsed_content_disposition = self.content_disposition + if parsed_content_disposition: + disp_type = parsed_content_disposition.get_content_disposition() + filename = parsed_content_disposition.get_filename() + if disp_type == "attachment" or filename: + self._is_file = True + return True + + if content_type.startswith("text/") and "csv" not in content_type: + return False + + if content_type.startswith("application/"): + if any( + text_type in content_type + for text_type in {"json", "xml", "javascript", "x-www-form-urlencoded", "yaml", "graphql"} + ): + self._is_file = False + return False + try: + content_sample = self.response.content[:1024] + content_sample.decode("utf-8") + text_markers = (b"{", b"[", b"<", b"function", b"var ", b"const ", b"let ") + if any(marker in content_sample for marker in text_markers): + return False + except UnicodeDecodeError: + self._is_file = True + return True + + main_type, _ = mimetypes.guess_type("dummy" + (mimetypes.guess_extension(content_type) or "")) + if main_type: + self._is_file = main_type.split("/")[0] in ("application", "image", "audio", "video") + return self._is_file + self._is_file = any(media_type in content_type for media_type in ("image/", "audio/", "video/")) + return self._is_file + + @property + def is_image(self): + if self.is_file: + kind = imghdr.what(None, h=self.response.content) + return kind is not None + return False + + @property + def url(self) -> str: + return str(self.response.url) + + @property + def body(self) -> str: + if self.is_file: + return f"{'!' if self.is_image else ''}[file]({self.url})" + return self.response.text + + @staticmethod + def get_file_type(file_bytes) -> tuple[FileType | None, str | None]: + mime = magic.from_buffer(file_bytes, mime=True) + + if mime.startswith("image"): + return FileType.IMAGE, mime + elif mime.startswith("video"): + return FileType.VIDEO, mime + elif mime.startswith("audio"): + return FileType.AUDIO, mime + elif mime in ["application/pdf", + "application/msword", + "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + "application/vnd.ms-excel", + "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "text/plain"]: + return FileType.DOCUMENT, mime + return None, None + + @property + def files(self) -> list[FileObject]: + file_type, mime_type = self.get_file_type(self.response.content) + origin_file_type = mime_to_file_type(mime_type) + if self.is_file and file_type and origin_file_type: + file_obj = FileObject( + type=file_type, + url=self.url, + transfer_method=TransferMethod.REMOTE_URL.value, + origin_file_type=origin_file_type, + file_id=None, + is_file=True + ) + file_obj.set_content(self.response.content) + return [ + file_obj + ] + return [] + + class HttpRequestNode(BaseNode): """ HTTP Request Workflow Node. @@ -44,6 +166,7 @@ class HttpRequestNode(BaseNode): "body": VariableType.STRING, "status_code": VariableType.NUMBER, "headers": VariableType.OBJECT, + "files": VariableType.ARRAY_FILE, "output": VariableType.STRING } @@ -232,10 +355,12 @@ class HttpRequestNode(BaseNode): ) resp.raise_for_status() logger.info(f"Node {self.node_id}: HTTP request succeeded") + response = HttpResponse(resp) return HttpRequestNodeOutput( - body=resp.text, + body=response.body, status_code=resp.status_code, headers=resp.headers, + files=response.files ).model_dump() except (httpx.HTTPStatusError, httpx.RequestError) as e: logger.error(f"HTTP request node exception: {e}") diff --git a/api/app/core/workflow/utils/file_processer.py b/api/app/core/workflow/utils/file_processer.py new file mode 100644 index 00000000..ae406ab0 --- /dev/null +++ b/api/app/core/workflow/utils/file_processer.py @@ -0,0 +1,56 @@ +# -*- coding: UTF-8 -*- +# Author: Eternity +# @Email: 1533512157@qq.com +# @Time : 2026/3/10 13:36 +TRANSFORM_FILE_TYPE = { + 'text/plain': 'document/text', + 'text/markdown': 'document/markdown', + 'text/x-markdown': 'document/x-markdown', + + 'application/pdf': 'document/pdf', + + 'application/msword': 'document/doc', + 'application/vnd.openxmlformats-officedocument.wordprocessingml.document': 'document/docx', + + 'application/vnd.ms-powerpoint': 'document/ppt', + 'application/vnd.openxmlformats-officedocument.presentationml.presentation': 'document/pptx', +} +ALLOWED_FILE_TYPES = [ + 'text/plain', + 'text/markdown', + 'text/x-markdown', + 'application/pdf', + 'application/msword', + 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', + 'application/vnd.ms-powerpoint', + 'application/vnd.openxmlformats-officedocument.presentationml.presentation', + 'image/jpg', + 'image/jpeg', + 'image/png', + 'image/gif', + 'image/bmp', + 'image/webp', + 'image/svg+xml', + 'video/mp4', + 'video/quicktime', + 'video/x-msvideo', + 'video/x-matroska', + 'video/webm', + 'video/x-flv', + 'video/x-ms-wmv', + 'audio/mpeg', + 'audio/wav', + 'audio/ogg', + 'audio/aac', + 'audio/flac', + 'audio/mp4', + 'audio/x-ms-wma', + 'audio/x-m4a', +] + + +def mime_to_file_type(mime_type): + if mime_type not in ALLOWED_FILE_TYPES: + return None + + return TRANSFORM_FILE_TYPE.get(mime_type, mime_type) diff --git a/api/app/core/workflow/variable/base_variable.py b/api/app/core/workflow/variable/base_variable.py index dd821ea7..aea40cf6 100644 --- a/api/app/core/workflow/variable/base_variable.py +++ b/api/app/core/workflow/variable/base_variable.py @@ -114,9 +114,16 @@ class FileObject(BaseModel): file_id: str | None content_cache: dict = Field(default_factory=dict) - is_file: bool + _byte_content: bytes | None = None + + def get_content(self): + return self._byte_content + + def set_content(self, byte_content): + self._byte_content = byte_content + class BaseVariable(ABC): """Abstract base class for all workflow variables. diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index f073a200..c0482ec3 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -45,11 +45,19 @@ class FileInput(BaseModel): url: Optional[str] = Field(None, description="远程URL（remote_url时必填）") file_type: Optional[str] = Field(None, description="具体文件格式（如image/jpg、audio/wav、document/docx、video/mp4）") + _content = None + def __init__(self, **data): if "type" in data: data['file_type'] = data['type'] super().__init__(**data) + def set_content(self, content: bytes): + self._content = content + + def get_content(self) -> bytes | None: + return self._content + @field_validator("type", mode="before") @classmethod def validate_type(cls, v): diff --git a/api/app/services/multimodal_service.py b/api/app/services/multimodal_service.py index 9b06c287..fffca2e5 100644 --- a/api/app/services/multimodal_service.py +++ b/api/app/services/multimodal_service.py @@ -8,32 +8,42 @@ - Bedrock/Anthropic: 仅支持 base64 格式 - OpenAI: 支持 URL 和 base64 格式 """ -import uuid -import httpx import base64 -from typing import List, Dict, Any, Optional -from abc import ABC, abstractmethod -from sqlalchemy.orm import Session -from docx import Document import io -import PyPDF2 +from abc import ABC, abstractmethod +from typing import List, Dict, Any, Optional + +import PyPDF2 +import httpx +import magic +from docx import Document +from sqlalchemy.orm import Session -from app.core.logging_config import get_business_logger -from app.core.exceptions import BusinessException -from app.core.error_codes import BizCode -from app.schemas.app_schema import FileInput, FileType, TransferMethod -from app.models.file_metadata_model import FileMetadata from app.core.config import settings +from app.core.error_codes import BizCode +from app.core.exceptions import BusinessException +from app.core.logging_config import get_business_logger +from app.models.file_metadata_model import FileMetadata +from app.schemas.app_schema import FileInput, FileType, TransferMethod from app.services.audio_transcription_service import AudioTranscriptionService logger = get_business_logger() +TEXT_MIME = ['text/plain', 'text/x-markdown'] +PDF_MIME = ['application/pdf'] +DOC_MIME = [ + 'application/msword', + 'application/vnd.openxmlformats-officedocument.wordprocessingml.document' +] + class MultimodalFormatStrategy(ABC): """多模态格式策略基类""" + def __init__(self, file: FileInput): + self.file = file @abstractmethod - async def format_image(self, url: str) -> Dict[str, Any]: + async def format_image(self, url: str, content: bytes | None = None) -> Dict[str, Any]: """格式化图片""" pass @@ -43,7 +53,7 @@ class MultimodalFormatStrategy(ABC): pass @abstractmethod - async def format_audio(self, file_type: str, url: str) -> Dict[str, Any]: + async def format_audio(self, file_type: str, url: str, content: bytes | None = None) -> Dict[str, Any]: """格式化音频""" pass @@ -56,7 +66,7 @@ class MultimodalFormatStrategy(ABC): class DashScopeFormatStrategy(MultimodalFormatStrategy): """通义千问策略""" - async def format_image(self, url: str) -> Dict[str, Any]: + async def format_image(self, url: str, content: bytes | None = None) -> Dict[str, Any]: """通义千问图片格式：{"type": "image", "image": "url"}""" return { "type": "image", @@ -70,7 +80,13 @@ class DashScopeFormatStrategy(MultimodalFormatStrategy): "text": f"\n{text}\n" } - async def format_audio(self, file_type: str, url: str, transcription: Optional[str] = None) -> Dict[str, Any]: + async def format_audio( + self, + file_type: str, + url: str, + content: bytes | None = None, + transcription: Optional[str] = None + ) -> Dict[str, Any]: """ 通义千问音频格式 - 原生支持: qwen-audio 系列 @@ -98,44 +114,37 @@ class DashScopeFormatStrategy(MultimodalFormatStrategy): class BedrockFormatStrategy(MultimodalFormatStrategy): """Bedrock/Anthropic 策略""" - async def format_image(self, url: str) -> Dict[str, Any]: + async def format_image(self, url: str, content: bytes | None = None) -> Dict[str, Any]: """ Bedrock/Anthropic 格式: base64 编码 {"type": "image", "source": {"type": "base64", "media_type": "...", "data": "..."}} """ - from mimetypes import guess_type logger.info(f"下载并编码图片: {url}") # 下载图片 - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.get(url) - response.raise_for_status() - - # 获取图片数据 - image_data = response.content + if content is None: + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.get(url) + response.raise_for_status() + content = response.content + self.file.set_content(content) # 确定 media type - content_type = response.headers.get("content-type") - if content_type and content_type.startswith("image/"): - media_type = content_type - else: - guessed_type, _ = guess_type(url) - media_type = guessed_type if guessed_type and guessed_type.startswith("image/") else "image/jpeg" + content_type = magic.from_buffer(content, mime=True) + media_type = content_type if content_type.startswith("image/") else "image/jpeg" + base64_data = base64.b64encode(content).decode("utf-8") - # 转换为 base64 - base64_data = base64.b64encode(image_data).decode("utf-8") + logger.info(f"图片编码完成: media_type={media_type}, size={len(base64_data)}") - logger.info(f"图片编码完成: media_type={media_type}, size={len(base64_data)}") - - return { - "type": "image", - "source": { - "type": "base64", - "media_type": media_type, - "data": base64_data - } + return { + "type": "image", + "source": { + "type": "base64", + "media_type": media_type, + "data": base64_data } + } async def format_document(self, file_name: str, text: str) -> Dict[str, Any]: """Bedrock/Anthropic 文档格式（需要 base64 编码）""" @@ -152,7 +161,12 @@ class BedrockFormatStrategy(MultimodalFormatStrategy): } } - async def format_audio(self, file_type: str, url: str, transcription: Optional[str] = None) -> Dict[str, Any]: + async def format_audio( + self, file_type: str, + url: str, + content: bytes | None = None, + transcription: Optional[str] = None + ) -> Dict[str, Any]: """ Bedrock/Anthropic 音频格式 不支持原生音频，必须转录为文本 @@ -178,7 +192,7 @@ class BedrockFormatStrategy(MultimodalFormatStrategy): class OpenAIFormatStrategy(MultimodalFormatStrategy): """OpenAI 策略""" - async def format_image(self, url: str) -> Dict[str, Any]: + async def format_image(self, url: str, content: bytes | None = None) -> Dict[str, Any]: """OpenAI 格式: {"type": "image_url", "image_url": {"url": "..."}}""" return { "type": "image_url", @@ -194,7 +208,13 @@ class OpenAIFormatStrategy(MultimodalFormatStrategy): "text": f"\n{text}\n" } - async def format_audio(self, file_type: str, url: str, transcription: Optional[str] = None) -> Dict[str, Any]: + async def format_audio( + self, + file_type: str, + url: str, + content: bytes | None = None, + transcription: Optional[str] = None + ) -> Dict[str, Any]: """ OpenAI 音频格式 - gpt-4o-audio 系列支持原生音频（需要 base64 编码） @@ -208,31 +228,35 @@ class OpenAIFormatStrategy(MultimodalFormatStrategy): # OpenAI 音频需要 base64 编码 try: - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.get(url) - response.raise_for_status() - audio_data = response.content - base64_audio = base64.b64encode(audio_data).decode('utf-8') - # 1. 优先从 file_type (MIME) 取扩展名 - file_ext = file_type.split('/')[-1] if file_type and '/' in file_type else None - # 2. 从响应头 content-type 取 - if not file_ext: - ct = response.headers.get("content-type", "") - file_ext = ct.split('/')[-1].split(';')[0].strip() if '/' in ct else None - # 3. 从 URL 路径取扩展名 - if not file_ext: - file_ext = url.split('?')[0].rsplit('.', 1)[-1].lower() or None - # 4. 默认 wav - # supported_ext = {"wav", "mp3", "mp4", "ogg", "flac", "webm", "m4a", "wave", "x-m4a"} - file_ext = "wav" if not file_ext else file_ext + audio_data = content + if content is None: + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.get(url) + response.raise_for_status() + audio_data = response.content + self.file.set_content(audio_data) + base64_audio = base64.b64encode(audio_data).decode('utf-8') - return { - "type": "input_audio", - "input_audio": { - "data": f"data:;base64,{base64_audio}", - "format": file_ext - } + # 1. 优先从 file_type (MIME) 取扩展名 + file_ext = file_type.split('/')[-1] if file_type and '/' in file_type else None + # 2. 从响应头 content-type 取 + if not file_ext: + content_type = magic.from_buffer(audio_data, mime=True) + file_ext = content_type.split('/')[-1].split(';')[0].strip() if '/' in content_type else None + # 3. 从 URL 路径取扩展名 + if not file_ext: + file_ext = url.split('?')[0].rsplit('.', 1)[-1].lower() or None + # 4. 默认 wav + # supported_ext = {"wav", "mp3", "mp4", "ogg", "flac", "webm", "m4a", "wave", "x-m4a"} + file_ext = "wav" if not file_ext else file_ext + + return { + "type": "input_audio", + "input_audio": { + "data": f"data:;base64,{base64_audio}", + "format": file_ext } + } except Exception as e: logger.error(f"下载音频失败: {e}") return { @@ -262,7 +286,8 @@ PROVIDER_STRATEGIES = { class MultimodalService: """多模态文件处理服务""" - def __init__(self, db: Session, provider: str = "dashscope", api_key: Optional[str] = None, enable_audio_transcription: bool = False, is_omni: bool = False): + def __init__(self, db: Session, provider: str = "dashscope", api_key: Optional[str] = None, + enable_audio_transcription: bool = False, is_omni: bool = False): """ 初始化多模态服务 @@ -305,10 +330,9 @@ class MultimodalService: logger.warning(f"未找到 provider '{self.provider}' 的策略，使用默认策略") strategy_class = DashScopeFormatStrategy - strategy = strategy_class() - result = [] for idx, file in enumerate(files): + strategy = strategy_class(file) try: if file.type == FileType.IMAGE: content = await self._process_image(file, strategy) @@ -355,7 +379,7 @@ class MultimodalService: """ try: url = await self.get_file_url(file) - return await strategy.format_image(url) + return await strategy.format_image(url, content=file.get_content()) except Exception as e: logger.error(f"处理图片失败: {e}", exc_info=True) return { @@ -415,11 +439,13 @@ class MultimodalService: # 远程文档暂不支持提取 return { "type": "text", - "text": f"\n[远程文档，暂不支持内容提取]\n" + "text": f"\n{await self._extract_document_text(file)}\n" } else: # 本地文件，提取文本内容 - text = await self._extract_document_text(file.upload_file_id) + server_url = settings.FILE_LOCAL_SERVER_URL + file.url = f"{server_url}/storage/permanent/{file.upload_file_id}" + text = await self._extract_document_text(file) file_metadata = self.db.query(FileMetadata).filter( FileMetadata.id == file.upload_file_id ).first() @@ -454,7 +480,7 @@ class MultimodalService: else: logger.warning(f"Provider {self.provider} 不支持音频转文本") - return await strategy.format_audio(file.file_type, url, transcription) + return await strategy.format_audio(file.file_type, url, file.get_content(), transcription) except Exception as e: logger.error(f"处理音频失败: {e}", exc_info=True) return { @@ -500,8 +526,6 @@ class MultimodalService: return file.url else: file_id = file.upload_file_id - print("="*50) - print("file_id",file_id) # 查询 FileMetadata file_metadata = self.db.query(FileMetadata).filter( @@ -519,66 +543,44 @@ class MultimodalService: server_url = settings.FILE_LOCAL_SERVER_URL return f"{server_url}/storage/permanent/{file_id}" - async def _extract_document_text(self, file_id: uuid.UUID) -> str: + async def _extract_document_text(self, file: FileInput) -> str: """ 提取文档文本内容 Args: - file_id: 文件ID + file: 文件输入 Returns: str: 提取的文本内容 """ - file_metadata = self.db.query(FileMetadata).filter( - FileMetadata.id == file_id, - FileMetadata.status == "completed" - ).first() - - if not file_metadata: - raise BusinessException( - f"文件不存在或已删除: {file_id}", - BizCode.NOT_FOUND - ) - - file_ext = file_metadata.file_ext.lower() - server_url = settings.FILE_LOCAL_SERVER_URL - file_url = f"{server_url}/storage/permanent/{file_id}" - - if file_ext in ['.txt', '.md', '.markdown']: - return await self._read_text_file(file_url) - elif file_ext == '.pdf': - return await self._extract_pdf_text(file_url) - elif file_ext in ['.doc', '.docx']: - return await self._extract_word_text(file_url) - else: - return f"[不支持的文档格式: {file_ext}]" - - @staticmethod - async def _read_text_file(file_url: str) -> str: - """读取纯文本文件""" try: - # 下载文件 - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.get(file_url) - response.raise_for_status() - return response.text + file_content = file.get_content() + if not file_content: + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.get(file.url) + response.raise_for_status() + file_content = response.content + file.set_content(file_content) + file_mime_type = magic.from_buffer(file_content, mime=True) + if file_mime_type in TEXT_MIME: + return file_content.decode("utf-8") + elif file_mime_type in PDF_MIME: + return await self._extract_pdf_text(file_content) + elif file_mime_type in DOC_MIME: + return await self._extract_word_text(file_content) + else: + return f"[Unsupported file type: {file_mime_type}]" except Exception as e: - logger.error(f"读取文本文件失败: {e}") - return f"[文件读取失败: {str(e)}]" + logger.error(f"Failed to load file. - {e}") + return "[Failed to load file.]" @staticmethod - async def _extract_pdf_text(file_url: str) -> str: + async def _extract_pdf_text(file_content: bytes) -> str: """提取 PDF 文本""" try: - # 下载 PDF 文件 - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.get(file_url) - response.raise_for_status() - pdf_data = response.content - # 使用 BytesIO 读取 PDF text_parts = [] - pdf_file = io.BytesIO(pdf_data) + pdf_file = io.BytesIO(file_content) pdf_reader = PyPDF2.PdfReader(pdf_file) for page in pdf_reader.pages: text_parts.append(page.extract_text()) @@ -588,17 +590,11 @@ class MultimodalService: return f"[PDF 提取失败: {str(e)}]" @staticmethod - async def _extract_word_text(file_url: str) -> str: + async def _extract_word_text(file_content: bytes) -> str: """提取 Word 文档文本""" try: - # 下载 Word 文件 - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.get(file_url) - response.raise_for_status() - word_data = response.content - # 使用 BytesIO 读取 Word 文档 - word_file = io.BytesIO(word_data) + word_file = io.BytesIO(file_content) doc = Document(word_file) text_parts = [paragraph.text for paragraph in doc.paragraphs] return '\n'.join(text_parts) diff --git a/api/app/services/workflow_service.py b/api/app/services/workflow_service.py index eaf78b90..4e7268d3 100644 --- a/api/app/services/workflow_service.py +++ b/api/app/services/workflow_service.py @@ -458,7 +458,7 @@ class WorkflowService: type=file.type, url=await self.multimodal_service.get_file_url(file), transfer_method=file.transfer_method, - file_id=str(file.upload_file_id), + file_id=str(file.upload_file_id) if file.upload_file_id else None, origin_file_type=file.file_type, is_file=True ).model_dump() diff --git a/api/pyproject.toml b/api/pyproject.toml index 0bb232c3..e6fddea8 100644 --- a/api/pyproject.toml +++ b/api/pyproject.toml @@ -145,6 +145,8 @@ dependencies = [ "lxml>=4.9.0", "httpx>=0.28.0", "modelscope>=1.34.0", + "python-magic>=0.4.14; sys_platform == 'linux' or sys_platform == 'darwin'", + "python-magic-bin>=0.4.14; sys_platform=='win32'", ] [tool.pytest.ini_options] From 8b0f8060799af1b7808a2d4991dcf8677d4f4dcf Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 19:00:30 +0800 Subject: [PATCH 048/196] [add] Different scenarios achieve different pruning effects. --- .../data_preprocessing/data_pruning.py | 251 ++++--------- .../data_preprocessing/scene_config.py | 344 ++---------------- .../prompt/prompts/extracat_Pruning.jinja2 | 56 +-- api/app/services/memory_config_service.py | 16 +- api/app/services/pilot_run_service.py | 3 +- redbear-mem-benchmark | 2 +- 6 files changed, 121 insertions(+), 551 deletions(-) diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py index 567c0347..a1953316 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py @@ -88,26 +88,17 @@ class SemanticPruner: self._detailed_prune_logging = True # 是否启用详细日志 self._max_debug_msgs_per_dialog = 20 # 每个对话最多记录前N条消息的详细日志 - # 加载场景特定配置（内置场景走专门规则，自定义场景 fallback 到通用规则） - self.scene_config: ScenePatterns = SceneConfigRegistry.get_config( - self.config.pruning_scene, - fallback_to_generic=True - ) + # 加载统一填充词库 + self.scene_config: ScenePatterns = SceneConfigRegistry.get_config(self.config.pruning_scene) - # 判断是否为内置专门场景 - self._is_builtin_scene = SceneConfigRegistry.is_scene_supported(self.config.pruning_scene) - - # 自定义场景的本体类型列表（用于注入提示词） + # 本体类型列表（用于注入提示词，所有场景均支持） self._ontology_classes = getattr(self.config, "ontology_classes", None) or [] - if self._is_builtin_scene: - self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 使用内置专门配置") + self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene}") + if self._ontology_classes: + self._log(f"[剪枝-初始化] 注入本体类型: {self._ontology_classes}") else: - self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 为自定义场景，使用通用规则 + 本体类型提示词注入") - if self._ontology_classes: - self._log(f"[剪枝-初始化] 注入本体类型: {self._ontology_classes}") - else: - self._log(f"[剪枝-初始化] 未找到本体类型，将使用通用提示词") + self._log(f"[剪枝-初始化] 未找到本体类型，将使用通用提示词") # Load Jinja2 template self.template = prompt_env.get_template("extracat_Pruning.jinja2") @@ -119,88 +110,9 @@ class SemanticPruner: # 运行日志：收集关键终端输出，便于写入 JSON self.run_logs: List[str] = [] - def _is_important_message(self, message: ConversationMessage) -> bool: - """基于启发式规则识别重要信息消息，优先保留。 - - 改进版：使用场景特定的模式进行识别 - - 根据 pruning_scene 动态加载对应的识别规则 - - 支持教育、在线服务、外呼三个场景的特定模式 - """ - text = message.msg.strip() - if not text: - return False - - # 使用场景特定的模式 - all_patterns = ( - self.scene_config.high_priority_patterns + - self.scene_config.medium_priority_patterns + - self.scene_config.low_priority_patterns - ) - - for pattern, _ in all_patterns: - if re.search(pattern, text, flags=re.IGNORECASE): - return True - - # 检查是否为问句（以问号结尾或包含疑问词） - if text.endswith("？") or text.endswith("?"): - return True - - # 检查是否包含问句关键词 - if any(keyword in text for keyword in self.scene_config.question_keywords): - return True - - # 检查是否包含决策性关键词 - if any(keyword in text for keyword in self.scene_config.decision_keywords): - return True - - return False - - def _importance_score(self, message: ConversationMessage) -> int: - """为重要消息打分，用于在保留比例内优先保留更关键的内容。 - - 改进版：使用场景特定的权重体系（0-10分） - - 根据场景动态调整不同信息类型的权重 - - 高优先级模式：4-6分 - - 中优先级模式：2-3分 - - 低优先级模式：1分 - """ - text = message.msg.strip() - score = 0 - - # 使用场景特定的权重 - for pattern, weight in self.scene_config.high_priority_patterns: - if re.search(pattern, text, flags=re.IGNORECASE): - score += weight - - for pattern, weight in self.scene_config.medium_priority_patterns: - if re.search(pattern, text, flags=re.IGNORECASE): - score += weight - - for pattern, weight in self.scene_config.low_priority_patterns: - if re.search(pattern, text, flags=re.IGNORECASE): - score += weight - - # 问句加分 - if text.endswith("？") or text.endswith("?"): - score += 2 - - # 包含问句关键词加分 - if any(keyword in text for keyword in self.scene_config.question_keywords): - score += 1 - - # 包含决策性关键词加分 - if any(keyword in text for keyword in self.scene_config.decision_keywords): - score += 2 - - # 长度加分（较长的消息通常包含更多信息） - if len(text) > 50: - score += 1 - if len(text) > 100: - score += 1 - - return min(score, 10) # 最高10分 - - # 情绪/兴趣/爱好安全防线正则已移除，改由 extracat_Pruning.jinja2 提示词中的 preserve_keywords 机制处理 + # _is_important_message 和 _importance_score 已移除： + # 重要性判断完全由 extracat_Pruning.jinja2 提示词 + LLM 的 preserve_tokens 机制承担。 + # LLM 根据注入的本体工程类型语义识别需要保护的内容，无需硬编码正则规则。 def _is_filler_message(self, message: ConversationMessage) -> bool: """检测典型寒暄/口头禅/确认类短消息。 @@ -419,14 +331,12 @@ class SemanticPruner: rendered = self.template.render( pruning_scene=self.config.pruning_scene, - is_builtin_scene=self._is_builtin_scene, ontology_classes=self._ontology_classes, dialog_text=dialog_text, language=self.language ) log_template_rendering("extracat_Pruning.jinja2", { "pruning_scene": self.config.pruning_scene, - "is_builtin_scene": self._is_builtin_scene, "ontology_classes_count": len(self._ontology_classes), "language": self.language }) @@ -491,60 +401,52 @@ class SemanticPruner: # 相关对话不剪枝 return dialog - # 在不相关对话中，识别重要/不重要消息 - tokens = extraction.times + extraction.ids + extraction.amounts + extraction.contacts + extraction.addresses + extraction.keywords + # 在不相关对话中，LLM 已通过 preserve_tokens 标记需要保护的内容 + preserve_tokens = ( + extraction.times + extraction.ids + extraction.amounts + + extraction.contacts + extraction.addresses + extraction.keywords + + extraction.preserve_keywords + ) msgs = dialog.context.msgs - imp_unrel_msgs: List[ConversationMessage] = [] - unimp_unrel_msgs: List[ConversationMessage] = [] + + # 分类：LLM保护 / 填充 / 其他可删 + protected_ids: set = set() + filler_ids: set = set() + deletable: List[ConversationMessage] = [] + for m in msgs: - if self._msg_matches_tokens(m, tokens) or self._is_important_message(m): - imp_unrel_msgs.append(m) + if self._msg_matches_tokens(m, preserve_tokens): + protected_ids.add(id(m)) + elif self._is_filler_message(m): + filler_ids.add(id(m)) else: - unimp_unrel_msgs.append(m) - # 计算总删除目标数量 + deletable.append(m) + + # 计算删除目标 total_unrel = len(msgs) delete_target = int(total_unrel * proportion) if proportion > 0 and total_unrel > 0 and delete_target == 0: delete_target = 1 - imp_del_cap = min(int(len(imp_unrel_msgs) * proportion), len(imp_unrel_msgs)) - unimp_del_cap = len(unimp_unrel_msgs) - max_capacity = max(0, len(msgs) - 1) - max_deletable = min(imp_del_cap + unimp_del_cap, max_capacity) + max_deletable = max(0, total_unrel - 1) delete_target = min(delete_target, max_deletable) - # 删除配额分配 - del_unimp = min(delete_target, unimp_del_cap) - rem = delete_target - del_unimp - del_imp = min(rem, imp_del_cap) - # 选取删除集合 - unimp_delete_ids = [] - imp_delete_ids = [] - if del_unimp > 0: - # 按出现顺序选取前 del_unimp 条不重要消息进行删除（确定性、可复现） - unimp_delete_ids = [id(m) for m in unimp_unrel_msgs[:del_unimp]] - if del_imp > 0: - imp_sorted = sorted(imp_unrel_msgs, key=lambda m: self._importance_score(m)) - imp_delete_ids = [id(m) for m in imp_sorted[:del_imp]] - - # 统计实际删除数量（重要/不重要） - actual_unimp_deleted = 0 - actual_imp_deleted = 0 - kept_msgs = [] - delete_targets = set(unimp_delete_ids) | set(imp_delete_ids) + # 优先删填充，再删其他可删消息（按出现顺序） + to_delete_ids: set = set() for m in msgs: - mid = id(m) - if mid in delete_targets: - if mid in set(unimp_delete_ids) and actual_unimp_deleted < del_unimp: - actual_unimp_deleted += 1 - continue - if mid in set(imp_delete_ids) and actual_imp_deleted < del_imp: - actual_imp_deleted += 1 - continue - kept_msgs.append(m) + if len(to_delete_ids) >= delete_target: + break + if id(m) in filler_ids: + to_delete_ids.add(id(m)) + for m in deletable: + if len(to_delete_ids) >= delete_target: + break + to_delete_ids.add(id(m)) + + kept_msgs = [m for m in msgs if id(m) not in to_delete_ids] if not kept_msgs and msgs: kept_msgs = [msgs[0]] - deleted_total = actual_unimp_deleted + actual_imp_deleted + deleted_total = len(msgs) - len(kept_msgs) self._log( f"[剪枝-对话] 对话ID={dialog.id} 总消息={len(msgs)} 删除目标={delete_target} 实删={deleted_total} 保留={len(kept_msgs)}" ) @@ -616,38 +518,29 @@ class SemanticPruner: if extraction.preserve_keywords: self._log(f" 对话[{d_idx}] LLM抽取到情绪/兴趣保护词: {extraction.preserve_keywords}") - # 消息级分类：每条消息独立判断 - llm_protected_msgs = [] # LLM 保护消息（情绪/兴趣/重要token）：绝对不可删除 - rule_important_msgs = [] # 规则层重要消息（场景规则）：配额不足时可少量删除 - unimportant_msgs = [] # 不重要消息（可删除） + # 消息级分类：LLM保护 / 填充 / 其他可删 + llm_protected_msgs = [] # LLM 保护消息（preserve_tokens 命中）：绝对不可删除 filler_msgs = [] # 填充消息（优先删除） + deletable_msgs = [] # 其余消息（按比例删除） for idx, m in enumerate(msgs): msg_text = m.msg.strip() - # LLM 保护：消息包含 preserve_keywords（情绪/兴趣词）或其他重要 token → 绝对不可删除 if self._msg_matches_tokens(m, preserve_tokens): llm_protected_msgs.append((idx, m)) if should_log_details or idx < self._max_debug_msgs_per_dialog: - self._log(f" [{idx}] '{msg_text[:30]}...' → 重要（LLM保护，不可删）") - # 填充消息（寒暄、表情等） + self._log(f" [{idx}] '{msg_text[:30]}...' → 保护（LLM，不可删）") elif self._is_filler_message(m): filler_msgs.append((idx, m)) if should_log_details or idx < self._max_debug_msgs_per_dialog: self._log(f" [{idx}] '{msg_text[:30]}...' → 填充") - # 规则层重要信息（学号、成绩、时间、金额等） - elif self._is_important_message(m): - rule_important_msgs.append((idx, m)) - if should_log_details or idx < self._max_debug_msgs_per_dialog: - self._log(f" [{idx}] '{msg_text[:30]}...' → 重要（场景规则）") - # 其他消息 else: - unimportant_msgs.append((idx, m)) + deletable_msgs.append((idx, m)) if should_log_details or idx < self._max_debug_msgs_per_dialog: - self._log(f" [{idx}] '{msg_text[:30]}...' → 不重要") + self._log(f" [{idx}] '{msg_text[:30]}...' → 可删") - # important_msgs 仅用于日志统计（兼容下方日志输出） - important_msgs = llm_protected_msgs + rule_important_msgs + # important_msgs 仅用于日志统计 + important_msgs = llm_protected_msgs # 计算删除配额 delete_target = int(original_count * proportion) @@ -658,37 +551,23 @@ class SemanticPruner: max_deletable = max(0, original_count - 1) delete_target = min(delete_target, max_deletable) - # 删除策略：优先删除填充消息，再删除不重要消息 + # 删除策略：优先删填充消息，再按出现顺序删其余可删消息 to_delete_indices = set() - deleted_details = [] # 记录删除的消息详情 - + deleted_details = [] + # 第一步：删除填充消息 - filler_to_delete = min(len(filler_msgs), delete_target) - for i in range(filler_to_delete): - idx, msg = filler_msgs[i] + for idx, msg in filler_msgs: + if len(to_delete_indices) >= delete_target: + break to_delete_indices.add(idx) deleted_details.append(f"[{idx}] 填充: '{msg.msg[:50]}'") - - # 第二步：如果还需要删除，删除不重要消息 - remaining_quota = delete_target - len(to_delete_indices) - if remaining_quota > 0: - unimp_to_delete = min(len(unimportant_msgs), remaining_quota) - for i in range(unimp_to_delete): - idx, msg = unimportant_msgs[i] - to_delete_indices.add(idx) - deleted_details.append(f"[{idx}] 不重要: '{msg.msg[:50]}'") - - # 第三步：如果还需要删除，按重要性分数删除规则层重要消息（LLM保护消息绝对不删） - remaining_quota = delete_target - len(to_delete_indices) - if remaining_quota > 0 and rule_important_msgs: - # 按重要性分数排序（分数低的优先删除） - imp_sorted = sorted(rule_important_msgs, key=lambda x: self._importance_score(x[1])) - imp_to_delete = min(len(imp_sorted), remaining_quota) - for i in range(imp_to_delete): - idx, msg = imp_sorted[i] - to_delete_indices.add(idx) - score = self._importance_score(msg) - deleted_details.append(f"[{idx}] 规则重要(分数{score}): '{msg.msg[:50]}'") + + # 第二步：如果还需要删除，按出现顺序删可删消息 + for idx, msg in deletable_msgs: + if len(to_delete_indices) >= delete_target: + break + to_delete_indices.add(idx) + deleted_details.append(f"[{idx}] 可删: '{msg.msg[:50]}'") # 执行删除 kept_msgs = [] @@ -716,7 +595,7 @@ class SemanticPruner: self._log( f"[剪枝-对话] 对话 {d_idx+1} 总消息={original_count} " - f"(重要={len(important_msgs)} 不重要={len(unimportant_msgs)} 填充={len(filler_msgs)}) " + f"(保护={len(important_msgs)} 填充={len(filler_msgs)} 可删={len(deletable_msgs)}) " f"删除={deleted_count} 保留={len(kept_msgs)}" ) diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py index a79ebea5..8e97163e 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py @@ -1,84 +1,25 @@ """ -场景特定配置 - 为不同场景提供定制化的剪枝规则 +场景特定配置 - 统一填充词库 -功能： -- 场景特定的重要信息识别模式 -- 场景特定的重要性评分权重 -- 场景特定的填充词库 -- 场景特定的问答对识别规则 +重要性判断已完全交由 extracat_Pruning.jinja2 提示词 + LLM preserve_tokens 机制承担。 +本模块仅保留统一填充词库（filler_phrases），用于识别无意义寒暄/表情/口头禅。 +所有场景共用同一份词库，场景差异由 LLM 语义判断处理。 """ -from typing import Dict, List, Set, Tuple +from typing import List, Set from dataclasses import dataclass, field @dataclass class ScenePatterns: - """场景特定的识别模式""" - - # 重要信息的正则模式（优先级从高到低） - high_priority_patterns: List[Tuple[str, int]] = field(default_factory=list) # (pattern, weight) - medium_priority_patterns: List[Tuple[str, int]] = field(default_factory=list) - low_priority_patterns: List[Tuple[str, int]] = field(default_factory=list) - - # 填充词库（无意义对话） + """场景特定的识别模式（仅保留填充词库）""" filler_phrases: Set[str] = field(default_factory=set) - - # 问句关键词（用于识别问答对） - question_keywords: Set[str] = field(default_factory=set) - - # 决策性/承诺性关键词 - decision_keywords: Set[str] = field(default_factory=set) class SceneConfigRegistry: - """场景配置注册表 - 管理所有场景的特定配置""" - - # 基础通用模式（所有场景共享） - BASE_HIGH_PRIORITY = [ - (r"订单号|工单|申请号|编号|ID|账号|账户", 5), - (r"金额|费用|价格|¥|￥|\d+元", 5), - (r"\d{11}", 4), # 手机号 - (r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", 4), # 邮箱 - ] - - BASE_MEDIUM_PRIORITY = [ - (r"\d{4}-\d{1,2}-\d{1,2}", 3), # 日期 - (r"\d{4}年\d{1,2}月\d{1,2}日", 3), - (r"电话|手机号|微信|QQ|联系方式", 3), - (r"地址|地点|位置", 2), - (r"时间|日期|有效期|截止", 2), - (r"今天|明天|后天|昨天|前天", 3), # 相对时间（提高权重） - (r"下周|下月|下年|上周|上月|上年|本周|本月|本年", 3), - (r"今年|去年|明年", 3), - # ---- 情绪内容（所有场景通用，用于情绪提取） ---- - (r"开心|高兴|快乐|兴奋|愉快|幸福|满足|喜悦|欣喜", 4), - (r"难过|悲伤|伤心|痛苦|委屈|失落|沮丧|郁闷|忧郁|绝望", 4), - (r"生气|愤怒|烦躁|焦虑|紧张|害怕|恐惧|担心|担忧|压力", 4), - (r"感动|温暖|感激|感谢|惊喜|期待|憧憬|向往", 3), - (r"无聊|无奈|尴尬|后悔|遗憾|羞愧|惭愧", 3), - (r"好[开高快]心|很[开高快]心|超[开高快]心|非常[开高快]心", 4), - (r"好难过|好伤心|好悲伤|好委屈|好痛苦", 4), - (r"好开心|好高兴|好快乐|好幸福|好感动", 4), - # ---- 兴趣/爱好内容（所有场景通用，用于兴趣提取） ---- - (r"喜欢|热爱|爱好|兴趣|擅长|享受|沉迷|着迷|痴迷", 4), - (r"不喜欢|讨厌|厌恶|反感|排斥", 3), - (r"羽毛球|篮球|足球|排球|乒乓球|网球|棒球|高尔夫", 4), - (r"游泳|跑步|健身|瑜伽|舞蹈|武术|骑行|登山|徒步", 4), - (r"音乐|唱歌|吉他|钢琴|绘画|摄影|书法|手工|烹饪", 4), - (r"游戏|电影|动漫|小说|阅读|旅游|美食|宠物", 3), - ] - - BASE_LOW_PRIORITY = [ - (r"\d{1,2}:\d{2}", 2), # 时间点 HH:MM - (r"\d{1,2}点\d{0,2}分?", 2), # 时间点 X点Y分 或 X点 - (r"上午|下午|中午|晚上|早上|傍晚|凌晨", 2), # 时段（提高权重并扩充） - (r"AM|PM|am|pm", 1), - # ---- 情绪程度副词（辅助情绪识别） ---- - (r"特别|非常|超级|极其|十分|很|好[开高快]|太.*了", 1), - ] - - BASE_FILLERS = { + """场景配置注册表 - 所有场景共用统一填充词库""" + + BASE_FILLERS: Set[str] = { # 基础寒暄 "你好", "您好", "在吗", "在的", "在呢", "嗯", "嗯嗯", "哦", "哦哦", "好的", "好", "行", "可以", "不可以", "谢谢", "多谢", "感谢", @@ -87,7 +28,26 @@ class SceneConfigRegistry: "哈哈", "呵呵", "哈哈哈", "嘿嘿", "嘻嘻", "hiahia", "额", "呃", "啊", "诶", "唉", "哎", "嗯哼", # 确认词 - "是的", "对", "对的", "没错", "嗯嗯", "好嘞", "收到", "明白", "了解", "知道了", + "是的", "对", "对的", "没错", "好嘞", "收到", "明白", "了解", "知道了", + # 服务类套话 + "请问", "请稍等", "稍等", "马上", "立即", + "正在查询", "正在处理", "正在为您", "帮您查一下", + "还有其他问题吗", "还需要什么帮助", "很高兴为您服务", + "感谢您的耐心等待", "抱歉让您久等了", + "已记录", "已反馈", "已转接", "已升级", + "祝您生活愉快", "欢迎下次咨询", + # 外呼套话 + "喂", "hello", "打扰了", "不好意思", + "方便接电话吗", "现在方便吗", "占用您一点时间", + "我是", "我们是", "我们公司", "我们这边", + "了解一下", "介绍一下", "简单说一下", + "考虑考虑", "想一想", "再说", "再看看", + "不需要", "不感兴趣", "没兴趣", "不用了", + "没问题", "那就这样", "再联系", "回头聊", "有需要再说", + # 教育场景套话 + "老师好", "同学们好", "上课", "下课", "起立", "坐下", + "举手", "请坐", "很好", "不错", "继续", + "下一个", "下一题", "下一位", "还有吗", "还有问题吗", # 标点和符号 "。。。", "...", "???", "？？？", "!!!", "！！！", # 表情符号 @@ -99,246 +59,8 @@ class SceneConfigRegistry: "hhh", "hhhh", "2333", "666", "gg", "ok", "OK", "okok", "emmm", "emm", "em", "mmp", "wtf", "omg", } - - BASE_QUESTION_KEYWORDS = { - "什么", "为什么", "怎么", "如何", "哪里", "哪个", "谁", "多少", "几点", "何时", "吗" - } - - BASE_DECISION_KEYWORDS = { - "必须", "一定", "务必", "需要", "要求", "规定", "应该", - "承诺", "保证", "确保", "负责", "同意", "答应" - } - + @classmethod - def get_education_config(cls) -> ScenePatterns: - """教育场景配置""" - return ScenePatterns( - high_priority_patterns=cls.BASE_HIGH_PRIORITY + [ - # 成绩相关（最高优先级） - (r"成绩|分数|得分|满分|及格|不及格", 6), - (r"GPA|绩点|学分|平均分", 6), - (r"\d+分|\d+\.?\d*分", 5), # 具体分数 - (r"排名|名次|第.{1,3}名", 5), # 支持"第三名"、"第1名"等 - - # 学籍信息 - (r"学号|学生证|教师工号|工号", 5), - (r"班级|年级|专业|院系", 4), - - # 课程相关 - (r"课程|科目|学科|必修|选修", 4), - (r"教材|课本|教科书|参考书", 4), - (r"章节|第.{1,3}章|第.{1,3}节", 3), # 支持"第三章"、"第1章"等 - - # 学科内容（新增） - (r"微积分|导数|积分|函数|极限|微分", 4), - (r"代数|几何|三角|概率|统计", 4), - (r"物理|化学|生物|历史|地理", 4), - (r"英语|语文|数学|政治|哲学", 4), - (r"定义|定理|公式|概念|原理|法则", 3), - (r"例题|解题|证明|推导|计算", 3), - ], - medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY + [ - # 教学活动 - (r"作业|练习|习题|题目", 3), - (r"考试|测验|测试|考核|期中|期末", 3), - (r"上课|下课|课堂|讲课", 2), - (r"提问|回答|发言|讨论", 2), - (r"问一下|请教|咨询|询问", 2), # 新增：问询相关 - (r"理解|明白|懂|掌握|学会", 2), # 新增：学习状态 - - # 时间安排 - (r"课表|课程表|时间表", 3), - (r"第.{1,3}节课|第.{1,3}周", 2), # 支持"第三节课"、"第1周"等 - ], - low_priority_patterns=cls.BASE_LOW_PRIORITY + [ - (r"老师|教师|同学|学生", 1), - (r"教室|实验室|图书馆", 1), - ], - filler_phrases=cls.BASE_FILLERS | { - # 教育场景特有填充词（移除了"明白了"、"懂了"、"不懂"等，这些在教育场景中有意义） - "老师好", "同学们好", "上课", "下课", "起立", "坐下", - "举手", "请坐", "很好", "不错", "继续", - "下一个", "下一题", "下一位", "还有吗", "还有问题吗", - }, - question_keywords=cls.BASE_QUESTION_KEYWORDS | { - "为啥", "咋", "咋办", "怎样", "如何做", - "能不能", "可不可以", "行不行", "对不对", "是不是", - }, - decision_keywords=cls.BASE_DECISION_KEYWORDS | { - "必考", "重点", "考点", "难点", "关键", - "记住", "背诵", "掌握", "理解", "复习", - } - ) - - @classmethod - def get_online_service_config(cls) -> ScenePatterns: - """在线服务场景配置""" - return ScenePatterns( - high_priority_patterns=cls.BASE_HIGH_PRIORITY + [ - # 工单相关（最高优先级） - (r"工单号|工单编号|ticket|TK\d+", 6), - (r"工单状态|处理中|已解决|已关闭|待处理", 5), - (r"优先级|紧急|高优先级|P0|P1|P2", 5), - - # 产品信息 - (r"产品型号|型号|SKU|产品编号", 5), - (r"序列号|SN|设备号", 5), - (r"版本号|软件版本|固件版本", 4), - - # 问题描述 - (r"故障|错误|异常|bug|问题", 4), - (r"错误代码|故障代码|error code", 5), - (r"无法|不能|失败|报错", 3), - ], - medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY + [ - # 服务相关 - (r"退款|退货|换货|补发", 4), - (r"发票|收据|凭证", 3), - (r"物流|快递|运单号", 3), - (r"保修|质保|售后", 3), - - # 时效相关 - (r"SLA|响应时间|处理时长", 4), - (r"超时|延迟|等待", 2), - ], - low_priority_patterns=cls.BASE_LOW_PRIORITY + [ - (r"客服|工程师|技术支持", 1), - (r"用户|客户|会员", 1), - ], - filler_phrases=cls.BASE_FILLERS | { - # 在线服务特有填充词 - "您好", "请问", "请稍等", "稍等", "马上", "立即", - "正在查询", "正在处理", "正在为您", "帮您查一下", - "还有其他问题吗", "还需要什么帮助", "很高兴为您服务", - "感谢您的耐心等待", "抱歉让您久等了", - "已记录", "已反馈", "已转接", "已升级", - "祝您生活愉快", "再见", "欢迎下次咨询", - }, - question_keywords=cls.BASE_QUESTION_KEYWORDS | { - "能否", "可否", "是否", "有没有", "能不能", - "怎么办", "如何处理", "怎么解决", - }, - decision_keywords=cls.BASE_DECISION_KEYWORDS | { - "立即处理", "马上解决", "尽快", "优先", - "升级", "转接", "派单", "跟进", - "补偿", "赔偿", "退款", "换货", - } - ) - - @classmethod - def get_outbound_config(cls) -> ScenePatterns: - """外呼场景配置""" - return ScenePatterns( - high_priority_patterns=cls.BASE_HIGH_PRIORITY + [ - # 意向相关（最高优先级） - (r"意向|意愿|兴趣|感兴趣", 6), - (r"A类|B类|C类|D类|高意向|低意向", 6), - (r"成交|签约|下单|购买|确认", 6), - - # 联系信息（外呼场景中更重要） - (r"预约|约定|安排|确定时间", 5), - (r"下次联系|回访|跟进", 5), - (r"方便|有空|可以|时间", 4), - - # 通话状态 - (r"接通|未接通|占线|关机|停机", 4), - (r"通话时长|通话时间", 3), - ], - medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY + [ - # 客户信息 - (r"姓名|称呼|先生|女士", 3), - (r"公司|单位|职位|职务", 3), - (r"需求|要求|期望", 3), - - # 跟进状态 - (r"跟进状态|进展|进度", 3), - (r"已联系|待联系|联系中", 2), - (r"拒绝|不感兴趣|考虑|再说", 3), - ], - low_priority_patterns=cls.BASE_LOW_PRIORITY + [ - (r"销售|客户经理|业务员", 1), - (r"产品|服务|方案", 1), - ], - filler_phrases=cls.BASE_FILLERS | { - # 外呼场景特有填充词 - "您好", "喂", "hello", "打扰了", "不好意思", - "方便接电话吗", "现在方便吗", "占用您一点时间", - "我是", "我们是", "我们公司", "我们这边", - "了解一下", "介绍一下", "简单说一下", - "考虑考虑", "想一想", "再说", "再看看", - "不需要", "不感兴趣", "没兴趣", "不用了", - "好的", "行", "可以", "没问题", "那就这样", - "再联系", "回头聊", "有需要再说", - }, - question_keywords=cls.BASE_QUESTION_KEYWORDS | { - "有没有", "需不需要", "要不要", "考虑不考虑", - "了解吗", "知道吗", "听说过吗", - "方便吗", "有空吗", "在吗", - }, - decision_keywords=cls.BASE_DECISION_KEYWORDS | { - "确定", "决定", "选择", "购买", "下单", - "预约", "安排", "约定", "确认", - "跟进", "回访", "联系", "沟通", - } - ) - - @classmethod - def get_config(cls, scene: str, fallback_to_generic: bool = True) -> ScenePatterns: - """根据场景名称获取配置 - - Args: - scene: 场景名称 ('education', 'online_service', 'outbound' 或其他) - fallback_to_generic: 如果场景不存在，是否降级到通用配置 - - Returns: - 对应场景的配置，如果场景不存在： - - fallback_to_generic=True: 返回通用配置（仅基础规则） - - fallback_to_generic=False: 抛出异常 - """ - scene_map = { - 'education': cls.get_education_config, - 'online_service': cls.get_online_service_config, - 'outbound': cls.get_outbound_config, - } - - if scene in scene_map: - return scene_map[scene]() - - if fallback_to_generic: - # 返回通用配置（仅包含基础规则，不包含场景特定规则） - return cls.get_generic_config() - else: - raise ValueError(f"不支持的场景: {scene}，支持的场景: {list(scene_map.keys())}") - - @classmethod - def get_generic_config(cls) -> ScenePatterns: - """通用场景配置 - 仅包含基础规则，适用于未定义的场景 - - 这是一个保守的配置，只使用最通用的规则，避免误删重要信息 - """ - return ScenePatterns( - high_priority_patterns=cls.BASE_HIGH_PRIORITY, - medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY, - low_priority_patterns=cls.BASE_LOW_PRIORITY, - filler_phrases=cls.BASE_FILLERS, - question_keywords=cls.BASE_QUESTION_KEYWORDS, - decision_keywords=cls.BASE_DECISION_KEYWORDS - ) - - @classmethod - def get_all_scenes(cls) -> List[str]: - """获取所有预定义场景的列表""" - return ['education', 'online_service', 'outbound'] - - @classmethod - def is_scene_supported(cls, scene: str) -> bool: - """检查场景是否有专门的配置支持 - - Args: - scene: 场景名称 - - Returns: - True: 有专门配置 - False: 将使用通用配置 - """ - return scene in cls.get_all_scenes() + def get_config(cls, scene: str = "") -> ScenePatterns: + """所有场景统一返回同一份填充词库""" + return ScenePatterns(filler_phrases=cls.BASE_FILLERS) diff --git a/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 b/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 index 47b3badb..e204b7f9 100644 --- a/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 +++ b/api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2 @@ -1,6 +1,6 @@ {# 对话级抽取与相关性判定模板（用于剪枝加速） - 输入：pruning_scene, is_builtin_scene, ontology_classes, dialog_text, language + 输入：pruning_scene, ontology_classes, dialog_text, language 输出：严格 JSON（不要包含任何多余文本），字段： - is_related: bool，是否与所选场景相关 - times: [string]，从对话中抽取的时间相关文本（日期、时间、时间段、有效期等） @@ -17,44 +17,22 @@ - 仅输出上述键；避免多余解释或字段。 #} -{# ── 内置场景的固定说明 ── #} -{% set builtin_scene_instructions = { - 'education': { - 'zh': '教育场景：教学、课程、考试、作业、老师/学生互动、学习资源、学校管理等。', - 'en': 'Education Scenario: Teaching, courses, exams, homework, teacher/student interaction, learning resources, school management, etc.' - }, - 'online_service': { - 'zh': '在线客服场景：客户咨询、问题排查、服务工单、售后支持、订单/退款、工单升级等。', - 'en': 'Online Service Scenario: Customer inquiries, troubleshooting, service tickets, after-sales support, orders/refunds, ticket escalation, etc.' - }, - 'outbound': { - 'zh': '外呼场景：电话外呼、邀约、调研问卷、线索跟进、对话脚本、回访记录等。', - 'en': 'Outbound Scenario: Outbound calls, invitations, survey questionnaires, lead follow-up, call scripts, follow-up records, etc.' - } -} %} - -{# ── 确定最终使用的场景说明 ── #} -{% if is_builtin_scene %} - {% set scene_key = pruning_scene %} - {% if scene_key not in builtin_scene_instructions %}{% set scene_key = 'education' %}{% endif %} - {% set instruction = builtin_scene_instructions[scene_key][language] if language in ['zh', 'en'] else builtin_scene_instructions[scene_key]['zh'] %} - {% set custom_types_str = '' %} -{% else %} - {% if ontology_classes and ontology_classes | length > 0 %} - {% if language == 'en' %} - {% set custom_types_str = ontology_classes | join(', ') %} - {% set instruction = 'Custom scene "' ~ pruning_scene ~ '": The dialogue is related to this scene if it involves any of the following entity types: ' ~ custom_types_str ~ '.' %} - {% else %} - {% set custom_types_str = ontology_classes | join('、') %} - {% set instruction = '自定义场景「' ~ pruning_scene ~ '」：对话涉及以下任意实体类型时视为相关：' ~ custom_types_str ~ '。' %} - {% endif %} +{# ── 确定场景说明 ── #} +{% if ontology_classes and ontology_classes | length > 0 %} + {% if language == 'en' %} + {% set custom_types_str = ontology_classes | join(', ') %} + {% set instruction = 'Scene "' ~ pruning_scene ~ '": The dialogue is related to this scene if it involves any of the following entity types: ' ~ custom_types_str ~ '.' %} {% else %} - {% if language == 'en' %} - {% set instruction = 'Custom scene "' ~ pruning_scene ~ '": Determine whether the dialogue content is relevant to this scene based on overall context.' %} - {% else %} - {% set instruction = '自定义场景「' ~ pruning_scene ~ '」：根据对话整体内容判断是否与该场景相关。' %} - {% endif %} + {% set custom_types_str = ontology_classes | join('、') %} + {% set instruction = '场景「' ~ pruning_scene ~ '」：对话涉及以下任意实体类型时视为相关：' ~ custom_types_str ~ '。' %} + {% endif %} +{% else %} + {% if language == 'en' %} {% set custom_types_str = '' %} + {% set instruction = 'Scene "' ~ pruning_scene ~ '": Determine whether the dialogue content is relevant to this scene based on overall context.' %} + {% else %} + {% set custom_types_str = '' %} + {% set instruction = '场景「' ~ pruning_scene ~ '」：根据对话整体内容判断是否与该场景相关。' %} {% endif %} {% endif %} @@ -64,7 +42,7 @@ 2. 从对话中抽取所有需要保留的重要信息片段。 场景说明：{{ instruction }} -{% if not is_builtin_scene and custom_types_str %} +{% if custom_types_str %} 重要提示：只要对话中出现与上述实体类型（{{ custom_types_str }}）相关的内容，即判定为相关（is_related=true）。 {% endif %} @@ -118,7 +96,7 @@ You are a dialogue content analysis assistant. Please analyze the full dialogue 2. Extract all important information fragments that must be preserved. Scenario Description: {{ instruction }} -{% if not is_builtin_scene and custom_types_str %} +{% if custom_types_str %} Important: If the dialogue contains content related to any of the entity types above ({{ custom_types_str }}), mark it as relevant (is_related=true). {% endif %} diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index 00757f8c..4d67673f 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -107,29 +107,19 @@ def _validate_config_id(config_id, db: Session = None): ) -# 专门场景的内置 key 集合，直接从 SceneConfigRegistry 派生，避免重复维护 -# 使用懒加载函数避免模块级循环导入 -def _get_builtin_pruning_scenes() -> set: - from app.core.memory.storage_services.extraction_engine.data_preprocessing.scene_config import SceneConfigRegistry - return set(SceneConfigRegistry.get_all_scenes()) - - def _load_ontology_classes(db: Session, scene_id, pruning_scene: Optional[str]) -> Optional[list]: - """当 pruning_scene 不是内置场景时，从 ontology_class 表加载类型名称列表。 + """从 ontology_class 表加载场景类型名称列表，用于注入提示词。 Args: db: 数据库会话 scene_id: 本体场景 UUID - pruning_scene: 语义剪枝场景名称 + pruning_scene: 语义剪枝场景名称（保留参数，暂未使用） Returns: - class_name 字符串列表，或 None（内置场景 / 无数据时） + class_name 字符串列表，或 None（无数据时） """ if not scene_id: return None - # 内置场景走 SceneConfigRegistry，不需要注入类型列表 - if pruning_scene in _get_builtin_pruning_scenes(): - return None try: from app.repositories.ontology_class_repository import OntologyClassRepository repo = OntologyClassRepository(db) diff --git a/api/app/services/pilot_run_service.py b/api/app/services/pilot_run_service.py index 5d00d8a5..b63bc0db 100644 --- a/api/app/services/pilot_run_service.py +++ b/api/app/services/pilot_run_service.py @@ -120,7 +120,8 @@ async def run_pilot_extraction( "pruning_switch": memory_config.pruning_enabled, "pruning_scene": memory_config.pruning_scene, "pruning_threshold": memory_config.pruning_threshold, - "llm_model_id": str(memory_config.llm_model_id), + "scene_id": str(memory_config.scene_id) if memory_config.scene_id else None, + "ontology_classes": memory_config.ontology_classes, } config = PruningConfig(**pruning_config_dict) diff --git a/redbear-mem-benchmark b/redbear-mem-benchmark index 8494e824..c3bbc693 160000 --- a/redbear-mem-benchmark +++ b/redbear-mem-benchmark @@ -1 +1 @@ -Subproject commit 8494e82498cb99c70ac67a64a544ff872432363a +Subproject commit c3bbc6931c570e6fac88c0b00658b4f08dc2ac77 From 86c51559bb4e25e974211dcaa6705c99009a48e9 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 19:06:50 +0800 Subject: [PATCH 049/196] [add] Remove unused protected_ids; cap delete_target by actual deletable count. --- .../data_preprocessing/data_pruning.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py index a1953316..28f7d8e0 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py @@ -409,14 +409,13 @@ class SemanticPruner: ) msgs = dialog.context.msgs - # 分类：LLM保护 / 填充 / 其他可删 - protected_ids: set = set() + # 分类：填充 / 其他可删（LLM保护消息通过不加入任何桶来隐式保护） filler_ids: set = set() deletable: List[ConversationMessage] = [] for m in msgs: if self._msg_matches_tokens(m, preserve_tokens): - protected_ids.add(id(m)) + pass # 保护消息：不加入任何桶，不会被删除 elif self._is_filler_message(m): filler_ids.add(id(m)) else: @@ -427,7 +426,7 @@ class SemanticPruner: delete_target = int(total_unrel * proportion) if proportion > 0 and total_unrel > 0 and delete_target == 0: delete_target = 1 - max_deletable = max(0, total_unrel - 1) + max_deletable = min(len(filler_ids) + len(deletable), max(0, total_unrel - 1)) delete_target = min(delete_target, max_deletable) # 优先删填充，再删其他可删消息（按出现顺序） @@ -447,8 +446,11 @@ class SemanticPruner: kept_msgs = [msgs[0]] deleted_total = len(msgs) - len(kept_msgs) + protected_count = len(msgs) - len(filler_ids) - len(deletable) self._log( - f"[剪枝-对话] 对话ID={dialog.id} 总消息={len(msgs)} 删除目标={delete_target} 实删={deleted_total} 保留={len(kept_msgs)}" + f"[剪枝-对话] 对话ID={dialog.id} 总消息={len(msgs)} " + f"(保护={protected_count} 填充={len(filler_ids)} 可删={len(deletable)}) " + f"删除目标={delete_target} 实删={deleted_total} 保留={len(kept_msgs)}" ) dialog.context = ConversationContext(msgs=kept_msgs) From 4f5ee24bc516daefc9611923380a57f0f58a3cf4 Mon Sep 17 00:00:00 2001 From: Mark Date: Wed, 11 Mar 2026 10:45:07 +0800 Subject: [PATCH 050/196] [add] i18n support zh,en --- api/app/controllers/__init__.py | 2 + api/app/controllers/auth_controller.py | 33 +- api/app/controllers/i18n_controller.py | 833 ++++++++++++++++++++ api/app/controllers/user_controller.py | 88 ++- api/app/controllers/workspace_controller.py | 108 ++- api/app/core/config.py | 38 + api/app/i18n/README.md | 61 ++ api/app/i18n/__init__.py | 113 +++ api/app/i18n/cache.py | 291 +++++++ api/app/i18n/dependencies.py | 158 ++++ api/app/i18n/exceptions.py | 495 ++++++++++++ api/app/i18n/loader.py | 199 +++++ api/app/i18n/logger.py | 382 +++++++++ api/app/i18n/metrics.py | 337 ++++++++ api/app/i18n/middleware.py | 202 +++++ api/app/i18n/serializers.py | 219 +++++ api/app/i18n/service.py | 370 +++++++++ api/app/locales/en/README.md | 26 + api/app/locales/en/auth.json | 55 ++ api/app/locales/en/common.json | 132 ++++ api/app/locales/en/enums.json | 132 ++++ api/app/locales/en/errors.json | 138 ++++ api/app/locales/en/i18n.json | 27 + api/app/locales/en/tenant.json | 63 ++ api/app/locales/en/users.json | 72 ++ api/app/locales/en/workspace.json | 44 ++ api/app/locales/zh/README.md | 26 + api/app/locales/zh/auth.json | 55 ++ api/app/locales/zh/common.json | 132 ++++ api/app/locales/zh/enums.json | 132 ++++ api/app/locales/zh/errors.json | 138 ++++ api/app/locales/zh/i18n.json | 27 + api/app/locales/zh/tenant.json | 63 ++ api/app/locales/zh/users.json | 72 ++ api/app/locales/zh/workspace.json | 44 ++ api/app/main.py | 196 ++++- api/app/models/tenant_model.py | 8 +- api/app/models/user_model.py | 5 +- api/app/schemas/i18n_schema.py | 73 ++ api/app/schemas/tenant_schema.py | 70 +- api/app/schemas/user_schema.py | 11 + api/app/services/auth_service.py | 11 +- api/app/services/tenant_service.py | 53 +- api/app/services/user_service.py | 71 +- 44 files changed, 5730 insertions(+), 75 deletions(-) create mode 100644 api/app/controllers/i18n_controller.py create mode 100644 api/app/i18n/README.md create mode 100644 api/app/i18n/__init__.py create mode 100644 api/app/i18n/cache.py create mode 100644 api/app/i18n/dependencies.py create mode 100644 api/app/i18n/exceptions.py create mode 100644 api/app/i18n/loader.py create mode 100644 api/app/i18n/logger.py create mode 100644 api/app/i18n/metrics.py create mode 100644 api/app/i18n/middleware.py create mode 100644 api/app/i18n/serializers.py create mode 100644 api/app/i18n/service.py create mode 100644 api/app/locales/en/README.md create mode 100644 api/app/locales/en/auth.json create mode 100644 api/app/locales/en/common.json create mode 100644 api/app/locales/en/enums.json create mode 100644 api/app/locales/en/errors.json create mode 100644 api/app/locales/en/i18n.json create mode 100644 api/app/locales/en/tenant.json create mode 100644 api/app/locales/en/users.json create mode 100644 api/app/locales/en/workspace.json create mode 100644 api/app/locales/zh/README.md create mode 100644 api/app/locales/zh/auth.json create mode 100644 api/app/locales/zh/common.json create mode 100644 api/app/locales/zh/enums.json create mode 100644 api/app/locales/zh/errors.json create mode 100644 api/app/locales/zh/i18n.json create mode 100644 api/app/locales/zh/tenant.json create mode 100644 api/app/locales/zh/users.json create mode 100644 api/app/locales/zh/workspace.json create mode 100644 api/app/schemas/i18n_schema.py diff --git a/api/app/controllers/__init__.py b/api/app/controllers/__init__.py index 85550f94..585de2ed 100644 --- a/api/app/controllers/__init__.py +++ b/api/app/controllers/__init__.py @@ -16,6 +16,7 @@ from . import ( file_controller, file_storage_controller, home_page_controller, + i18n_controller, implicit_memory_controller, knowledge_controller, knowledgeshare_controller, @@ -94,5 +95,6 @@ manager_router.include_router(memory_working_controller.router) manager_router.include_router(file_storage_controller.router) manager_router.include_router(ontology_controller.router) manager_router.include_router(skill_controller.router) +manager_router.include_router(i18n_controller.router) __all__ = ["manager_router"] diff --git a/api/app/controllers/auth_controller.py b/api/app/controllers/auth_controller.py index 708cbaa2..2cc72a3b 100644 --- a/api/app/controllers/auth_controller.py +++ b/api/app/controllers/auth_controller.py @@ -1,4 +1,5 @@ from datetime import datetime, timedelta, timezone +from typing import Callable from fastapi import APIRouter, Depends from sqlalchemy.orm import Session @@ -16,6 +17,7 @@ from app.core.exceptions import BusinessException from app.core.error_codes import BizCode from app.dependencies import get_current_user, oauth2_scheme from app.models.user_model import User +from app.i18n.dependencies import get_translator # 获取专用日志器 auth_logger = get_auth_logger() @@ -26,7 +28,8 @@ router = APIRouter(tags=["Authentication"]) @router.post("/token", response_model=ApiResponse) async def login_for_access_token( form_data: TokenRequest, - db: Session = Depends(get_db) + db: Session = Depends(get_db), + t: Callable = Depends(get_translator) ): """用户登录获取token""" auth_logger.info(f"用户登录请求: {form_data.email}") @@ -40,10 +43,10 @@ async def login_for_access_token( invite_info = workspace_service.validate_invite_token(db, form_data.invite) if not invite_info.is_valid: - raise BusinessException("邀请码无效或已过期", code=BizCode.BAD_REQUEST) + raise BusinessException(t("auth.invite.invalid"), code=BizCode.BAD_REQUEST) if invite_info.email != form_data.email: - raise BusinessException("邀请邮箱与登录邮箱不匹配", code=BizCode.BAD_REQUEST) + raise BusinessException(t("auth.invite.email_mismatch"), code=BizCode.BAD_REQUEST) auth_logger.info(f"邀请码验证成功: workspace={invite_info.workspace_name}") try: # 尝试认证用户 @@ -69,7 +72,7 @@ async def login_for_access_token( elif e.code == BizCode.PASSWORD_ERROR: # 用户存在但密码错误 auth_logger.warning(f"接受邀请失败，密码验证错误: {form_data.email}") - raise BusinessException("接受邀请失败，密码验证错误", BizCode.LOGIN_FAILED) + raise BusinessException(t("auth.invite.password_verification_failed"), BizCode.LOGIN_FAILED) else: # 其他认证失败情况，直接抛出 raise @@ -82,7 +85,7 @@ async def login_for_access_token( except BusinessException as e: # 其他认证失败情况，直接抛出 - raise BusinessException(e.message,BizCode.LOGIN_FAILED) + raise BusinessException(e.message, BizCode.LOGIN_FAILED) # 创建 tokens access_token, access_token_id = security.create_access_token(subject=user.id) @@ -110,14 +113,15 @@ async def login_for_access_token( expires_at=access_expires_at, refresh_expires_at=refresh_expires_at ), - msg="登录成功" + msg=t("auth.login.success") ) @router.post("/refresh", response_model=ApiResponse) async def refresh_token( refresh_request: RefreshTokenRequest, - db: Session = Depends(get_db) + db: Session = Depends(get_db), + t: Callable = Depends(get_translator) ): """刷新token""" auth_logger.info("收到token刷新请求") @@ -125,18 +129,18 @@ async def refresh_token( # 验证 refresh token userId = security.verify_token(refresh_request.refresh_token, "refresh") if not userId: - raise BusinessException("无效的refresh token", code=BizCode.TOKEN_INVALID) + raise BusinessException(t("auth.token.invalid_refresh_token"), code=BizCode.TOKEN_INVALID) # 检查用户是否存在 user = auth_service.get_user_by_id(db, userId) if not user: - raise BusinessException("用户不存在", code=BizCode.USER_NOT_FOUND) + raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NOT_FOUND) # 检查 refresh token 黑名单 if settings.ENABLE_SINGLE_SESSION: refresh_token_id = security.get_token_id(refresh_request.refresh_token) if refresh_token_id and await SessionService.is_token_blacklisted(refresh_token_id): - raise BusinessException("Refresh token已失效", code=BizCode.TOKEN_BLACKLISTED) + raise BusinessException(t("auth.token.refresh_token_blacklisted"), code=BizCode.TOKEN_BLACKLISTED) # 生成新 tokens new_access_token, new_access_token_id = security.create_access_token(subject=user.id) @@ -167,7 +171,7 @@ async def refresh_token( expires_at=access_expires_at, refresh_expires_at=refresh_expires_at ), - msg="token刷新成功" + msg=t("auth.token.refresh_success") ) @@ -175,14 +179,15 @@ async def refresh_token( async def logout( token: str = Depends(oauth2_scheme), current_user: User = Depends(get_current_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), + t: Callable = Depends(get_translator) ): """登出当前用户：加入token黑名单并清理会话""" auth_logger.info(f"用户 {current_user.username} 请求登出") token_id = security.get_token_id(token) if not token_id: - raise BusinessException("无效的access token", code=BizCode.TOKEN_INVALID) + raise BusinessException(t("auth.token.invalid"), code=BizCode.TOKEN_INVALID) # 加入黑名单 await SessionService.blacklist_token(token_id) @@ -192,5 +197,5 @@ async def logout( await SessionService.clear_user_session(current_user.username) auth_logger.info(f"用户 {current_user.username} 登出成功") - return success(msg="登出成功") + return success(msg=t("auth.logout.success")) diff --git a/api/app/controllers/i18n_controller.py b/api/app/controllers/i18n_controller.py new file mode 100644 index 00000000..5dd07797 --- /dev/null +++ b/api/app/controllers/i18n_controller.py @@ -0,0 +1,833 @@ +""" +I18n Management API Controller + +This module provides management APIs for: +- Language management (list, get, add, update languages) +- Translation management (get, update, reload translations) +""" + +from fastapi import APIRouter, Depends, HTTPException, status +from sqlalchemy.orm import Session +from typing import Callable, Optional + +from app.core.logging_config import get_api_logger +from app.core.response_utils import success +from app.db import get_db +from app.dependencies import get_current_user, get_current_superuser +from app.i18n.dependencies import get_translator +from app.i18n.service import get_translation_service +from app.models.user_model import User +from app.schemas.i18n_schema import ( + LanguageInfo, + LanguageListResponse, + LanguageCreateRequest, + LanguageUpdateRequest, + TranslationResponse, + TranslationUpdateRequest, + MissingTranslationsResponse, + ReloadResponse +) +from app.schemas.response_schema import ApiResponse + +api_logger = get_api_logger() + +router = APIRouter( + prefix="/i18n", + tags=["I18n Management"], +) + + +# ============================================================================ +# Language Management APIs +# ============================================================================ + +@router.get("/languages", response_model=ApiResponse) +def get_languages( + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_user) +): + """ + Get list of all supported languages. + + Returns: + List of language information including code, name, and status + """ + api_logger.info(f"Get languages request from user: {current_user.username}") + + from app.core.config import settings + translation_service = get_translation_service() + + # Get available locales from translation service + available_locales = translation_service.get_available_locales() + + # Build language info list + languages = [] + for locale in available_locales: + is_default = locale == settings.I18N_DEFAULT_LANGUAGE + is_enabled = locale in settings.I18N_SUPPORTED_LANGUAGES + + # Get native names + native_names = { + "zh": "中文（简体）", + "en": "English", + "ja": "日本語", + "ko": "한국어", + "fr": "Français", + "de": "Deutsch", + "es": "Español" + } + + language_info = LanguageInfo( + code=locale, + name=f"{locale.upper()}", + native_name=native_names.get(locale, locale), + is_enabled=is_enabled, + is_default=is_default + ) + languages.append(language_info) + + response = LanguageListResponse(languages=languages) + + api_logger.info(f"Returning {len(languages)} languages") + return success(data=response.dict(), msg=t("common.success.retrieved")) + + +@router.get("/languages/{locale}", response_model=ApiResponse) +def get_language( + locale: str, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_user) +): + """ + Get information about a specific language. + + Args: + locale: Language code (e.g., 'zh', 'en') + + Returns: + Language information + """ + api_logger.info(f"Get language info request: locale={locale}, user={current_user.username}") + + from app.core.config import settings + translation_service = get_translation_service() + + # Check if locale exists + available_locales = translation_service.get_available_locales() + if locale not in available_locales: + api_logger.warning(f"Language not found: {locale}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=t("i18n.language.not_found", locale=locale) + ) + + # Build language info + is_default = locale == settings.I18N_DEFAULT_LANGUAGE + is_enabled = locale in settings.I18N_SUPPORTED_LANGUAGES + + native_names = { + "zh": "中文（简体）", + "en": "English", + "ja": "日本語", + "ko": "한국어", + "fr": "Français", + "de": "Deutsch", + "es": "Español" + } + + language_info = LanguageInfo( + code=locale, + name=f"{locale.upper()}", + native_name=native_names.get(locale, locale), + is_enabled=is_enabled, + is_default=is_default + ) + + api_logger.info(f"Returning language info for: {locale}") + return success(data=language_info.dict(), msg=t("common.success.retrieved")) + + +@router.post("/languages", response_model=ApiResponse) +def add_language( + request: LanguageCreateRequest, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Add a new language (admin only). + + Note: This endpoint validates the request but actual language addition + requires creating translation files in the locales directory. + + Args: + request: Language creation request + + Returns: + Success message + """ + api_logger.info( + f"Add language request: code={request.code}, admin={current_user.username}" + ) + + from app.core.config import settings + translation_service = get_translation_service() + + # Check if language already exists + available_locales = translation_service.get_available_locales() + if request.code in available_locales: + api_logger.warning(f"Language already exists: {request.code}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=t("i18n.language.already_exists", locale=request.code) + ) + + # Note: Actual language addition requires creating translation files + # This endpoint serves as a validation and documentation point + + api_logger.info( + f"Language addition validated: {request.code}. " + "Translation files need to be created manually." + ) + + return success( + msg=t( + "i18n.language.add_instructions", + locale=request.code, + dir=settings.I18N_CORE_LOCALES_DIR + ) + ) + + +@router.put("/languages/{locale}", response_model=ApiResponse) +def update_language( + locale: str, + request: LanguageUpdateRequest, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Update language configuration (admin only). + + Note: This endpoint validates the request but actual configuration + changes require updating environment variables or config files. + + Args: + locale: Language code + request: Language update request + + Returns: + Success message + """ + api_logger.info( + f"Update language request: locale={locale}, admin={current_user.username}" + ) + + translation_service = get_translation_service() + + # Check if language exists + available_locales = translation_service.get_available_locales() + if locale not in available_locales: + api_logger.warning(f"Language not found: {locale}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=t("i18n.language.not_found", locale=locale) + ) + + # Note: Actual configuration changes require updating settings + # This endpoint serves as a validation and documentation point + + api_logger.info( + f"Language update validated: {locale}. " + "Configuration changes require environment variable updates." + ) + + return success(msg=t("i18n.language.update_instructions", locale=locale)) + + +# ============================================================================ +# Translation Management APIs +# ============================================================================ + +@router.get("/translations", response_model=ApiResponse) +def get_all_translations( + locale: Optional[str] = None, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_user) +): + """ + Get all translations for all or specific locale. + + Args: + locale: Optional locale filter + + Returns: + All translations organized by locale and namespace + """ + api_logger.info( + f"Get all translations request: locale={locale}, user={current_user.username}" + ) + + translation_service = get_translation_service() + + if locale: + # Get translations for specific locale + available_locales = translation_service.get_available_locales() + if locale not in available_locales: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=t("i18n.language.not_found", locale=locale) + ) + + translations = { + locale: translation_service._cache.get(locale, {}) + } + else: + # Get all translations + translations = translation_service._cache + + response = TranslationResponse(translations=translations) + + api_logger.info(f"Returning translations for: {locale or 'all locales'}") + return success(data=response.dict(), msg=t("common.success.retrieved")) + + +@router.get("/translations/{locale}", response_model=ApiResponse) +def get_locale_translations( + locale: str, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_user) +): + """ + Get all translations for a specific locale. + + Args: + locale: Language code + + Returns: + All translations for the locale organized by namespace + """ + api_logger.info( + f"Get locale translations request: locale={locale}, user={current_user.username}" + ) + + translation_service = get_translation_service() + + # Check if locale exists + available_locales = translation_service.get_available_locales() + if locale not in available_locales: + api_logger.warning(f"Language not found: {locale}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=t("i18n.language.not_found", locale=locale) + ) + + translations = translation_service._cache.get(locale, {}) + + api_logger.info(f"Returning {len(translations)} namespaces for locale: {locale}") + return success(data={"locale": locale, "translations": translations}, msg=t("common.success.retrieved")) + + +@router.get("/translations/{locale}/{namespace}", response_model=ApiResponse) +def get_namespace_translations( + locale: str, + namespace: str, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_user) +): + """ + Get translations for a specific namespace in a locale. + + Args: + locale: Language code + namespace: Translation namespace (e.g., 'common', 'auth') + + Returns: + Translations for the specified namespace + """ + api_logger.info( + f"Get namespace translations request: locale={locale}, " + f"namespace={namespace}, user={current_user.username}" + ) + + translation_service = get_translation_service() + + # Check if locale exists + available_locales = translation_service.get_available_locales() + if locale not in available_locales: + api_logger.warning(f"Language not found: {locale}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=t("i18n.language.not_found", locale=locale) + ) + + # Get namespace translations + locale_translations = translation_service._cache.get(locale, {}) + namespace_translations = locale_translations.get(namespace, {}) + + if not namespace_translations: + api_logger.warning(f"Namespace not found: {namespace} in locale: {locale}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=t("i18n.namespace.not_found", namespace=namespace, locale=locale) + ) + + api_logger.info( + f"Returning translations for namespace: {namespace} in locale: {locale}" + ) + return success( + data={ + "locale": locale, + "namespace": namespace, + "translations": namespace_translations + }, + msg=t("common.success.retrieved") + ) + + +@router.put("/translations/{locale}/{key:path}", response_model=ApiResponse) +def update_translation( + locale: str, + key: str, + request: TranslationUpdateRequest, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Update a single translation (admin only). + + Note: This endpoint validates the request but actual translation updates + require modifying translation files in the locales directory. + + Args: + locale: Language code + key: Translation key (format: "namespace.key.subkey") + request: Translation update request + + Returns: + Success message + """ + api_logger.info( + f"Update translation request: locale={locale}, key={key}, " + f"admin={current_user.username}" + ) + + translation_service = get_translation_service() + + # Check if locale exists + available_locales = translation_service.get_available_locales() + if locale not in available_locales: + api_logger.warning(f"Language not found: {locale}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=t("i18n.language.not_found", locale=locale) + ) + + # Validate key format + if "." not in key: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=t("i18n.translation.invalid_key_format", key=key) + ) + + # Note: Actual translation updates require modifying JSON files + # This endpoint serves as a validation and documentation point + + api_logger.info( + f"Translation update validated: {locale}/{key}. " + "Translation files need to be updated manually." + ) + + return success( + msg=t("i18n.translation.update_instructions", locale=locale, key=key) + ) + + +@router.get("/translations/missing", response_model=ApiResponse) +def get_missing_translations( + locale: Optional[str] = None, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_user) +): + """ + Get list of missing translations. + + Compares translations across locales to find missing keys. + + Args: + locale: Optional locale to check (defaults to checking all non-default locales) + + Returns: + List of missing translation keys + """ + api_logger.info( + f"Get missing translations request: locale={locale}, user={current_user.username}" + ) + + from app.core.config import settings + translation_service = get_translation_service() + + default_locale = settings.I18N_DEFAULT_LANGUAGE + available_locales = translation_service.get_available_locales() + + # Get default locale translations as reference + default_translations = translation_service._cache.get(default_locale, {}) + + # Collect all keys from default locale + def collect_keys(data, prefix=""): + keys = [] + for key, value in data.items(): + full_key = f"{prefix}.{key}" if prefix else key + if isinstance(value, dict): + keys.extend(collect_keys(value, full_key)) + else: + keys.append(full_key) + return keys + + default_keys = set() + for namespace, translations in default_translations.items(): + namespace_keys = collect_keys(translations, namespace) + default_keys.update(namespace_keys) + + # Find missing keys in target locale(s) + missing_by_locale = {} + + target_locales = [locale] if locale else [ + loc for loc in available_locales if loc != default_locale + ] + + for target_locale in target_locales: + if target_locale not in available_locales: + continue + + target_translations = translation_service._cache.get(target_locale, {}) + target_keys = set() + + for namespace, translations in target_translations.items(): + namespace_keys = collect_keys(translations, namespace) + target_keys.update(namespace_keys) + + missing_keys = default_keys - target_keys + if missing_keys: + missing_by_locale[target_locale] = sorted(list(missing_keys)) + + response = MissingTranslationsResponse(missing_translations=missing_by_locale) + + total_missing = sum(len(keys) for keys in missing_by_locale.values()) + api_logger.info(f"Found {total_missing} missing translations across {len(missing_by_locale)} locales") + + return success(data=response.dict(), msg=t("common.success.retrieved")) + + +@router.post("/reload", response_model=ApiResponse) +def reload_translations( + locale: Optional[str] = None, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Trigger hot reload of translation files (admin only). + + Args: + locale: Optional locale to reload (defaults to reloading all locales) + + Returns: + Reload status and statistics + """ + api_logger.info( + f"Reload translations request: locale={locale or 'all'}, " + f"admin={current_user.username}" + ) + + from app.core.config import settings + + if not settings.I18N_ENABLE_HOT_RELOAD: + api_logger.warning("Hot reload is disabled in configuration") + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail=t("i18n.reload.disabled") + ) + + translation_service = get_translation_service() + + try: + # Reload translations + translation_service.reload(locale) + + # Get statistics + available_locales = translation_service.get_available_locales() + reloaded_locales = [locale] if locale else available_locales + + response = ReloadResponse( + success=True, + reloaded_locales=reloaded_locales, + total_locales=len(available_locales) + ) + + api_logger.info( + f"Successfully reloaded translations for: {', '.join(reloaded_locales)}" + ) + + return success(data=response.dict(), msg=t("i18n.reload.success")) + + except Exception as e: + api_logger.error(f"Failed to reload translations: {str(e)}") + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=t("i18n.reload.failed", error=str(e)) + ) + + +# ============================================================================ +# Performance Monitoring APIs +# ============================================================================ + +@router.get("/metrics", response_model=ApiResponse) +def get_metrics( + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Get i18n performance metrics (admin only). + + Returns: + Performance metrics including: + - Request counts + - Missing translations + - Timing statistics + - Locale usage + - Error counts + """ + api_logger.info(f"Get metrics request: admin={current_user.username}") + + translation_service = get_translation_service() + metrics = translation_service.get_metrics_summary() + + api_logger.info("Returning i18n metrics") + return success(data=metrics, msg=t("common.success.retrieved")) + + +@router.get("/metrics/cache", response_model=ApiResponse) +def get_cache_stats( + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Get cache statistics (admin only). + + Returns: + Cache statistics including: + - Hit/miss rates + - LRU cache performance + - Loaded locales + - Memory usage + """ + api_logger.info(f"Get cache stats request: admin={current_user.username}") + + translation_service = get_translation_service() + cache_stats = translation_service.get_cache_stats() + memory_usage = translation_service.get_memory_usage() + + data = { + "cache": cache_stats, + "memory": memory_usage + } + + api_logger.info("Returning cache statistics") + return success(data=data, msg=t("common.success.retrieved")) + + +@router.get("/metrics/prometheus") +def get_prometheus_metrics( + current_user: User = Depends(get_current_superuser) +): + """ + Get metrics in Prometheus format (admin only). + + Returns: + Prometheus-formatted metrics as plain text + """ + api_logger.info(f"Get Prometheus metrics request: admin={current_user.username}") + + from app.i18n.metrics import get_metrics + metrics = get_metrics() + prometheus_output = metrics.export_prometheus() + + from fastapi.responses import PlainTextResponse + return PlainTextResponse(content=prometheus_output) + + +@router.post("/metrics/reset", response_model=ApiResponse) +def reset_metrics( + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Reset all metrics (admin only). + + Returns: + Success message + """ + api_logger.info(f"Reset metrics request: admin={current_user.username}") + + from app.i18n.metrics import get_metrics + metrics = get_metrics() + metrics.reset() + + translation_service = get_translation_service() + translation_service.cache.reset_stats() + + api_logger.info("Metrics reset completed") + return success(msg=t("i18n.metrics.reset_success")) + + +# ============================================================================ +# Missing Translation Logging and Reporting APIs +# ============================================================================ + +@router.get("/logs/missing", response_model=ApiResponse) +def get_missing_translation_logs( + locale: Optional[str] = None, + limit: Optional[int] = 100, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Get missing translation logs (admin only). + + Returns logged missing translations with context information. + + Args: + locale: Optional locale filter + limit: Maximum number of entries to return (default: 100) + + Returns: + Missing translation logs with context + """ + api_logger.info( + f"Get missing translation logs request: locale={locale}, " + f"limit={limit}, admin={current_user.username}" + ) + + translation_service = get_translation_service() + translation_logger = translation_service.translation_logger + + # Get missing translations + missing_translations = translation_logger.get_missing_translations(locale) + + # Get missing with context + missing_with_context = translation_logger.get_missing_with_context(locale, limit) + + # Get statistics + statistics = translation_logger.get_statistics() + + data = { + "missing_translations": missing_translations, + "recent_context": missing_with_context, + "statistics": statistics + } + + api_logger.info( + f"Returning {statistics['total_missing']} missing translations" + ) + return success(data=data, msg=t("common.success.retrieved")) + + +@router.get("/logs/missing/report", response_model=ApiResponse) +def generate_missing_translation_report( + locale: Optional[str] = None, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Generate a comprehensive missing translation report (admin only). + + Args: + locale: Optional locale filter + + Returns: + Comprehensive report with missing translations and statistics + """ + api_logger.info( + f"Generate missing translation report request: locale={locale}, " + f"admin={current_user.username}" + ) + + translation_service = get_translation_service() + translation_logger = translation_service.translation_logger + + # Generate report + report = translation_logger.generate_report(locale) + + api_logger.info( + f"Generated report with {report['total_missing']} missing translations" + ) + return success(data=report, msg=t("common.success.retrieved")) + + +@router.post("/logs/missing/export", response_model=ApiResponse) +def export_missing_translations( + locale: Optional[str] = None, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Export missing translations to JSON file (admin only). + + Args: + locale: Optional locale filter + + Returns: + Export status and file path + """ + api_logger.info( + f"Export missing translations request: locale={locale}, " + f"admin={current_user.username}" + ) + + from datetime import datetime + translation_service = get_translation_service() + translation_logger = translation_service.translation_logger + + # Generate filename with timestamp + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + locale_suffix = f"_{locale}" if locale else "_all" + output_file = f"logs/i18n/missing_translations{locale_suffix}_{timestamp}.json" + + # Export to file + translation_logger.export_to_json(output_file) + + api_logger.info(f"Missing translations exported to: {output_file}") + return success( + data={"file_path": output_file}, + msg=t("i18n.logs.export_success", file=output_file) + ) + + +@router.delete("/logs/missing", response_model=ApiResponse) +def clear_missing_translation_logs( + locale: Optional[str] = None, + t: Callable = Depends(get_translator), + current_user: User = Depends(get_current_superuser) +): + """ + Clear missing translation logs (admin only). + + Args: + locale: Optional locale to clear (clears all if not specified) + + Returns: + Success message + """ + api_logger.info( + f"Clear missing translation logs request: locale={locale or 'all'}, " + f"admin={current_user.username}" + ) + + translation_service = get_translation_service() + translation_logger = translation_service.translation_logger + + # Clear logs + translation_logger.clear(locale) + + api_logger.info(f"Cleared missing translation logs for: {locale or 'all locales'}") + return success(msg=t("i18n.logs.clear_success")) diff --git a/api/app/controllers/user_controller.py b/api/app/controllers/user_controller.py index 2806da1a..16213690 100644 --- a/api/app/controllers/user_controller.py +++ b/api/app/controllers/user_controller.py @@ -1,6 +1,7 @@ from fastapi import APIRouter, Depends from sqlalchemy.orm import Session import uuid +from typing import Callable from app.core.error_codes import BizCode from app.core.exceptions import BusinessException @@ -19,6 +20,7 @@ from app.services import user_service from app.core.logging_config import get_api_logger from app.core.response_utils import success from app.core.security import verify_password +from app.i18n.dependencies import get_translator # 获取API专用日志器 api_logger = get_api_logger() @@ -33,7 +35,8 @@ router = APIRouter( def create_superuser( user: user_schema.UserCreate, db: Session = Depends(get_db), - current_superuser: User = Depends(get_current_superuser) + current_superuser: User = Depends(get_current_superuser), + t: Callable = Depends(get_translator) ): """创建超级管理员（仅超级管理员可访问）""" api_logger.info(f"超级管理员创建请求: {user.username}, email: {user.email}") @@ -42,7 +45,7 @@ def create_superuser( api_logger.info(f"超级管理员创建成功: {result.username} (ID: {result.id})") result_schema = user_schema.User.model_validate(result) - return success(data=result_schema, msg="超级管理员创建成功") + return success(data=result_schema, msg=t("users.create.superuser_success")) @router.delete("/{user_id}", response_model=ApiResponse) @@ -50,6 +53,7 @@ def delete_user( user_id: uuid.UUID, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """停用用户（软删除）""" api_logger.info(f"用户停用请求: user_id={user_id}, 操作者: {current_user.username}") @@ -57,13 +61,14 @@ def delete_user( db=db, user_id_to_deactivate=user_id, current_user=current_user ) api_logger.info(f"用户停用成功: {result.username} (ID: {result.id})") - return success(msg="用户停用成功") + return success(msg=t("users.delete.deactivate_success")) @router.post("/{user_id}/activate", response_model=ApiResponse) def activate_user( user_id: uuid.UUID, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """激活用户""" api_logger.info(f"用户激活请求: user_id={user_id}, 操作者: {current_user.username}") @@ -74,13 +79,14 @@ def activate_user( api_logger.info(f"用户激活成功: {result.username} (ID: {result.id})") result_schema = user_schema.User.model_validate(result) - return success(data=result_schema, msg="用户激活成功") + return success(data=result_schema, msg=t("users.activate.success")) @router.get("", response_model=ApiResponse) def get_current_user_info( db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """获取当前用户信息""" api_logger.info(f"当前用户信息请求: {current_user.username}") @@ -105,7 +111,7 @@ def get_current_user_info( break api_logger.info(f"当前用户信息获取成功: {result.username}, 角色: {result_schema.role}, 工作空间: {result_schema.current_workspace_name}") - return success(data=result_schema, msg="用户信息获取成功") + return success(data=result_schema, msg=t("users.info.get_success")) @router.get("/superusers", response_model=ApiResponse) @@ -113,6 +119,7 @@ def get_tenant_superusers( include_inactive: bool = False, db: Session = Depends(get_db), current_user: User = Depends(get_current_superuser), + t: Callable = Depends(get_translator) ): """获取当前租户下的超管账号列表（仅超级管理员可访问）""" api_logger.info(f"获取租户超管列表请求: {current_user.username}") @@ -125,7 +132,7 @@ def get_tenant_superusers( api_logger.info(f"租户超管列表获取成功: count={len(superusers)}") superusers_schema = [user_schema.User.model_validate(u) for u in superusers] - return success(data=superusers_schema, msg="租户超管列表获取成功") + return success(data=superusers_schema, msg=t("users.list.superusers_success")) @@ -134,6 +141,7 @@ def get_user_info_by_id( user_id: uuid.UUID, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """根据用户ID获取用户信息""" api_logger.info(f"获取用户信息请求: user_id={user_id}, 操作者: {current_user.username}") @@ -144,7 +152,7 @@ def get_user_info_by_id( api_logger.info(f"用户信息获取成功: {result.username}") result_schema = user_schema.User.model_validate(result) - return success(data=result_schema, msg="用户信息获取成功") + return success(data=result_schema, msg=t("users.info.get_success")) @router.put("/change-password", response_model=ApiResponse) @@ -152,6 +160,7 @@ async def change_password( request: ChangePasswordRequest, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """修改当前用户密码""" api_logger.info(f"用户密码修改请求: {current_user.username}") @@ -164,7 +173,7 @@ async def change_password( current_user=current_user ) api_logger.info(f"用户密码修改成功: {current_user.username}") - return success(msg="密码修改成功") + return success(msg=t("auth.password.change_success")) @router.put("/admin/change-password", response_model=ApiResponse) @@ -172,6 +181,7 @@ async def admin_change_password( request: AdminChangePasswordRequest, db: Session = Depends(get_db), current_user: User = Depends(get_current_superuser), + t: Callable = Depends(get_translator) ): """超级管理员修改指定用户的密码""" api_logger.info(f"管理员密码修改请求: 管理员 {current_user.username} 修改用户 {request.user_id}") @@ -186,16 +196,17 @@ async def admin_change_password( # 根据是否生成了随机密码来构造响应 if request.new_password: api_logger.info(f"管理员密码修改成功: 用户 {request.user_id}") - return success(msg="密码修改成功") + return success(msg=t("auth.password.change_success")) else: api_logger.info(f"管理员密码重置成功: 用户 {request.user_id}, 随机密码已生成") - return success(data=generated_password, msg="密码重置成功") + return success(data=generated_password, msg=t("auth.password.reset_success")) @router.post("/verify_pwd", response_model=ApiResponse) def verify_pwd( request: VerifyPasswordRequest, current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """验证当前用户密码""" api_logger.info(f"用户验证密码请求: {current_user.username}") @@ -203,8 +214,8 @@ def verify_pwd( is_valid = verify_password(request.password, current_user.hashed_password) api_logger.info(f"用户密码验证结果: {current_user.username}, valid={is_valid}") if not is_valid: - raise BusinessException("密码验证失败", code=BizCode.VALIDATION_FAILED) - return success(data={"valid": is_valid}, msg="验证完成") + raise BusinessException(t("users.errors.password_verification_failed"), code=BizCode.VALIDATION_FAILED) + return success(data={"valid": is_valid}, msg=t("common.success.retrieved")) @router.post("/send-email-code", response_model=ApiResponse) @@ -212,6 +223,7 @@ async def send_email_code( request: SendEmailCodeRequest, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """发送邮箱验证码""" api_logger.info(f"用户请求发送邮箱验证码: {current_user.username}, email={request.email}") @@ -219,7 +231,7 @@ async def send_email_code( await user_service.send_email_code_method(db=db, email=request.email, user_id=current_user.id) api_logger.info(f"邮箱验证码已发送: {current_user.username}") - return success(msg="验证码已发送到您的邮箱，请查收") + return success(msg=t("users.email.code_sent")) @router.put("/change-email", response_model=ApiResponse) @@ -227,6 +239,7 @@ async def change_email( request: VerifyEmailCodeRequest, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) ): """验证验证码并修改邮箱""" api_logger.info(f"用户修改邮箱: {current_user.username}, new_email={request.new_email}") @@ -239,4 +252,51 @@ async def change_email( ) api_logger.info(f"用户邮箱修改成功: {current_user.username}") - return success(msg="邮箱修改成功") + return success(msg=t("users.email.change_success")) + + + +@router.get("/me/language", response_model=ApiResponse) +def get_current_user_language( + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) +): + """获取当前用户的语言偏好""" + api_logger.info(f"获取用户语言偏好: {current_user.username}") + + language = user_service.get_user_language_preference( + db=db, + user_id=current_user.id, + current_user=current_user + ) + + api_logger.info(f"用户语言偏好获取成功: {current_user.username}, language={language}") + return success( + data=user_schema.LanguagePreferenceResponse(language=language), + msg=t("users.language.get_success") + ) + + +@router.put("/me/language", response_model=ApiResponse) +def update_current_user_language( + request: user_schema.LanguagePreferenceRequest, + db: Session = Depends(get_db), + current_user: User = Depends(get_current_user), + t: Callable = Depends(get_translator) +): + """设置当前用户的语言偏好""" + api_logger.info(f"更新用户语言偏好: {current_user.username}, language={request.language}") + + updated_user = user_service.update_user_language_preference( + db=db, + user_id=current_user.id, + language=request.language, + current_user=current_user + ) + + api_logger.info(f"用户语言偏好更新成功: {current_user.username}, language={request.language}") + return success( + data=user_schema.LanguagePreferenceResponse(language=updated_user.preferred_language), + msg=t("users.language.update_success") + ) diff --git a/api/app/controllers/workspace_controller.py b/api/app/controllers/workspace_controller.py index 9bcd8571..6f4a4fa8 100644 --- a/api/app/controllers/workspace_controller.py +++ b/api/app/controllers/workspace_controller.py @@ -14,6 +14,12 @@ from app.dependencies import ( get_current_user, workspace_access_guard, ) +from app.i18n.dependencies import get_current_language, get_translator +from app.i18n.serializers import ( + WorkspaceSerializer, + WorkspaceMemberSerializer, + WorkspaceInviteSerializer +) from app.models.tenant_model import Tenants from app.models.user_model import User from app.models.workspace_model import InviteStatus @@ -65,7 +71,9 @@ def get_workspaces( include_current: bool = Query(True, description="是否包含当前工作空间"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user), - current_tenant: Tenants = Depends(get_current_tenant) + current_tenant: Tenants = Depends(get_current_tenant), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """获取当前租户下用户参与的所有工作空间 @@ -88,8 +96,13 @@ def get_workspaces( ) api_logger.info(f"成功获取 {len(workspaces)} 个工作空间") - workspaces_schema = [WorkspaceResponse.model_validate(w) for w in workspaces] - return success(data=workspaces_schema, msg="工作空间列表获取成功") + + # 使用序列化器添加国际化字段 + serializer = WorkspaceSerializer() + workspaces_data = [WorkspaceResponse.model_validate(w).model_dump() for w in workspaces] + workspaces_i18n = serializer.serialize_list(workspaces_data, language) + + return success(data=workspaces_i18n, msg=t("workspace.list_retrieved")) @router.post("", response_model=ApiResponse) @@ -98,6 +111,8 @@ def create_workspace( language_type: str = Header(default="zh", alias="X-Language-Type"), db: Session = Depends(get_db), current_user: User = Depends(get_current_superuser), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """创建新的工作空间""" from app.core.language_utils import get_language_from_header @@ -118,8 +133,13 @@ def create_workspace( f"工作空间创建成功 - 名称: {workspace.name}, ID: {result.id}, " f"创建者: {current_user.username}, language={language}" ) - result_schema = WorkspaceResponse.model_validate(result) - return success(data=result_schema, msg="工作空间创建成功") + + # 使用序列化器添加国际化字段 + serializer = WorkspaceSerializer() + result_data = WorkspaceResponse.model_validate(result).model_dump() + result_i18n = serializer.serialize(result_data, language) + + return success(data=result_i18n, msg=t("workspace.created")) @router.put("", response_model=ApiResponse) @cur_workspace_access_guard() @@ -127,6 +147,8 @@ def update_workspace( workspace: WorkspaceUpdate, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """更新工作空间""" workspace_id = current_user.current_workspace_id @@ -139,14 +161,21 @@ def update_workspace( user=current_user, ) api_logger.info(f"工作空间更新成功 - ID: {workspace_id}, 用户: {current_user.username}") - result_schema = WorkspaceResponse.model_validate(result) - return success(data=result_schema, msg="工作空间更新成功") + + # 使用序列化器添加国际化字段 + serializer = WorkspaceSerializer() + result_data = WorkspaceResponse.model_validate(result).model_dump() + result_i18n = serializer.serialize(result_data, language) + + return success(data=result_i18n, msg=t("workspace.updated")) @router.get("/members", response_model=ApiResponse) @cur_workspace_access_guard() def get_cur_workspace_members( db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """获取工作空间成员列表（关系序列化）""" api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {current_user.current_workspace_id} 的成员列表") @@ -157,8 +186,14 @@ def get_cur_workspace_members( user=current_user, ) api_logger.info(f"工作空间成员列表获取成功 - ID: {current_user.current_workspace_id}, 数量: {len(members)}") + + # 转换为表格项并使用序列化器添加国际化字段 table_items = _convert_members_to_table_items(members) - return success(data=table_items, msg="工作空间成员列表获取成功") + serializer = WorkspaceMemberSerializer() + members_data = [item.model_dump() for item in table_items] + members_i18n = serializer.serialize_list(members_data, language) + + return success(data=members_i18n, msg=t("workspace.members.list_retrieved")) @router.put("/members", response_model=ApiResponse) @@ -168,6 +203,7 @@ def update_workspace_members( updates: List[WorkspaceMemberUpdate], db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: callable = Depends(get_translator) ): workspace_id = current_user.current_workspace_id api_logger.info(f"用户 {current_user.username} 请求更新工作空间 {workspace_id} 的成员角色") @@ -178,7 +214,7 @@ def update_workspace_members( user=current_user, ) api_logger.info(f"工作空间成员角色更新成功 - ID: {workspace_id}, 数量: {len(members)}") - return success(msg="成员角色更新成功") + return success(msg=t("workspace.members.role_updated")) @router.delete("/members/{member_id}", response_model=ApiResponse) @@ -187,6 +223,7 @@ def delete_workspace_member( member_id: uuid.UUID, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: callable = Depends(get_translator) ): workspace_id = current_user.current_workspace_id api_logger.info(f"用户 {current_user.username} 请求删除工作空间 {workspace_id} 的成员 {member_id}") @@ -198,7 +235,7 @@ def delete_workspace_member( user=current_user, ) api_logger.info(f"工作空间成员删除成功 - ID: {workspace_id}, 成员: {member_id}") - return success(msg="成员删除成功") + return success(msg=t("workspace.members.deleted")) # 创建空间协作邀请 @@ -208,6 +245,8 @@ def create_workspace_invite( invite_data: WorkspaceInviteCreate, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """创建工作空间邀请""" workspace_id = current_user.current_workspace_id @@ -220,7 +259,12 @@ def create_workspace_invite( user=current_user ) api_logger.info(f"工作空间邀请创建成功 - 工作空间: {workspace_id}, 邮箱: {invite_data.email}") - return success(data=result, msg="邀请创建成功") + + # 使用序列化器添加国际化字段 + serializer = WorkspaceInviteSerializer() + result_i18n = serializer.serialize(result, language) + + return success(data=result_i18n, msg=t("workspace.invites.created")) @router.get("/invites", response_model=ApiResponse) @@ -232,6 +276,8 @@ def get_workspace_invites( offset: int = Query(0, ge=0), db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """获取工作空间邀请列表""" workspace_id = current_user.current_workspace_id @@ -246,18 +292,30 @@ def get_workspace_invites( offset=offset ) api_logger.info(f"成功获取 {len(invites)} 个邀请记录") - return success(data=invites, msg="邀请列表获取成功") + + # 使用序列化器添加国际化字段 + serializer = WorkspaceInviteSerializer() + invites_i18n = serializer.serialize_list(invites, language) + + return success(data=invites_i18n, msg=t("workspace.invites.list_retrieved")) @public_router.get("/invites/validate/{token}", response_model=ApiResponse) def get_workspace_invite_info( token: str, db: Session = Depends(get_db), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """获取工作空间邀请用户信息（无需认证）""" result = workspace_service.validate_invite_token(db=db, token=token) api_logger.info(f"工作空间邀请验证成功 - 邀请: {token}") - return success(data=result, msg="邀请验证成功") + + # 使用序列化器添加国际化字段 + serializer = WorkspaceInviteSerializer() + result_i18n = serializer.serialize(result, language) + + return success(data=result_i18n, msg=t("workspace.invites.validated")) @router.delete("/invites/{invite_id}", response_model=ApiResponse) @@ -267,6 +325,8 @@ def revoke_workspace_invite( invite_id: uuid.UUID, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """撤销工作空间邀请""" workspace_id = current_user.current_workspace_id @@ -279,7 +339,12 @@ def revoke_workspace_invite( user=current_user ) api_logger.info(f"工作空间邀请撤销成功 - 邀请: {invite_id}") - return success(data=result, msg="邀请撤销成功") + + # 使用序列化器添加国际化字段 + serializer = WorkspaceInviteSerializer() + result_i18n = serializer.serialize(result, language) + + return success(data=result_i18n, msg=t("workspace.invites.revoked")) # ==================== 公开邀请接口（无需认证） ==================== @@ -302,6 +367,7 @@ def switch_workspace( workspace_id: uuid.UUID, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: callable = Depends(get_translator) ): """切换工作空间""" api_logger.info(f"用户 {current_user.username} 请求切换工作空间为 {workspace_id}") @@ -312,7 +378,7 @@ def switch_workspace( user=current_user, ) api_logger.info(f"成功切换工作空间为 {workspace_id}") - return success(msg="工作空间切换成功") + return success(msg=t("workspace.switched")) @router.get("/storage", response_model=ApiResponse) @@ -320,6 +386,7 @@ def switch_workspace( def get_workspace_storage_type( db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: callable = Depends(get_translator) ): """获取当前工作空间的存储类型""" workspace_id = current_user.current_workspace_id @@ -331,7 +398,7 @@ def get_workspace_storage_type( user=current_user ) api_logger.info(f"成功获取工作空间 {workspace_id} 的存储类型: {storage_type}") - return success(data={"storage_type": storage_type}, msg="存储类型获取成功") + return success(data={"storage_type": storage_type}, msg=t("workspace.storage.type_retrieved")) @router.get("/workspace_models", response_model=ApiResponse) @@ -339,6 +406,8 @@ def get_workspace_storage_type( def workspace_models_configs( db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + language: str = Depends(get_current_language), + t: callable = Depends(get_translator) ): """获取当前工作空间的模型配置（llm, embedding, rerank）""" workspace_id = current_user.current_workspace_id @@ -354,14 +423,14 @@ def workspace_models_configs( api_logger.warning(f"工作空间 {workspace_id} 不存在或无权访问") raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail="工作空间不存在或无权访问" + detail=t("workspace.not_found") ) api_logger.info( f"成功获取工作空间 {workspace_id} 的模型配置: " f"llm={configs.get('llm')}, embedding={configs.get('embedding')}, rerank={configs.get('rerank')}" ) - return success(data=WorkspaceModelsConfig.model_validate(configs), msg="模型配置获取成功") + return success(data=WorkspaceModelsConfig.model_validate(configs), msg=t("workspace.models.config_retrieved")) @router.put("/workspace_models", response_model=ApiResponse) @@ -370,6 +439,7 @@ def update_workspace_models_configs( models_update: WorkspaceModelsUpdate, db: Session = Depends(get_db), current_user: User = Depends(get_current_user), + t: callable = Depends(get_translator) ): """更新当前工作空间的模型配置（llm, embedding, rerank）""" workspace_id = current_user.current_workspace_id @@ -386,5 +456,5 @@ def update_workspace_models_configs( f"成功更新工作空间 {workspace_id} 的模型配置: " f"llm={updated_workspace.llm}, embedding={updated_workspace.embedding}, rerank={updated_workspace.rerank}" ) - return success(data=WorkspaceModelsConfig.model_validate(updated_workspace), msg="模型配置更新成功") + return success(data=WorkspaceModelsConfig.model_validate(updated_workspace), msg=t("workspace.models.config_updated")) diff --git a/api/app/core/config.py b/api/app/core/config.py index 25713967..cdaa13cc 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -162,6 +162,44 @@ class Settings: # This controls the language used for memory summary titles and other generated content DEFAULT_LANGUAGE: str = os.getenv("DEFAULT_LANGUAGE", "zh") + # ======================================================================== + # Internationalization (i18n) Configuration + # ======================================================================== + # Default language for API responses + I18N_DEFAULT_LANGUAGE: str = os.getenv("I18N_DEFAULT_LANGUAGE", "zh") + + # Supported languages (comma-separated) + I18N_SUPPORTED_LANGUAGES: list[str] = [ + lang.strip() + for lang in os.getenv("I18N_SUPPORTED_LANGUAGES", "zh,en").split(",") + if lang.strip() + ] + + # Core locales directory (community edition) + # Use absolute path to work from any working directory + I18N_CORE_LOCALES_DIR: str = os.getenv( + "I18N_CORE_LOCALES_DIR", + os.path.join(os.path.dirname(os.path.dirname(__file__)), "locales") + ) + + # Premium locales directory (enterprise edition, optional) + I18N_PREMIUM_LOCALES_DIR: Optional[str] = os.getenv("I18N_PREMIUM_LOCALES_DIR", None) + + # Enable translation cache + I18N_ENABLE_TRANSLATION_CACHE: bool = os.getenv("I18N_ENABLE_TRANSLATION_CACHE", "true").lower() == "true" + + # LRU cache size for hot translations + I18N_LRU_CACHE_SIZE: int = int(os.getenv("I18N_LRU_CACHE_SIZE", "1000")) + + # Enable hot reload of translation files + I18N_ENABLE_HOT_RELOAD: bool = os.getenv("I18N_ENABLE_HOT_RELOAD", "false").lower() == "true" + + # Fallback language when translation is missing + I18N_FALLBACK_LANGUAGE: str = os.getenv("I18N_FALLBACK_LANGUAGE", "zh") + + # Log missing translations + I18N_LOG_MISSING_TRANSLATIONS: bool = os.getenv("I18N_LOG_MISSING_TRANSLATIONS", "true").lower() == "true" + # Logging settings LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO") LOG_FORMAT: str = os.getenv("LOG_FORMAT", "%(asctime)s - %(name)s - %(levelname)s - %(message)s") diff --git a/api/app/i18n/README.md b/api/app/i18n/README.md new file mode 100644 index 00000000..7374e966 --- /dev/null +++ b/api/app/i18n/README.md @@ -0,0 +1,61 @@ +# Internationalization (i18n) Module + +This module provides internationalization support for the MemoryBear API. + +## Components + +- `service.py` - Translation service and core translation logic +- `middleware.py` - Language detection middleware +- `dependencies.py` - FastAPI dependency injection functions +- `exceptions.py` - Internationalized exception classes + +## Usage + +### Basic Translation + +```python +from app.i18n import t + +# Simple translation +message = t("common.success.created") + +# Parameterized translation +message = t("common.validation.required", field="Name") +``` + +### Enum Translation + +```python +from app.i18n import t_enum + +# Translate enum value +role_display = t_enum("workspace_role", "manager") +``` + +### In FastAPI Endpoints + +```python +from fastapi import Depends +from app.i18n.dependencies import get_translator + +@router.post("/workspaces") +async def create_workspace( + data: WorkspaceCreate, + t: Callable = Depends(get_translator) +): + workspace = await workspace_service.create(data) + return { + "success": True, + "message": t("workspace.created_successfully"), + "data": workspace + } +``` + +## Configuration + +See `app/core/config.py` for i18n configuration options: + +- `I18N_DEFAULT_LANGUAGE` - Default language (default: "zh") +- `I18N_SUPPORTED_LANGUAGES` - Supported languages (default: "zh,en") +- `I18N_ENABLE_TRANSLATION_CACHE` - Enable caching (default: true) +- `I18N_LOG_MISSING_TRANSLATIONS` - Log missing translations (default: true) diff --git a/api/app/i18n/__init__.py b/api/app/i18n/__init__.py new file mode 100644 index 00000000..23561bec --- /dev/null +++ b/api/app/i18n/__init__.py @@ -0,0 +1,113 @@ +""" +Internationalization (i18n) module for MemoryBear Enterprise. + +This module provides complete i18n support for the backend API including: +- Translation loading from multiple directories (community + enterprise) +- Translation service with caching and fallback +- Language detection middleware +- Dependency injection for FastAPI +- Convenience functions for easy usage + +Usage: + from app.i18n import t, t_enum + + # Simple translation + message = t("common.success.created") + + # Parameterized translation + error = t("common.validation.required", field="名称") + + # Enum translation + role_display = t_enum("workspace_role", "manager") +""" + +from app.i18n.dependencies import ( + get_current_language, + get_enum_translator, + get_translator, +) +from app.i18n.exceptions import ( + BadRequestError, + ConflictError, + FileNotFoundError, + FileTooLargeError, + ForbiddenError, + I18nException, + InternalServerError, + InvalidCredentialsError, + InvalidFileTypeError, + NotFoundError, + QuotaExceededError, + RateLimitExceededError, + ServiceUnavailableError, + TenantNotFoundError, + TenantSuspendedError, + TokenExpiredError, + TokenInvalidError, + UnauthorizedError, + UserAlreadyExistsError, + UserNotFoundError, + ValidationError, + WorkspaceNotFoundError, + WorkspacePermissionDeniedError, + get_current_locale, + set_current_locale, +) +from app.i18n.loader import TranslationLoader +from app.i18n.logger import ( + TranslationLogger, + get_translation_logger, + log_missing_translation, + log_translation_error, +) +from app.i18n.middleware import LanguageMiddleware +from app.i18n.service import ( + TranslationService, + get_translation_service, + t, + t_enum, +) + +__all__ = [ + "TranslationLoader", + "LanguageMiddleware", + "TranslationService", + "get_translation_service", + "t", + "t_enum", + "get_current_language", + "get_translator", + "get_enum_translator", + # Context management + "get_current_locale", + "set_current_locale", + # Logging + "TranslationLogger", + "get_translation_logger", + "log_missing_translation", + "log_translation_error", + # Exception classes + "I18nException", + "BadRequestError", + "UnauthorizedError", + "ForbiddenError", + "NotFoundError", + "ConflictError", + "ValidationError", + "InternalServerError", + "ServiceUnavailableError", + "WorkspaceNotFoundError", + "WorkspacePermissionDeniedError", + "UserNotFoundError", + "UserAlreadyExistsError", + "TenantNotFoundError", + "TenantSuspendedError", + "InvalidCredentialsError", + "TokenExpiredError", + "TokenInvalidError", + "FileNotFoundError", + "FileTooLargeError", + "InvalidFileTypeError", + "RateLimitExceededError", + "QuotaExceededError", +] diff --git a/api/app/i18n/cache.py b/api/app/i18n/cache.py new file mode 100644 index 00000000..5b0837d9 --- /dev/null +++ b/api/app/i18n/cache.py @@ -0,0 +1,291 @@ +""" +Advanced caching system for i18n translations. + +This module provides: +- LRU cache for hot translations +- Lazy loading mechanism +- Memory optimization +- Cache statistics +""" + +import logging +from functools import lru_cache +from typing import Any, Dict, Optional +from collections import OrderedDict +import time + +logger = logging.getLogger(__name__) + + +class TranslationCache: + """ + Advanced translation cache with LRU eviction and lazy loading. + + Features: + - LRU cache for frequently accessed translations + - Lazy loading to reduce startup time + - Memory-efficient storage + - Cache hit/miss statistics + """ + + def __init__(self, max_lru_size: int = 1000, enable_lazy_load: bool = True): + """ + Initialize the translation cache. + + Args: + max_lru_size: Maximum size of LRU cache for hot translations + enable_lazy_load: Enable lazy loading of locales + """ + self.max_lru_size = max_lru_size + self.enable_lazy_load = enable_lazy_load + + # Main cache: {locale: {namespace: {key: value}}} + self._main_cache: Dict[str, Dict[str, Any]] = {} + + # LRU cache for hot translations + self._lru_cache: OrderedDict = OrderedDict() + + # Loaded locales tracker + self._loaded_locales: set = set() + + # Statistics + self._stats = { + "hits": 0, + "misses": 0, + "lru_hits": 0, + "lru_misses": 0, + "lazy_loads": 0 + } + + logger.info( + f"TranslationCache initialized with LRU size: {max_lru_size}, " + f"lazy loading: {enable_lazy_load}" + ) + + def set_locale_data(self, locale: str, data: Dict[str, Any]): + """ + Set translation data for a locale. + + Args: + locale: Locale code + data: Translation data dictionary + """ + self._main_cache[locale] = data + self._loaded_locales.add(locale) + logger.debug(f"Loaded locale '{locale}' into cache") + + def get_translation( + self, + locale: str, + namespace: str, + key_path: list + ) -> Optional[str]: + """ + Get translation from cache with LRU optimization. + + Args: + locale: Locale code + namespace: Translation namespace + key_path: List of nested keys + + Returns: + Translation string or None if not found + """ + # Build cache key for LRU + cache_key = f"{locale}:{namespace}:{'.'.join(key_path)}" + + # Check LRU cache first (hot translations) + if cache_key in self._lru_cache: + self._stats["lru_hits"] += 1 + self._stats["hits"] += 1 + # Move to end (most recently used) + self._lru_cache.move_to_end(cache_key) + return self._lru_cache[cache_key] + + self._stats["lru_misses"] += 1 + + # Check main cache + if locale not in self._main_cache: + self._stats["misses"] += 1 + return None + + if namespace not in self._main_cache[locale]: + self._stats["misses"] += 1 + return None + + # Navigate through nested keys + current = self._main_cache[locale][namespace] + for key in key_path: + if isinstance(current, dict) and key in current: + current = current[key] + else: + self._stats["misses"] += 1 + return None + + # Return only if it's a string value + if not isinstance(current, str): + self._stats["misses"] += 1 + return None + + self._stats["hits"] += 1 + + # Add to LRU cache + self._add_to_lru(cache_key, current) + + return current + + def _add_to_lru(self, key: str, value: str): + """ + Add translation to LRU cache. + + Args: + key: Cache key + value: Translation value + """ + # Remove oldest if cache is full + if len(self._lru_cache) >= self.max_lru_size: + self._lru_cache.popitem(last=False) + + self._lru_cache[key] = value + + def is_locale_loaded(self, locale: str) -> bool: + """ + Check if a locale is loaded. + + Args: + locale: Locale code + + Returns: + True if locale is loaded + """ + return locale in self._loaded_locales + + def get_loaded_locales(self) -> list: + """ + Get list of loaded locales. + + Returns: + List of locale codes + """ + return list(self._loaded_locales) + + def clear_lru(self): + """Clear the LRU cache.""" + self._lru_cache.clear() + logger.info("LRU cache cleared") + + def clear_locale(self, locale: str): + """ + Clear cache for a specific locale. + + Args: + locale: Locale code + """ + if locale in self._main_cache: + del self._main_cache[locale] + self._loaded_locales.discard(locale) + + # Clear related LRU entries + keys_to_remove = [k for k in self._lru_cache if k.startswith(f"{locale}:")] + for key in keys_to_remove: + del self._lru_cache[key] + + logger.info(f"Cleared cache for locale '{locale}'") + + def clear_all(self): + """Clear all caches.""" + self._main_cache.clear() + self._lru_cache.clear() + self._loaded_locales.clear() + logger.info("All caches cleared") + + def get_stats(self) -> Dict[str, Any]: + """ + Get cache statistics. + + Returns: + Dictionary with cache statistics + """ + total_requests = self._stats["hits"] + self._stats["misses"] + hit_rate = ( + self._stats["hits"] / total_requests * 100 + if total_requests > 0 + else 0 + ) + + lru_total = self._stats["lru_hits"] + self._stats["lru_misses"] + lru_hit_rate = ( + self._stats["lru_hits"] / lru_total * 100 + if lru_total > 0 + else 0 + ) + + return { + "total_requests": total_requests, + "hits": self._stats["hits"], + "misses": self._stats["misses"], + "hit_rate": round(hit_rate, 2), + "lru_hits": self._stats["lru_hits"], + "lru_misses": self._stats["lru_misses"], + "lru_hit_rate": round(lru_hit_rate, 2), + "lru_size": len(self._lru_cache), + "lru_max_size": self.max_lru_size, + "loaded_locales": len(self._loaded_locales), + "lazy_loads": self._stats["lazy_loads"] + } + + def reset_stats(self): + """Reset cache statistics.""" + self._stats = { + "hits": 0, + "misses": 0, + "lru_hits": 0, + "lru_misses": 0, + "lazy_loads": 0 + } + logger.info("Cache statistics reset") + + def get_memory_usage(self) -> Dict[str, Any]: + """ + Estimate memory usage of the cache. + + Returns: + Dictionary with memory usage information + """ + import sys + + main_cache_size = sys.getsizeof(self._main_cache) + lru_cache_size = sys.getsizeof(self._lru_cache) + + # Rough estimate of nested data + for locale_data in self._main_cache.values(): + main_cache_size += sys.getsizeof(locale_data) + for namespace_data in locale_data.values(): + main_cache_size += sys.getsizeof(namespace_data) + + return { + "main_cache_bytes": main_cache_size, + "lru_cache_bytes": lru_cache_size, + "total_bytes": main_cache_size + lru_cache_size, + "main_cache_mb": round(main_cache_size / 1024 / 1024, 2), + "lru_cache_mb": round(lru_cache_size / 1024 / 1024, 2), + "total_mb": round((main_cache_size + lru_cache_size) / 1024 / 1024, 2) + } + + +@lru_cache(maxsize=128) +def get_cached_translation_key(locale: str, namespace: str, key: str) -> str: + """ + LRU cached function for building translation cache keys. + + This reduces string concatenation overhead for frequently accessed keys. + + Args: + locale: Locale code + namespace: Translation namespace + key: Translation key + + Returns: + Cache key string + """ + return f"{locale}:{namespace}:{key}" diff --git a/api/app/i18n/dependencies.py b/api/app/i18n/dependencies.py new file mode 100644 index 00000000..4c8e9a11 --- /dev/null +++ b/api/app/i18n/dependencies.py @@ -0,0 +1,158 @@ +""" +FastAPI dependency injection functions for i18n. + +This module provides dependency injection functions that can be used +in FastAPI route handlers to access the current language and translator. +""" + +import logging +from typing import Callable + +from fastapi import Request + +from app.i18n.service import get_translation_service + +logger = logging.getLogger(__name__) + + +async def get_current_language(request: Request) -> str: + """ + Get the current language from the request context. + + This dependency extracts the language that was determined by the + LanguageMiddleware and stored in request.state. + + Args: + request: FastAPI request object + + Returns: + Language code (e.g., "zh", "en") + + Usage: + @router.get("/example") + async def example(language: str = Depends(get_current_language)): + return {"language": language} + """ + # Get language from request state (set by LanguageMiddleware) + language = getattr(request.state, "language", None) + + if language is None: + # Fallback to default language if not set + from app.core.config import settings + language = settings.I18N_DEFAULT_LANGUAGE + logger.warning( + "Language not found in request.state, using default: " + f"{language}" + ) + + return language + + +async def get_translator(request: Request) -> Callable: + """ + Get a translator function bound to the current request's language. + + This dependency returns a translation function that automatically + uses the current request's language, making it easy to translate + strings in route handlers. + + Args: + request: FastAPI request object + + Returns: + Translation function with signature: t(key: str, **params) -> str + + Usage: + @router.post("/workspaces") + async def create_workspace( + data: WorkspaceCreate, + t: Callable = Depends(get_translator) + ): + workspace = await workspace_service.create(data) + return { + "success": True, + "message": t("workspace.created_successfully"), + "data": workspace + } + + # With parameters + @router.get("/items") + async def get_items(t: Callable = Depends(get_translator)): + count = 5 + return { + "message": t("items.found", count=count) + } + """ + # Get current language + language = await get_current_language(request) + + # Get translation service + service = get_translation_service() + + # Return a bound translation function + def translate(key: str, **params) -> str: + """ + Translate a key using the current request's language. + + Args: + key: Translation key (e.g., "common.success.created") + **params: Parameters for parameterized messages + + Returns: + Translated string + """ + return service.translate(key, language, **params) + + return translate + + +async def get_enum_translator(request: Request) -> Callable: + """ + Get an enum translator function bound to the current request's language. + + This dependency returns a function for translating enum values + that automatically uses the current request's language. + + Args: + request: FastAPI request object + + Returns: + Enum translation function with signature: + t_enum(enum_type: str, value: str) -> str + + Usage: + @router.get("/workspace/{id}") + async def get_workspace( + id: str, + t_enum: Callable = Depends(get_enum_translator) + ): + workspace = await workspace_service.get(id) + return { + "id": workspace.id, + "role": workspace.role, + "role_display": t_enum("workspace_role", workspace.role), + "status": workspace.status, + "status_display": t_enum("workspace_status", workspace.status) + } + """ + # Get current language + language = await get_current_language(request) + + # Get translation service + service = get_translation_service() + + # Return a bound enum translation function + def translate_enum(enum_type: str, value: str) -> str: + """ + Translate an enum value using the current request's language. + + Args: + enum_type: Enum type name (e.g., "workspace_role") + value: Enum value (e.g., "manager") + + Returns: + Translated enum display name + """ + return service.translate_enum(enum_type, value, language) + + return translate_enum diff --git a/api/app/i18n/exceptions.py b/api/app/i18n/exceptions.py new file mode 100644 index 00000000..b81369ed --- /dev/null +++ b/api/app/i18n/exceptions.py @@ -0,0 +1,495 @@ +""" +Internationalized exception classes for i18n system. + +This module provides exception classes that automatically translate +error messages based on the current request's language. +""" + +import logging +from contextvars import ContextVar +from typing import Any, Dict, Optional + +from fastapi import HTTPException, Request + +from app.i18n.service import get_translation_service + +logger = logging.getLogger(__name__) + +# Context variable to store current locale +_current_locale: ContextVar[Optional[str]] = ContextVar("current_locale", default=None) + + +def set_current_locale(locale: str) -> None: + """ + Set the current locale in the context variable. + + This should be called by the LanguageMiddleware. + + Args: + locale: Locale code (e.g., "zh", "en") + """ + _current_locale.set(locale) + + +def get_current_locale() -> Optional[str]: + """ + Get the current locale from the context variable. + + Returns: + Locale code or None if not set + """ + return _current_locale.get() + + +class I18nException(HTTPException): + """ + Base exception class with automatic i18n support. + + This exception automatically translates error messages based on: + 1. The current request's language (from request.state.language) + 2. The fallback language if request language is not available + 3. The error key itself if no translation is found + + Features: + - Automatic error message translation + - Parameterized error messages support + - Consistent error response format + - Language-aware error handling + + Usage: + # Simple error + raise I18nException( + error_key="errors.workspace.not_found", + status_code=404 + ) + + # Error with parameters + raise I18nException( + error_key="errors.validation.missing_field", + status_code=400, + field="name" + ) + + # Custom error code + raise I18nException( + error_key="errors.workspace.not_found", + error_code="WORKSPACE_NOT_FOUND", + status_code=404, + workspace_id="123" + ) + """ + + def __init__( + self, + error_key: str, + status_code: int = 400, + error_code: Optional[str] = None, + locale: Optional[str] = None, + headers: Optional[Dict[str, str]] = None, + **params + ): + """ + Initialize the i18n exception. + + Args: + error_key: Translation key for the error message + (e.g., "errors.workspace.not_found") + status_code: HTTP status code (default: 400) + error_code: Custom error code for API clients + (default: derived from error_key) + locale: Target locale for translation (optional) + If not provided, uses current request's language + headers: Additional HTTP headers + **params: Parameters for parameterized error messages + """ + self.error_key = error_key + self.error_code = error_code or self._generate_error_code(error_key) + self.params = params + + # Get locale from request context if not provided + if locale is None: + locale = self._get_current_locale() + + # Translate error message + translation_service = get_translation_service() + message = translation_service.translate( + error_key, + locale, + **params + ) + + # Build error detail + detail = { + "error_code": self.error_code, + "message": message, + } + + # Add parameters to detail if provided + if params: + detail["params"] = params + + # Initialize HTTPException + super().__init__( + status_code=status_code, + detail=detail, + headers=headers + ) + + logger.debug( + f"I18nException raised: {self.error_code} " + f"(key: {error_key}, locale: {locale})" + ) + + def _get_current_locale(self) -> str: + """ + Get the current locale from request context. + + Returns: + Locale code (e.g., "zh", "en") + """ + try: + # Try to get locale from context variable + locale = _current_locale.get() + if locale: + return locale + except Exception as e: + logger.debug(f"Could not get locale from context: {e}") + + # Fallback to default locale + from app.core.config import settings + return settings.I18N_DEFAULT_LANGUAGE + + def _generate_error_code(self, error_key: str) -> str: + """ + Generate error code from error key. + + Converts "errors.workspace.not_found" to "WORKSPACE_NOT_FOUND" + + Args: + error_key: Translation key + + Returns: + Error code in UPPER_SNAKE_CASE + """ + # Remove "errors." prefix if present + if error_key.startswith("errors."): + error_key = error_key[7:] + + # Convert to UPPER_SNAKE_CASE + parts = error_key.split(".") + return "_".join(parts).upper() + + +# Specific exception classes for common errors + +class BadRequestError(I18nException): + """Bad request error (400).""" + + def __init__( + self, + error_key: str = "errors.common.bad_request", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=400, + error_code=error_code, + **params + ) + + +class UnauthorizedError(I18nException): + """Unauthorized error (401).""" + + def __init__( + self, + error_key: str = "errors.auth.unauthorized", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=401, + error_code=error_code, + **params + ) + + +class ForbiddenError(I18nException): + """Forbidden error (403).""" + + def __init__( + self, + error_key: str = "errors.auth.forbidden", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=403, + error_code=error_code, + **params + ) + + +class NotFoundError(I18nException): + """Not found error (404).""" + + def __init__( + self, + error_key: str = "errors.common.not_found", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=404, + error_code=error_code, + **params + ) + + +class ConflictError(I18nException): + """Conflict error (409).""" + + def __init__( + self, + error_key: str = "errors.common.conflict", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=409, + error_code=error_code, + **params + ) + + +class ValidationError(I18nException): + """Validation error (422).""" + + def __init__( + self, + error_key: str = "errors.common.validation_failed", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=422, + error_code=error_code, + **params + ) + + +class InternalServerError(I18nException): + """Internal server error (500).""" + + def __init__( + self, + error_key: str = "errors.common.internal_error", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=500, + error_code=error_code, + **params + ) + + +class ServiceUnavailableError(I18nException): + """Service unavailable error (503).""" + + def __init__( + self, + error_key: str = "errors.common.service_unavailable", + error_code: Optional[str] = None, + **params + ): + super().__init__( + error_key=error_key, + status_code=503, + error_code=error_code, + **params + ) + + +# Domain-specific exception classes + +class WorkspaceNotFoundError(NotFoundError): + """Workspace not found error.""" + + def __init__(self, workspace_id: Optional[str] = None, **params): + if workspace_id: + params["workspace_id"] = workspace_id + super().__init__( + error_key="errors.workspace.not_found", + error_code="WORKSPACE_NOT_FOUND", + **params + ) + + +class WorkspacePermissionDeniedError(ForbiddenError): + """Workspace permission denied error.""" + + def __init__(self, workspace_id: Optional[str] = None, **params): + if workspace_id: + params["workspace_id"] = workspace_id + super().__init__( + error_key="errors.workspace.permission_denied", + error_code="WORKSPACE_PERMISSION_DENIED", + **params + ) + + +class UserNotFoundError(NotFoundError): + """User not found error.""" + + def __init__(self, user_id: Optional[str] = None, **params): + if user_id: + params["user_id"] = user_id + super().__init__( + error_key="errors.user.not_found", + error_code="USER_NOT_FOUND", + **params + ) + + +class UserAlreadyExistsError(ConflictError): + """User already exists error.""" + + def __init__(self, identifier: Optional[str] = None, **params): + if identifier: + params["identifier"] = identifier + super().__init__( + error_key="errors.user.already_exists", + error_code="USER_ALREADY_EXISTS", + **params + ) + + +class TenantNotFoundError(NotFoundError): + """Tenant not found error.""" + + def __init__(self, tenant_id: Optional[str] = None, **params): + if tenant_id: + params["tenant_id"] = tenant_id + super().__init__( + error_key="errors.tenant.not_found", + error_code="TENANT_NOT_FOUND", + **params + ) + + +class TenantSuspendedError(ForbiddenError): + """Tenant suspended error.""" + + def __init__(self, tenant_id: Optional[str] = None, **params): + if tenant_id: + params["tenant_id"] = tenant_id + super().__init__( + error_key="errors.tenant.suspended", + error_code="TENANT_SUSPENDED", + **params + ) + + +class InvalidCredentialsError(UnauthorizedError): + """Invalid credentials error.""" + + def __init__(self, **params): + super().__init__( + error_key="errors.auth.invalid_credentials", + error_code="INVALID_CREDENTIALS", + **params + ) + + +class TokenExpiredError(UnauthorizedError): + """Token expired error.""" + + def __init__(self, **params): + super().__init__( + error_key="errors.auth.token_expired", + error_code="TOKEN_EXPIRED", + **params + ) + + +class TokenInvalidError(UnauthorizedError): + """Token invalid error.""" + + def __init__(self, **params): + super().__init__( + error_key="errors.auth.token_invalid", + error_code="TOKEN_INVALID", + **params + ) + + +class FileNotFoundError(NotFoundError): + """File not found error.""" + + def __init__(self, file_id: Optional[str] = None, **params): + if file_id: + params["file_id"] = file_id + super().__init__( + error_key="errors.file.not_found", + error_code="FILE_NOT_FOUND", + **params + ) + + +class FileTooLargeError(BadRequestError): + """File too large error.""" + + def __init__(self, max_size: Optional[str] = None, **params): + if max_size: + params["max_size"] = max_size + super().__init__( + error_key="errors.file.too_large", + error_code="FILE_TOO_LARGE", + **params + ) + + +class InvalidFileTypeError(BadRequestError): + """Invalid file type error.""" + + def __init__(self, file_type: Optional[str] = None, **params): + if file_type: + params["file_type"] = file_type + super().__init__( + error_key="errors.file.invalid_type", + error_code="INVALID_FILE_TYPE", + **params + ) + + +class RateLimitExceededError(I18nException): + """Rate limit exceeded error (429).""" + + def __init__(self, **params): + super().__init__( + error_key="errors.api.rate_limit_exceeded", + status_code=429, + error_code="RATE_LIMIT_EXCEEDED", + **params + ) + + +class QuotaExceededError(ForbiddenError): + """Quota exceeded error.""" + + def __init__(self, resource: Optional[str] = None, **params): + if resource: + params["resource"] = resource + super().__init__( + error_key="errors.api.quota_exceeded", + error_code="QUOTA_EXCEEDED", + **params + ) diff --git a/api/app/i18n/loader.py b/api/app/i18n/loader.py new file mode 100644 index 00000000..3865378b --- /dev/null +++ b/api/app/i18n/loader.py @@ -0,0 +1,199 @@ +""" +Translation file loader for i18n system. + +This module handles loading translation files from multiple directories +(community edition + enterprise edition) and provides hot reload support. +""" + +import json +import logging +from pathlib import Path +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class TranslationLoader: + """ + Translation file loader that supports: + - Loading from multiple directories (community + enterprise) + - Hot reload of translation files + - Automatic locale detection + """ + + def __init__(self, locales_dirs: Optional[List[str]] = None): + """ + Initialize the translation loader. + + Args: + locales_dirs: List of directories containing translation files. + If None, will auto-detect from settings. + """ + if locales_dirs is None: + locales_dirs = self._detect_locales_dirs() + + self.locales_dirs = [Path(d) for d in locales_dirs] + logger.info(f"TranslationLoader initialized with directories: {self.locales_dirs}") + + def _detect_locales_dirs(self) -> List[str]: + """ + Auto-detect translation directories from settings. + + Returns: + List of translation directory paths + """ + from app.core.config import settings + + dirs = [] + + # 1. Core locales directory (community edition, required) + core_dir = Path(settings.I18N_CORE_LOCALES_DIR) + if core_dir.exists(): + dirs.append(str(core_dir)) + logger.debug(f"Found core locales directory: {core_dir}") + else: + logger.warning(f"Core locales directory not found: {core_dir}") + + # 2. Premium locales directory (enterprise edition, optional) + if settings.I18N_PREMIUM_LOCALES_DIR: + premium_dir = Path(settings.I18N_PREMIUM_LOCALES_DIR) + if premium_dir.exists(): + dirs.append(str(premium_dir)) + logger.debug(f"Found premium locales directory: {premium_dir}") + else: + # Auto-detect premium directory + premium_dir = Path("premium/locales") + if premium_dir.exists(): + dirs.append(str(premium_dir)) + logger.debug(f"Auto-detected premium locales directory: {premium_dir}") + + if not dirs: + logger.error("No translation directories found!") + + return dirs + + def get_available_locales(self) -> List[str]: + """ + Get list of all available locales across all directories. + + Returns: + List of locale codes (e.g., ['zh', 'en']) + """ + locales = set() + + for locales_dir in self.locales_dirs: + if not locales_dir.exists(): + continue + + for locale_dir in locales_dir.iterdir(): + if locale_dir.is_dir() and not locale_dir.name.startswith('.'): + locales.add(locale_dir.name) + + return sorted(list(locales)) + + def load_locale(self, locale: str) -> Dict[str, Any]: + """ + Load all translation files for a specific locale from all directories. + + Translation files are merged with priority: + - Later directories override earlier directories + - Enterprise translations override community translations + + Args: + locale: Locale code (e.g., 'zh', 'en') + + Returns: + Dictionary of translations organized by namespace + Format: {namespace: {key: value, ...}, ...} + """ + translations = {} + + # Load from each directory in order (later directories override earlier) + for locales_dir in self.locales_dirs: + locale_dir = locales_dir / locale + if not locale_dir.exists(): + logger.debug(f"Locale directory not found: {locale_dir}") + continue + + # Load all JSON files in this locale directory + for json_file in locale_dir.glob("*.json"): + namespace = json_file.stem + + try: + with open(json_file, "r", encoding="utf-8") as f: + new_translations = json.load(f) + + # Merge translations (deep merge) + if namespace in translations: + translations[namespace] = self._deep_merge( + translations[namespace], + new_translations + ) + logger.debug( + f"Merged translations: {locale}/{namespace} from {json_file}" + ) + else: + translations[namespace] = new_translations + logger.debug( + f"Loaded translations: {locale}/{namespace} from {json_file}" + ) + + except json.JSONDecodeError as e: + logger.error( + f"Failed to parse JSON file {json_file}: {e}" + ) + except Exception as e: + logger.error( + f"Failed to load translation file {json_file}: {e}" + ) + + if not translations: + logger.warning(f"No translations found for locale: {locale}") + + return translations + + def reload(self, locale: Optional[str] = None) -> Dict[str, Dict[str, Any]]: + """ + Reload translation files. + + Args: + locale: Specific locale to reload. If None, reloads all locales. + + Returns: + Dictionary of reloaded translations + Format: {locale: {namespace: {key: value}}} + """ + if locale: + logger.info(f"Reloading translations for locale: {locale}") + return {locale: self.load_locale(locale)} + else: + logger.info("Reloading all translations") + all_translations = {} + for loc in self.get_available_locales(): + all_translations[loc] = self.load_locale(loc) + return all_translations + + def _deep_merge(self, base: Dict, override: Dict) -> Dict: + """ + Deep merge two dictionaries. + + Args: + base: Base dictionary + override: Dictionary with values to override + + Returns: + Merged dictionary + """ + result = base.copy() + + for key, value in override.items(): + if ( + key in result + and isinstance(result[key], dict) + and isinstance(value, dict) + ): + result[key] = self._deep_merge(result[key], value) + else: + result[key] = value + + return result diff --git a/api/app/i18n/logger.py b/api/app/i18n/logger.py new file mode 100644 index 00000000..9a81fc79 --- /dev/null +++ b/api/app/i18n/logger.py @@ -0,0 +1,382 @@ +""" +Translation logging for i18n system. + +This module provides: +- TranslationLogger for recording missing translations +- Missing translation report generation +- Integration with existing logging system +- Structured logging for translation events +""" + +import logging +from typing import Dict, List, Optional, Set +from datetime import datetime +from collections import defaultdict +from pathlib import Path +import json + +from app.core.logging_config import get_logger + +logger = get_logger(__name__) + + +class TranslationLogger: + """ + Logger for translation events and missing translations. + + Features: + - Records missing translations with context + - Generates missing translation reports + - Integrates with existing logging system + - Provides structured logging for analysis + """ + + def __init__(self, log_file: Optional[str] = None): + """ + Initialize translation logger. + + Args: + log_file: Optional custom log file path for missing translations + """ + self.log_file = log_file or "logs/i18n/missing_translations.log" + self._missing_translations: Dict[str, Set[str]] = defaultdict(set) + self._missing_with_context: List[Dict] = [] + self._max_context_entries = 10000 # Keep last 10k entries + + # Ensure log directory exists + log_path = Path(self.log_file) + log_path.parent.mkdir(parents=True, exist_ok=True) + + # Create dedicated file handler for missing translations + self._file_handler = logging.FileHandler( + self.log_file, + encoding='utf-8' + ) + self._file_handler.setLevel(logging.WARNING) + + # Create formatter + formatter = logging.Formatter( + fmt='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' + ) + self._file_handler.setFormatter(formatter) + + # Create dedicated logger for missing translations + self._logger = logging.getLogger("i18n.missing_translations") + self._logger.setLevel(logging.WARNING) + self._logger.addHandler(self._file_handler) + self._logger.propagate = False # Don't propagate to root logger + + logger.info(f"TranslationLogger initialized with log file: {self.log_file}") + + def log_missing_translation( + self, + key: str, + locale: str, + context: Optional[Dict] = None + ): + """ + Log a missing translation. + + Args: + key: Translation key that was not found + locale: Locale code + context: Optional context information (e.g., request path, user info) + """ + # Add to missing set + self._missing_translations[locale].add(key) + + # Create context entry + entry = { + "timestamp": datetime.now().isoformat(), + "key": key, + "locale": locale, + "context": context or {} + } + + # Keep only recent entries to avoid memory bloat + if len(self._missing_with_context) >= self._max_context_entries: + self._missing_with_context.pop(0) + + self._missing_with_context.append(entry) + + # Log to file + context_str = f" (context: {context})" if context else "" + self._logger.warning( + f"Missing translation: key='{key}', locale='{locale}'{context_str}" + ) + + def log_translation_error( + self, + error_type: str, + message: str, + key: Optional[str] = None, + locale: Optional[str] = None, + context: Optional[Dict] = None + ): + """ + Log a translation error. + + Args: + error_type: Type of error (e.g., "format_error", "parameter_missing") + message: Error message + key: Translation key (optional) + locale: Locale code (optional) + context: Optional context information + """ + error_data = { + "error_type": error_type, + "message": message, + "key": key, + "locale": locale, + "context": context or {}, + "timestamp": datetime.now().isoformat() + } + + self._logger.error( + f"Translation error: {error_type} - {message} " + f"(key: {key}, locale: {locale})" + ) + + def log_translation_success( + self, + key: str, + locale: str, + duration_ms: Optional[float] = None + ): + """ + Log a successful translation (debug level). + + Args: + key: Translation key + locale: Locale code + duration_ms: Optional duration in milliseconds + """ + duration_str = f" ({duration_ms:.3f}ms)" if duration_ms else "" + logger.debug( + f"Translation success: key='{key}', locale='{locale}'{duration_str}" + ) + + def get_missing_translations( + self, + locale: Optional[str] = None + ) -> Dict[str, List[str]]: + """ + Get missing translations. + + Args: + locale: Specific locale (optional, returns all if None) + + Returns: + Dictionary of missing translations by locale + """ + if locale: + return {locale: sorted(list(self._missing_translations.get(locale, set())))} + + return { + loc: sorted(list(keys)) + for loc, keys in self._missing_translations.items() + } + + def get_missing_with_context( + self, + locale: Optional[str] = None, + limit: Optional[int] = None + ) -> List[Dict]: + """ + Get missing translations with context. + + Args: + locale: Filter by locale (optional) + limit: Maximum number of entries to return (optional) + + Returns: + List of missing translation entries with context + """ + entries = self._missing_with_context + + # Filter by locale if specified + if locale: + entries = [e for e in entries if e["locale"] == locale] + + # Apply limit if specified + if limit: + entries = entries[-limit:] + + return entries + + def generate_report( + self, + locale: Optional[str] = None, + output_file: Optional[str] = None + ) -> Dict: + """ + Generate a missing translation report. + + Args: + locale: Specific locale (optional, generates for all if None) + output_file: Optional file path to save report as JSON + + Returns: + Report dictionary + """ + missing = self.get_missing_translations(locale) + + report = { + "generated_at": datetime.now().isoformat(), + "total_missing": sum(len(keys) for keys in missing.values()), + "missing_by_locale": { + loc: { + "count": len(keys), + "keys": keys + } + for loc, keys in missing.items() + }, + "recent_context": self.get_missing_with_context(locale, limit=100) + } + + # Save to file if specified + if output_file: + output_path = Path(output_file) + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(report, f, indent=2, ensure_ascii=False) + + logger.info(f"Missing translation report saved to: {output_file}") + + return report + + def get_statistics(self) -> Dict: + """ + Get statistics about missing translations. + + Returns: + Dictionary with statistics + """ + total_missing = sum(len(keys) for keys in self._missing_translations.values()) + + # Count by namespace + namespace_counts = defaultdict(int) + for locale, keys in self._missing_translations.items(): + for key in keys: + namespace = key.split('.')[0] if '.' in key else 'unknown' + namespace_counts[namespace] += 1 + + return { + "total_missing": total_missing, + "locales_affected": len(self._missing_translations), + "missing_by_locale": { + loc: len(keys) + for loc, keys in self._missing_translations.items() + }, + "missing_by_namespace": dict(namespace_counts), + "total_context_entries": len(self._missing_with_context) + } + + def clear(self, locale: Optional[str] = None): + """ + Clear missing translation records. + + Args: + locale: Specific locale to clear (optional, clears all if None) + """ + if locale: + self._missing_translations.pop(locale, None) + self._missing_with_context = [ + e for e in self._missing_with_context + if e["locale"] != locale + ] + logger.info(f"Cleared missing translations for locale: {locale}") + else: + self._missing_translations.clear() + self._missing_with_context.clear() + logger.info("Cleared all missing translations") + + def export_to_json(self, output_file: str): + """ + Export all missing translations to JSON file. + + Args: + output_file: Output file path + """ + data = { + "exported_at": datetime.now().isoformat(), + "missing_translations": self.get_missing_translations(), + "statistics": self.get_statistics(), + "recent_context": self.get_missing_with_context(limit=1000) + } + + output_path = Path(output_file) + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=2, ensure_ascii=False) + + logger.info(f"Missing translations exported to: {output_file}") + + def __del__(self): + """Cleanup file handler on deletion.""" + try: + if hasattr(self, '_file_handler'): + self._file_handler.close() + self._logger.removeHandler(self._file_handler) + except Exception: + pass + + +# Global translation logger instance +_translation_logger: Optional[TranslationLogger] = None + + +def get_translation_logger() -> TranslationLogger: + """ + Get the global translation logger instance. + + Returns: + TranslationLogger singleton + """ + global _translation_logger + if _translation_logger is None: + _translation_logger = TranslationLogger() + return _translation_logger + + +def log_missing_translation( + key: str, + locale: str, + context: Optional[Dict] = None +): + """ + Log a missing translation (convenience function). + + Args: + key: Translation key + locale: Locale code + context: Optional context information + """ + translation_logger = get_translation_logger() + translation_logger.log_missing_translation(key, locale, context) + + +def log_translation_error( + error_type: str, + message: str, + key: Optional[str] = None, + locale: Optional[str] = None, + context: Optional[Dict] = None +): + """ + Log a translation error (convenience function). + + Args: + error_type: Type of error + message: Error message + key: Translation key (optional) + locale: Locale code (optional) + context: Optional context information + """ + translation_logger = get_translation_logger() + translation_logger.log_translation_error( + error_type, message, key, locale, context + ) diff --git a/api/app/i18n/metrics.py b/api/app/i18n/metrics.py new file mode 100644 index 00000000..781ba83e --- /dev/null +++ b/api/app/i18n/metrics.py @@ -0,0 +1,337 @@ +""" +Performance monitoring and metrics for i18n system. + +This module provides: +- Translation request counters +- Translation timing metrics +- Missing translation tracking +- Performance monitoring decorators +- Prometheus-compatible metrics +""" + +import logging +import time +from functools import wraps +from typing import Any, Callable, Dict, Optional +from collections import defaultdict +from datetime import datetime + +logger = logging.getLogger(__name__) + + +class TranslationMetrics: + """ + Metrics collector for translation operations. + + Tracks: + - Translation request counts + - Translation timing (latency) + - Missing translations + - Cache performance + - Locale usage + """ + + def __init__(self): + """Initialize metrics collector.""" + # Request counters by locale + self._request_counts: Dict[str, int] = defaultdict(int) + + # Missing translation tracker + self._missing_translations: Dict[str, set] = defaultdict(set) + + # Timing metrics (in milliseconds) + self._timing_data: list = [] + self._max_timing_samples = 10000 # Keep last 10k samples + + # Locale usage + self._locale_usage: Dict[str, int] = defaultdict(int) + + # Namespace usage + self._namespace_usage: Dict[str, int] = defaultdict(int) + + # Error counts + self._error_counts: Dict[str, int] = defaultdict(int) + + # Start time + self._start_time = datetime.now() + + logger.info("TranslationMetrics initialized") + + def record_request(self, locale: str, namespace: str = None): + """ + Record a translation request. + + Args: + locale: Locale code + namespace: Translation namespace (optional) + """ + self._request_counts[locale] += 1 + self._locale_usage[locale] += 1 + + if namespace: + self._namespace_usage[namespace] += 1 + + def record_missing(self, key: str, locale: str): + """ + Record a missing translation. + + Args: + key: Translation key + locale: Locale code + """ + self._missing_translations[locale].add(key) + logger.debug(f"Missing translation recorded: {key} (locale: {locale})") + + def record_timing(self, duration_ms: float, locale: str, operation: str = "translate"): + """ + Record translation operation timing. + + Args: + duration_ms: Duration in milliseconds + locale: Locale code + operation: Operation type + """ + # Keep only recent samples to avoid memory bloat + if len(self._timing_data) >= self._max_timing_samples: + self._timing_data.pop(0) + + self._timing_data.append({ + "duration_ms": duration_ms, + "locale": locale, + "operation": operation, + "timestamp": time.time() + }) + + def record_error(self, error_type: str): + """ + Record an error. + + Args: + error_type: Type of error + """ + self._error_counts[error_type] += 1 + + def get_summary(self) -> Dict[str, Any]: + """ + Get metrics summary. + + Returns: + Dictionary with metrics summary + """ + total_requests = sum(self._request_counts.values()) + total_missing = sum(len(keys) for keys in self._missing_translations.values()) + + # Calculate timing statistics + timing_stats = self._calculate_timing_stats() + + # Calculate uptime + uptime_seconds = (datetime.now() - self._start_time).total_seconds() + + return { + "uptime_seconds": round(uptime_seconds, 2), + "total_requests": total_requests, + "requests_per_locale": dict(self._request_counts), + "total_missing_translations": total_missing, + "missing_by_locale": { + locale: len(keys) + for locale, keys in self._missing_translations.items() + }, + "timing": timing_stats, + "locale_usage": dict(self._locale_usage), + "namespace_usage": dict(self._namespace_usage), + "error_counts": dict(self._error_counts) + } + + def _calculate_timing_stats(self) -> Dict[str, Any]: + """ + Calculate timing statistics. + + Returns: + Dictionary with timing statistics + """ + if not self._timing_data: + return { + "count": 0, + "avg_ms": 0, + "min_ms": 0, + "max_ms": 0, + "p50_ms": 0, + "p95_ms": 0, + "p99_ms": 0 + } + + durations = [d["duration_ms"] for d in self._timing_data] + durations.sort() + + count = len(durations) + avg = sum(durations) / count + + # Calculate percentiles + p50_idx = int(count * 0.50) + p95_idx = int(count * 0.95) + p99_idx = int(count * 0.99) + + return { + "count": count, + "avg_ms": round(avg, 3), + "min_ms": round(durations[0], 3), + "max_ms": round(durations[-1], 3), + "p50_ms": round(durations[p50_idx], 3), + "p95_ms": round(durations[p95_idx], 3), + "p99_ms": round(durations[p99_idx], 3) + } + + def get_missing_translations(self, locale: Optional[str] = None) -> Dict[str, list]: + """ + Get missing translations. + + Args: + locale: Specific locale (optional, returns all if None) + + Returns: + Dictionary of missing translations by locale + """ + if locale: + return {locale: list(self._missing_translations.get(locale, set()))} + + return { + locale: list(keys) + for locale, keys in self._missing_translations.items() + } + + def reset(self): + """Reset all metrics.""" + self._request_counts.clear() + self._missing_translations.clear() + self._timing_data.clear() + self._locale_usage.clear() + self._namespace_usage.clear() + self._error_counts.clear() + self._start_time = datetime.now() + logger.info("Metrics reset") + + def export_prometheus(self) -> str: + """ + Export metrics in Prometheus format. + + Returns: + Prometheus-formatted metrics string + """ + lines = [] + + # Translation requests counter + lines.append("# HELP i18n_translation_requests_total Total number of translation requests") + lines.append("# TYPE i18n_translation_requests_total counter") + for locale, count in self._request_counts.items(): + lines.append(f'i18n_translation_requests_total{{locale="{locale}"}} {count}') + + # Missing translations counter + lines.append("# HELP i18n_missing_translations_total Total number of missing translations") + lines.append("# TYPE i18n_missing_translations_total counter") + for locale, keys in self._missing_translations.items(): + lines.append(f'i18n_missing_translations_total{{locale="{locale}"}} {len(keys)}') + + # Timing metrics + timing_stats = self._calculate_timing_stats() + lines.append("# HELP i18n_translation_duration_ms Translation operation duration in milliseconds") + lines.append("# TYPE i18n_translation_duration_ms summary") + lines.append(f'i18n_translation_duration_ms{{quantile="0.5"}} {timing_stats["p50_ms"]}') + lines.append(f'i18n_translation_duration_ms{{quantile="0.95"}} {timing_stats["p95_ms"]}') + lines.append(f'i18n_translation_duration_ms{{quantile="0.99"}} {timing_stats["p99_ms"]}') + lines.append(f'i18n_translation_duration_ms_sum {sum(d["duration_ms"] for d in self._timing_data)}') + lines.append(f'i18n_translation_duration_ms_count {timing_stats["count"]}') + + # Error counter + lines.append("# HELP i18n_errors_total Total number of i18n errors") + lines.append("# TYPE i18n_errors_total counter") + for error_type, count in self._error_counts.items(): + lines.append(f'i18n_errors_total{{type="{error_type}"}} {count}') + + return "\n".join(lines) + + +# Global metrics instance +_metrics: Optional[TranslationMetrics] = None + + +def get_metrics() -> TranslationMetrics: + """ + Get the global metrics instance. + + Returns: + TranslationMetrics singleton + """ + global _metrics + if _metrics is None: + _metrics = TranslationMetrics() + return _metrics + + +def monitor_performance(operation: str = "translate"): + """ + Decorator to monitor translation operation performance. + + Args: + operation: Operation name for metrics + + Returns: + Decorated function + + Example: + @monitor_performance("translate") + def translate(key: str, locale: str) -> str: + ... + """ + def decorator(func: Callable) -> Callable: + @wraps(func) + def wrapper(*args, **kwargs): + start_time = time.perf_counter() + + try: + result = func(*args, **kwargs) + + # Record timing + duration_ms = (time.perf_counter() - start_time) * 1000 + + # Try to extract locale from args/kwargs + locale = kwargs.get("locale", "unknown") + if not locale and len(args) > 1: + locale = args[1] if isinstance(args[1], str) else "unknown" + + metrics = get_metrics() + metrics.record_timing(duration_ms, locale, operation) + + return result + + except Exception as e: + # Record error + metrics = get_metrics() + metrics.record_error(type(e).__name__) + raise + + return wrapper + return decorator + + +def track_missing_translation(key: str, locale: str): + """ + Track a missing translation. + + Args: + key: Translation key + locale: Locale code + """ + metrics = get_metrics() + metrics.record_missing(key, locale) + + +def track_translation_request(locale: str, namespace: str = None): + """ + Track a translation request. + + Args: + locale: Locale code + namespace: Translation namespace (optional) + """ + metrics = get_metrics() + metrics.record_request(locale, namespace) diff --git a/api/app/i18n/middleware.py b/api/app/i18n/middleware.py new file mode 100644 index 00000000..2e945dde --- /dev/null +++ b/api/app/i18n/middleware.py @@ -0,0 +1,202 @@ +""" +Language detection middleware for i18n system. + +This middleware determines the language to use for each request based on: +1. Query parameter (?lang=en) +2. Accept-Language HTTP header +3. User language preference (from database) +4. Tenant default language +5. System default language + +The detected language is injected into request.state.language and +added to the response Content-Language header. +""" + +import logging +import re +from typing import Optional + +from fastapi import Request +from starlette.middleware.base import BaseHTTPMiddleware + +logger = logging.getLogger(__name__) + + +class LanguageMiddleware(BaseHTTPMiddleware): + """ + Language detection middleware. + + Determines the language for each request based on multiple sources + with a clear priority order, validates the language is supported, + and injects it into the request context. + """ + + async def dispatch(self, request: Request, call_next): + """ + Process the request and determine the language. + + Args: + request: The incoming request + call_next: The next middleware/handler in the chain + + Returns: + Response with Content-Language header added + """ + # Determine the language for this request + language = await self._determine_language(request) + + # Validate language is supported + from app.core.config import settings + if language not in settings.I18N_SUPPORTED_LANGUAGES: + logger.warning( + f"Unsupported language '{language}' requested, " + f"falling back to default: {settings.I18N_DEFAULT_LANGUAGE}" + ) + language = settings.I18N_DEFAULT_LANGUAGE + + # Inject language into request state + request.state.language = language + + # Also set in context variable for exception handling + from app.i18n.exceptions import set_current_locale + set_current_locale(language) + + logger.debug(f"Request language set to: {language}") + + # Process the request + response = await call_next(request) + + # Add Content-Language header to response + response.headers["Content-Language"] = language + + return response + + async def _determine_language(self, request: Request) -> str: + """ + Determine the language to use based on priority order. + + Priority: + 1. Query parameter (?lang=en) + 2. Accept-Language HTTP header + 3. User language preference (from database) + 4. Tenant default language + 5. System default language + + Args: + request: The incoming request + + Returns: + Language code (e.g., "zh", "en") + """ + from app.core.config import settings + + # 1. Check query parameter (?lang=en) + if "lang" in request.query_params: + lang = request.query_params["lang"].strip().lower() + if lang: + logger.debug(f"Language from query parameter: {lang}") + return lang + + # 2. Check Accept-Language HTTP header + if "Accept-Language" in request.headers: + lang = self._parse_accept_language( + request.headers["Accept-Language"] + ) + if lang: + logger.debug(f"Language from Accept-Language header: {lang}") + return lang + + # 3. Check user language preference (requires authentication) + # Note: This assumes user is already loaded into request.state by auth middleware + if hasattr(request.state, "user") and request.state.user: + user = request.state.user + if hasattr(user, "preferred_language") and user.preferred_language: + logger.debug( + f"Language from user preference: {user.preferred_language}" + ) + return user.preferred_language + + # 4. Check tenant default language + # Note: This assumes tenant is already loaded into request.state + if hasattr(request.state, "tenant") and request.state.tenant: + tenant = request.state.tenant + if hasattr(tenant, "default_language") and tenant.default_language: + logger.debug( + f"Language from tenant default: {tenant.default_language}" + ) + return tenant.default_language + + # 5. Fall back to system default language + logger.debug( + f"Using system default language: {settings.I18N_DEFAULT_LANGUAGE}" + ) + return settings.I18N_DEFAULT_LANGUAGE + + def _parse_accept_language(self, header: str) -> Optional[str]: + """ + Parse the Accept-Language HTTP header. + + The Accept-Language header format: + Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-US;q=0.7 + + This method: + 1. Parses all language codes and their quality values + 2. Extracts the base language code (zh-CN -> zh) + 3. Sorts by quality value (higher first) + 4. Returns the first supported language + + Args: + header: Accept-Language header value + + Returns: + Language code if found and supported, None otherwise + + Examples: + _parse_accept_language("zh-CN,zh;q=0.9,en;q=0.8") + # => "zh" (if zh is supported) + + _parse_accept_language("en-US,en;q=0.9") + # => "en" (if en is supported) + """ + from app.core.config import settings + + if not header: + return None + + # Parse language preferences with quality values + languages = [] + + for item in header.split(","): + item = item.strip() + if not item: + continue + + # Split language code and quality value + parts = item.split(";") + lang_code = parts[0].strip() + + # Extract base language code (zh-CN -> zh, en-US -> en) + base_lang = lang_code.split("-")[0].lower() + + # Extract quality value (default: 1.0) + quality = 1.0 + if len(parts) > 1: + # Look for q=0.9 pattern + q_match = re.search(r"q=([\d.]+)", parts[1]) + if q_match: + try: + quality = float(q_match.group(1)) + except ValueError: + quality = 1.0 + + languages.append((base_lang, quality)) + + # Sort by quality value (descending) + languages.sort(key=lambda x: x[1], reverse=True) + + # Return the first supported language + for lang_code, _ in languages: + if lang_code in settings.I18N_SUPPORTED_LANGUAGES: + return lang_code + + return None diff --git a/api/app/i18n/serializers.py b/api/app/i18n/serializers.py new file mode 100644 index 00000000..9381b8f0 --- /dev/null +++ b/api/app/i18n/serializers.py @@ -0,0 +1,219 @@ +""" +国际化响应序列化器 + +提供基础的 I18nResponseMixin 类，用于为 API 响应添加国际化字段。 +""" + +from typing import Any, Dict, List, Union +from pydantic import BaseModel + + +class I18nResponseMixin: + """国际化响应混入类 + + 为响应数据添加国际化字段，特别是为枚举值添加 _display 后缀的翻译字段。 + + 使用方法： + 1. 继承此类 + 2. 实现 _get_enum_fields() 方法定义需要翻译的枚举字段 + 3. 调用 serialize_with_i18n() 方法序列化数据 + + 示例： + class WorkspaceSerializer(I18nResponseMixin): + def _get_enum_fields(self) -> Dict[str, str]: + return { + "role": "workspace_role", + "status": "workspace_status" + } + + def serialize(self, workspace: Workspace, locale: str = "zh") -> Dict: + data = { + "id": str(workspace.id), + "name": workspace.name, + "role": workspace.role, + "status": workspace.status + } + return self.serialize_with_i18n(data, locale) + """ + + def serialize_with_i18n( + self, + data: Any, + locale: str = "zh" + ) -> Union[Dict, List[Dict], Any]: + """序列化数据并添加国际化字段 + + Args: + data: 要序列化的数据（字典、列表或 Pydantic 模型） + locale: 语言代码 + + Returns: + 序列化后的数据，包含国际化字段 + """ + # 如果是 Pydantic 模型，转换为字典 + if isinstance(data, BaseModel): + data = data.model_dump() + + # 处理不同类型的数据 + if isinstance(data, dict): + return self._serialize_dict(data, locale) + elif isinstance(data, list): + return [self._serialize_dict(item, locale) if isinstance(item, dict) else item for item in data] + else: + return data + + def _serialize_dict(self, data: Dict, locale: str) -> Dict: + """序列化字典并添加 _display 字段 + + Args: + data: 字典数据 + locale: 语言代码 + + Returns: + 添加了 _display 字段的字典 + """ + from app.i18n.service import translation_service + + result = data.copy() + + # 获取需要翻译的枚举字段 + enum_fields = self._get_enum_fields() + + # 为每个枚举字段添加 _display 字段 + for field, enum_type in enum_fields.items(): + if field in result and result[field] is not None: + value = result[field] + # 翻译枚举值 + display_value = translation_service.translate_enum( + enum_type=enum_type, + value=str(value), + locale=locale + ) + # 添加 _display 字段 + result[f"{field}_display"] = display_value + + return result + + def _get_enum_fields(self) -> Dict[str, str]: + """获取需要翻译的枚举字段 + + 子类必须实现此方法，返回字段名到枚举类型的映射。 + + Returns: + 字段名到枚举类型的映射 + 例如: {"role": "workspace_role", "status": "workspace_status"} + """ + return {} + + +class WorkspaceSerializer(I18nResponseMixin): + """工作空间序列化器 + + 为工作空间响应添加国际化字段。 + """ + + def _get_enum_fields(self) -> Dict[str, str]: + """定义工作空间的枚举字段""" + return { + "role": "workspace_role", + "status": "workspace_status" + } + + def serialize(self, workspace_data: Union[Dict, BaseModel], locale: str = "zh") -> Dict: + """序列化工作空间数据 + + Args: + workspace_data: 工作空间数据（字典或 Pydantic 模型） + locale: 语言代码 + + Returns: + 序列化后的工作空间数据，包含国际化字段 + """ + return self.serialize_with_i18n(workspace_data, locale) + + def serialize_list(self, workspaces: List[Union[Dict, BaseModel]], locale: str = "zh") -> List[Dict]: + """序列化工作空间列表 + + Args: + workspaces: 工作空间列表 + locale: 语言代码 + + Returns: + 序列化后的工作空间列表 + """ + return [self.serialize(ws, locale) for ws in workspaces] + + +class WorkspaceMemberSerializer(I18nResponseMixin): + """工作空间成员序列化器 + + 为工作空间成员响应添加国际化字段。 + """ + + def _get_enum_fields(self) -> Dict[str, str]: + """定义工作空间成员的枚举字段""" + return { + "role": "workspace_role" + } + + def serialize(self, member_data: Union[Dict, BaseModel], locale: str = "zh") -> Dict: + """序列化工作空间成员数据 + + Args: + member_data: 成员数据（字典或 Pydantic 模型） + locale: 语言代码 + + Returns: + 序列化后的成员数据，包含国际化字段 + """ + return self.serialize_with_i18n(member_data, locale) + + def serialize_list(self, members: List[Union[Dict, BaseModel]], locale: str = "zh") -> List[Dict]: + """序列化工作空间成员列表 + + Args: + members: 成员列表 + locale: 语言代码 + + Returns: + 序列化后的成员列表 + """ + return [self.serialize(member, locale) for member in members] + + +class WorkspaceInviteSerializer(I18nResponseMixin): + """工作空间邀请序列化器 + + 为工作空间邀请响应添加国际化字段。 + """ + + def _get_enum_fields(self) -> Dict[str, str]: + """定义工作空间邀请的枚举字段""" + return { + "status": "invite_status", + "role": "workspace_role" + } + + def serialize(self, invite_data: Union[Dict, BaseModel], locale: str = "zh") -> Dict: + """序列化工作空间邀请数据 + + Args: + invite_data: 邀请数据（字典或 Pydantic 模型） + locale: 语言代码 + + Returns: + 序列化后的邀请数据，包含国际化字段 + """ + return self.serialize_with_i18n(invite_data, locale) + + def serialize_list(self, invites: List[Union[Dict, BaseModel]], locale: str = "zh") -> List[Dict]: + """序列化工作空间邀请列表 + + Args: + invites: 邀请列表 + locale: 语言代码 + + Returns: + 序列化后的邀请列表 + """ + return [self.serialize(invite, locale) for invite in invites] diff --git a/api/app/i18n/service.py b/api/app/i18n/service.py new file mode 100644 index 00000000..9cbc0926 --- /dev/null +++ b/api/app/i18n/service.py @@ -0,0 +1,370 @@ +""" +Translation service for i18n system. + +This module provides the core translation functionality including: +- Translation lookup with fallback mechanism +- Parameterized message support +- Enum value translation +- Memory caching for performance +- Performance monitoring and metrics +""" + +import logging +from functools import lru_cache +from typing import Any, Dict, Optional + +from app.i18n.loader import TranslationLoader +from app.i18n.cache import TranslationCache +from app.i18n.metrics import get_metrics, monitor_performance, track_missing_translation, track_translation_request +from app.i18n.logger import get_translation_logger + +logger = logging.getLogger(__name__) + + +class TranslationService: + """ + Translation service that provides: + - Fast translation lookup with memory cache + - Parameterized message support ({param} syntax) + - Fallback mechanism (current locale → default locale → key) + - Enum value translation + - Deep merge of multi-directory translations + """ + + def __init__(self, locales_dirs: Optional[list] = None): + """ + Initialize the translation service. + + Args: + locales_dirs: List of directories containing translation files. + If None, will auto-detect from settings. + """ + from app.core.config import settings + + self.loader = TranslationLoader(locales_dirs) + self.default_locale = settings.I18N_DEFAULT_LANGUAGE + self.fallback_locale = settings.I18N_FALLBACK_LANGUAGE + self.log_missing = settings.I18N_LOG_MISSING_TRANSLATIONS + self.enable_cache = settings.I18N_ENABLE_TRANSLATION_CACHE + + # Initialize advanced cache with LRU + lru_cache_size = getattr(settings, 'I18N_LRU_CACHE_SIZE', 1000) + self.cache = TranslationCache( + max_lru_size=lru_cache_size, + enable_lazy_load=False # Load all at startup for now + ) + + # Load all translations into cache + self._load_all_locales() + + # Initialize metrics + self.metrics = get_metrics() + + # Initialize translation logger + self.translation_logger = get_translation_logger() + + logger.info( + f"TranslationService initialized with default locale: {self.default_locale}, " + f"LRU cache size: {lru_cache_size}" + ) + + def _load_all_locales(self): + """Load all available locales into memory cache.""" + available_locales = self.loader.get_available_locales() + logger.info(f"Loading translations for locales: {available_locales}") + + for locale in available_locales: + locale_data = self.loader.load_locale(locale) + self.cache.set_locale_data(locale, locale_data) + + logger.info(f"Loaded {len(available_locales)} locales into cache") + + @monitor_performance("translate") + def translate( + self, + key: str, + locale: Optional[str] = None, + **params + ) -> str: + """ + Translate a key to the target locale. + + Supports: + - Dot-separated keys (e.g., "common.success.created") + - Parameterized messages (e.g., "Hello {name}") + - Fallback mechanism + + Args: + key: Translation key (format: "namespace.key.subkey") + locale: Target locale (defaults to default locale) + **params: Parameters for parameterized messages + + Returns: + Translated string, or the key itself if translation not found + + Examples: + translate("common.success.created", "zh") + # => "创建成功" + + translate("common.validation.required", "zh", field="名称") + # => "名称不能为空" + """ + if locale is None: + locale = self.default_locale + + # Parse key (namespace.key.subkey) + parts = key.split(".", 1) + if len(parts) < 2: + if self.log_missing: + logger.warning(f"Invalid translation key format: {key}") + return key + + namespace = parts[0] + key_path = parts[1].split(".") + + # Track request + track_translation_request(locale, namespace) + + # Get translation from cache + translation = self.cache.get_translation(locale, namespace, key_path) + + # Fallback to default locale if not found + if translation is None and locale != self.fallback_locale: + translation = self.cache.get_translation( + self.fallback_locale, namespace, key_path + ) + + # If still not found, return the key itself + if translation is None: + if self.log_missing: + logger.warning( + f"Missing translation: {key} (locale: {locale})" + ) + track_missing_translation(key, locale) + + # Log to translation logger with context + self.translation_logger.log_missing_translation( + key=key, + locale=locale, + context={"namespace": namespace} + ) + return key + + # Apply parameters if provided + if params: + try: + translation = translation.format(**params) + except KeyError as e: + error_msg = f"Missing parameter in translation '{key}': {e}" + logger.error(error_msg) + self.translation_logger.log_translation_error( + error_type="parameter_missing", + message=error_msg, + key=key, + locale=locale, + context={"params": list(params.keys())} + ) + except Exception as e: + error_msg = f"Error formatting translation '{key}': {e}" + logger.error(error_msg) + self.translation_logger.log_translation_error( + error_type="format_error", + message=error_msg, + key=key, + locale=locale + ) + + return translation + + def _get_translation( + self, + locale: str, + namespace: str, + key_path: list + ) -> Optional[str]: + """ + Get translation from cache (deprecated, use cache.get_translation). + + Args: + locale: Locale code + namespace: Translation namespace + key_path: List of nested keys + + Returns: + Translation string or None if not found + """ + return self.cache.get_translation(locale, namespace, key_path) + + @monitor_performance("translate_enum") + def translate_enum( + self, + enum_type: str, + value: str, + locale: Optional[str] = None + ) -> str: + """ + Translate an enum value. + + Args: + enum_type: Enum type name (e.g., "workspace_role") + value: Enum value (e.g., "manager") + locale: Target locale + + Returns: + Translated enum display name + + Examples: + translate_enum("workspace_role", "manager", "zh") + # => "管理员" + + translate_enum("invite_status", "pending", "en") + # => "Pending" + """ + key = f"enums.{enum_type}.{value}" + return self.translate(key, locale) + + def has_translation(self, key: str, locale: str) -> bool: + """ + Check if a translation exists for the given key and locale. + + Args: + key: Translation key + locale: Locale code + + Returns: + True if translation exists, False otherwise + """ + parts = key.split(".", 1) + if len(parts) < 2: + return False + + namespace = parts[0] + key_path = parts[1].split(".") + + translation = self.cache.get_translation(locale, namespace, key_path) + return translation is not None + + def reload(self, locale: Optional[str] = None): + """ + Reload translation files. + + Args: + locale: Specific locale to reload. If None, reloads all locales. + """ + logger.info(f"Reloading translations for locale: {locale or 'all'}") + + if locale: + locale_data = self.loader.load_locale(locale) + self.cache.set_locale_data(locale, locale_data) + # Clear LRU cache for this locale + self.cache.clear_locale(locale) + else: + self._load_all_locales() + # Clear all LRU cache + self.cache.clear_lru() + + logger.info("Translation reload completed") + + def get_available_locales(self) -> list: + """ + Get list of all available locales. + + Returns: + List of locale codes + """ + return self.cache.get_loaded_locales() + + def get_cache_stats(self) -> Dict[str, Any]: + """ + Get cache statistics. + + Returns: + Dictionary with cache statistics + """ + return self.cache.get_stats() + + def get_metrics_summary(self) -> Dict[str, Any]: + """ + Get metrics summary. + + Returns: + Dictionary with metrics summary + """ + return self.metrics.get_summary() + + def get_memory_usage(self) -> Dict[str, Any]: + """ + Get memory usage information. + + Returns: + Dictionary with memory usage information + """ + return self.cache.get_memory_usage() + + def get_loaded_dirs(self) -> list: + """ + Get list of loaded translation directories. + + Returns: + List of directory paths + """ + return self.loader.locales_dirs + + +# Global singleton instance +_translation_service: Optional[TranslationService] = None + + +def get_translation_service() -> TranslationService: + """ + Get the global translation service instance. + + Returns: + TranslationService singleton + """ + global _translation_service + if _translation_service is None: + _translation_service = TranslationService() + return _translation_service + + +# Convenience functions for easy access +def t(key: str, locale: Optional[str] = None, **params) -> str: + """ + Translate a key (convenience function). + + Args: + key: Translation key + locale: Target locale (optional, uses default if not provided) + **params: Parameters for parameterized messages + + Returns: + Translated string + + Examples: + t("common.success.created") + t("common.validation.required", field="名称") + t("workspace.member_count", count=5) + """ + service = get_translation_service() + return service.translate(key, locale, **params) + + +def t_enum(enum_type: str, value: str, locale: Optional[str] = None) -> str: + """ + Translate an enum value (convenience function). + + Args: + enum_type: Enum type name + value: Enum value + locale: Target locale + + Returns: + Translated enum display name + + Examples: + t_enum("workspace_role", "manager") + t_enum("invite_status", "pending", "en") + """ + service = get_translation_service() + return service.translate_enum(enum_type, value, locale) diff --git a/api/app/locales/en/README.md b/api/app/locales/en/README.md new file mode 100644 index 00000000..0a605a60 --- /dev/null +++ b/api/app/locales/en/README.md @@ -0,0 +1,26 @@ +# English Translation Files + +This directory contains English translation files. + +## File Structure + +- `common.json` - Common translations (success messages, actions, validation) +- `auth.json` - Authentication module translations +- `workspace.json` - Workspace module translations +- `tenant.json` - Tenant module translations +- `errors.json` - Error message translations +- `enums.json` - Enum value translations + +## Translation File Format + +All translation files use JSON format and support nested structures. + +Example: +```json +{ + "success": { + "created": "Created successfully", + "updated": "Updated successfully" + } +} +``` diff --git a/api/app/locales/en/auth.json b/api/app/locales/en/auth.json new file mode 100644 index 00000000..50ba866b --- /dev/null +++ b/api/app/locales/en/auth.json @@ -0,0 +1,55 @@ +{ + "login": { + "success": "Login successful", + "failed": "Login failed", + "invalid_credentials": "Invalid username or password", + "account_locked": "Account has been locked", + "account_disabled": "Account has been disabled" + }, + "logout": { + "success": "Logout successful", + "failed": "Logout failed" + }, + "token": { + "refresh_success": "Token refreshed successfully", + "invalid": "Invalid token", + "expired": "Token has expired", + "blacklisted": "Token has been invalidated", + "invalid_refresh_token": "Invalid refresh token", + "refresh_token_blacklisted": "Refresh token has been invalidated" + }, + "registration": { + "success": "Registration successful", + "failed": "Registration failed", + "email_exists": "Email already in use", + "username_exists": "Username already taken" + }, + "password": { + "reset_success": "Password reset successful", + "reset_failed": "Password reset failed", + "change_success": "Password changed successfully", + "change_failed": "Password change failed", + "incorrect": "Incorrect password", + "too_weak": "Password is too weak", + "mismatch": "Passwords do not match" + }, + "invite": { + "invalid": "Invalid or expired invite code", + "email_mismatch": "Invite email does not match login email", + "accept_success": "Invite accepted successfully", + "accept_failed": "Failed to accept invite", + "password_verification_failed": "Failed to accept invite, password verification error", + "bind_workspace_success": "Workspace bound successfully", + "bind_workspace_failed": "Failed to bind workspace" + }, + "user": { + "not_found": "User not found", + "already_exists": "User already exists", + "created_with_invite": "User created successfully and joined workspace" + }, + "session": { + "expired": "Session expired, please login again", + "invalid": "Invalid session", + "single_session_enabled": "Single sign-on enabled, other device sessions will be logged out" + } +} diff --git a/api/app/locales/en/common.json b/api/app/locales/en/common.json new file mode 100644 index 00000000..505f83e3 --- /dev/null +++ b/api/app/locales/en/common.json @@ -0,0 +1,132 @@ +{ + "success": { + "created": "Created successfully", + "updated": "Updated successfully", + "deleted": "Deleted successfully", + "retrieved": "Retrieved successfully", + "saved": "Saved successfully", + "uploaded": "Uploaded successfully", + "downloaded": "Downloaded successfully", + "sent": "Sent successfully", + "completed": "Completed", + "confirmed": "Confirmed", + "cancelled": "Cancelled", + "archived": "Archived", + "restored": "Restored" + }, + "actions": { + "create": "Create", + "update": "Update", + "delete": "Delete", + "view": "View", + "edit": "Edit", + "save": "Save", + "cancel": "Cancel", + "confirm": "Confirm", + "submit": "Submit", + "upload": "Upload", + "download": "Download", + "send": "Send", + "search": "Search", + "filter": "Filter", + "sort": "Sort", + "export": "Export", + "import": "Import", + "refresh": "Refresh", + "reset": "Reset", + "back": "Back", + "next": "Next", + "previous": "Previous", + "finish": "Finish", + "close": "Close", + "open": "Open", + "archive": "Archive", + "restore": "Restore", + "duplicate": "Duplicate", + "share": "Share", + "invite": "Invite", + "remove": "Remove", + "add": "Add", + "select": "Select", + "clear": "Clear" + }, + "validation": { + "required": "{field} is required", + "invalid_format": "{field} format is invalid", + "too_long": "{field} cannot exceed {max} characters", + "too_short": "{field} must be at least {min} characters", + "invalid_email": "Invalid email format", + "invalid_url": "Invalid URL format", + "invalid_phone": "Invalid phone number format", + "invalid_date": "Invalid date format", + "invalid_number": "Must be a valid number", + "out_of_range": "{field} must be between {min} and {max}", + "already_exists": "{field} already exists", + "not_found": "{field} not found", + "invalid_value": "Invalid value for {field}", + "password_mismatch": "Passwords do not match", + "weak_password": "Password is too weak, please use a stronger password", + "invalid_credentials": "Invalid username or password", + "unauthorized": "Unauthorized access", + "forbidden": "Permission denied", + "expired": "{field} has expired", + "invalid_token": "Invalid token", + "file_too_large": "File size cannot exceed {max}", + "invalid_file_type": "Unsupported file type", + "duplicate": "Duplicate {field}" + }, + "status": { + "active": "Active", + "inactive": "Inactive", + "pending": "Pending", + "processing": "Processing", + "completed": "Completed", + "failed": "Failed", + "cancelled": "Cancelled", + "archived": "Archived", + "deleted": "Deleted", + "draft": "Draft", + "published": "Published", + "suspended": "Suspended", + "expired": "Expired" + }, + "messages": { + "loading": "Loading...", + "saving": "Saving...", + "processing": "Processing...", + "uploading": "Uploading...", + "downloading": "Downloading...", + "no_data": "No data available", + "no_results": "No results found", + "confirm_delete": "Are you sure you want to delete? This action cannot be undone.", + "confirm_action": "Are you sure you want to perform this action?", + "operation_success": "Operation successful", + "operation_failed": "Operation failed", + "please_wait": "Please wait...", + "try_again": "Please try again", + "contact_support": "If the problem persists, please contact support" + }, + "pagination": { + "page": "Page {page}", + "of": "of {total}", + "items": "{total} items", + "per_page": "{count} per page", + "showing": "Showing {from} to {to} of {total}", + "first": "First", + "last": "Last", + "next": "Next", + "previous": "Previous" + }, + "time": { + "just_now": "Just now", + "minutes_ago": "{count} minutes ago", + "hours_ago": "{count} hours ago", + "days_ago": "{count} days ago", + "weeks_ago": "{count} weeks ago", + "months_ago": "{count} months ago", + "years_ago": "{count} years ago", + "today": "Today", + "yesterday": "Yesterday", + "tomorrow": "Tomorrow" + } +} diff --git a/api/app/locales/en/enums.json b/api/app/locales/en/enums.json new file mode 100644 index 00000000..da7a3ace --- /dev/null +++ b/api/app/locales/en/enums.json @@ -0,0 +1,132 @@ +{ + "workspace_role": { + "owner": "Owner", + "manager": "Manager", + "member": "Member", + "guest": "Guest" + }, + "workspace_status": { + "active": "Active", + "inactive": "Inactive", + "archived": "Archived", + "suspended": "Suspended", + "deleted": "Deleted" + }, + "invite_status": { + "pending": "Pending", + "accepted": "Accepted", + "rejected": "Rejected", + "revoked": "Revoked", + "expired": "Expired" + }, + "user_status": { + "active": "Active", + "inactive": "Inactive", + "suspended": "Suspended", + "deleted": "Deleted", + "pending": "Pending" + }, + "tenant_status": { + "active": "Active", + "inactive": "Inactive", + "suspended": "Suspended", + "expired": "Expired", + "trial": "Trial" + }, + "file_status": { + "uploading": "Uploading", + "processing": "Processing", + "completed": "Completed", + "failed": "Failed", + "deleted": "Deleted" + }, + "task_status": { + "pending": "Pending", + "running": "Running", + "completed": "Completed", + "failed": "Failed", + "cancelled": "Cancelled", + "paused": "Paused" + }, + "priority": { + "low": "Low", + "medium": "Medium", + "high": "High", + "urgent": "Urgent" + }, + "visibility": { + "public": "Public", + "private": "Private", + "internal": "Internal", + "shared": "Shared" + }, + "permission": { + "read": "Read", + "write": "Write", + "delete": "Delete", + "admin": "Admin", + "owner": "Owner" + }, + "notification_type": { + "info": "Info", + "warning": "Warning", + "error": "Error", + "success": "Success" + }, + "language": { + "zh": "Chinese (Simplified)", + "en": "English", + "ja": "Japanese", + "ko": "Korean", + "fr": "French", + "de": "German", + "es": "Spanish" + }, + "timezone": { + "utc": "UTC", + "asia_shanghai": "Asia/Shanghai", + "asia_tokyo": "Asia/Tokyo", + "america_new_york": "America/New_York", + "europe_london": "Europe/London" + }, + "date_format": { + "short": "Short", + "medium": "Medium", + "long": "Long", + "full": "Full" + }, + "sort_order": { + "asc": "Ascending", + "desc": "Descending" + }, + "filter_operator": { + "equals": "Equals", + "not_equals": "Not Equals", + "contains": "Contains", + "not_contains": "Not Contains", + "starts_with": "Starts With", + "ends_with": "Ends With", + "greater_than": "Greater Than", + "less_than": "Less Than", + "greater_or_equal": "Greater or Equal", + "less_or_equal": "Less or Equal", + "in": "In", + "not_in": "Not In", + "is_null": "Is Null", + "is_not_null": "Is Not Null" + }, + "log_level": { + "debug": "Debug", + "info": "Info", + "warning": "Warning", + "error": "Error", + "critical": "Critical" + }, + "api_method": { + "get": "GET", + "post": "POST", + "put": "PUT", + "patch": "PATCH", + "delete": "DELETE" + } +} diff --git a/api/app/locales/en/errors.json b/api/app/locales/en/errors.json new file mode 100644 index 00000000..d0276dc9 --- /dev/null +++ b/api/app/locales/en/errors.json @@ -0,0 +1,138 @@ +{ + "common": { + "internal_error": "Internal server error", + "network_error": "Network connection error", + "timeout": "Request timeout", + "service_unavailable": "Service temporarily unavailable", + "bad_request": "Bad request parameters", + "unauthorized": "Unauthorized access", + "forbidden": "Access forbidden", + "not_found": "Resource not found", + "method_not_allowed": "Method not allowed", + "conflict": "Resource conflict", + "too_many_requests": "Too many requests, please try again later", + "validation_failed": "Validation failed", + "database_error": "Database operation failed", + "file_operation_error": "File operation failed" + }, + "auth": { + "invalid_credentials": "Invalid username or password", + "token_expired": "Session expired, please login again", + "token_invalid": "Invalid authentication token", + "token_missing": "Authentication token missing", + "unauthorized": "Unauthorized access", + "forbidden": "Permission denied", + "account_locked": "Account has been locked", + "account_disabled": "Account has been disabled", + "account_not_verified": "Account not verified", + "password_incorrect": "Incorrect password", + "password_too_weak": "Password is too weak", + "password_expired": "Password expired, please change it", + "email_not_verified": "Email not verified", + "phone_not_verified": "Phone number not verified", + "verification_code_invalid": "Invalid verification code", + "verification_code_expired": "Verification code expired", + "login_failed": "Login failed", + "logout_failed": "Logout failed", + "session_expired": "Session expired", + "already_logged_in": "Already logged in", + "not_logged_in": "Not logged in" + }, + "user": { + "not_found": "User not found", + "already_exists": "User already exists", + "email_already_exists": "Email already in use", + "phone_already_exists": "Phone number already in use", + "username_already_exists": "Username already taken", + "invalid_email": "Invalid email format", + "invalid_phone": "Invalid phone number format", + "invalid_username": "Invalid username format", + "create_failed": "Failed to create user", + "update_failed": "Failed to update user", + "delete_failed": "Failed to delete user", + "cannot_delete_self": "Cannot delete yourself", + "cannot_update_self_role": "Cannot update your own role", + "profile_update_failed": "Failed to update profile", + "avatar_upload_failed": "Failed to upload avatar", + "password_change_failed": "Failed to change password", + "old_password_incorrect": "Old password is incorrect" + }, + "workspace": { + "not_found": "Workspace not found", + "already_exists": "Workspace already exists", + "name_required": "Workspace name is required", + "name_too_long": "Workspace name is too long", + "create_failed": "Failed to create workspace", + "update_failed": "Failed to update workspace", + "delete_failed": "Failed to delete workspace", + "permission_denied": "Permission denied to access this workspace", + "not_member": "Not a workspace member", + "already_member": "Already a workspace member", + "member_limit_reached": "Member limit reached", + "cannot_leave_last_manager": "Cannot leave, you are the last manager", + "cannot_remove_last_manager": "Cannot remove the last manager", + "cannot_remove_self": "Cannot remove yourself", + "invite_not_found": "Invite not found", + "invite_expired": "Invite has expired", + "invite_already_accepted": "Invite already accepted", + "invite_already_revoked": "Invite already revoked", + "invite_send_failed": "Failed to send invite", + "archived": "Workspace is archived", + "suspended": "Workspace is suspended" + }, + "tenant": { + "not_found": "Tenant not found", + "already_exists": "Tenant already exists", + "create_failed": "Failed to create tenant", + "update_failed": "Failed to update tenant", + "delete_failed": "Failed to delete tenant", + "suspended": "Tenant is suspended", + "expired": "Tenant has expired", + "license_invalid": "Invalid license", + "license_expired": "License has expired", + "quota_exceeded": "Quota exceeded" + }, + "file": { + "not_found": "File not found", + "upload_failed": "File upload failed", + "download_failed": "File download failed", + "delete_failed": "File deletion failed", + "too_large": "File size exceeds limit", + "invalid_type": "Unsupported file type", + "invalid_format": "Invalid file format", + "corrupted": "File is corrupted", + "storage_full": "Storage is full", + "access_denied": "Access denied to this file" + }, + "api": { + "rate_limit_exceeded": "API rate limit exceeded", + "quota_exceeded": "API quota exceeded", + "invalid_api_key": "Invalid API key", + "api_key_expired": "API key has expired", + "api_key_revoked": "API key has been revoked", + "endpoint_not_found": "API endpoint not found", + "method_not_allowed": "Method not allowed", + "invalid_request": "Invalid request", + "missing_parameter": "Missing required parameter: {param}", + "invalid_parameter": "Invalid parameter: {param}" + }, + "database": { + "connection_failed": "Database connection failed", + "query_failed": "Database query failed", + "transaction_failed": "Database transaction failed", + "constraint_violation": "Data constraint violation", + "duplicate_key": "Duplicate data", + "foreign_key_violation": "Foreign key constraint violation", + "deadlock": "Database deadlock" + }, + "validation": { + "invalid_input": "Invalid input data", + "missing_field": "Missing required field: {field}", + "invalid_field": "Invalid field: {field}", + "field_too_long": "Field too long: {field}", + "field_too_short": "Field too short: {field}", + "invalid_format": "Invalid format: {field}", + "invalid_value": "Invalid value: {field}", + "out_of_range": "Value out of range: {field}" + } +} diff --git a/api/app/locales/en/i18n.json b/api/app/locales/en/i18n.json new file mode 100644 index 00000000..1662836d --- /dev/null +++ b/api/app/locales/en/i18n.json @@ -0,0 +1,27 @@ +{ + "language": { + "not_found": "Language {locale} not found", + "already_exists": "Language {locale} already exists", + "add_instructions": "Language {locale} validated successfully. Please create translation files in {dir} directory to complete the addition.", + "update_instructions": "Language {locale} update validated successfully. Please update I18N_SUPPORTED_LANGUAGES environment variable to apply configuration changes." + }, + "namespace": { + "not_found": "Namespace {namespace} not found in language {locale}" + }, + "translation": { + "invalid_key_format": "Invalid translation key format: {key}. Should use format: namespace.key.subkey", + "update_instructions": "Translation {locale}/{key} update validated successfully. Please modify the corresponding JSON translation file to apply changes." + }, + "reload": { + "disabled": "Translation hot reload is disabled. Please enable I18N_ENABLE_HOT_RELOAD in configuration.", + "success": "Translations reloaded successfully", + "failed": "Translation reload failed: {error}" + }, + "metrics": { + "reset_success": "Performance metrics reset successfully" + }, + "logs": { + "export_success": "Missing translations exported to: {file}", + "clear_success": "Missing translation logs cleared successfully" + } +} diff --git a/api/app/locales/en/tenant.json b/api/app/locales/en/tenant.json new file mode 100644 index 00000000..8c3b4b02 --- /dev/null +++ b/api/app/locales/en/tenant.json @@ -0,0 +1,63 @@ +{ + "info": { + "get_success": "Tenant information retrieved successfully", + "get_failed": "Failed to retrieve tenant information", + "update_success": "Tenant information updated successfully", + "update_failed": "Failed to update tenant information" + }, + "create": { + "success": "Tenant created successfully", + "failed": "Failed to create tenant" + }, + "delete": { + "success": "Tenant deleted successfully", + "failed": "Failed to delete tenant" + }, + "status": { + "activate_success": "Tenant activated successfully", + "activate_failed": "Failed to activate tenant", + "deactivate_success": "Tenant deactivated successfully", + "deactivate_failed": "Failed to deactivate tenant" + }, + "language": { + "get_success": "Tenant language configuration retrieved successfully", + "get_failed": "Failed to retrieve tenant language configuration", + "update_success": "Tenant language configuration updated successfully", + "update_failed": "Failed to update tenant language configuration", + "invalid_language": "Unsupported language code", + "default_not_in_supported": "Default language must be in the supported languages list" + }, + "list": { + "get_success": "Tenant list retrieved successfully", + "get_failed": "Failed to retrieve tenant list" + }, + "users": { + "list_success": "Tenant user list retrieved successfully", + "list_failed": "Failed to retrieve tenant user list", + "assign_success": "User assigned to tenant successfully", + "assign_failed": "Failed to assign user to tenant", + "remove_success": "User removed from tenant successfully", + "remove_failed": "Failed to remove user from tenant" + }, + "statistics": { + "get_success": "Tenant statistics retrieved successfully", + "get_failed": "Failed to retrieve tenant statistics" + }, + "validation": { + "name_required": "Tenant name is required", + "name_invalid": "Invalid tenant name format", + "name_too_long": "Tenant name cannot exceed {max} characters", + "description_too_long": "Tenant description cannot exceed {max} characters", + "language_code_invalid": "Invalid language code format", + "supported_languages_empty": "Supported languages list cannot be empty" + }, + "errors": { + "not_found": "Tenant not found", + "already_exists": "Tenant name already exists", + "permission_denied": "Permission denied to access this tenant", + "has_users": "Cannot delete tenant, associated users exist", + "has_workspaces": "Cannot delete tenant, associated workspaces exist", + "already_active": "Tenant is already active", + "already_inactive": "Tenant is already inactive" + } +} diff --git a/api/app/locales/en/users.json b/api/app/locales/en/users.json new file mode 100644 index 00000000..efd5d034 --- /dev/null +++ b/api/app/locales/en/users.json @@ -0,0 +1,72 @@ +{ + "info": { + "get_success": "User information retrieved successfully", + "get_failed": "Failed to retrieve user information", + "update_success": "User information updated successfully", + "update_failed": "Failed to update user information" + }, + "create": { + "success": "User created successfully", + "failed": "Failed to create user", + "superuser_success": "Superuser created successfully", + "superuser_failed": "Failed to create superuser" + }, + "delete": { + "success": "User deleted successfully", + "failed": "Failed to delete user", + "deactivate_success": "User deactivated successfully", + "deactivate_failed": "Failed to deactivate user" + }, + "activate": { + "success": "User activated successfully", + "failed": "Failed to activate user" + }, + "language": { + "get_success": "Language preference retrieved successfully", + "get_failed": "Failed to retrieve language preference", + "update_success": "Language preference updated successfully", + "update_failed": "Failed to update language preference", + "invalid_language": "Unsupported language code", + "current": "Current language preference" + }, + "email": { + "change_success": "Email changed successfully", + "change_failed": "Failed to change email", + "code_sent": "Verification code has been sent to your email", + "code_send_failed": "Failed to send verification code", + "code_invalid": "Invalid or expired verification code", + "already_exists": "Email already in use" + }, + "list": { + "get_success": "User list retrieved successfully", + "get_failed": "Failed to retrieve user list", + "superusers_success": "Tenant superuser list retrieved successfully", + "superusers_failed": "Failed to retrieve tenant superuser list" + }, + "validation": { + "username_required": "Username is required", + "username_invalid": "Invalid username format", + "username_too_long": "Username cannot exceed {max} characters", + "email_required": "Email is required", + "email_invalid": "Invalid email format", + "password_required": "Password is required", + "password_too_short": "Password must be at least {min} characters", + "password_too_long": "Password cannot exceed {max} characters", + "old_password_required": "Old password is required", + "new_password_required": "New password is required", + "verification_code_required": "Verification code is required", + "verification_code_invalid": "Invalid verification code format" + }, + "errors": { + "not_found": "User not found", + "already_exists": "User already exists", + "permission_denied": "Permission denied to access this user", + "cannot_delete_self": "Cannot delete yourself", + "cannot_deactivate_self": "Cannot deactivate yourself", + "already_deactivated": "User is already deactivated", + "already_activated": "User is already activated", + "password_verification_failed": "Password verification failed", + "old_password_incorrect": "Old password is incorrect", + "same_as_old_password": "New password cannot be the same as old password" + } +} diff --git a/api/app/locales/en/workspace.json b/api/app/locales/en/workspace.json new file mode 100644 index 00000000..cca29698 --- /dev/null +++ b/api/app/locales/en/workspace.json @@ -0,0 +1,44 @@ +{ + "list_retrieved": "Workspace list retrieved successfully", + "created": "Workspace created successfully", + "updated": "Workspace updated successfully", + "deleted": "Workspace deleted successfully", + "switched": "Workspace switched successfully", + "not_found": "Workspace not found or access denied", + "already_exists": "Workspace already exists", + "permission_denied": "No permission to access this workspace", + "name_required": "Workspace name is required", + "invalid_name": "Invalid workspace name format", + "members": { + "list_retrieved": "Workspace members list retrieved successfully", + "role_updated": "Member role updated successfully", + "deleted": "Member deleted successfully", + "not_found": "Member not found", + "cannot_remove_self": "Cannot remove yourself", + "cannot_remove_last_manager": "Cannot remove the last manager", + "already_member": "User is already a workspace member" + }, + "invites": { + "created": "Invite created successfully", + "list_retrieved": "Invite list retrieved successfully", + "validated": "Invite validated successfully", + "revoked": "Invite revoked successfully", + "accepted": "Invite accepted", + "not_found": "Invite not found", + "expired": "Invite has expired", + "already_used": "Invite has already been used", + "invalid_token": "Invalid invite token", + "email_required": "Email address is required", + "invalid_email": "Invalid email address format" + }, + "storage": { + "type_retrieved": "Storage type retrieved successfully", + "type_updated": "Storage type updated successfully", + "invalid_type": "Invalid storage type" + }, + "models": { + "config_retrieved": "Model configuration retrieved successfully", + "config_updated": "Model configuration updated successfully", + "invalid_config": "Invalid model configuration" + } +} diff --git a/api/app/locales/zh/README.md b/api/app/locales/zh/README.md new file mode 100644 index 00000000..edaa0fb4 --- /dev/null +++ b/api/app/locales/zh/README.md @@ -0,0 +1,26 @@ +# 中文翻译文件 + +此目录包含中文（简体）的翻译文件。 + +## 文件结构 + +- `common.json` - 通用翻译（成功消息、操作、验证） +- `auth.json` - 认证模块翻译 +- `workspace.json` - 工作空间模块翻译 +- `tenant.json` - 租户模块翻译 +- `errors.json` - 错误消息翻译 +- `enums.json` - 枚举值翻译 + +## 翻译文件格式 + +所有翻译文件使用 JSON 格式，支持嵌套结构。 + +示例： +```json +{ + "success": { + "created": "创建成功", + "updated": "更新成功" + } +} +``` diff --git a/api/app/locales/zh/auth.json b/api/app/locales/zh/auth.json new file mode 100644 index 00000000..283d2ffb --- /dev/null +++ b/api/app/locales/zh/auth.json @@ -0,0 +1,55 @@ +{ + "login": { + "success": "登录成功", + "failed": "登录失败", + "invalid_credentials": "用户名或密码错误", + "account_locked": "账户已被锁定", + "account_disabled": "账户已被禁用" + }, + "logout": { + "success": "登出成功", + "failed": "登出失败" + }, + "token": { + "refresh_success": "token刷新成功", + "invalid": "无效的token", + "expired": "token已过期", + "blacklisted": "token已失效", + "invalid_refresh_token": "无效的refresh token", + "refresh_token_blacklisted": "Refresh token已失效" + }, + "registration": { + "success": "注册成功", + "failed": "注册失败", + "email_exists": "邮箱已被使用", + "username_exists": "用户名已被使用" + }, + "password": { + "reset_success": "密码重置成功", + "reset_failed": "密码重置失败", + "change_success": "密码修改成功", + "change_failed": "密码修改失败", + "incorrect": "密码错误", + "too_weak": "密码强度不够", + "mismatch": "两次输入的密码不一致" + }, + "invite": { + "invalid": "邀请码无效或已过期", + "email_mismatch": "邀请邮箱与登录邮箱不匹配", + "accept_success": "接受邀请成功", + "accept_failed": "接受邀请失败", + "password_verification_failed": "接受邀请失败，密码验证错误", + "bind_workspace_success": "绑定工作空间成功", + "bind_workspace_failed": "绑定工作空间失败" + }, + "user": { + "not_found": "用户不存在", + "already_exists": "用户已存在", + "created_with_invite": "用户创建成功并已加入工作空间" + }, + "session": { + "expired": "会话已过期，请重新登录", + "invalid": "无效的会话", + "single_session_enabled": "单点登录已启用，其他设备的登录将被注销" + } +} diff --git a/api/app/locales/zh/common.json b/api/app/locales/zh/common.json new file mode 100644 index 00000000..b3c62adc --- /dev/null +++ b/api/app/locales/zh/common.json @@ -0,0 +1,132 @@ +{ + "success": { + "created": "创建成功", + "updated": "更新成功", + "deleted": "删除成功", + "retrieved": "获取成功", + "saved": "保存成功", + "uploaded": "上传成功", + "downloaded": "下载成功", + "sent": "发送成功", + "completed": "完成", + "confirmed": "已确认", + "cancelled": "已取消", + "archived": "已归档", + "restored": "已恢复" + }, + "actions": { + "create": "创建", + "update": "更新", + "delete": "删除", + "view": "查看", + "edit": "编辑", + "save": "保存", + "cancel": "取消", + "confirm": "确认", + "submit": "提交", + "upload": "上传", + "download": "下载", + "send": "发送", + "search": "搜索", + "filter": "筛选", + "sort": "排序", + "export": "导出", + "import": "导入", + "refresh": "刷新", + "reset": "重置", + "back": "返回", + "next": "下一步", + "previous": "上一步", + "finish": "完成", + "close": "关闭", + "open": "打开", + "archive": "归档", + "restore": "恢复", + "duplicate": "复制", + "share": "分享", + "invite": "邀请", + "remove": "移除", + "add": "添加", + "select": "选择", + "clear": "清除" + }, + "validation": { + "required": "{field}不能为空", + "invalid_format": "{field}格式不正确", + "too_long": "{field}长度不能超过{max}个字符", + "too_short": "{field}长度不能少于{min}个字符", + "invalid_email": "邮箱格式不正确", + "invalid_url": "URL格式不正确", + "invalid_phone": "手机号格式不正确", + "invalid_date": "日期格式不正确", + "invalid_number": "必须是有效的数字", + "out_of_range": "{field}必须在{min}和{max}之间", + "already_exists": "{field}已存在", + "not_found": "{field}不存在", + "invalid_value": "{field}的值无效", + "password_mismatch": "两次输入的密码不一致", + "weak_password": "密码强度不够，请使用更复杂的密码", + "invalid_credentials": "用户名或密码错误", + "unauthorized": "未授权访问", + "forbidden": "没有权限执行此操作", + "expired": "{field}已过期", + "invalid_token": "无效的令牌", + "file_too_large": "文件大小不能超过{max}", + "invalid_file_type": "不支持的文件类型", + "duplicate": "重复的{field}" + }, + "status": { + "active": "活跃", + "inactive": "未激活", + "pending": "待处理", + "processing": "处理中", + "completed": "已完成", + "failed": "失败", + "cancelled": "已取消", + "archived": "已归档", + "deleted": "已删除", + "draft": "草稿", + "published": "已发布", + "suspended": "已暂停", + "expired": "已过期" + }, + "messages": { + "loading": "加载中...", + "saving": "保存中...", + "processing": "处理中...", + "uploading": "上传中...", + "downloading": "下载中...", + "no_data": "暂无数据", + "no_results": "没有找到结果", + "confirm_delete": "确定要删除吗？此操作不可恢复。", + "confirm_action": "确定要执行此操作吗？", + "operation_success": "操作成功", + "operation_failed": "操作失败", + "please_wait": "请稍候...", + "try_again": "请重试", + "contact_support": "如果问题持续，请联系技术支持" + }, + "pagination": { + "page": "第{page}页", + "of": "共{total}页", + "items": "共{total}条", + "per_page": "每页{count}条", + "showing": "显示第{from}到第{to}条，共{total}条", + "first": "首页", + "last": "末页", + "next": "下一页", + "previous": "上一页" + }, + "time": { + "just_now": "刚刚", + "minutes_ago": "{count}分钟前", + "hours_ago": "{count}小时前", + "days_ago": "{count}天前", + "weeks_ago": "{count}周前", + "months_ago": "{count}个月前", + "years_ago": "{count}年前", + "today": "今天", + "yesterday": "昨天", + "tomorrow": "明天" + } +} diff --git a/api/app/locales/zh/enums.json b/api/app/locales/zh/enums.json new file mode 100644 index 00000000..9a241817 --- /dev/null +++ b/api/app/locales/zh/enums.json @@ -0,0 +1,132 @@ +{ + "workspace_role": { + "owner": "所有者", + "manager": "管理员", + "member": "成员", + "guest": "访客" + }, + "workspace_status": { + "active": "活跃", + "inactive": "未激活", + "archived": "已归档", + "suspended": "已暂停", + "deleted": "已删除" + }, + "invite_status": { + "pending": "待处理", + "accepted": "已接受", + "rejected": "已拒绝", + "revoked": "已撤销", + "expired": "已过期" + }, + "user_status": { + "active": "活跃", + "inactive": "未激活", + "suspended": "已暂停", + "deleted": "已删除", + "pending": "待激活" + }, + "tenant_status": { + "active": "活跃", + "inactive": "未激活", + "suspended": "已暂停", + "expired": "已过期", + "trial": "试用中" + }, + "file_status": { + "uploading": "上传中", + "processing": "处理中", + "completed": "已完成", + "failed": "失败", + "deleted": "已删除" + }, + "task_status": { + "pending": "待处理", + "running": "运行中", + "completed": "已完成", + "failed": "失败", + "cancelled": "已取消", + "paused": "已暂停" + }, + "priority": { + "low": "低", + "medium": "中", + "high": "高", + "urgent": "紧急" + }, + "visibility": { + "public": "公开", + "private": "私有", + "internal": "内部", + "shared": "共享" + }, + "permission": { + "read": "读取", + "write": "写入", + "delete": "删除", + "admin": "管理", + "owner": "所有者" + }, + "notification_type": { + "info": "信息", + "warning": "警告", + "error": "错误", + "success": "成功" + }, + "language": { + "zh": "中文（简体）", + "en": "English", + "ja": "日本語", + "ko": "한국어", + "fr": "Français", + "de": "Deutsch", + "es": "Español" + }, + "timezone": { + "utc": "UTC", + "asia_shanghai": "亚洲/上海", + "asia_tokyo": "亚洲/东京", + "america_new_york": "美洲/纽约", + "europe_london": "欧洲/伦敦" + }, + "date_format": { + "short": "短日期", + "medium": "中等日期", + "long": "长日期", + "full": "完整日期" + }, + "sort_order": { + "asc": "升序", + "desc": "降序" + }, + "filter_operator": { + "equals": "等于", + "not_equals": "不等于", + "contains": "包含", + "not_contains": "不包含", + "starts_with": "开始于", + "ends_with": "结束于", + "greater_than": "大于", + "less_than": "小于", + "greater_or_equal": "大于等于", + "less_or_equal": "小于等于", + "in": "在列表中", + "not_in": "不在列表中", + "is_null": "为空", + "is_not_null": "不为空" + }, + "log_level": { + "debug": "调试", + "info": "信息", + "warning": "警告", + "error": "错误", + "critical": "严重" + }, + "api_method": { + "get": "GET", + "post": "POST", + "put": "PUT", + "patch": "PATCH", + "delete": "DELETE" + } +} diff --git a/api/app/locales/zh/errors.json b/api/app/locales/zh/errors.json new file mode 100644 index 00000000..eafadad4 --- /dev/null +++ b/api/app/locales/zh/errors.json @@ -0,0 +1,138 @@ +{ + "common": { + "internal_error": "服务器内部错误", + "network_error": "网络连接错误", + "timeout": "请求超时", + "service_unavailable": "服务暂时不可用", + "bad_request": "请求参数错误", + "unauthorized": "未授权访问", + "forbidden": "没有权限访问", + "not_found": "请求的资源不存在", + "method_not_allowed": "不支持的请求方法", + "conflict": "资源冲突", + "too_many_requests": "请求过于频繁，请稍后再试", + "validation_failed": "数据验证失败", + "database_error": "数据库操作失败", + "file_operation_error": "文件操作失败" + }, + "auth": { + "invalid_credentials": "用户名或密码错误", + "token_expired": "登录已过期，请重新登录", + "token_invalid": "无效的登录令牌", + "token_missing": "缺少登录令牌", + "unauthorized": "未授权访问", + "forbidden": "没有权限执行此操作", + "account_locked": "账户已被锁定", + "account_disabled": "账户已被禁用", + "account_not_verified": "账户未验证", + "password_incorrect": "密码错误", + "password_too_weak": "密码强度不够", + "password_expired": "密码已过期，请修改密码", + "email_not_verified": "邮箱未验证", + "phone_not_verified": "手机号未验证", + "verification_code_invalid": "验证码无效", + "verification_code_expired": "验证码已过期", + "login_failed": "登录失败", + "logout_failed": "登出失败", + "session_expired": "会话已过期", + "already_logged_in": "已经登录", + "not_logged_in": "未登录" + }, + "user": { + "not_found": "用户不存在", + "already_exists": "用户已存在", + "email_already_exists": "邮箱已被使用", + "phone_already_exists": "手机号已被使用", + "username_already_exists": "用户名已被使用", + "invalid_email": "邮箱格式不正确", + "invalid_phone": "手机号格式不正确", + "invalid_username": "用户名格式不正确", + "create_failed": "创建用户失败", + "update_failed": "更新用户失败", + "delete_failed": "删除用户失败", + "cannot_delete_self": "不能删除自己", + "cannot_update_self_role": "不能修改自己的角色", + "profile_update_failed": "更新个人资料失败", + "avatar_upload_failed": "上传头像失败", + "password_change_failed": "修改密码失败", + "old_password_incorrect": "原密码错误" + }, + "workspace": { + "not_found": "工作空间不存在", + "already_exists": "工作空间已存在", + "name_required": "工作空间名称不能为空", + "name_too_long": "工作空间名称过长", + "create_failed": "创建工作空间失败", + "update_failed": "更新工作空间失败", + "delete_failed": "删除工作空间失败", + "permission_denied": "没有权限访问此工作空间", + "not_member": "不是工作空间成员", + "already_member": "已经是工作空间成员", + "member_limit_reached": "成员数量已达上限", + "cannot_leave_last_manager": "不能离开，您是最后一个管理员", + "cannot_remove_last_manager": "不能移除最后一个管理员", + "cannot_remove_self": "不能移除自己", + "invite_not_found": "邀请不存在", + "invite_expired": "邀请已过期", + "invite_already_accepted": "邀请已被接受", + "invite_already_revoked": "邀请已被撤销", + "invite_send_failed": "发送邀请失败", + "archived": "工作空间已归档", + "suspended": "工作空间已暂停" + }, + "tenant": { + "not_found": "租户不存在", + "already_exists": "租户已存在", + "create_failed": "创建租户失败", + "update_failed": "更新租户失败", + "delete_failed": "删除租户失败", + "suspended": "租户已暂停", + "expired": "租户已过期", + "license_invalid": "许可证无效", + "license_expired": "许可证已过期", + "quota_exceeded": "配额已超限" + }, + "file": { + "not_found": "文件不存在", + "upload_failed": "文件上传失败", + "download_failed": "文件下载失败", + "delete_failed": "文件删除失败", + "too_large": "文件大小超过限制", + "invalid_type": "不支持的文件类型", + "invalid_format": "文件格式不正确", + "corrupted": "文件已损坏", + "storage_full": "存储空间已满", + "access_denied": "没有权限访问此文件" + }, + "api": { + "rate_limit_exceeded": "API调用频率超限", + "quota_exceeded": "API调用配额已用完", + "invalid_api_key": "无效的API密钥", + "api_key_expired": "API密钥已过期", + "api_key_revoked": "API密钥已被撤销", + "endpoint_not_found": "API端点不存在", + "method_not_allowed": "不支持的请求方法", + "invalid_request": "无效的请求", + "missing_parameter": "缺少必需参数：{param}", + "invalid_parameter": "参数无效：{param}" + }, + "database": { + "connection_failed": "数据库连接失败", + "query_failed": "数据库查询失败", + "transaction_failed": "数据库事务失败", + "constraint_violation": "数据约束冲突", + "duplicate_key": "数据重复", + "foreign_key_violation": "外键约束冲突", + "deadlock": "数据库死锁" + }, + "validation": { + "invalid_input": "输入数据无效", + "missing_field": "缺少必需字段：{field}", + "invalid_field": "字段无效：{field}", + "field_too_long": "字段过长：{field}", + "field_too_short": "字段过短：{field}", + "invalid_format": "格式不正确：{field}", + "invalid_value": "值无效：{field}", + "out_of_range": "值超出范围：{field}" + } +} diff --git a/api/app/locales/zh/i18n.json b/api/app/locales/zh/i18n.json new file mode 100644 index 00000000..a072f332 --- /dev/null +++ b/api/app/locales/zh/i18n.json @@ -0,0 +1,27 @@ +{ + "language": { + "not_found": "语言 {locale} 不存在", + "already_exists": "语言 {locale} 已存在", + "add_instructions": "语言 {locale} 验证成功。请在 {dir} 目录下创建翻译文件以完成添加。", + "update_instructions": "语言 {locale} 更新验证成功。请更新环境变量 I18N_SUPPORTED_LANGUAGES 以应用配置更改。" + }, + "namespace": { + "not_found": "命名空间 {namespace} 在语言 {locale} 中不存在" + }, + "translation": { + "invalid_key_format": "翻译键格式无效: {key}。应使用格式: namespace.key.subkey", + "update_instructions": "翻译 {locale}/{key} 更新验证成功。请修改对应的 JSON 翻译文件以应用更改。" + }, + "reload": { + "disabled": "翻译热重载功能已禁用。请在配置中启用 I18N_ENABLE_HOT_RELOAD。", + "success": "翻译重载成功", + "failed": "翻译重载失败: {error}" + }, + "metrics": { + "reset_success": "性能指标已重置" + }, + "logs": { + "export_success": "缺失翻译已导出到: {file}", + "clear_success": "缺失翻译日志已清除" + } +} diff --git a/api/app/locales/zh/tenant.json b/api/app/locales/zh/tenant.json new file mode 100644 index 00000000..a8bdc124 --- /dev/null +++ b/api/app/locales/zh/tenant.json @@ -0,0 +1,63 @@ +{ + "info": { + "get_success": "租户信息获取成功", + "get_failed": "租户信息获取失败", + "update_success": "租户信息更新成功", + "update_failed": "租户信息更新失败" + }, + "create": { + "success": "租户创建成功", + "failed": "租户创建失败" + }, + "delete": { + "success": "租户删除成功", + "failed": "租户删除失败" + }, + "status": { + "activate_success": "租户启用成功", + "activate_failed": "租户启用失败", + "deactivate_success": "租户禁用成功", + "deactivate_failed": "租户禁用失败" + }, + "language": { + "get_success": "租户语言配置获取成功", + "get_failed": "租户语言配置获取失败", + "update_success": "租户语言配置更新成功", + "update_failed": "租户语言配置更新失败", + "invalid_language": "不支持的语言代码", + "default_not_in_supported": "默认语言必须在支持的语言列表中" + }, + "list": { + "get_success": "租户列表获取成功", + "get_failed": "租户列表获取失败" + }, + "users": { + "list_success": "租户用户列表获取成功", + "list_failed": "租户用户列表获取失败", + "assign_success": "用户分配到租户成功", + "assign_failed": "用户分配到租户失败", + "remove_success": "用户从租户移除成功", + "remove_failed": "用户从租户移除失败" + }, + "statistics": { + "get_success": "租户统计信息获取成功", + "get_failed": "租户统计信息获取失败" + }, + "validation": { + "name_required": "租户名称不能为空", + "name_invalid": "租户名称格式不正确", + "name_too_long": "租户名称长度不能超过{max}个字符", + "description_too_long": "租户描述长度不能超过{max}个字符", + "language_code_invalid": "语言代码格式不正确", + "supported_languages_empty": "支持的语言列表不能为空" + }, + "errors": { + "not_found": "租户不存在", + "already_exists": "租户名称已存在", + "permission_denied": "没有权限访问此租户", + "has_users": "无法删除租户，存在关联的用户", + "has_workspaces": "无法删除租户，存在关联的工作空间", + "already_active": "租户已处于激活状态", + "already_inactive": "租户已处于禁用状态" + } +} diff --git a/api/app/locales/zh/users.json b/api/app/locales/zh/users.json new file mode 100644 index 00000000..a446ed8d --- /dev/null +++ b/api/app/locales/zh/users.json @@ -0,0 +1,72 @@ +{ + "info": { + "get_success": "用户信息获取成功", + "get_failed": "用户信息获取失败", + "update_success": "用户信息更新成功", + "update_failed": "用户信息更新失败" + }, + "create": { + "success": "用户创建成功", + "failed": "用户创建失败", + "superuser_success": "超级管理员创建成功", + "superuser_failed": "超级管理员创建失败" + }, + "delete": { + "success": "用户删除成功", + "failed": "用户删除失败", + "deactivate_success": "用户停用成功", + "deactivate_failed": "用户停用失败" + }, + "activate": { + "success": "用户激活成功", + "failed": "用户激活失败" + }, + "language": { + "get_success": "语言偏好获取成功", + "get_failed": "语言偏好获取失败", + "update_success": "语言偏好更新成功", + "update_failed": "语言偏好更新失败", + "invalid_language": "不支持的语言代码", + "current": "当前语言偏好" + }, + "email": { + "change_success": "邮箱修改成功", + "change_failed": "邮箱修改失败", + "code_sent": "验证码已发送到您的邮箱，请查收", + "code_send_failed": "验证码发送失败", + "code_invalid": "验证码无效或已过期", + "already_exists": "该邮箱已被使用" + }, + "list": { + "get_success": "用户列表获取成功", + "get_failed": "用户列表获取失败", + "superusers_success": "租户超管列表获取成功", + "superusers_failed": "租户超管列表获取失败" + }, + "validation": { + "username_required": "用户名不能为空", + "username_invalid": "用户名格式不正确", + "username_too_long": "用户名长度不能超过{max}个字符", + "email_required": "邮箱不能为空", + "email_invalid": "邮箱格式不正确", + "password_required": "密码不能为空", + "password_too_short": "密码长度不能少于{min}个字符", + "password_too_long": "密码长度不能超过{max}个字符", + "old_password_required": "旧密码不能为空", + "new_password_required": "新密码不能为空", + "verification_code_required": "验证码不能为空", + "verification_code_invalid": "验证码格式不正确" + }, + "errors": { + "not_found": "用户不存在", + "already_exists": "用户已存在", + "permission_denied": "没有权限访问此用户", + "cannot_delete_self": "不能删除自己", + "cannot_deactivate_self": "不能停用自己", + "already_deactivated": "用户已被停用", + "already_activated": "用户已处于激活状态", + "password_verification_failed": "密码验证失败", + "old_password_incorrect": "旧密码不正确", + "same_as_old_password": "新密码不能与旧密码相同" + } +} diff --git a/api/app/locales/zh/workspace.json b/api/app/locales/zh/workspace.json new file mode 100644 index 00000000..e7dba7dc --- /dev/null +++ b/api/app/locales/zh/workspace.json @@ -0,0 +1,44 @@ +{ + "list_retrieved": "工作空间列表获取成功", + "created": "工作空间创建成功", + "updated": "工作空间更新成功", + "deleted": "工作空间删除成功", + "switched": "工作空间切换成功", + "not_found": "工作空间不存在或无权访问", + "already_exists": "工作空间已存在", + "permission_denied": "没有权限访问此工作空间", + "name_required": "工作空间名称不能为空", + "invalid_name": "工作空间名称格式不正确", + "members": { + "list_retrieved": "工作空间成员列表获取成功", + "role_updated": "成员角色更新成功", + "deleted": "成员删除成功", + "not_found": "成员不存在", + "cannot_remove_self": "不能删除自己", + "cannot_remove_last_manager": "不能删除最后一个管理员", + "already_member": "用户已经是工作空间成员" + }, + "invites": { + "created": "邀请创建成功", + "list_retrieved": "邀请列表获取成功", + "validated": "邀请验证成功", + "revoked": "邀请撤销成功", + "accepted": "邀请已接受", + "not_found": "邀请不存在", + "expired": "邀请已过期", + "already_used": "邀请已被使用", + "invalid_token": "无效的邀请令牌", + "email_required": "邮箱地址不能为空", + "invalid_email": "邮箱地址格式不正确" + }, + "storage": { + "type_retrieved": "存储类型获取成功", + "type_updated": "存储类型更新成功", + "invalid_type": "无效的存储类型" + }, + "models": { + "config_retrieved": "模型配置获取成功", + "config_updated": "模型配置更新成功", + "invalid_config": "无效的模型配置" + } +} diff --git a/api/app/main.py b/api/app/main.py index af5ed796..c6256e3c 100644 --- a/api/app/main.py +++ b/api/app/main.py @@ -92,6 +92,10 @@ app.add_middleware( allow_headers=["*"], ) +# Add i18n language detection middleware +from app.i18n.middleware import LanguageMiddleware +app.add_middleware(LanguageMiddleware) + logger.info("FastAPI应用程序启动") @@ -129,6 +133,11 @@ from app.core.exceptions import ( from app.core.sensitive_filter import SensitiveDataFilter import traceback +# Import i18n exception support +from app.i18n.exceptions import I18nException +from app.i18n.service import get_translation_service +from pydantic import ValidationError as PydanticValidationError + # 处理验证异常 @app.exception_handler(ValidationException) @@ -156,6 +165,131 @@ async def validation_exception_handler(request: Request, exc: ValidationExceptio ) +# 处理 i18n 异常（国际化异常） +@app.exception_handler(I18nException) +async def i18n_exception_handler(request: Request, exc: I18nException): + """ + 处理国际化异常 + + I18nException 已经自动翻译了错误消息，直接返回即可 + """ + # 获取当前语言 + language = getattr(request.state, "language", settings.I18N_DEFAULT_LANGUAGE) + + # 获取异常详情（已经包含翻译后的消息） + detail = exc.detail + + # 过滤敏感信息 + if isinstance(detail, dict): + filtered_message = SensitiveDataFilter.filter_string(detail.get("message", "")) + filtered_detail = { + **detail, + "message": filtered_message + } + else: + filtered_detail = SensitiveDataFilter.filter_string(str(detail)) + + logger.warning( + f"I18n exception: {exc.error_key}", + extra={ + "path": request.url.path, + "method": request.method, + "error_code": exc.error_code, + "error_key": exc.error_key, + "language": language, + "status_code": exc.status_code, + "params": exc.params + } + ) + + return JSONResponse( + status_code=exc.status_code, + content={ + "success": False, + **filtered_detail + }, + headers=exc.headers + ) + + +# 处理 Pydantic 验证错误（国际化支持） +@app.exception_handler(PydanticValidationError) +async def pydantic_validation_exception_handler(request: Request, exc: PydanticValidationError): + """ + 处理 Pydantic 验证错误，支持国际化 + """ + # 获取当前语言 + language = getattr(request.state, "language", settings.I18N_DEFAULT_LANGUAGE) + + # 获取翻译服务 + translation_service = get_translation_service() + + # 翻译验证错误消息 + errors = [] + for error in exc.errors(): + field = ".".join(str(loc) for loc in error["loc"]) + error_type = error["type"] + + # 尝试翻译错误消息 + if error_type == "value_error.missing": + message = translation_service.translate( + "errors.validation.missing_field", + language, + field=field + ) + elif error_type == "value_error.any_str.max_length": + message = translation_service.translate( + "errors.validation.field_too_long", + language, + field=field + ) + elif error_type == "value_error.any_str.min_length": + message = translation_service.translate( + "errors.validation.field_too_short", + language, + field=field + ) + else: + # 使用通用验证错误消息 + message = translation_service.translate( + "errors.validation.invalid_field", + language, + field=field + ) + + errors.append({ + "field": field, + "message": message, + "type": error_type + }) + + # 翻译主错误消息 + main_message = translation_service.translate( + "errors.common.validation_failed", + language + ) + + logger.warning( + f"Pydantic validation error: {len(errors)} errors", + extra={ + "path": request.url.path, + "method": request.method, + "language": language, + "errors": errors + } + ) + + return JSONResponse( + status_code=422, + content={ + "success": False, + "error_code": "VALIDATION_FAILED", + "message": main_message, + "errors": errors + } + ) + + # 处理资源不存在异常 @app.exception_handler(ResourceNotFoundException) async def not_found_exception_handler(request: Request, exc: ResourceNotFoundException): @@ -354,31 +488,66 @@ async def business_exception_handler(request: Request, exc: BusinessException): ) -# 统一异常处理：将HTTPException转换为统一响应结构 +# 统一异常处理：将HTTPException转换为统一响应结构（支持国际化） @app.exception_handler(HTTPException) async def http_exception_handler(request: Request, exc: HTTPException): - """处理HTTP异常""" - # 过滤敏感信息 - filtered_detail = SensitiveDataFilter.filter_string(str(exc.detail)) - + """处理HTTP异常，支持国际化""" + # 获取当前语言 + language = getattr(request.state, "language", settings.I18N_DEFAULT_LANGUAGE) + + # 获取翻译服务 + translation_service = get_translation_service() + + # 尝试翻译标准HTTP错误 + error_key_map = { + 400: "errors.common.bad_request", + 401: "errors.common.unauthorized", + 403: "errors.common.forbidden", + 404: "errors.common.not_found", + 405: "errors.common.method_not_allowed", + 409: "errors.common.conflict", + 422: "errors.common.validation_failed", + 429: "errors.common.too_many_requests", + 500: "errors.common.internal_error", + 503: "errors.common.service_unavailable", + } + + # 如果有对应的翻译键，使用翻译 + if exc.status_code in error_key_map: + translated_message = translation_service.translate( + error_key_map[exc.status_code], + language + ) + else: + # 否则过滤原始消息 + translated_message = SensitiveDataFilter.filter_string(str(exc.detail)) + logger.warning( - f"HTTP exception: {filtered_detail}", + f"HTTP exception: {translated_message}", extra={ "path": request.url.path, "method": request.method, - "status_code": exc.status_code + "status_code": exc.status_code, + "language": language } ) + return JSONResponse( status_code=exc.status_code, - content=fail(code=exc.status_code, msg=filtered_detail, error=filtered_detail) + content=fail(code=exc.status_code, msg=translated_message, error=translated_message) ) -# 捕获未处理的异常，返回统一错误结构 +# 捕获未处理的异常，返回统一错误结构（支持国际化） @app.exception_handler(Exception) async def unhandled_exception_handler(request: Request, exc: Exception): - """处理未捕获的异常""" + """处理未捕获的异常，支持国际化""" + # 获取当前语言 + language = getattr(request.state, "language", settings.I18N_DEFAULT_LANGUAGE) + + # 获取翻译服务 + translation_service = get_translation_service() + # 记录完整的堆栈跟踪（日志过滤器会自动过滤敏感信息） logger.error( f"Unhandled exception: {exc}", @@ -386,6 +555,7 @@ async def unhandled_exception_handler(request: Request, exc: Exception): "path": request.url.path, "method": request.method, "exception_type": type(exc).__name__, + "language": language, "traceback": traceback.format_exc() }, exc_info=True @@ -394,7 +564,11 @@ async def unhandled_exception_handler(request: Request, exc: Exception): # 生产环境隐藏详细错误信息 environment = os.getenv("ENVIRONMENT", "development") if environment == "production": - message = "服务器内部错误，请稍后重试" + # 使用翻译的通用错误消息 + message = translation_service.translate( + "errors.common.internal_error", + language + ) else: # 开发环境也要过滤敏感信息 message = SensitiveDataFilter.filter_string(str(exc)) diff --git a/api/app/models/tenant_model.py b/api/app/models/tenant_model.py index 54a3e347..044857d2 100644 --- a/api/app/models/tenant_model.py +++ b/api/app/models/tenant_model.py @@ -1,7 +1,7 @@ import datetime import uuid -from sqlalchemy import Column, String, DateTime, Boolean -from sqlalchemy.dialects.postgresql import UUID +from sqlalchemy import Column, String, DateTime, Boolean, text +from sqlalchemy.dialects.postgresql import UUID, ARRAY from sqlalchemy.orm import relationship from app.db import Base @@ -20,6 +20,10 @@ class Tenants(Base): external_id = Column(String(100), nullable=True, index=True) # 外部企业ID external_source = Column(String(50), nullable=True) # 来源系统 + # 国际化语言配置字段 + default_language = Column(String(10), nullable=False, default='zh', server_default='zh', index=True) # 租户默认语言 + supported_languages = Column(ARRAY(String(10)), nullable=False, default=lambda: ['zh', 'en'], server_default=text("'{zh,en}'")) # 租户支持的语言列表 + # Relationship to users - one tenant has many users users = relationship("User", back_populates="tenant") diff --git a/api/app/models/user_model.py b/api/app/models/user_model.py index 663bfc71..b6de28ec 100644 --- a/api/app/models/user_model.py +++ b/api/app/models/user_model.py @@ -1,6 +1,6 @@ import datetime import uuid -from sqlalchemy import Column, String, Boolean, DateTime, ForeignKey +from sqlalchemy import Column, String, Boolean, DateTime, ForeignKey, text from sqlalchemy.dialects.postgresql import UUID from sqlalchemy.orm import relationship from app.db import Base @@ -22,6 +22,9 @@ class User(Base): external_id = Column(String(100), nullable=True) # 外部用户ID external_source = Column(String(50), nullable=True) # 来源系统 + # 用户语言偏好 + preferred_language = Column(String(10), server_default=text("'zh'"), default='zh', nullable=False, index=True) # 用户偏好语言，默认中文 + current_workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id"), nullable=True) # 当前工作空间ID，可为空 # Foreign key to tenant - each user belongs to exactly one tenant diff --git a/api/app/schemas/i18n_schema.py b/api/app/schemas/i18n_schema.py new file mode 100644 index 00000000..b2ae93c6 --- /dev/null +++ b/api/app/schemas/i18n_schema.py @@ -0,0 +1,73 @@ +""" +I18n Management API Schemas + +This module defines Pydantic schemas for i18n management APIs. +""" + +from pydantic import BaseModel, Field +from typing import Dict, List, Optional, Any + + +# ============================================================================ +# Language Management Schemas +# ============================================================================ + +class LanguageInfo(BaseModel): + """Language information""" + code: str = Field(..., description="Language code (e.g., 'zh', 'en')") + name: str = Field(..., description="Language name (e.g., 'Chinese', 'English')") + native_name: str = Field(..., description="Native language name (e.g., '中文', 'English')") + is_enabled: bool = Field(..., description="Whether the language is enabled") + is_default: bool = Field(..., description="Whether this is the default language") + + +class LanguageListResponse(BaseModel): + """Response for language list""" + languages: List[LanguageInfo] = Field(..., description="List of available languages") + + +class LanguageCreateRequest(BaseModel): + """Request to add a new language""" + code: str = Field(..., description="Language code (e.g., 'ja', 'ko')", min_length=2, max_length=10) + name: str = Field(..., description="Language name", min_length=1, max_length=100) + native_name: str = Field(..., description="Native language name", min_length=1, max_length=100) + is_enabled: bool = Field(default=True, description="Whether to enable the language") + + +class LanguageUpdateRequest(BaseModel): + """Request to update language configuration""" + is_enabled: Optional[bool] = Field(None, description="Whether the language is enabled") + is_default: Optional[bool] = Field(None, description="Whether this is the default language") + + +# ============================================================================ +# Translation Management Schemas +# ============================================================================ + +class TranslationResponse(BaseModel): + """Response for translation data""" + translations: Dict[str, Dict[str, Any]] = Field( + ..., + description="Translations organized by locale and namespace" + ) + + +class TranslationUpdateRequest(BaseModel): + """Request to update a translation""" + value: str = Field(..., description="New translation value", min_length=1) + description: Optional[str] = Field(None, description="Optional description of the translation") + + +class MissingTranslationsResponse(BaseModel): + """Response for missing translations""" + missing_translations: Dict[str, List[str]] = Field( + ..., + description="Missing translation keys organized by locale" + ) + + +class ReloadResponse(BaseModel): + """Response for translation reload""" + success: bool = Field(..., description="Whether the reload was successful") + reloaded_locales: List[str] = Field(..., description="List of reloaded locales") + total_locales: int = Field(..., description="Total number of available locales") diff --git a/api/app/schemas/tenant_schema.py b/api/app/schemas/tenant_schema.py index 6e8bd158..4f49ee88 100644 --- a/api/app/schemas/tenant_schema.py +++ b/api/app/schemas/tenant_schema.py @@ -11,6 +11,8 @@ class TenantBase(BaseModel): name: str = Field(..., description="租户名称", max_length=255) description: Optional[str] = Field(None, description="租户描述", max_length=1000) is_active: bool = Field(True, description="是否激活") + default_language: Optional[str] = Field('zh', description="租户默认语言", max_length=10) + supported_languages: Optional[List[str]] = Field(['zh', 'en'], description="租户支持的语言列表") @field_validator('name') @classmethod @@ -18,6 +20,26 @@ class TenantBase(BaseModel): if not v or not v.strip(): raise ValidationException('租户名称不能为空', code=BizCode.VALIDATION_FAILED) return v.strip() + + @field_validator('default_language') + @classmethod + def validate_default_language(cls, v): + if v: + # Validate language code format (2-letter code, optionally with region) + import re + if not re.match(r'^[a-z]{2}(-[A-Z]{2})?$', v): + raise ValidationException('语言代码格式不正确', code=BizCode.VALIDATION_FAILED) + return v + + @field_validator('supported_languages') + @classmethod + def validate_supported_languages(cls, v): + if v: + import re + for lang in v: + if not re.match(r'^[a-z]{2}(-[A-Z]{2})?$', lang): + raise ValidationException(f'语言代码格式不正确: {lang}', code=BizCode.VALIDATION_FAILED) + return v class TenantCreate(TenantBase): @@ -30,6 +52,8 @@ class TenantUpdate(BaseModel): name: Optional[str] = Field(None, description="租户名称", max_length=255) description: Optional[str] = Field(None, description="租户描述", max_length=1000) is_active: Optional[bool] = Field(None, description="是否激活") + default_language: Optional[str] = Field(None, description="租户默认语言", max_length=10) + supported_languages: Optional[List[str]] = Field(None, description="租户支持的语言列表") @field_validator('name') @classmethod @@ -37,6 +61,25 @@ class TenantUpdate(BaseModel): if v is not None and (not v or not v.strip()): raise ValidationException('租户名称不能为空', code=BizCode.VALIDATION_FAILED) return v.strip() if v else v + + @field_validator('default_language') + @classmethod + def validate_default_language(cls, v): + if v: + import re + if not re.match(r'^[a-z]{2}(-[A-Z]{2})?$', v): + raise ValidationException('语言代码格式不正确', code=BizCode.VALIDATION_FAILED) + return v + + @field_validator('supported_languages') + @classmethod + def validate_supported_languages(cls, v): + if v: + import re + for lang in v: + if not re.match(r'^[a-z]{2}(-[A-Z]{2})?$', lang): + raise ValidationException(f'语言代码格式不正确: {lang}', code=BizCode.VALIDATION_FAILED) + return v class Tenant(TenantBase): @@ -62,4 +105,29 @@ class TenantList(BaseModel): total: int page: int size: int - pages: int \ No newline at end of file + pages: int + + +class TenantLanguageConfig(BaseModel): + """租户语言配置Schema""" + default_language: str = Field(..., description="租户默认语言", max_length=10) + supported_languages: List[str] = Field(..., description="租户支持的语言列表") + + @field_validator('default_language') + @classmethod + def validate_default_language(cls, v): + import re + if not re.match(r'^[a-z]{2}(-[A-Z]{2})?$', v): + raise ValidationException('语言代码格式不正确', code=BizCode.VALIDATION_FAILED) + return v + + @field_validator('supported_languages') + @classmethod + def validate_supported_languages(cls, v): + if not v: + raise ValidationException('支持的语言列表不能为空', code=BizCode.VALIDATION_FAILED) + import re + for lang in v: + if not re.match(r'^[a-z]{2}(-[A-Z]{2})?$', lang): + raise ValidationException(f'语言代码格式不正确: {lang}', code=BizCode.VALIDATION_FAILED) + return v diff --git a/api/app/schemas/user_schema.py b/api/app/schemas/user_schema.py index 7b9e201d..6b880696 100644 --- a/api/app/schemas/user_schema.py +++ b/api/app/schemas/user_schema.py @@ -58,6 +58,16 @@ class VerifyPasswordRequest(BaseModel): password: str = Field(..., description="密码") +class LanguagePreferenceRequest(BaseModel): + """语言偏好设置请求""" + language: str = Field(..., min_length=2, max_length=10, description="语言代码，如 'zh', 'en'") + + +class LanguagePreferenceResponse(BaseModel): + """语言偏好响应""" + language: str = Field(..., description="当前语言偏好") + + class ChangePasswordResponse(BaseModel): """修改密码响应""" message: str @@ -74,6 +84,7 @@ class User(UserBase): current_workspace_id: Optional[uuid.UUID] = None current_workspace_name: Optional[str] = None role: Optional[WorkspaceRole] = None + preferred_language: Optional[str] = "zh" # 用户语言偏好 # 将 datetime 转换为毫秒时间戳 @validator("created_at", pre=True) diff --git a/api/app/services/auth_service.py b/api/app/services/auth_service.py index 03e1ebc0..436a5c96 100644 --- a/api/app/services/auth_service.py +++ b/api/app/services/auth_service.py @@ -80,6 +80,7 @@ def authenticate_user_or_raise(db: Session, email: str, password: str) -> User: from app.core.exceptions import BusinessException from app.core.error_codes import BizCode from app.core.logging_config import get_auth_logger + from app.i18n.service import t logger = get_auth_logger() @@ -87,17 +88,17 @@ def authenticate_user_or_raise(db: Session, email: str, password: str) -> User: user = user_repository.get_user_by_email(db, email=email) if not user: logger.warning(f"用户不存在: {email}") - raise BusinessException("用户不存在", code=BizCode.USER_NOT_FOUND) + raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NOT_FOUND) # 检查用户状态 if not user.is_active: logger.warning(f"用户未激活: {email}") - raise BusinessException("用户未激活", code=BizCode.USER_NOT_FOUND) + raise BusinessException(t("auth.login.account_disabled"), code=BizCode.USER_NOT_FOUND) # 验证密码 if not verify_password(password, user.hashed_password): logger.warning(f"密码错误: {email}") - raise BusinessException("密码错误", code=BizCode.PASSWORD_ERROR) + raise BusinessException(t("auth.password.incorrect"), code=BizCode.PASSWORD_ERROR) logger.info(f"用户认证成功: {email}") return user @@ -254,6 +255,8 @@ def decode_access_token(token: str) -> dict: Raises: BusinessException: token 无效 """ + from app.i18n.service import t + try: payload = jwt.decode(token, TOKEN_SECRET_KEY, algorithms=[TOKEN_ALGORITHM]) return { @@ -261,4 +264,4 @@ def decode_access_token(token: str) -> dict: "share_token": payload["share_token"] } except jwt.InvalidTokenError: - raise BusinessException("无效的访问 token", BizCode.INVALID_TOKEN) \ No newline at end of file + raise BusinessException(t("auth.token.invalid"), BizCode.INVALID_TOKEN) \ No newline at end of file diff --git a/api/app/services/tenant_service.py b/api/app/services/tenant_service.py index 2edb46df..066edf57 100644 --- a/api/app/services/tenant_service.py +++ b/api/app/services/tenant_service.py @@ -217,4 +217,55 @@ class TenantService: skip=skip, limit=limit, is_active=is_active - ) \ No newline at end of file + ) + + def get_tenant_language_config(self, tenant_id: uuid.UUID) -> Optional[dict]: + """获取租户语言配置""" + tenant = self.tenant_repo.get_tenant_by_id(tenant_id) + if not tenant: + raise BusinessException("租户不存在", code=BizCode.TENANT_NOT_FOUND) + + return { + "default_language": tenant.default_language, + "supported_languages": tenant.supported_languages + } + + def update_tenant_language_config( + self, + tenant_id: uuid.UUID, + default_language: str, + supported_languages: list + ) -> Optional[dict]: + """更新租户语言配置""" + # 检查租户是否存在 + tenant = self.tenant_repo.get_tenant_by_id(tenant_id) + if not tenant: + raise BusinessException("租户不存在", code=BizCode.TENANT_NOT_FOUND) + + # 验证默认语言在支持的语言列表中 + if default_language not in supported_languages: + raise BusinessException( + "默认语言必须在支持的语言列表中", + code=BizCode.VALIDATION_FAILED + ) + + try: + # 更新语言配置 + tenant.default_language = default_language + tenant.supported_languages = supported_languages + self.db.commit() + self.db.refresh(tenant) + + business_logger.info( + f"更新租户语言配置成功: {tenant.name} (ID: {tenant.id}), " + f"默认语言: {default_language}, 支持语言: {supported_languages}" + ) + + return { + "default_language": tenant.default_language, + "supported_languages": tenant.supported_languages + } + except Exception as e: + self.db.rollback() + business_logger.error(f"更新租户语言配置失败: {str(e)}") + raise BusinessException(f"更新租户语言配置失败: {str(e)}", code=BizCode.DB_ERROR) diff --git a/api/app/services/user_service.py b/api/app/services/user_service.py index 22dabed7..e23b1ac3 100644 --- a/api/app/services/user_service.py +++ b/api/app/services/user_service.py @@ -438,24 +438,26 @@ def update_last_login_time(db: Session, user_id: uuid.UUID) -> User: async def change_password(db: Session, user_id: uuid.UUID, old_password: str, new_password: str, current_user: User) -> User: """普通用户修改自己的密码""" + from app.i18n.service import t + business_logger.info(f"用户修改密码请求: user_id={user_id}, current_user={current_user.id}") # 检查权限：只能修改自己的密码 if current_user.id != user_id: business_logger.warning(f"用户尝试修改他人密码: current_user={current_user.id}, target_user={user_id}") - raise PermissionDeniedException("You can only change your own password") + raise PermissionDeniedException(t("auth.password.change_failed")) try: # 获取用户 db_user = user_repository.get_user_by_id(db=db, user_id=user_id) if not db_user: business_logger.warning(f"用户不存在: {user_id}") - raise BusinessException("User not found", code=BizCode.USER_NOT_FOUND) + raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NOT_FOUND) # 验证旧密码 if not verify_password(old_password, db_user.hashed_password): business_logger.warning(f"用户旧密码验证失败: {user_id}") - raise BusinessException("当前密码不正确", code=BizCode.VALIDATION_FAILED) + raise BusinessException(t("auth.password.incorrect"), code=BizCode.VALIDATION_FAILED) # 更新密码 db_user.hashed_password = get_password_hash(new_password) @@ -471,7 +473,7 @@ async def change_password(db: Session, user_id: uuid.UUID, old_password: str, ne except Exception as e: business_logger.error(f"修改用户密码失败: user_id={user_id} - {str(e)}") db.rollback() - raise BusinessException(f"修改用户密码失败: user_id={user_id} - {str(e)}", code=BizCode.DB_ERROR) + raise BusinessException(t("auth.password.change_failed"), code=BizCode.DB_ERROR) async def admin_change_password(db: Session, target_user_id: uuid.UUID, new_password: str = None, current_user: User = None) -> tuple[User, str]: @@ -487,6 +489,8 @@ async def admin_change_password(db: Session, target_user_id: uuid.UUID, new_pass Returns: tuple[User, str]: (更新后的用户对象, 实际使用的密码) """ + from app.i18n.service import t + business_logger.info(f"管理员修改用户密码请求: admin={current_user.id}, target_user={target_user_id}") # 检查权限：只有超级管理员可以修改他人密码 @@ -496,7 +500,7 @@ async def admin_change_password(db: Session, target_user_id: uuid.UUID, new_pass try: permission_service.check_superuser( subject, - error_message="只有超级管理员可以修改他人密码" + error_message=t("auth.password.change_failed") ) except PermissionDeniedException as e: business_logger.warning(f"非超管用户尝试修改他人密码: current_user={current_user.id}") @@ -507,12 +511,12 @@ async def admin_change_password(db: Session, target_user_id: uuid.UUID, new_pass target_user = user_repository.get_user_by_id(db=db, user_id=target_user_id) if not target_user: business_logger.warning(f"目标用户不存在: {target_user_id}") - raise BusinessException("目标用户不存在", code=BizCode.USER_NOT_FOUND) + raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NOT_FOUND) # 检查租户权限：超管只能修改同租户用户的密码 if current_user.tenant_id != target_user.tenant_id: business_logger.warning(f"跨租户密码修改尝试: admin_tenant={current_user.tenant_id}, target_tenant={target_user.tenant_id}") - raise BusinessException("不可跨租户修改用户密码", code=BizCode.FORBIDDEN) + raise BusinessException(t("auth.password.change_failed"), code=BizCode.FORBIDDEN) # 如果没有提供新密码，则生成随机密码 actual_password = new_password if new_password else generate_random_password() @@ -532,7 +536,7 @@ async def admin_change_password(db: Session, target_user_id: uuid.UUID, new_pass except Exception as e: business_logger.error(f"管理员修改用户密码失败: admin={current_user.id}, target_user={target_user_id} - {str(e)}") db.rollback() - raise BusinessException(f"管理员修改用户密码失败: admin={current_user.id}, target_user={target_user_id} - {str(e)}", code=BizCode.DB_ERROR) + raise BusinessException(t("auth.password.change_failed"), code=BizCode.DB_ERROR) def generate_random_password(length: int = 12) -> str: @@ -740,3 +744,54 @@ async def verify_and_change_email(db: Session, user_id: uuid.UUID, new_email: Em # # business_logger.info(f"用户邮箱修改成功: {db_user.username}, new_email={new_email}") # return db_user + + +def get_user_language_preference(db: Session, user_id: uuid.UUID, current_user: User) -> str: + """获取用户语言偏好""" + business_logger.info(f"获取用户语言偏好: user_id={user_id}") + + # 权限检查：只能获取自己的语言偏好 + if current_user.id != user_id: + raise PermissionDeniedException("只能获取自己的语言偏好") + + db_user = user_repository.get_user_by_id(db=db, user_id=user_id) + if not db_user: + raise BusinessException("用户不存在", code=BizCode.USER_NOT_FOUND) + + language = db_user.preferred_language or "zh" + business_logger.info(f"用户语言偏好: {db_user.username}, language={language}") + return language + + +def update_user_language_preference( + db: Session, + user_id: uuid.UUID, + language: str, + current_user: User +) -> User: + """更新用户语言偏好""" + business_logger.info(f"更新用户语言偏好: user_id={user_id}, language={language}") + + # 权限检查：只能修改自己的语言偏好 + if current_user.id != user_id: + raise PermissionDeniedException("只能修改自己的语言偏好") + + # 验证语言代码是否支持 + from app.core.config import settings + if language not in settings.I18N_SUPPORTED_LANGUAGES: + raise BusinessException( + f"不支持的语言代码: {language}。支持的语言: {', '.join(settings.I18N_SUPPORTED_LANGUAGES)}", + code=BizCode.VALIDATION_FAILED + ) + + db_user = user_repository.get_user_by_id(db=db, user_id=user_id) + if not db_user: + raise BusinessException("用户不存在", code=BizCode.USER_NOT_FOUND) + + # 更新语言偏好 + db_user.preferred_language = language + db.commit() + db.refresh(db_user) + + business_logger.info(f"用户语言偏好更新成功: {db_user.username}, language={language}") + return db_user From b9340ba02d347a97ed4e41a8b04747eee7892c26 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 11 Mar 2026 15:16:02 +0800 Subject: [PATCH 051/196] feat(web): model api key add request abort --- web/src/api/models.ts | 16 +++++++------- .../components/CustomModelModal.tsx | 19 ++++++++++------- .../components/KeyConfigModal.tsx | 21 ++++++++++++------- .../components/MultiKeyConfigModal.tsx | 12 +++++++---- 4 files changed, 42 insertions(+), 26 deletions(-) diff --git a/web/src/api/models.ts b/web/src/api/models.ts index 2d590287..7af412f8 100644 --- a/web/src/api/models.ts +++ b/web/src/api/models.ts @@ -41,12 +41,12 @@ export const deleteCompositeModel = (model_id: string) => { return request.delete(`/models/composite/${model_id}`) } // Create API keys for all matching models by provider -export const updateProviderApiKeys = (data: KeyConfigModalForm) => { - return request.post('/models/provider/apikeys', data) +export const updateProviderApiKeys = (data: KeyConfigModalForm, signal?: AbortSignal) => { + return request.post('/models/provider/apikeys', data, { signal }) } // Create model API key -export const addModelApiKey = (model_id: string, data: MultiKeyForm) => { - return request.post(`/models/${model_id}/apikeys`, data) +export const addModelApiKey = (model_id: string, data: MultiKeyForm, signal?: AbortSignal) => { + return request.post(`/models/${model_id}/apikeys`, data, { signal }) } // Delete model API key export const deleteModelApiKey = (api_key_id: string) => { @@ -65,10 +65,10 @@ export const addModelPlaza = (model_base_id: string) => { return request.post(`/models/model_plaza/${model_base_id}/add`) } // Create custom model -export const addCustomModel = (data: CustomModelForm) => { - return request.post('/models', data) +export const addCustomModel = (data: CustomModelForm, signal?: AbortSignal) => { + return request.post('/models', data, { signal }) } // Update custom model -export const updateCustomModel = (model_base_id: string, data: CustomModelForm) => { - return request.put(`/models/${model_base_id}`, data) +export const updateCustomModel = (model_base_id: string, data: CustomModelForm, signal?: AbortSignal) => { + return request.put(`/models/${model_base_id}`, data, { signal }) } \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/CustomModelModal.tsx b/web/src/views/ModelManagement/components/CustomModelModal.tsx index d47fc996..25c7385c 100644 --- a/web/src/views/ModelManagement/components/CustomModelModal.tsx +++ b/web/src/views/ModelManagement/components/CustomModelModal.tsx @@ -2,7 +2,7 @@ * @Author: ZhaoYing * @Date: 2026-02-03 16:49:28 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-03-04 11:31:43 + * @Last Modified time: 2026-03-11 15:08:24 */ /** * Custom Model Modal @@ -11,7 +11,7 @@ */ import { forwardRef, useEffect, useImperativeHandle, useState } from 'react'; -import { Form, Input, App, Checkbox } from 'antd'; +import { Form, Input, App, Checkbox, Button } from 'antd'; import { useTranslation } from 'react-i18next'; import type { CustomModelForm, ModelListItem, CustomModelModalRef, CustomModelModalProps } from '../types'; @@ -35,6 +35,7 @@ const CustomModelModal = forwardRef( const [isEdit, setIsEdit] = useState(false); const [form] = Form.useForm(); const [loading, setLoading] = useState(false) + const [abortController, setAbortController] = useState(null) const modelType = Form.useWatch(['type'], form); const isOmni = Form.useWatch(['is_omni'], form); @@ -46,6 +47,8 @@ const CustomModelModal = forwardRef( /** Close modal and reset state */ const handleClose = () => { + abortController?.abort() + setAbortController(null) setModel({} as ModelListItem); form.resetFields(); setLoading(false) @@ -73,8 +76,10 @@ const CustomModelModal = forwardRef( /** Update or create custom model */ const handleUpdate = (data: CustomModelForm) => { setLoading(true) + const controller = new AbortController() + setAbortController(controller) const { type, provider, ...rest} = data - const res = isEdit ? updateCustomModel(model.id, rest) : addCustomModel(data) + const res = isEdit ? updateCustomModel(model.id, rest, controller.signal) : addCustomModel(data, controller.signal) res.then(() => { refresh?.(isEdit) @@ -124,15 +129,15 @@ const CustomModelModal = forwardRef( useImperativeHandle(ref, () => ({ handleOpen, })); - console.log('modelType', modelType) return ( {t('common.cancel')}, + , + ]} >
(({ const [model, setModel] = useState({} as ProviderModelItem); const [form] = Form.useForm(); const [loading, setLoading] = useState(false) + const [abortController, setAbortController] = useState(null) /** Close modal and reset state */ const handleClose = () => { + abortController?.abort() + setAbortController(null) setModel({} as ProviderModelItem); form.resetFields(); setLoading(false) @@ -51,10 +54,13 @@ const KeyConfigModal = forwardRef(({ .then((values) => { setLoading(true) + const controller = new AbortController() + setAbortController(controller) + updateProviderApiKeys({ ...values, provider: model.provider - }).then((res) => { + }, controller.signal).then((res) => { if (refresh) { refresh(); } @@ -81,9 +87,10 @@ const KeyConfigModal = forwardRef(({ title={`${model.provider} - ${t('modelNew.keyConfig')}`} open={visible} onCancel={handleClose} - okText={t(`common.save`)} - onOk={handleSave} - confirmLoading={loading} + footer={[ + , + , + ]} > ({} as ModelListItem); const [form] = Form.useForm(); const [loading, setLoading] = useState(false) + const [abortController, setAbortController] = useState(null) /** Close modal and refresh parent */ const handleClose = () => { + abortController?.abort() + setAbortController(null) setModel({} as ModelListItem); refresh?.() @@ -60,12 +63,14 @@ const MultiKeyConfigModal = forwardRef { setLoading(true) + const controller = new AbortController() + setAbortController(controller) addModelApiKey(model.id, { ...values, model_config_id: model.id, model_name: model.name, provider: model.provider, - }).then(() => { + }, controller.signal).then(() => { message.success(t('common.saveSuccess')) form.resetFields(); getData(model) @@ -98,7 +103,6 @@ const MultiKeyConfigModal = forwardRef {model.api_keys && model.api_keys.length > 0 && (
From 168cce1678395d926347174c1a217dacc2403729 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Wed, 11 Mar 2026 17:11:16 +0800 Subject: [PATCH 052/196] feat(web): improve MCP market UI responsiveness and add refresh after service addition - Change getMarketTools parameter type from Query to optional Record for flexibility - Rename marketConfig i18n key to marketConfigBtn for clarity and consistency - Add handleRefreshAfterAdd function to refresh MCP list after successful service addition - Update grid layout to use auto-fill responsive columns instead of fixed 3-column layout - Disable Add button for services already in database to prevent duplicate additions - Connect McpServiceModal refresh callback to handleRefreshAfterAdd for cache invalidation - Improves user experience by automatically updating market list after adding services --- web/src/api/tools.ts | 2 +- web/src/i18n/en.ts | 2 +- web/src/i18n/zh.ts | 2 +- web/src/views/ToolManagement/Market.tsx | 28 +++++++++++++++++++++---- 4 files changed, 27 insertions(+), 7 deletions(-) diff --git a/web/src/api/tools.ts b/web/src/api/tools.ts index 2aed3f80..7c7a0e3d 100644 --- a/web/src/api/tools.ts +++ b/web/src/api/tools.ts @@ -36,7 +36,7 @@ export const getToolMethods = (tool_id: string) => { } // MCP市场列表 -export const getMarketTools = (data: Query) => { +export const getMarketTools = (data?: Record) => { return request.get('/mcp_markets/mcp_markets', data) } // 市场配置创建 diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 11be15b4..80d51554 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1818,7 +1818,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re marketInDatabase: 'In Database', marketAdd: 'Add', marketRefresh: 'Refresh', - marketConfig: 'Configure', + marketConfigBtn: 'Configure', marketConfigConnection: 'Configure Connection', marketNoServices: 'No MCP Services Available', marketNotConnected: 'Not Connected to This Market', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 94c145eb..d2a39ad4 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1814,7 +1814,7 @@ export const zh = { marketInDatabase: '已入库', marketAdd: '添加', marketRefresh: '刷新', - marketConfig: '配置', + marketConfigBtn: '配置', marketConfigConnection: '配置连接', marketNoServices: '暂无可用的 MCP 服务', marketNotConnected: '尚未连接此市场', diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index 5297903e..9bcf5f67 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -279,6 +279,20 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => } }; + const handleRefreshAfterAdd = async () => { + // 添加成功后，刷新当前选中的市场源的 MCP 列表 + if (!selectedSource) return; + + // 清除缓存并重新加载，这样会重新获取工具列表并更新 inDatabase 标记 + setMcpCache(prev => { + const next = { ...prev }; + delete next[selectedSource]; + return next; + }); + setCurrentPage(1); + await fetchMcpList(selectedSource, 1); + }; + const renderSourceDetail = () => { if (!selectedSource) { return ( @@ -355,7 +369,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => )}
@@ -523,7 +543,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => /> {}} + refresh={handleRefreshAfterAdd} />
); From a3e6f67ff707c169b04a22cd3c41d826197d56aa Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Wed, 11 Mar 2026 17:19:07 +0800 Subject: [PATCH 053/196] fix(tool): The MCP tool checks for duplicate additions from the main screen and performs a test before adding. --- api/app/controllers/tool_controller.py | 5 +- api/app/services/tool_service.py | 81 ++++++++++++++++++++++++-- 2 files changed, 81 insertions(+), 5 deletions(-) diff --git a/api/app/controllers/tool_controller.py b/api/app/controllers/tool_controller.py index ce5b15c0..10ca83af 100644 --- a/api/app/controllers/tool_controller.py +++ b/api/app/controllers/tool_controller.py @@ -14,6 +14,7 @@ from app.models import User from app.models.tool_model import ToolType, ToolStatus, AuthType from app.services.tool_service import ToolService from app.schemas.response_schema import ApiResponse +from app.core.exceptions import BusinessException router = APIRouter(prefix="/tools", tags=["Tool System"]) @@ -103,7 +104,7 @@ async def create_tool( val = getattr(request, key, None) if val is not None: request.config[key] = val - tool_id = service.create_tool( + tool_id = await service.create_tool( name=request.name, tool_type=request.tool_type, tenant_id=current_user.tenant_id, @@ -113,6 +114,8 @@ async def create_tool( tags=request.tags ) return success(data={"tool_id": tool_id}, msg="工具创建成功") + except BusinessException as e: + raise HTTPException(status_code=400, detail=e.message) except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) except Exception as e: diff --git a/api/app/services/tool_service.py b/api/app/services/tool_service.py index 4fe1e9e6..23def7f8 100644 --- a/api/app/services/tool_service.py +++ b/api/app/services/tool_service.py @@ -93,7 +93,44 @@ class ToolService: if query.first(): raise BusinessException(f"工具名称 '{name}' 已存在", BizCode.DUPLICATE_NAME) - def create_tool( + def _check_mcp_duplicate(self, name: str, tool_type: ToolType, tenant_id: uuid.UUID, config: Dict[str, Any]): + """检查MCP工具是否重复：市场来源按market_id+market_config_id+mcp_service_id判断（名称无关），自建按name+tool_type判断""" + from app.models.tool_model import MCPSourceChannel + source_channel = config.get("source_channel") + is_market_source = ( + source_channel is not None + and source_channel != MCPSourceChannel.SELF_HOSTED + ) + if is_market_source: + exists = ( + self.db.query(ToolConfig) + .join(MCPToolConfig, MCPToolConfig.id == ToolConfig.id) + .filter( + ToolConfig.tenant_id == tenant_id, + ToolConfig.tool_type == tool_type, + MCPToolConfig.source_channel == source_channel, + MCPToolConfig.market_id == config.get("market_id"), + MCPToolConfig.market_config_id == config.get("market_config_id"), + MCPToolConfig.mcp_service_id == config.get("mcp_service_id"), + ) + .first() + ) + if exists: + raise BusinessException(f"该MCP服务已添加", BizCode.DUPLICATE_NAME) + else: + exists = ( + self.db.query(ToolConfig) + .filter( + ToolConfig.name == name, + ToolConfig.tool_type == tool_type, + ToolConfig.tenant_id == tenant_id, + ) + .first() + ) + if exists: + raise BusinessException(f"工具 '{name}' 已存在", BizCode.DUPLICATE_NAME) + + async def create_tool( self, name: str, tool_type: ToolType, @@ -106,7 +143,19 @@ class ToolService: """创建工具""" if tool_type == ToolType.BUILTIN: raise ValueError("内置工具不允许创建") - self._check_name_duplicate(name, tool_type, tenant_id) + + cfg = config or {} + if tool_type == ToolType.MCP: + self._check_mcp_duplicate(name, tool_type, tenant_id, cfg) + # 创建前测试连接 + test_result = await self._test_mcp_connection_by_config(cfg) + if not test_result["success"]: + raise BusinessException(f"MCP连接测试失败: {test_result['message']}", BizCode.INVALID_PARAMETER) + # 将发现的工具列表写回 config + if "available_tools" in test_result: + cfg["available_tools"] = test_result["available_tools"] + else: + self._check_name_duplicate(name, tool_type, tenant_id) try: # 创建基础配置 @@ -117,19 +166,22 @@ class ToolService: tool_type=tool_type.value, tenant_id=tenant_id, status=ToolStatus.AVAILABLE.value, - config_data=config or {}, + config_data=cfg, tags=tags ) self.db.add(tool_config) self.db.flush() # 创建类型特定配置 - self._create_type_config(tool_config, config or {}) + self._create_type_config(tool_config, cfg) self.db.commit() logger.info(f"工具创建成功: {tool_config.id}") return str(tool_config.id) + except BusinessException: + self.db.rollback() + raise except Exception as e: self.db.rollback() logger.error(f"创建工具失败: {e}") @@ -1165,6 +1217,27 @@ class ToolService: logger.error(f"加载内置工具配置失败: {e}") return {} + async def _test_mcp_connection_by_config(self, config: Dict[str, Any]) -> Dict[str, Any]: + """根据配置参数直接测试MCP连接（创建前调用，无需已存在的工具记录）""" + server_url = config.get("server_url") + if not server_url: + return {"success": False, "message": "server_url不能为空"} + connection_config = config.get("connection_config") or {} + try: + test_result = await self.mcp_tool_manager.test_tool_connection(server_url, connection_config) + if not test_result["success"]: + return test_result + success_flag, tools, error = await self.mcp_tool_manager.discover_tools(server_url, connection_config) + if not success_flag: + return {"success": False, "message": f"获取工具列表失败: {error}"} + tool_list = [ + {tool["name"]: {"description": tool.get("description", ""), "inputSchema": tool.get("inputSchema", {})}} + for tool in tools if tool.get("name") + ] + return {"success": True, "message": "MCP连接测试成功", "available_tools": tool_list} + except Exception as e: + return {"success": False, "message": f"连接测试异常: {str(e)}"} + async def _test_mcp_connection(self, config: ToolConfig) -> Dict[str, Any]: """测试MCP连接并自动同步工具列表""" try: From fc58ac0408c6110fcbd852ce85e17c9e95353ff0 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Wed, 11 Mar 2026 18:04:04 +0800 Subject: [PATCH 054/196] [changes] Initial stage of community integration --- .../clustering_engine/label_propagation.py | 94 ++++++++++++++----- .../neo4j/community_repository.py | 20 ++++ api/app/repositories/neo4j/cypher_queries.py | 48 +++++----- 3 files changed, 115 insertions(+), 47 deletions(-) diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 80e238fd..cb6e5804 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -141,8 +141,18 @@ class LabelPropagationEngine: # 将最终标签写入 Neo4j await self._flush_labels(labels, end_user_id) + pre_merge_count = len(set(labels.values())) logger.info( - f"[Clustering] 全量聚类完成，共 {len(set(labels.values()))} 个社区，" + f"[Clustering] 全量迭代完成，共 {pre_merge_count} 个社区，" + f"{len(labels)} 个实体，开始后处理合并" + ) + + # 全量初始化后做一轮社区合并（基于 name_embedding 余弦相似度） + all_community_ids = list(set(labels.values())) + await self._evaluate_merge(all_community_ids, end_user_id) + + logger.info( + f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，" f"{len(labels)} 个实体" ) @@ -221,30 +231,50 @@ class LabelPropagationEngine: 策略：计算各社区成员 embedding 的平均向量，若两两余弦相似度 > 0.75 则合并。 合并时保留成员数最多的社区，其余成员迁移过来。 + + 全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。 """ MERGE_THRESHOLD = 0.75 + BATCH_THRESHOLD = 20 # 超过此数量走批量查询 community_embeddings: Dict[str, Optional[List[float]]] = {} community_sizes: Dict[str, int] = {} - for cid in community_ids: - members = await self.repo.get_community_members(cid, end_user_id) - community_sizes[cid] = len(members) - # 计算社区成员 embedding 的平均向量 - valid_embeddings = [ - m["name_embedding"] - for m in members - if m.get("name_embedding") - ] - if valid_embeddings: - dim = len(valid_embeddings[0]) - avg = [ - sum(e[i] for e in valid_embeddings) / len(valid_embeddings) - for i in range(dim) + if len(community_ids) > BATCH_THRESHOLD: + # 批量查询：一次拉取所有社区成员 + all_members = await self.repo.get_all_community_members_batch( + community_ids, end_user_id + ) + for cid in community_ids: + members = all_members.get(cid, []) + community_sizes[cid] = len(members) + valid_embeddings = [ + m["name_embedding"] for m in members if m.get("name_embedding") ] - community_embeddings[cid] = avg - else: - community_embeddings[cid] = None + if valid_embeddings: + dim = len(valid_embeddings[0]) + community_embeddings[cid] = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + else: + community_embeddings[cid] = None + else: + # 增量场景：逐个查询 + for cid in community_ids: + members = await self.repo.get_community_members(cid, end_user_id) + community_sizes[cid] = len(members) + valid_embeddings = [ + m["name_embedding"] for m in members if m.get("name_embedding") + ] + if valid_embeddings: + dim = len(valid_embeddings[0]) + community_embeddings[cid] = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + else: + community_embeddings[cid] = None # 找出应合并的社区对 to_merge: List[tuple] = [] @@ -258,14 +288,32 @@ class LabelPropagationEngine: if sim > MERGE_THRESHOLD: to_merge.append((cids[i], cids[j])) + logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区") + + # 执行合并：用 union-find 思路避免重复迁移已被合并的社区 + # 维护一个 canonical 映射，确保链式合并正确收敛 + canonical: Dict[str, str] = {cid: cid for cid in cids} + + def find(x: str) -> str: + while canonical[x] != x: + canonical[x] = canonical[canonical[x]] + x = canonical[x] + return x + for c1, c2 in to_merge: - keep = c1 if community_sizes.get(c1, 0) >= community_sizes.get(c2, 0) else c2 - dissolve = c2 if keep == c1 else c1 + root1, root2 = find(c1), find(c2) + if root1 == root2: + continue # 已经在同一社区，跳过 + keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2 + dissolve = root2 if keep == root1 else root1 + canonical[dissolve] = keep + members = await self.repo.get_community_members(dissolve, end_user_id) for m in members: - await self.repo.assign_entity_to_community( - m["id"], keep, end_user_id - ) + await self.repo.assign_entity_to_community(m["id"], keep, end_user_id) + # 更新 sizes 以便后续合并决策准确 + community_sizes[keep] = community_sizes.get(keep, 0) + len(members) + community_sizes[dissolve] = 0 await self.repo.refresh_member_count(keep, end_user_id) logger.info( f"[Clustering] 社区合并: {dissolve} → {keep}，" diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 16e30a10..2a1f4f2b 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/api/app/repositories/neo4j/community_repository.py @@ -14,6 +14,7 @@ from app.repositories.neo4j.cypher_queries import ( GET_ENTITY_NEIGHBORS, GET_ALL_ENTITIES_FOR_USER, GET_COMMUNITY_MEMBERS, + GET_ALL_COMMUNITY_MEMBERS_BATCH, CHECK_USER_HAS_COMMUNITIES, UPDATE_COMMUNITY_MEMBER_COUNT, ) @@ -101,6 +102,25 @@ class CommunityRepository: logger.error(f"get_community_members failed: {e}") return [] + async def get_all_community_members_batch( + self, community_ids: List[str], end_user_id: str + ) -> Dict[str, List[Dict]]: + """批量查询多个社区的成员，返回 {community_id: [members]} 字典。""" + try: + rows = await self.connector.execute_query( + GET_ALL_COMMUNITY_MEMBERS_BATCH, + community_ids=community_ids, + end_user_id=end_user_id, + ) + result: Dict[str, List[Dict]] = {} + for row in rows: + cid = row["community_id"] + result.setdefault(cid, []).append(row) + return result + except Exception as e: + logger.error(f"get_all_community_members_batch failed: {e}") + return {} + async def has_communities(self, end_user_id: str) -> bool: """检查该用户是否已有 Community 节点（用于判断全量 vs 增量）。""" try: diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 947097a2..84889d65 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1065,26 +1065,6 @@ Graph_Node_query = """ # Community 节点 & BELONGS_TO_COMMUNITY 边 # ============================================================ -COMMUNITY_NODE_SAVE = """ -MERGE (c:Community {community_id: $community_id}) -SET c.end_user_id = $end_user_id, - c.formed_at = $formed_at, - c.updated_at = datetime(), - c.status = $status, - c.member_count = $member_count -RETURN c.community_id AS community_id -""" - -COMMUNITY_ADD_MEMBER = """ -MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) -MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) -MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) -SET c.updated_at = datetime(), - c.member_count = $member_count -""" - - - # ─── Community 聚类相关 Cypher 模板 ─────────────────────────────────────────── COMMUNITY_NODE_UPSERT = """ @@ -1111,12 +1091,23 @@ DELETE r GET_ENTITY_NEIGHBORS = """ MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) -OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源一：直接关系邻居（EXTRACTED_RELATIONSHIP 边） +OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb1:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源二：同 Statement 共现邻居（REFERENCES_ENTITY 边） +OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e) +OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(nb2:ExtractedEntity {end_user_id: $end_user_id}) +WHERE nb2.id <> e.id + +WITH collect(DISTINCT nb1) + collect(DISTINCT nb2) AS all_neighbors +UNWIND all_neighbors AS nb +WITH nb WHERE nb IS NOT NULL OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community) RETURN DISTINCT - nb.id AS id, - nb.name AS name, - nb.name_embedding AS name_embedding, + nb.id AS id, + nb.name AS name, + nb.name_embedding AS name_embedding, nb.activation_value AS activation_value, CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id """ @@ -1139,6 +1130,15 @@ RETURN e.id AS id, e.name AS name, e.entity_type AS entity_type, ORDER BY coalesce(e.activation_value, 0) DESC """ +GET_ALL_COMMUNITY_MEMBERS_BATCH = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community) +WHERE c.community_id IN $community_ids +RETURN c.community_id AS community_id, + e.id AS id, + e.name_embedding AS name_embedding, + e.activation_value AS activation_value +""" + CHECK_USER_HAS_COMMUNITIES = """ MATCH (c:Community {end_user_id: $end_user_id}) RETURN count(c) AS community_count From c612dfbc1f5da4d88ef274a77481738664c16f24 Mon Sep 17 00:00:00 2001 From: lixiangcheng1 Date: Wed, 11 Mar 2026 18:07:43 +0800 Subject: [PATCH 055/196] =?UTF-8?q?=E3=80=90ADD]update=20=20mcp=20count=20?= =?UTF-8?q?at=20mcp=20market?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/mcp_market_config_controller.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/api/app/controllers/mcp_market_config_controller.py b/api/app/controllers/mcp_market_config_controller.py index 7f73663e..5b71190d 100644 --- a/api/app/controllers/mcp_market_config_controller.py +++ b/api/app/controllers/mcp_market_config_controller.py @@ -19,7 +19,7 @@ from app.models import mcp_market_config_model from app.models.user_model import User from app.schemas import mcp_market_config_schema from app.schemas.response_schema import ApiResponse -from app.services import mcp_market_config_service +from app.services import mcp_market_config_service, mcp_market_service # Obtain a dedicated API logger api_logger = get_api_logger() @@ -115,6 +115,17 @@ async def get_mcp_servers( "has_next": True if page * pagesize < total else False } } + # 5. Update mck_market.mcp_count + db_mcp_market = mcp_market_service.get_mcp_market_by_id(db, mcp_market_id=db_mcp_market_config.mcp_market_id, current_user=current_user) + if not db_mcp_market: + api_logger.warning(f"The mcp market does not exist or access is denied: mcp_market_id={db_mcp_market_config.mcp_market_id}") + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail="The mcp market does not exist or access is denied" + ) + db_mcp_market.mcp_count = total + db.commit() + db.refresh(db_mcp_market) return success(data=result, msg="Query of mcp servers list successful") From 59618457df158fadb7fbcceb3ad483b2fc1605a7 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Wed, 11 Mar 2026 18:24:46 +0800 Subject: [PATCH 056/196] feat(web): add search functionality and empty states to MCP market - Add search input with debouncing (500ms) to filter MCP services by keywords - Implement server-side search via keywords parameter in getMarketMCPs API call - Add new i18n strings for empty states: marketNoData, marketNoDataDesc, marketNoSearchResult, marketNoSearchResultDesc - Replace client-side filtering with server-side search for better performance - Update Empty component display to show different messages for no data vs no search results - Remove BodyWrapper component and implement custom empty state handling - Add searchTimerRef to manage debounce timer lifecycle - Update loadMore callback to include searchKeyword parameter for pagination consistency - Add allowClear prop to search input for better UX - Remove conditional rendering of search input to keep it always visible --- web/src/i18n/en.ts | 4 + web/src/i18n/zh.ts | 4 + web/src/views/ToolManagement/Market.tsx | 87 ++++++++++++++----- .../components/McpServiceModal.tsx | 14 +-- web/src/views/ToolManagement/types.ts | 1 + 5 files changed, 84 insertions(+), 26 deletions(-) diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 6d1e7f6d..62f404aa 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1820,6 +1820,10 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re marketRefresh: 'Refresh', marketConfigBtn: 'Configure', marketConfigConnection: 'Configure Connection', + marketNoData: 'No Data', + marketNoDataDesc: 'This market currently has no available services', + marketNoSearchResult: 'No Search Results', + marketNoSearchResultDesc: 'No matching services found, please try other keywords', marketNoServices: 'No MCP Services Available', marketNotConnected: 'Not Connected to This Market', marketNoServicesDesc: 'This market currently has no available services', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index e2308aee..387c67c3 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1816,6 +1816,10 @@ export const zh = { marketRefresh: '刷新', marketConfigBtn: '配置', marketConfigConnection: '配置连接', + marketNoData: '暂无数据', + marketNoDataDesc: '该市场暂时没有可用的服务', + marketNoSearchResult: '无搜索结果', + marketNoSearchResultDesc: '未找到匹配的服务，请尝试其他关键词', marketNoServices: '暂无可用的 MCP 服务', marketNotConnected: '尚未连接此市场', marketNoServicesDesc: '该市场暂时没有可用的服务', diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index 9bcf5f67..351ae8b7 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -9,7 +9,6 @@ import type { McpServiceModalRef } from './types'; import pageEmptyIcon from '@/assets/images/empty/pageEmpty.png' import Empty from '@/components/Empty/index' import { getMarketTools, getMarketConfig, getMarketMCPs, getMarketMCPDetail, getMarketMCPsActivated, getTools } from '@/api/tools'; -import BodyWrapper from '@/components/Empty/BodyWrapper'; interface MarketSource { id: string; name: string; @@ -75,6 +74,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => const [activatedMcps, setActivatedMcps] = useState([]); const [currentPage, setCurrentPage] = useState(1); const pageSize = 20; + const searchTimerRef = useRef(null); // 获取市场数据 useEffect(() => { @@ -109,7 +109,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => fetchMarketData(); }, [message]); - const fetchMcpList = async (sourceId: string, page = 1, append = false) => { + const fetchMcpList = async (sourceId: string, page = 1, append = false, keywords = '') => { setLoading(true); try { let configId = configIdMap[sourceId]; @@ -139,7 +139,12 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => const allTools: any = await getTools({ tool_type: 'mcp' }); const toolsList = Array.isArray(allTools) ? allTools : []; - const res: any = await getMarketMCPs({ mcp_market_config_id: configId, page, pagesize: pageSize }); + const res: any = await getMarketMCPs({ + mcp_market_config_id: configId, + page, + pagesize: pageSize, + ...(keywords ? { keywords } : {}) + }); if (res?.items && Array.isArray(res.items)) { // 标记已激活和已入库的 MCP const mcpsWithActivated = res.items.map((item: MarketMcp) => { @@ -176,8 +181,46 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => const loadMore = useCallback(() => { if (!selectedSource || loading) return; - fetchMcpList(selectedSource, currentPage + 1, true); - }, [selectedSource, currentPage, loading]); + fetchMcpList(selectedSource, currentPage + 1, true, searchKeyword); + }, [selectedSource, currentPage, loading, searchKeyword]); + + const handleSearchChange = (value: string) => { + setSearchKeyword(value); + + // 清除之前的定时器 + if (searchTimerRef.current) { + clearTimeout(searchTimerRef.current); + } + + // 如果清空搜索框，恢复原始列表 + if (!value.trim()) { + if (selectedSource) { + // 清除缓存，重新加载原始列表 + setMcpCache(prev => { + const next = { ...prev }; + delete next[selectedSource]; + return next; + }); + setCurrentPage(1); + fetchMcpList(selectedSource, 1, false, ''); + } + return; + } + + // 设置新的定时器，500ms 后执行搜索 + searchTimerRef.current = setTimeout(() => { + if (selectedSource) { + // 清除缓存，重新搜索 + setMcpCache(prev => { + const next = { ...prev }; + delete next[selectedSource]; + return next; + }); + setCurrentPage(1); + fetchMcpList(selectedSource, 1, false, value); + } + }, 500); + }; const handleSelectSource = async (sourceId: string) => { setSelectedSource(sourceId); @@ -313,12 +356,6 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => if (!source) return null; const mcpList = mcpCache[selectedSource] || []; - const filteredList = mcpList.filter(mcp => { - const name = getLocaleField(mcp, 'name'); - const desc = getLocaleField(mcp, 'description'); - return name.toLowerCase().includes(searchKeyword.toLowerCase()) || - desc.toLowerCase().includes(searchKeyword.toLowerCase()); - }); return ( <> @@ -358,15 +395,17 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => {t('tool.marketRefresh')} )} - {mcpList.length > 0 && ( + } placeholder={t('tool.marketSearchPlaceholder')} value={searchKeyword} - onChange={(e) => setSearchKeyword(e.target.value)} + onChange={(e) => handleSearchChange(e.target.value)} + allowClear style={{ width: 200 }} + /> - )} +
-
+ {!loading && mcpList.length === 0 ? ( + + ) : ( } @@ -394,7 +441,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => gridTemplateColumns: 'repeat(auto-fill, minmax(300px, 1fr))' }} > - {filteredList.map(mcp => ( + {mcpList.map(mcp => (
ReactNode }> = () => ))}
+ )}
-
); @@ -515,9 +562,9 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => {source.name} - + {/* {source.mcp_count} - + */} {source.connected && ( ● )} diff --git a/web/src/views/ToolManagement/components/McpServiceModal.tsx b/web/src/views/ToolManagement/components/McpServiceModal.tsx index 8bd44ccf..4b523231 100644 --- a/web/src/views/ToolManagement/components/McpServiceModal.tsx +++ b/web/src/views/ToolManagement/components/McpServiceModal.tsx @@ -132,12 +132,14 @@ const McpServiceModal = forwardRef(({ const request = editVo?.id ? updateTool(editVo.id, newService) : addTool(newService) request.then((res: any) => { message.success(t('common.saveSuccess')); - testConnection(res.tool_id || editVo?.id) - .finally(() => { - setLoading(false); - handleClose(); - refresh() - }) + setLoading(false); + handleClose(); + refresh(); + + // 在后台测试连接，不阻塞用户操作 + testConnection(res.tool_id || editVo?.id).catch((err) => { + console.error('测试连接失败:', err); + }); }) .catch(() => { setLoading(false); diff --git a/web/src/views/ToolManagement/types.ts b/web/src/views/ToolManagement/types.ts index 5508b200..621b52fa 100644 --- a/web/src/views/ToolManagement/types.ts +++ b/web/src/views/ToolManagement/types.ts @@ -146,4 +146,5 @@ export interface MarketQuery { mcp_market_config_id?: string; page?: number; pagesize?: number; + keywords?: string; } \ No newline at end of file From 5774a95f618a3394c6b1cd659af962e200ad0649 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 12:00:56 +0800 Subject: [PATCH 057/196] [changes] Time zone modification --- api/app/celery_app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index cac4eff1..21ee291d 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -65,7 +65,7 @@ celery_app.conf.update( # 时区 timezone='Asia/Shanghai', - enable_utc=True, + enable_utc=False, # 任务追踪 task_track_started=True, From 71d9ae15a1b668513a060518b610f6c24c7cc85f Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 13:53:47 +0800 Subject: [PATCH 058/196] [changes] Remove the non-existent "storage_type" --- api/app/repositories/end_user_repository.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/api/app/repositories/end_user_repository.py b/api/app/repositories/end_user_repository.py index 0b828a8b..61faf6d4 100644 --- a/api/app/repositories/end_user_repository.py +++ b/api/app/repositories/end_user_repository.py @@ -247,7 +247,6 @@ class EndUserRepository: EndUser.user_summary: user_summary, EndUser.rag_tags: rag_tags, EndUser.rag_personas: rag_personas, - EndUser.storage_type: "rag", EndUser.rag_summary_updated_at: datetime.datetime.now(), }, synchronize_session=False @@ -286,7 +285,6 @@ class EndUserRepository: .update( { EndUser.memory_insight: memory_insight, - EndUser.storage_type: "rag", EndUser.memory_insight_updated_at: datetime.datetime.now(), }, synchronize_session=False From 2a66775e45581ae3fb08229d83f1f9b3013bb8ad Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 12 Mar 2026 14:17:44 +0800 Subject: [PATCH 059/196] add_remark --- .../memory_reflection_controller.py | 225 ++++++++++- .../memory_short_term_controller.py | 92 ++++- .../agent/langgraph_graph/nodes/data_nodes.py | 29 +- .../langgraph_graph/nodes/problem_nodes.py | 54 ++- .../langgraph_graph/nodes/retrieve_nodes.py | 111 ++++-- .../langgraph_graph/nodes/summary_nodes.py | 172 +++++++- .../nodes/verification_nodes.py | 38 +- .../agent/langgraph_graph/read_graph.py | 123 +----- .../langgraph_graph/routing/write_router.py | 145 ++++--- .../agent/langgraph_graph/tools/tool.py | 221 +++++++---- .../agent/langgraph_graph/tools/write_tool.py | 39 +- .../agent/langgraph_graph/write_graph.py | 43 +- .../reflection_engine/self_reflexion.py | 372 ++++++++++++------ api/app/core/memory/utils/data/text_utils.py | 30 +- api/app/core/memory/utils/data/time_utils.py | 41 +- api/app/schemas/memory_agent_schema.py | 1 + 16 files changed, 1274 insertions(+), 462 deletions(-) diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index 5a32372a..f827eaaf 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -1,3 +1,19 @@ +""" +Memory Reflection Controller + +This module provides REST API endpoints for managing memory reflection configurations +and operations. It handles reflection engine setup, configuration management, and +execution of self-reflection processes across memory systems. + +Key Features: +- Reflection configuration management (save, retrieve, update) +- Workspace-wide reflection execution across multiple applications +- Individual configuration-based reflection runs +- Multi-language support for reflection outputs +- Integration with Neo4j memory storage and LLM models +- Comprehensive error handling and logging +""" + import asyncio import time import uuid @@ -28,9 +44,13 @@ from sqlalchemy.orm import Session from app.utils.config_utils import resolve_config_id +# Load environment variables for configuration load_dotenv() + +# Initialize API logger for request tracking and debugging api_logger = get_api_logger() +# Configure router with prefix and tags for API organization router = APIRouter( prefix="/memory", tags=["Memory"], @@ -43,7 +63,38 @@ async def save_reflection_config( current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """Save reflection configuration to data_comfig table""" + """ + Save reflection configuration to memory config table + + Persists reflection engine configuration settings to the data_config table, + including reflection parameters, model settings, and evaluation criteria. + Validates configuration parameters and ensures data consistency. + + Args: + request: Memory reflection configuration data including: + - config_id: Configuration identifier to update + - reflection_enabled: Whether reflection is enabled + - reflection_period_in_hours: Reflection execution interval + - reflexion_range: Scope of reflection (partial/all) + - baseline: Reflection strategy (time/fact/hybrid) + - reflection_model_id: LLM model for reflection operations + - memory_verify: Enable memory verification checks + - quality_assessment: Enable quality assessment evaluation + current_user: Authenticated user saving the configuration + db: Database session for data operations + + Returns: + dict: Success response with saved reflection configuration data + + Raises: + HTTPException 400: If config_id is missing or parameters are invalid + HTTPException 500: If configuration save operation fails + + Database Operations: + - Updates memory_config table with reflection settings + - Commits transaction and refreshes entity + - Maintains configuration consistency + """ try: config_id = request.config_id config_id = resolve_config_id(config_id, db) @@ -54,6 +105,7 @@ async def save_reflection_config( ) api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}") + # Update reflection configuration in database memory_config = MemoryConfigRepository.update_reflection_config( db, config_id=config_id, @@ -66,6 +118,7 @@ async def save_reflection_config( quality_assessment=request.quality_assessment ) + # Commit transaction and refresh entity db.commit() db.refresh(memory_config) @@ -102,13 +155,55 @@ async def start_workspace_reflection( current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """启动工作空间中所有匹配应用的反思功能""" + """ + Start reflection functionality for all matching applications in workspace + + Initiates reflection processes across all applications within the user's current + workspace that have valid memory configurations. Processes each application's + configurations and associated end users, executing reflection operations + with proper error isolation and transaction management. + + This endpoint serves as a workspace-wide reflection orchestrator, ensuring + that reflection failures for individual users don't affect other operations. + + Args: + current_user: Authenticated user initiating workspace reflection + db: Database session for configuration queries + + Returns: + dict: Success response with reflection results for all processed applications: + - app_id: Application identifier + - config_id: Memory configuration identifier + - end_user_id: End user identifier + - reflection_result: Individual reflection operation result + + Processing Logic: + 1. Retrieve all applications in the current workspace + 2. Filter applications with valid memory configurations + 3. For each configuration, find matching releases + 4. Execute reflection for each end user with isolated transactions + 5. Aggregate results with error handling per user + + Error Handling: + - Individual user reflection failures are isolated + - Failed operations are logged and included in results + - Database transactions are isolated per user to prevent cascading failures + - Comprehensive error reporting for debugging + + Raises: + HTTPException 500: If workspace reflection initialization fails + + Performance Notes: + - Uses independent database sessions for each user operation + - Prevents transaction failures from affecting other users + - Comprehensive logging for operation tracking + """ workspace_id = current_user.current_workspace_id try: api_logger.info(f"用户 {current_user.username} 启动workspace反思，workspace_id: {workspace_id}") - # 使用独立的数据库会话来获取工作空间应用详情，避免事务失败 + # Use independent database session to get workspace app details, avoiding transaction failures from app.db import get_db_context with get_db_context() as query_db: service = WorkspaceAppService(query_db) @@ -116,8 +211,9 @@ async def start_workspace_reflection( reflection_results = [] + # Process each application in the workspace for data in result['apps_detailed_info']: - # 跳过没有配置的应用 + # Skip applications without configurations if not data['memory_configs']: api_logger.debug(f"应用 {data['id']} 没有memory_configs，跳过") continue @@ -126,22 +222,22 @@ async def start_workspace_reflection( memory_configs = data['memory_configs'] end_users = data['end_users'] - # 为每个配置和用户组合执行反思 + # Execute reflection for each configuration and user combination for config in memory_configs: config_id_str = str(config['config_id']) - # 找到匹配此配置的所有release + # Find all releases matching this configuration matching_releases = [r for r in releases if str(r['config']) == config_id_str] if not matching_releases: api_logger.debug(f"配置 {config_id_str} 没有匹配的release") continue - # 为每个用户执行反思 - 使用独立的数据库会话 + # Execute reflection for each user - using independent database sessions for user in end_users: api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config_id_str}") - # 为每个用户创建独立的数据库会话，避免事务失败影响其他用户 + # Create independent database session for each user to avoid transaction failure impact with get_db_context() as user_db: try: reflection_service = MemoryReflectionService(user_db) @@ -184,14 +280,51 @@ async def start_reflection_configs( current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """通过config_id查询memory_config表中的反思配置信息""" + """ + Query reflection configuration information by config_id + + Retrieves detailed reflection configuration settings from the memory_config + table for a specific configuration ID. Provides comprehensive reflection + parameters including model settings, evaluation criteria, and operational flags. + + Args: + config_id: Configuration identifier (UUID or integer) to query + current_user: Authenticated user making the request + db: Database session for data operations + + Returns: + dict: Success response with detailed reflection configuration: + - config_id: Resolved configuration identifier + - reflection_enabled: Whether reflection is enabled for this config + - reflection_period_in_hours: Reflection execution interval + - reflexion_range: Scope of reflection operations (partial/all) + - baseline: Reflection strategy (time/fact/hybrid) + - reflection_model_id: LLM model identifier for reflection + - memory_verify: Memory verification flag + - quality_assessment: Quality assessment flag + + Database Operations: + - Queries memory_config table by resolved config_id + - Retrieves all reflection-related configuration fields + - Resolves configuration ID for consistent formatting + + Raises: + HTTPException 404: If configuration with specified ID is not found + HTTPException 500: If configuration query operation fails + + ID Resolution: + - Supports both UUID and integer config_id formats + - Automatically resolves to appropriate internal format + - Maintains consistency across different ID representations + """ config_id = resolve_config_id(config_id, db) try: config_id=resolve_config_id(config_id,db) api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) memory_config_id = resolve_config_id(result.config_id, db) - # 构建返回数据 + + # Build response data with comprehensive configuration details reflection_config = { "config_id": memory_config_id, "reflection_enabled": result.enable_self_reflexion, @@ -204,10 +337,12 @@ async def start_reflection_configs( } api_logger.info(f"成功查询反思配置，config_id: {config_id}") return success(data=reflection_config, msg="反思配置查询成功") - + api_logger.info(f"Successfully queried reflection config, config_id: {config_id}") + return success(data=reflection_config, msg="Reflection configuration query successful") + except HTTPException: - # 重新抛出HTTP异常 + # Re-raise HTTP exceptions without modification raise except Exception as e: api_logger.error(f"查询反思配置失败: {str(e)}") @@ -223,13 +358,66 @@ async def reflection_run( current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """Activate the reflection function for all matching applications in the workspace""" - # 使用集中化的语言校验 + """ + Execute reflection engine with specified configuration + + Runs the reflection engine using configuration parameters from the database. + Validates model availability, sets up the reflection engine with proper + configuration, and executes the reflection process with multi-language support. + + This endpoint provides a test run capability for reflection configurations, + allowing users to validate their reflection settings and see results before + deploying to production environments. + + Args: + config_id: Configuration identifier (UUID or integer) for reflection settings + language_type: Language preference header for output localization (optional) + current_user: Authenticated user executing the reflection + db: Database session for configuration queries + + Returns: + dict: Success response with reflection execution results including: + - baseline: Reflection strategy used + - source_data: Input data processed + - memory_verifies: Memory verification results (if enabled) + - quality_assessments: Quality assessment results (if enabled) + - reflexion_data: Generated reflection insights and solutions + + Configuration Validation: + - Verifies configuration exists in database + - Validates LLM model availability + - Falls back to default model if specified model is unavailable + - Ensures all required parameters are properly set + + Reflection Engine Setup: + - Creates ReflectionConfig with database parameters + - Initializes Neo4j connector for memory access + - Sets up ReflectionEngine with validated model + - Configures language preferences for output + + Error Handling: + - Model validation with fallback to default + - Configuration validation and error reporting + - Comprehensive logging for debugging + - Graceful handling of missing configurations + + Raises: + HTTPException 404: If configuration is not found + HTTPException 500: If reflection execution fails + + Performance Notes: + - Direct database query for configuration retrieval + - Model validation to prevent runtime failures + - Efficient reflection engine initialization + - Language-aware output processing + """ + # Use centralized language validation for consistent localization language = get_language_from_header(language_type) api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") config_id = resolve_config_id(config_id, db) - # 使用MemoryConfigRepository查询反思配置 + + # Query reflection configuration using MemoryConfigRepository result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) if not result: raise HTTPException( @@ -239,7 +427,7 @@ async def reflection_run( api_logger.info(f"成功查询反思配置，config_id: {config_id}") - # 验证模型ID是否存在 + # Validate model ID existence model_id = result.reflection_model_id if model_id: try: @@ -250,6 +438,7 @@ async def reflection_run( # 可以设置为None，让反思引擎使用默认模型 model_id = None + # Create reflection configuration with database parameters config = ReflectionConfig( enabled=result.enable_self_reflexion, iteration_period=result.iteration_period, @@ -262,11 +451,13 @@ async def reflection_run( model_id=model_id, language_type=language_type ) + + # Initialize Neo4j connector and reflection engine connector = Neo4jConnector() engine = ReflectionEngine( config=config, neo4j_connector=connector, - llm_client=model_id # 传入验证后的 model_id + llm_client=model_id # Pass validated model_id ) result=await (engine.reflection_run()) diff --git a/api/app/controllers/memory_short_term_controller.py b/api/app/controllers/memory_short_term_controller.py index 0acac6ce..b69406a8 100644 --- a/api/app/controllers/memory_short_term_controller.py +++ b/api/app/controllers/memory_short_term_controller.py @@ -1,3 +1,18 @@ +""" +Memory Short Term Controller + +This module provides REST API endpoints for managing short-term and long-term memory +data retrieval and analysis. It handles memory system statistics, data aggregation, +and provides comprehensive memory insights for end users. + +Key Features: +- Short-term memory data retrieval and statistics +- Long-term memory data aggregation +- Entity count integration +- Multi-language response support +- Memory system analytics and reporting +""" + from typing import Optional from dotenv import load_dotenv @@ -13,9 +28,13 @@ from app.models.user_model import User from app.services.memory_short_service import LongService, ShortService from app.services.memory_storage_service import search_entity +# Load environment variables for configuration load_dotenv() + +# Initialize API logger for request tracking and debugging api_logger = get_api_logger() +# Configure router with prefix and tags for API organization router = APIRouter( prefix="/memory/short", tags=["Memory"], @@ -27,24 +46,73 @@ async def short_term_configs( current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): - # 使用集中化的语言校验 + """ + Retrieve comprehensive short-term and long-term memory statistics + + Provides a comprehensive overview of memory system data for a specific end user, + including short-term memory entries, long-term memory aggregations, entity counts, + and retrieval statistics. Supports multi-language responses based on request headers. + + This endpoint serves as a central dashboard for memory system analytics, combining + data from multiple memory subsystems to provide a holistic view of user memory state. + + Args: + end_user_id: Unique identifier for the end user whose memory data to retrieve + language_type: Language preference header for response localization (optional) + current_user: Authenticated user making the request (injected by dependency) + db: Database session for data operations (injected by dependency) + + Returns: + dict: Success response containing comprehensive memory statistics: + - short_term: List of short-term memory entries with detailed data + - long_term: List of long-term memory aggregations and summaries + - entity: Count of entities associated with the end user + - retrieval_number: Total count of short-term memory retrievals + - long_term_number: Total count of long-term memory entries + + Response Structure: + { + "code": 200, + "msg": "Short-term memory system data retrieved successfully", + "data": { + "short_term": [...], # Short-term memory entries + "long_term": [...], # Long-term memory data + "entity": 42, # Entity count + "retrieval_number": 156, # Short-term retrieval count + "long_term_number": 23 # Long-term memory count + } + } + + Raises: + HTTPException: If end_user_id is invalid or data retrieval fails + + Performance Notes: + - Combines multiple service calls for comprehensive data + - Entity search is performed asynchronously for better performance + - Response time depends on memory data volume for the specified user + """ + # Use centralized language validation for consistent localization language = get_language_from_header(language_type) - # 获取短期记忆数据 - short_term=ShortService(end_user_id, db) - short_result=short_term.get_short_databasets() - short_count=short_term.get_short_count() + # Retrieve short-term memory data and statistics + short_term = ShortService(end_user_id, db) + short_result = short_term.get_short_databasets() # Get short-term memory entries + short_count = short_term.get_short_count() # Get short-term retrieval count - long_term=LongService(end_user_id, db) - long_result=long_term.get_long_databasets() + # Retrieve long-term memory data and aggregations + long_term = LongService(end_user_id, db) + long_result = long_term.get_long_databasets() # Get long-term memory entries + # Get entity count for the specified end user entity_result = await search_entity(end_user_id) + + # Compile comprehensive memory statistics response result = { - 'short_term': short_result, - 'long_term': long_result, - 'entity': entity_result.get('num', 0), - "retrieval_number":short_count, - "long_term_number":len(long_result) + 'short_term': short_result, # Short-term memory entries + 'long_term': long_result, # Long-term memory data + 'entity': entity_result.get('num', 0), # Entity count (default to 0 if not found) + "retrieval_number": short_count, # Short-term retrieval statistics + "long_term_number": len(long_result) # Long-term memory entry count } return success(data=result, msg="短期记忆系统数据获取成功") \ No newline at end of file diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py index 6595a2ce..829f26c4 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py @@ -2,15 +2,36 @@ from app.core.memory.agent.utils.llm_tools import ReadState, WriteState def content_input_node(state: ReadState) -> ReadState: - """开始节点 - 提取内容并保持状态信息""" + """ + Start node - Extract content and maintain state information + + Extracts the content from the first message in the state and returns it + as the data field while preserving all other state information. + + Args: + state: ReadState containing messages and other state data + + Returns: + ReadState: Updated state with extracted content in data field + """ content = state['messages'][0].content if state.get('messages') else '' - # 返回内容并保持所有状态信息 + # Return content and maintain all state information return {"data": content} def content_input_write(state: WriteState) -> WriteState: - """开始节点 - 提取内容并保持状态信息""" + """ + Start node - Extract content and maintain state information for write operations + + Extracts the content from the first message in the state for write operations. + + Args: + state: WriteState containing messages and other state data + + Returns: + WriteState: Updated state with extracted content in data field + """ content = state['messages'][0].content if state.get('messages') else '' - # 返回内容并保持所有状态信息 + # Return content and maintain all state information return {"data": content} \ No newline at end of file diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py index 784e5802..3030669c 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py @@ -19,19 +19,39 @@ logger = get_agent_logger(__name__) class ProblemNodeService(LLMServiceMixin): - """问题处理节点服务类""" + """ + Problem processing node service class + + Handles problem decomposition and extension operations using LLM services. + Inherits from LLMServiceMixin to provide structured LLM calling capabilities. + + Attributes: + template_service: Service for rendering Jinja2 templates + """ def __init__(self): super().__init__() self.template_service = TemplateService(template_root) -# 创建全局服务实例 +# Create global service instance problem_service = ProblemNodeService() async def Split_The_Problem(state: ReadState) -> ReadState: - """问题分解节点""" + """ + Problem decomposition node + + Breaks down complex user queries into smaller, more manageable sub-problems. + Uses LLM to analyze the input and generate structured problem decomposition + with question types and reasoning. + + Args: + state: ReadState containing user input and configuration + + Returns: + ReadState: Updated state with problem decomposition results + """ # 从状态中获取数据 content = state.get('data', '') end_user_id = state.get('end_user_id', '') @@ -64,7 +84,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState: # 添加更详细的日志记录 logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}") - # 验证结构化响应 + # Validate structured response if not structured or not hasattr(structured, 'root'): logger.warning("Split_The_Problem: 结构化响应为空或格式不正确") split_result = json.dumps([], ensure_ascii=False) @@ -106,7 +126,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState: exc_info=True ) - # 提供更详细的错误信息 + # Provide more detailed error information error_details = { "error_type": type(e).__name__, "error_message": str(e), @@ -116,7 +136,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState: logger.error(f"Split_The_Problem error details: {error_details}") - # 创建默认的空结果 + # Create default empty result result = { "context": json.dumps([], ensure_ascii=False), "original": content, @@ -130,13 +150,25 @@ async def Split_The_Problem(state: ReadState) -> ReadState: } } - # 返回更新后的状态，包含spit_context字段 + # Return updated state including spit_context field return {"spit_data": result} async def Problem_Extension(state: ReadState) -> ReadState: - """问题扩展节点""" - # 获取原始数据和分解结果 + """ + Problem extension node + + Extends the decomposed problems from Split_The_Problem node by generating + additional related questions and organizing them by original question. + Uses LLM to create comprehensive question extensions for better memory retrieval. + + Args: + state: ReadState containing decomposed problems and configuration + + Returns: + ReadState: Updated state with extended problem results + """ + # Get original data and decomposition results start = time.time() content = state.get('data', '') data = state.get('spit_data', '')['context'] @@ -182,7 +214,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: logger.info(f"Problem_Extension: 开始处理问题扩展，问题数量: {len(databasets)}") - # 验证结构化响应 + # Validate structured response if not response_content or not hasattr(response_content, 'root'): logger.warning("Problem_Extension: 结构化响应为空或格式不正确") aggregated_dict = {} @@ -216,7 +248,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: exc_info=True ) - # 提供更详细的错误信息 + # Provide more detailed error information error_details = { "error_type": type(e).__name__, "error_message": str(e), diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py index 06539ad1..f2cd0d3d 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py @@ -29,6 +29,18 @@ logger = get_agent_logger(__name__) async def rag_config(state): + """ + Configure RAG (Retrieval-Augmented Generation) settings + + Creates configuration for knowledge base retrieval including similarity thresholds, + weights, and reranker settings. + + Args: + state: Current state containing user_rag_memory_id + + Returns: + dict: RAG configuration dictionary + """ user_rag_memory_id = state.get('user_rag_memory_id', '') kb_config = { "knowledge_bases": [ @@ -48,6 +60,19 @@ async def rag_config(state): async def rag_knowledge(state, question): + """ + Retrieve knowledge using RAG approach + + Performs knowledge retrieval from configured knowledge bases using the + provided question and returns formatted results. + + Args: + state: Current state containing configuration + question: Question to search for + + Returns: + tuple: (retrieval_knowledge, clean_content, cleaned_query, raw_results) + """ kb_config = await rag_config(state) end_user_id = state.get('end_user_id', '') user_rag_memory_id = state.get("user_rag_memory_id", '') @@ -68,12 +93,24 @@ async def rag_knowledge(state, question): async def llm_infomation(state: ReadState) -> ReadState: + """ + Get LLM configuration information from state + + Retrieves model configuration details including model ID and tenant ID + from the memory configuration in the current state. + + Args: + state: ReadState containing memory configuration + + Returns: + ReadState: Model configuration as Pydantic model + """ memory_config = state.get('memory_config', None) model_id = memory_config.llm_model_id tenant_id = memory_config.tenant_id - # 使用现有的 memory_config 而不是重新查询数据库 - # 或者使用线程安全的数据库访问 + # Use existing memory_config instead of re-querying database + # or use thread-safe database access with get_db_context() as db: result_orm = ModelConfigService.get_model_by_id(db=db, model_id=model_id, tenant_id=tenant_id) result_pydantic = model_schema.ModelConfig.model_validate(result_orm) @@ -82,16 +119,20 @@ async def llm_infomation(state: ReadState) -> ReadState: async def clean_databases(data) -> str: """ - 简化的数据库搜索结果清理函数 + Simplified database search result cleaning function + + Processes and cleans search results from various sources including + reranked results and time-based search results. Extracts text content + from structured data and returns as formatted string. Args: - data: 搜索结果数据 + data: Search result data (can be string, dict, or other types) Returns: - 清理后的内容字符串 + str: Cleaned content string """ try: - # 解析JSON字符串 + # Parse JSON string if isinstance(data, str): try: data = json.loads(data) @@ -101,24 +142,24 @@ async def clean_databases(data) -> str: if not isinstance(data, dict): return str(data) - # 获取结果数据 + # Get result data # with open("搜索结果.json","w",encoding='utf-8') as f: # f.write(json.dumps(data, indent=4, ensure_ascii=False)) results = data.get('results', data) if not isinstance(results, dict): return str(results) - # 收集所有内容 + # Collect all content content_list = [] - # 处理重排序结果 + # Process reranked results reranked = results.get('reranked_results', {}) if reranked: for category in ['summaries', 'statements', 'chunks', 'entities']: items = reranked.get(category, []) if isinstance(items, list): content_list.extend(items) - # 处理时间搜索结果 + # Process time search results time_search = results.get('time_search', {}) if time_search: if isinstance(time_search, dict): @@ -128,7 +169,7 @@ async def clean_databases(data) -> str: elif isinstance(time_search, list): content_list.extend(time_search) - # 提取文本内容 + # Extract text content text_parts = [] for item in content_list: if isinstance(item, dict): @@ -146,10 +187,19 @@ async def clean_databases(data) -> str: async def retrieve_nodes(state: ReadState) -> ReadState: - ''' - - 模型信息 - ''' + """ + Retrieve information using simplified search approach + + Processes extended problems from previous nodes and performs retrieval + using either RAG or hybrid search based on storage type. Handles concurrent + processing of multiple questions and deduplicates results. + + Args: + state: ReadState containing problem extensions and configuration + + Returns: + ReadState: Updated state with retrieval results and intermediate outputs + """ problem_extension = state.get('problem_extension', '')['context'] storage_type = state.get('storage_type', '') @@ -163,7 +213,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: problem_list.append(data) logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}") - # 创建异步任务处理单个问题 + # Create async task to process individual questions async def process_question_nodes(idx, question): try: # Prepare search parameters based on storage type @@ -209,7 +259,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: } } - # 并发处理所有问题 + # Process all questions concurrently tasks = [process_question_nodes(idx, question) for idx, question in enumerate(problem_list)] databases_anser = await asyncio.gather(*tasks) databases_data = { @@ -257,7 +307,20 @@ async def retrieve_nodes(state: ReadState) -> ReadState: async def retrieve(state: ReadState) -> ReadState: - # 从state中获取end_user_id + """ + Advanced retrieve function using LangChain agents and tools + + Uses LangChain agents with specialized retrieval tools (time-based and hybrid) + to perform sophisticated information retrieval. Supports both RAG and traditional + memory storage approaches with concurrent processing and result deduplication. + + Args: + state: ReadState containing problem extensions and configuration + + Returns: + ReadState: Updated state with retrieval results and intermediate outputs + """ + # Get end_user_id from state import time start = time.time() problem_extension = state.get('problem_extension', '')['context'] @@ -299,21 +362,21 @@ async def retrieve(state: ReadState) -> ReadState: system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}" ) - # 创建异步任务处理单个问题 + # Create async task to process individual questions import asyncio - # 在模块级别定义信号量，限制最大并发数 - SEMAPHORE = asyncio.Semaphore(5) # 限制最多5个并发数据库操作 + # Define semaphore at module level to limit maximum concurrency + SEMAPHORE = asyncio.Semaphore(5) # Limit to maximum 5 concurrent database operations async def process_question(idx, question): - async with SEMAPHORE: # 限制并发 + async with SEMAPHORE: # Limit concurrency try: if storage_type == "rag" and user_rag_memory_id: retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state, question) else: cleaned_query = question - # 使用 asyncio 在线程池中运行同步的 agent.invoke + # Use asyncio to run synchronous agent.invoke in thread pool import asyncio response = await asyncio.get_event_loop().run_in_executor( None, @@ -362,7 +425,7 @@ async def retrieve(state: ReadState) -> ReadState: } } - # 并发处理所有问题 + # Process all questions concurrently import asyncio tasks = [process_question(idx, question) for idx, question in enumerate(problem_list)] databases_anser = await asyncio.gather(*tasks) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py index 87606bf8..030acc9a 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py @@ -23,18 +23,39 @@ logger = get_agent_logger(__name__) class SummaryNodeService(LLMServiceMixin): - """总结节点服务类""" + """ + Summary node service class + + Handles summary generation operations using LLM services. Inherits from + LLMServiceMixin to provide structured LLM calling capabilities for + generating summaries from retrieved information. + + Attributes: + template_service: Service for rendering Jinja2 templates + """ def __init__(self): super().__init__() self.template_service = TemplateService(template_root) -# 创建全局服务实例 +# Create global service instance summary_service = SummaryNodeService() async def rag_config(state): + """ + Configure RAG (Retrieval-Augmented Generation) settings for summary operations + + Creates configuration for knowledge base retrieval including similarity thresholds, + weights, and reranker settings specifically for summary generation. + + Args: + state: Current state containing user_rag_memory_id + + Returns: + dict: RAG configuration dictionary with knowledge base settings + """ user_rag_memory_id = state.get('user_rag_memory_id', '') kb_config = { "knowledge_bases": [ @@ -54,6 +75,23 @@ async def rag_config(state): async def rag_knowledge(state, question): + """ + Retrieve knowledge using RAG approach for summary generation + + Performs knowledge retrieval from configured knowledge bases using the + provided question and returns formatted results for summary processing. + + Args: + state: Current state containing configuration + question: Question to search for in knowledge base + + Returns: + tuple: (retrieval_knowledge, clean_content, cleaned_query, raw_results) + - retrieval_knowledge: List of retrieved knowledge chunks + - clean_content: Formatted content string + - cleaned_query: Processed query string + - raw_results: Raw retrieval results + """ kb_config = await rag_config(state) end_user_id = state.get('end_user_id', '') user_rag_memory_id = state.get("user_rag_memory_id", '') @@ -74,6 +112,18 @@ async def rag_knowledge(state, question): async def summary_history(state: ReadState) -> ReadState: + """ + Retrieve conversation history for summary context + + Gets the conversation history for the current user to provide context + for summary generation operations. + + Args: + state: ReadState containing end_user_id + + Returns: + ReadState: Conversation history data + """ end_user_id = state.get("end_user_id", '') history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) return history @@ -82,11 +132,26 @@ async def summary_history(state: ReadState) -> ReadState: async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model, search_mode) -> str: """ - 增强的summary_llm函数，包含更好的错误处理和数据验证 + Enhanced summary_llm function with better error handling and data validation + + Generates summaries using LLM with structured output. Includes fallback mechanisms + for handling LLM failures and provides robust error recovery. + + Args: + state: ReadState containing current context + history: Conversation history for context + retrieve_info: Retrieved information to summarize + template_name: Jinja2 template name for prompt generation + operation_name: Type of operation (summary, input_summary, retrieve_summary) + response_model: Pydantic model for structured output + search_mode: Search mode flag ("0" for simple, "1" for complex) + + Returns: + str: Generated summary text or fallback message """ data = state.get("data", '') - # 构建系统提示词 + # Build system prompt if str(search_mode) == "0": system_prompt = await summary_service.template_service.render_template( template_name=template_name, @@ -103,7 +168,7 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o retrieve_info=retrieve_info ) try: - # 使用优化的LLM服务进行结构化输出 + # Use optimized LLM service for structured output with get_db_context() as db_session: structured = await summary_service.call_llm_structured( state=state, @@ -112,23 +177,23 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o response_model=response_model, fallback_value=None ) - # 验证结构化响应 + # Validate structured response if structured is None: logger.warning("LLM返回None，使用默认回答") return "信息不足，无法回答" - # 根据操作类型提取答案 + # Extract answer based on operation type if operation_name == "summary": aimessages = getattr(structured, 'query_answer', None) or "信息不足，无法回答" else: - # 处理RetrieveSummaryResponse + # Handle RetrieveSummaryResponse if hasattr(structured, 'data') and structured.data: aimessages = getattr(structured.data, 'query_answer', None) or "信息不足，无法回答" else: logger.warning("结构化响应缺少data字段") aimessages = "信息不足，无法回答" - # 验证答案不为空 + # Validate answer is not empty if not aimessages or aimessages.strip() == "": aimessages = "信息不足，无法回答" @@ -137,7 +202,7 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o except Exception as e: logger.error(f"结构化输出失败: {e}", exc_info=True) - # 尝试非结构化输出作为fallback + # Try unstructured output as fallback try: logger.info("尝试非结构化输出作为fallback") response = await summary_service.call_llm_simple( @@ -148,9 +213,9 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o ) if response and response.strip(): - # 简单清理响应 + # Simple response cleaning cleaned_response = response.strip() - # 移除可能的JSON标记 + # Remove possible JSON markers if cleaned_response.startswith('```'): lines = cleaned_response.split('\n') cleaned_response = '\n'.join(lines[1:-1]) @@ -165,6 +230,19 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o async def summary_redis_save(state: ReadState, aimessages) -> ReadState: + """ + Save summary results to Redis session storage + + Stores the generated summary and user query in Redis for session management + and conversation history tracking. + + Args: + state: ReadState containing user and query information + aimessages: Generated summary message to save + + Returns: + ReadState: Updated state after saving to Redis + """ data = state.get("data", '') end_user_id = state.get("end_user_id", '') await SessionService(store).save_session( @@ -179,6 +257,20 @@ async def summary_redis_save(state: ReadState, aimessages) -> ReadState: async def summary_prompt(state: ReadState, aimessages, raw_results) -> ReadState: + """ + Format summary results for different output types + + Creates structured output formats for both input summary and retrieval summary + operations, including metadata and intermediate results for frontend display. + + Args: + state: ReadState containing storage and user information + aimessages: Generated summary message + raw_results: Raw search/retrieval results + + Returns: + tuple: (input_summary, retrieve_summary) formatted result dictionaries + """ storage_type = state.get("storage_type", '') user_rag_memory_id = state.get("user_rag_memory_id", '') data = state.get("data", '') @@ -217,6 +309,19 @@ async def summary_prompt(state: ReadState, aimessages, raw_results) -> ReadState async def Input_Summary(state: ReadState) -> ReadState: + """ + Generate quick input summary from retrieved information + + Performs fast retrieval and generates a quick summary response for user queries. + This function prioritizes speed by only searching summary nodes and provides + immediate feedback to users. + + Args: + state: ReadState containing user query, storage configuration, and context + + Returns: + ReadState: Dictionary containing summary results with status and metadata + """ start = time.time() storage_type = state.get("storage_type", '') memory_config = state.get('memory_config', None) @@ -266,6 +371,19 @@ async def Input_Summary(state: ReadState) -> ReadState: async def Retrieve_Summary(state: ReadState) -> ReadState: + """ + Generate comprehensive summary from retrieved expansion issues + + Processes retrieved expansion issues and generates a detailed summary using LLM. + This function handles complex retrieval results and provides comprehensive answers + based on expanded query results. + + Args: + state: ReadState containing retrieve data with expansion issues + + Returns: + ReadState: Dictionary containing comprehensive summary results + """ retrieve = state.get("retrieve", '') history = await summary_history(state) import json @@ -299,13 +417,26 @@ async def Retrieve_Summary(state: ReadState) -> ReadState: duration = 0.0 log_time('Retrieval summary', duration) - # 修复协程调用 - 先await，然后访问返回值 + # Fixed coroutine call - await first, then access return value summary_result = await summary_prompt(state, aimessages, retrieve_info_str) summary = summary_result[1] return {"summary": summary} async def Summary(state: ReadState) -> ReadState: + """ + Generate final comprehensive summary from verified data + + Creates the final summary using verified expansion issues and conversation history. + This function processes verified data to generate the most comprehensive and + accurate response to user queries. + + Args: + state: ReadState containing verified data and query information + + Returns: + ReadState: Dictionary containing final summary results + """ start = time.time() query = state.get("data", '') verify = state.get("verify", '') @@ -336,13 +467,26 @@ async def Summary(state: ReadState) -> ReadState: duration = 0.0 log_time('Retrieval summary', duration) - # 修复协程调用 - 先await，然后访问返回值 + # Fixed coroutine call - await first, then access return value summary_result = await summary_prompt(state, aimessages, retrieve_info_str) summary = summary_result[1] return {"summary": summary} async def Summary_fails(state: ReadState) -> ReadState: + """ + Generate fallback summary when normal summary process fails + + Provides a fallback summary generation mechanism when the standard summary + process encounters errors or fails to produce satisfactory results. Uses + a specialized failure template to handle edge cases. + + Args: + state: ReadState containing verified data and failure context + + Returns: + ReadState: Dictionary containing fallback summary results + """ storage_type = state.get("storage_type", '') user_rag_memory_id = state.get("user_rag_memory_id", '') history = await summary_history(state) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py index 3f7b491e..3a04b411 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py @@ -18,24 +18,46 @@ logger = get_agent_logger(__name__) class VerificationNodeService(LLMServiceMixin): - """验证节点服务类""" + """ + Verification node service class + + Handles data verification operations using LLM services. Inherits from + LLMServiceMixin to provide structured LLM calling capabilities for + verifying and validating retrieved information. + + Attributes: + template_service: Service for rendering Jinja2 templates + """ def __init__(self): super().__init__() self.template_service = TemplateService(template_root) -# 创建全局服务实例 +# Create global service instance verification_service = VerificationNodeService() async def Verify_prompt(state: ReadState, messages_deal: VerificationResult): - """处理验证结果并生成输出格式""" + """ + Process verification results and generate output format + + Transforms VerificationResult objects into structured output format suitable + for frontend consumption. Handles conversion of VerificationItem objects to + dictionary format and adds metadata for tracking. + + Args: + state: ReadState containing storage and user configuration + messages_deal: VerificationResult containing verification outcomes + + Returns: + dict: Formatted verification result with status and metadata + """ storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') data = state.get('data', '') - # 将 VerificationItem 对象转换为字典列表 + # Convert VerificationItem objects to dictionary list verified_data = [] if messages_deal.expansion_issue: for item in messages_deal.expansion_issue: @@ -89,7 +111,7 @@ async def Verify(state: ReadState): logger.info("Verify: 开始渲染模板") - # 生成 JSON schema 以指导 LLM 输出正确格式 + # Generate JSON schema to guide LLM output format json_schema = VerificationResult.model_json_schema() system_prompt = await verification_service.template_service.render_template( @@ -104,8 +126,8 @@ async def Verify(state: ReadState): # 使用优化的LLM服务，添加超时保护 logger.info("Verify: 开始调用 LLM") try: - # 添加 asyncio.wait_for 超时包裹，防止无限等待 - # 超时时间设置为 150 秒（比 LLM 配置的 120 秒稍长） + # Add asyncio.wait_for timeout wrapper to prevent infinite waiting + # Timeout set to 150 seconds (slightly longer than LLM config's 120 seconds) with get_db_context() as db_session: structured = await asyncio.wait_for( @@ -122,7 +144,7 @@ async def Verify(state: ReadState): "reason": "验证失败或超时" } ), - timeout=150.0 # 150秒超时 + timeout=150.0 # 150 second timeout ) logger.info(f"Verify: LLM 调用完成，result={structured}") except asyncio.TimeoutError: diff --git a/api/app/core/memory/agent/langgraph_graph/read_graph.py b/api/app/core/memory/agent/langgraph_graph/read_graph.py index cba1b230..bddae618 100644 --- a/api/app/core/memory/agent/langgraph_graph/read_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/read_graph.py @@ -33,7 +33,19 @@ from app.core.memory.agent.langgraph_graph.routing.routers import ( @asynccontextmanager async def make_read_graph(): - """创建并返回 LangGraph 工作流""" + """ + Create and return a LangGraph workflow for memory reading operations + + Builds a state graph workflow that handles memory retrieval, problem analysis, + verification, and summarization. The workflow includes nodes for content input, + problem splitting, retrieval, verification, and various summary operations. + + Yields: + StateGraph: Compiled LangGraph workflow for memory reading + + Raises: + Exception: If workflow creation fails + """ try: # Build workflow graph workflow = StateGraph(ReadState) @@ -48,7 +60,7 @@ async def make_read_graph(): workflow.add_node("Summary", Summary) workflow.add_node("Summary_fails", Summary_fails) - # 添加边 + # Add edges to define workflow flow workflow.add_edge(START, "content_input") workflow.add_conditional_edges("content_input", Split_continue) workflow.add_edge("Input_Summary", END) @@ -63,7 +75,7 @@ async def make_read_graph(): '''-----''' # workflow.add_edge("Retrieve", END) - # 编译工作流 + # Compile workflow graph = workflow.compile() yield graph @@ -72,108 +84,3 @@ async def make_read_graph(): raise finally: print("工作流创建完成") - - -async def main(): - """主函数 - 运行工作流""" - message = "昨天有什么好看的电影" - end_user_id = '88a459f5_text09' # 组ID - storage_type = 'neo4j' # 存储类型 - search_switch = '1' # 搜索开关 - user_rag_memory_id = 'wwwwwwww' # 用户RAG记忆ID - - # 获取数据库会话 - db_session = next(get_db()) - config_service = MemoryConfigService(db_session) - memory_config = config_service.load_memory_config( - config_id=17, # 改为整数 - service_name="MemoryAgentService" - ) - import time - start = time.time() - try: - async with make_read_graph() as graph: - config = {"configurable": {"thread_id": end_user_id}} - # 初始状态 - 包含所有必要字段 - initial_state = {"messages": [HumanMessage(content=message)], "search_switch": search_switch, - "end_user_id": end_user_id - , "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id, - "memory_config": memory_config} - # 获取节点更新信息 - _intermediate_outputs = [] - summary = '' - - async for update_event in graph.astream( - initial_state, - stream_mode="updates", - config=config - ): - for node_name, node_data in update_event.items(): - print(f"处理节点: {node_name}") - - # 处理不同Summary节点的返回结构 - if 'Summary' in node_name: - if 'InputSummary' in node_data and 'summary_result' in node_data['InputSummary']: - summary = node_data['InputSummary']['summary_result'] - elif 'RetrieveSummary' in node_data and 'summary_result' in node_data['RetrieveSummary']: - summary = node_data['RetrieveSummary']['summary_result'] - elif 'summary' in node_data and 'summary_result' in node_data['summary']: - summary = node_data['summary']['summary_result'] - elif 'SummaryFails' in node_data and 'summary_result' in node_data['SummaryFails']: - summary = node_data['SummaryFails']['summary_result'] - - spit_data = node_data.get('spit_data', {}).get('_intermediate', None) - if spit_data and spit_data != [] and spit_data != {}: - _intermediate_outputs.append(spit_data) - - # Problem_Extension 节点 - problem_extension = node_data.get('problem_extension', {}).get('_intermediate', None) - if problem_extension and problem_extension != [] and problem_extension != {}: - _intermediate_outputs.append(problem_extension) - - # Retrieve 节点 - retrieve_node = node_data.get('retrieve', {}).get('_intermediate_outputs', None) - if retrieve_node and retrieve_node != [] and retrieve_node != {}: - _intermediate_outputs.extend(retrieve_node) - - # Verify 节点 - verify_n = node_data.get('verify', {}).get('_intermediate', None) - if verify_n and verify_n != [] and verify_n != {}: - _intermediate_outputs.append(verify_n) - - # Summary 节点 - summary_n = node_data.get('summary', {}).get('_intermediate', None) - if summary_n and summary_n != [] and summary_n != {}: - _intermediate_outputs.append(summary_n) - - # # 过滤掉空值 - # _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}] - # - # # 优化搜索结果 - # print("=== 开始优化搜索结果 ===") - # optimized_outputs = merge_multiple_search_results(_intermediate_outputs) - # result=reorder_output_results(optimized_outputs) - # # 保存优化后的结果到文件 - # with open('_intermediate_outputs_optimized.json', 'w', encoding='utf-8') as f: - # import json - # f.write(json.dumps(result, indent=4, ensure_ascii=False)) - # - print(f"=== 最终摘要 ===") - print(summary) - - except Exception as e: - import traceback - traceback.print_exc() - finally: - db_session.close() - - end = time.time() - print(100 * 'y') - print(f"总耗时: {end - start}s") - print(100 * 'y') - - -if __name__ == "__main__": - import asyncio - - asyncio.run(main()) diff --git a/api/app/core/memory/agent/langgraph_graph/routing/write_router.py b/api/app/core/memory/agent/langgraph_graph/routing/write_router.py index 895f61ac..ddb6ca3e 100644 --- a/api/app/core/memory/agent/langgraph_graph/routing/write_router.py +++ b/api/app/core/memory/agent/langgraph_graph/routing/write_router.py @@ -22,57 +22,73 @@ logger = get_agent_logger(__name__) template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') async def write_rag_agent(end_user_id, user_message, ai_message, user_rag_memory_id): - # RAG 模式：组合消息为字符串格式（保持原有逻辑） + """ + Write messages to RAG storage system + + Combines user and AI messages into a single string format and stores them + in the RAG (Retrieval-Augmented Generation) knowledge base for future retrieval. + + Args: + end_user_id: User identifier for the conversation + user_message: User's input message content + ai_message: AI's response message content + user_rag_memory_id: RAG memory identifier for storage location + """ + # RAG mode: combine messages into string format (maintain original logic) combined_message = f"user: {user_message}\nassistant: {ai_message}" await write_rag(end_user_id, combined_message, user_rag_memory_id) logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}') async def write(storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id, long_term_messages=[]): """ - 写入记忆（支持结构化消息） - + Write memory with structured message support + + Handles memory writing operations for different storage types (Neo4j/RAG). + Supports both individual message pairs and batch long-term message processing. + Args: - storage_type: 存储类型 (neo4j/rag) - end_user_id: 终端用户ID - user_message: 用户消息内容 - ai_message: AI 回复内容 - user_rag_memory_id: RAG 记忆ID - actual_end_user_id: 实际用户ID - actual_config_id: 配置ID - - 逻辑说明： - - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变 - - Neo4j 模式：使用结构化消息列表 - 1. 如果 user_message 和 ai_message 都不为空：创建配对消息 [user, assistant] - 2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景） - 3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段 + storage_type: Storage type identifier ("neo4j" or "rag") + end_user_id: Terminal user identifier + user_message: User message content + ai_message: AI response content + user_rag_memory_id: RAG memory identifier + actual_end_user_id: Actual user identifier for storage + actual_config_id: Configuration identifier + long_term_messages: Optional list of structured messages for batch processing + + Logic explanation: + - RAG mode: Combines user_message and ai_message into string format, maintains original logic + - Neo4j mode: Uses structured message lists + 1. If both user_message and ai_message are not empty: Creates paired messages [user, assistant] + 2. If only user_message exists: Creates single user message [user] (for historical memory scenarios) + 3. Each message is converted to independent Chunk, preserving speaker field """ db = next(get_db()) try: actual_config_id = resolve_config_id(actual_config_id, db) - # Neo4j 模式：使用结构化消息列表 + # Neo4j mode: Use structured message lists structured_messages = [] - # 始终添加用户消息（如果不为空） + # Always add user message (if not empty) if isinstance(user_message, str) and user_message.strip() != "": structured_messages.append({"role": "user", "content": user_message}) - # 只有当 AI 回复不为空时才添加 assistant 消息 + # Only add assistant message when AI reply is not empty if isinstance(ai_message, str) and ai_message.strip() != "": structured_messages.append({"role": "assistant", "content": ai_message}) - # 如果提供了 long_term_messages，使用它替代 structured_messages + # If long_term_messages provided, use it to replace structured_messages if long_term_messages and isinstance(long_term_messages, list): structured_messages = long_term_messages elif long_term_messages and isinstance(long_term_messages, str): - # 如果是 JSON 字符串，先解析 + # If it's a JSON string, parse it first try: structured_messages = json.loads(long_term_messages) except json.JSONDecodeError: logger.error(f"Failed to parse long_term_messages as JSON: {long_term_messages}") - # 如果没有消息，直接返回 + # If no messages, return directly if not structured_messages: logger.warning(f"No messages to write for user {actual_end_user_id}") return @@ -80,11 +96,11 @@ async def write(storage_type, end_user_id, user_message, ai_message, user_rag_me logger.info( f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}") write_id = write_message_task.delay( - actual_end_user_id, # end_user_id: 用户ID - structured_messages, # message: JSON 字符串格式的消息列表 - str(actual_config_id), # config_id: 配置ID字符串 + actual_end_user_id, # end_user_id: User ID + structured_messages, # message: JSON string format message list + str(actual_config_id), # config_id: Configuration ID string storage_type, # storage_type: "neo4j" - user_rag_memory_id or "" # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用） + user_rag_memory_id or "" # user_rag_memory_id: RAG memory ID (not used in Neo4j mode) ) logger.info(f"[WRITE] Celery task submitted - task_id={write_id}") write_status = get_task_memory_write_result(str(write_id)) @@ -93,6 +109,20 @@ async def write(storage_type, end_user_id, user_message, ai_message, user_rag_me db.close() async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type,scope): + """ + Save long-term memory data to database + + Handles the storage of long-term memory data based on different strategies + (chunk-based or aggregate-based) and manages the transition from short-term + to long-term memory storage. + + Args: + long_term_messages: Long-term message data to be saved + actual_config_id: Configuration identifier for memory settings + end_user_id: User identifier for memory association + type: Memory storage strategy type (STRATEGY_CHUNK or STRATEGY_AGGREGATE) + scope: Scope/window size for memory processing + """ with get_db_context() as db_session: repo = LongTermMemoryRepository(db_session) @@ -113,16 +143,20 @@ async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type, -'''根据窗口''' +"""Window-based dialogue processing""" async def window_dialogue(end_user_id,langchain_messages,memory_config,scope): - ''' - 根据窗口获取redis数据,写入neo4j： - Args: - end_user_id: 终端用户ID - memory_config: 内存配置对象 - langchain_messages：原始数据LIST - scope：窗口大小 - ''' + """ + Process dialogue based on window size and write to Neo4j + + Manages conversation data based on a sliding window approach. When the window + reaches the specified scope size, it triggers long-term memory storage to Neo4j. + + Args: + end_user_id: Terminal user identifier + memory_config: Memory configuration object containing settings + langchain_messages: Original message data list + scope: Window size determining when to trigger long-term storage + """ scope=scope is_end_user_id = count_store.get_sessions_count(end_user_id) if is_end_user_id is not False: @@ -135,7 +169,7 @@ async def window_dialogue(end_user_id,langchain_messages,memory_config,scope): elif int(is_end_user_id) == int(scope): logger.info('写入长期记忆NEO4J') formatted_messages = (redis_messages) - # 获取 config_id（如果 memory_config 是对象，提取 config_id；否则直接使用） + # Get config_id (if memory_config is an object, extract config_id; otherwise use directly) if hasattr(memory_config, 'config_id'): config_id = memory_config.config_id else: @@ -148,14 +182,19 @@ async def window_dialogue(end_user_id,langchain_messages,memory_config,scope): count_store.save_sessions_count(end_user_id, 1, langchain_messages) -"""根据时间""" +"""Time-based memory processing""" async def memory_long_term_storage(end_user_id,memory_config,time): - ''' - 根据时间获取redis数据,写入neo4j： - Args: - end_user_id: 终端用户ID - memory_config: 内存配置对象 - ''' + """ + Process memory storage based on time intervals and write to Neo4j + + Retrieves Redis data based on time intervals and writes it to Neo4j for + long-term storage. This function handles time-based memory consolidation. + + Args: + end_user_id: Terminal user identifier + memory_config: Memory configuration object containing settings + time: Time interval for data retrieval + """ long_time_data = write_store.find_user_recent_sessions(end_user_id, time) format_messages = (long_time_data) messages=[] @@ -166,19 +205,25 @@ async def memory_long_term_storage(end_user_id,memory_config,time): if format_messages!=[]: await write(AgentMemory_Long_Term.STORAGE_NEO4J, end_user_id, "", "", None, end_user_id, memory_config, messages) -'''聚合判断''' +"""Aggregation judgment processing""" async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config) -> dict: """ - 聚合判断函数：判断输入句子和历史消息是否描述同一事件 + Aggregation judgment function: determine if input sentence and historical messages describe the same event + + Uses LLM-based analysis to determine whether new messages should be aggregated with existing + historical data or stored as separate events. This helps optimize memory storage and retrieval. Args: - end_user_id: 终端用户ID - ori_messages: 原始消息列表，格式如 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] - memory_config: 内存配置对象 + end_user_id: Terminal user identifier + ori_messages: Original message list, format like [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] + memory_config: Memory configuration object containing LLM settings + + Returns: + dict: Aggregation judgment result containing is_same_event flag and processed output """ try: - # 1. 获取历史会话数据（使用新方法） + # 1. Get historical session data (using new method) result = write_store.get_all_sessions_by_end_user_id(end_user_id) history = await format_parsing(result) if not result: diff --git a/api/app/core/memory/agent/langgraph_graph/tools/tool.py b/api/app/core/memory/agent/langgraph_graph/tools/tool.py index fcbb18e3..bee77ddf 100644 --- a/api/app/core/memory/agent/langgraph_graph/tools/tool.py +++ b/api/app/core/memory/agent/langgraph_graph/tools/tool.py @@ -13,30 +13,43 @@ from app.core.memory.src.search import ( ) def extract_tool_message_content(response): - """从agent响应中提取ToolMessage内容和工具名称""" + """ + Extract ToolMessage content and tool names from agent response + + Parses agent response messages to extract tool execution results and metadata. + Handles JSON parsing and provides structured access to tool output data. + + Args: + response: Agent response dictionary containing messages + + Returns: + dict: Dictionary containing tool_name and parsed content, or None if no tool message found + - tool_name: Name of the executed tool + - content: Parsed tool execution result (JSON or raw text) + """ messages = response.get('messages', []) for message in messages: if hasattr(message, 'tool_call_id') and hasattr(message, 'content'): - # 这是一个ToolMessage + # This is a ToolMessage tool_content = message.content tool_name = None - # 尝试获取工具名称 + # Try to get tool name if hasattr(message, 'name'): tool_name = message.name elif hasattr(message, 'tool_name'): tool_name = message.tool_name try: - # 解析JSON内容 + # Parse JSON content parsed_content = json.loads(tool_content) return { 'tool_name': tool_name, 'content': parsed_content } except json.JSONDecodeError: - # 如果不是JSON格式，直接返回内容 + # If not JSON format, return content directly return { 'tool_name': tool_name, 'content': tool_content @@ -46,26 +59,48 @@ def extract_tool_message_content(response): class TimeRetrievalInput(BaseModel): - """时间检索工具的输入模式""" + """ + Input schema for time retrieval tool + + Defines the expected input parameters for time-based retrieval operations. + Used for validation and documentation of tool parameters. + + Attributes: + context: User input query content for search + end_user_id: Group ID for filtering search results, defaults to test user + """ context: str = Field(description="用户输入的查询内容") end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") def create_time_retrieval_tool(end_user_id: str): """ - 创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements) + Create a TimeRetrieval tool with specific end_user_id (synchronous version) for searching statements by time range + + Creates a specialized time-based retrieval tool that searches for statements within + specified time ranges. Includes field cleaning functionality to remove unnecessary + metadata from search results. + + Args: + end_user_id: User identifier for scoping search results + + Returns: + function: Configured TimeRetrievalWithGroupId tool function """ def clean_temporal_result_fields(data): """ - 清理时间搜索结果中不需要的字段，并修改结构 + Clean unnecessary fields from temporal search results and modify structure + + Removes metadata fields that are not needed for end-user consumption and + restructures the response format for better usability. Args: - data: 要清理的数据 + data: Data to be cleaned (dict, list, or other types) Returns: - 清理后的数据 + Cleaned data with unnecessary fields removed """ - # 需要过滤的字段列表 + # List of fields to filter out fields_to_remove = { 'id', 'apply_id', 'user_id', 'chunk_id', 'created_at', 'valid_at', 'invalid_at', 'statement_ids' @@ -75,9 +110,9 @@ def create_time_retrieval_tool(end_user_id: str): cleaned = {} for key, value in data.items(): if key == 'statements' and isinstance(value, dict) and 'statements' in value: - # 将 statements: {"statements": [...]} 改为 time_search: {"statements": [...]} + # Change statements: {"statements": [...]} to time_search: {"statements": [...]} cleaned_value = clean_temporal_result_fields(value) - # 进一步将内部的 statements 改为 time_search + # Further change internal statements to time_search if 'statements' in cleaned_value: cleaned['results'] = { 'time_search': cleaned_value['statements'] @@ -95,22 +130,29 @@ def create_time_retrieval_tool(end_user_id: str): @tool def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str: """ - 优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段 - 显式接收参数： - - context: 查询上下文内容 - - start_date: 开始时间（可选，格式：YYYY-MM-DD） - - end_date: 结束时间（可选，格式：YYYY-MM-DD） - - end_user_id_param: 组ID（可选，用于覆盖默认组ID） - - clean_output: 是否清理输出中的元数据字段 - -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d") + Optimized time retrieval tool, combines time range search only (synchronous version), automatically filters unnecessary metadata fields + + Performs time-based search operations with automatic metadata filtering. Supports + flexible date range specification and provides clean, user-friendly output. + + Explicit parameters: + - context: Query context content + - start_date: Start time (optional, format: YYYY-MM-DD) + - end_date: End time (optional, format: YYYY-MM-DD) + - end_user_id_param: Group ID (optional, overrides default group ID) + - clean_output: Whether to clean metadata fields from output + - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d") + + Returns: + str: JSON formatted search results with temporal data """ async def _async_search(): - # 使用传入的参数或默认值 + # Use passed parameters or default values actual_end_user_id = end_user_id_param or end_user_id actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d") actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d") - # 基本时间搜索 + # Basic time search results = await search_by_temporal( end_user_id=actual_end_user_id, start_date=actual_start_date, @@ -118,7 +160,7 @@ def create_time_retrieval_tool(end_user_id: str): limit=10 ) - # 清理结果中不需要的字段 + # Clean unnecessary fields from results if clean_output: cleaned_results = clean_temporal_result_fields(results) else: @@ -131,20 +173,28 @@ def create_time_retrieval_tool(end_user_id: str): @tool def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None, clean_output: bool = True) -> str: """ - 优化的关键词时间检索工具，结合关键词和时间范围搜索（同步版本），自动过滤不需要的元数据字段 - 显式接收参数： - - context: 查询内容 - - days_back: 向前搜索的天数，默认7天 - - start_date: 开始时间（可选，格式：YYYY-MM-DD） - - end_date: 结束时间（可选，格式：YYYY-MM-DD） - - clean_output: 是否清理输出中的元数据字段 - - end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d") + Optimized keyword time retrieval tool, combines keyword and time range search (synchronous version), automatically filters unnecessary metadata fields + + Performs combined keyword and temporal search operations with automatic metadata + filtering. Provides more targeted search results by combining content relevance + with time-based filtering. + + Explicit parameters: + - context: Query content for keyword matching + - days_back: Number of days to search backwards, default 7 days + - start_date: Start time (optional, format: YYYY-MM-DD) + - end_date: End time (optional, format: YYYY-MM-DD) + - clean_output: Whether to clean metadata fields from output + - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d") + + Returns: + str: JSON formatted search results combining keyword and temporal data """ async def _async_search(): actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d") actual_start_date = start_date or (datetime.now() - timedelta(days=days_back)).strftime("%Y-%m-%d") - # 关键词时间搜索 + # Keyword time search results = await search_by_keyword_temporal( query_text=context, end_user_id=end_user_id, @@ -153,7 +203,7 @@ def create_time_retrieval_tool(end_user_id: str): limit=15 ) - # 清理结果中不需要的字段 + # Clean unnecessary fields from results if clean_output: cleaned_results = clean_temporal_result_fields(results) else: @@ -168,25 +218,34 @@ def create_time_retrieval_tool(end_user_id: str): def create_hybrid_retrieval_tool_async(memory_config, **search_params): """ - 创建混合检索工具，使用run_hybrid_search进行混合检索，优化输出格式并过滤不需要的字段 + Create hybrid retrieval tool using run_hybrid_search for hybrid retrieval, optimize output format and filter unnecessary fields + + Creates an advanced hybrid search tool that combines multiple search strategies + (keyword, vector, hybrid) with automatic result cleaning and formatting. Args: - memory_config: 内存配置对象 - **search_params: 搜索参数，包含end_user_id, limit, include等 + memory_config: Memory configuration object containing LLM and search settings + **search_params: Search parameters including end_user_id, limit, include, etc. + + Returns: + function: Configured HybridSearch tool function with async capabilities """ def clean_result_fields(data): """ - 递归清理结果中不需要的字段 + Recursively clean unnecessary fields from results + + Removes metadata fields that are not needed for end-user consumption, + improving readability and reducing response size. Args: - data: 要清理的数据（可能是字典、列表或其他类型） + data: Data to be cleaned (can be dict, list, or other types) Returns: - 清理后的数据 + Cleaned data with unnecessary fields removed """ - # 需要过滤的字段列表 - # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用 + # List of fields to filter out + # TODO: fact_summary functionality temporarily disabled, will be enabled after future development fields_to_remove = { 'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids', 'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id', @@ -194,17 +253,17 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): } if isinstance(data, dict): - # 对字典进行清理 + # Clean dictionary cleaned = {} for key, value in data.items(): if key not in fields_to_remove: - cleaned[key] = clean_result_fields(value) # 递归清理嵌套数据 + cleaned[key] = clean_result_fields(value) # Recursively clean nested data return cleaned elif isinstance(data, list): - # 对列表中的每个元素进行清理 + # Clean each element in list return [clean_result_fields(item) for item in data] else: - # 其他类型直接返回 + # Return other types directly return data @tool @@ -216,49 +275,55 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): rerank_alpha: float = 0.6, use_forgetting_rerank: bool = False, use_llm_rerank: bool = False, - clean_output: bool = True # 新增：是否清理输出字段 + clean_output: bool = True # New: whether to clean output fields ) -> str: """ - 优化的混合检索工具，支持关键词、向量和混合搜索，自动过滤不需要的元数据字段 + Optimized hybrid retrieval tool, supports keyword, vector and hybrid search, automatically filters unnecessary metadata fields + + Provides comprehensive search capabilities combining multiple search strategies + with intelligent result ranking and automatic metadata filtering for clean output. Args: - context: 查询内容 - search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') - limit: 结果数量限制 - end_user_id: 组ID，用于过滤搜索结果 - rerank_alpha: 重排序权重参数 - use_forgetting_rerank: 是否使用遗忘重排序 - use_llm_rerank: 是否使用LLM重排序 - clean_output: 是否清理输出中的元数据字段 + context: Query content for search + search_type: Search type ('keyword', 'embedding', 'hybrid') + limit: Result quantity limit + end_user_id: Group ID for filtering search results + rerank_alpha: Reranking weight parameter for result scoring + use_forgetting_rerank: Whether to use forgetting-based reranking + use_llm_rerank: Whether to use LLM-based reranking + clean_output: Whether to clean metadata fields from output + + Returns: + str: JSON formatted comprehensive search results """ try: - # 导入run_hybrid_search函数 + # Import run_hybrid_search function from app.core.memory.src.search import run_hybrid_search - # 合并参数，优先使用传入的参数 + # Merge parameters, prioritize passed parameters final_params = { "query_text": context, "search_type": search_type, "end_user_id": end_user_id or search_params.get("end_user_id"), "limit": limit or search_params.get("limit", 10), "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]), - "output_path": None, # 不保存到文件 + "output_path": None, # Don't save to file "memory_config": memory_config, "rerank_alpha": rerank_alpha, "use_forgetting_rerank": use_forgetting_rerank, "use_llm_rerank": use_llm_rerank } - # 执行混合检索 + # Execute hybrid retrieval raw_results = await run_hybrid_search(**final_params) - # 清理结果中不需要的字段 + # Clean unnecessary fields from results if clean_output: cleaned_results = clean_result_fields(raw_results) else: cleaned_results = raw_results - # 格式化返回结果 + # Format return results formatted_results = { "search_query": context, "search_type": search_type, @@ -281,11 +346,17 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): def create_hybrid_retrieval_tool_sync(memory_config, **search_params): """ - 创建同步版本的混合检索工具，优化输出格式并过滤不需要的字段 + Create synchronous version of hybrid retrieval tool, optimize output format and filter unnecessary fields + + Creates a synchronous wrapper around the async hybrid search functionality, + making it compatible with synchronous tool execution environments. Args: - memory_config: 内存配置对象 - **search_params: 搜索参数 + memory_config: Memory configuration object containing search settings + **search_params: Search parameters for configuration + + Returns: + function: Configured HybridSearchSync tool function """ @tool def HybridSearchSync( @@ -296,17 +367,23 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): clean_output: bool = True ) -> str: """ - 优化的混合检索工具（同步版本），自动过滤不需要的元数据字段 + Optimized hybrid retrieval tool (synchronous version), automatically filters unnecessary metadata fields + + Provides the same hybrid search capabilities as the async version but in a + synchronous execution context. Automatically handles async-to-sync conversion. Args: - context: 查询内容 - search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') - limit: 结果数量限制 - end_user_id: 组ID，用于过滤搜索结果 - clean_output: 是否清理输出中的元数据字段 + context: Query content for search + search_type: Search type ('keyword', 'embedding', 'hybrid') + limit: Result quantity limit + end_user_id: Group ID for filtering search results + clean_output: Whether to clean metadata fields from output + + Returns: + str: JSON formatted search results """ async def _async_search(): - # 创建异步工具并执行 + # Create async tool and execute async_tool = create_hybrid_retrieval_tool_async(memory_config, **search_params) return await async_tool.ainvoke({ "context": context, diff --git a/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py b/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py index 9ce581ee..aa5e09a6 100644 --- a/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py +++ b/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py @@ -3,14 +3,20 @@ import json from langchain_core.messages import HumanMessage, AIMessage async def format_parsing(messages: list,type:str='string'): """ - 格式化解析消息列表 + Format and parse message lists into different output types + + Processes message lists from storage and converts them into either string format + or dictionary format based on the specified type parameter. Handles JSON parsing + and role-based message organization. Args: - messages: 消息列表 - type: 返回类型 ('string' 或 'dict') + messages: List of message objects from storage containing message data + type: Return type specification ('string' for text format, 'dict' for key-value pairs) Returns: - 格式化后的消息列表 + list: Formatted message list in the specified format + - 'string': List of formatted text messages with role prefixes + - 'dict': List of dictionaries mapping user messages to AI responses """ result = [] user=[] @@ -40,6 +46,18 @@ async def format_parsing(messages: list,type:str='string'): return result async def messages_parse(messages: list | dict): + """ + Parse messages from storage format into user-AI conversation pairs + + Extracts and organizes conversation data from stored message format, + separating user and AI messages and pairing them for database storage. + + Args: + messages: List or dictionary containing stored message data with Query fields + + Returns: + list: List of dictionaries containing user-AI message pairs for database storage + """ user=[] ai=[] database=[] @@ -58,6 +76,19 @@ async def messages_parse(messages: list | dict): async def agent_chat_messages(user_content,ai_content): + """ + Create structured chat message format for agent conversations + + Formats user and AI content into a standardized message structure suitable + for agent processing and storage. Creates role-based message objects. + + Args: + user_content: User's message content string + ai_content: AI's response content string + + Returns: + list: List of structured message dictionaries with role and content fields + """ messages = [ { "role": "user", diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py index 1134acc7..15009955 100644 --- a/api/app/core/memory/agent/langgraph_graph/write_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py @@ -43,6 +43,19 @@ async def make_write_graph(): yield graph async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[],memory_config:str='',end_user_id:str='',scope:int=6): + """ + Handle long-term memory storage with different strategies + + Supports multiple storage strategies including chunk-based, time-based, + and aggregate judgment approaches for long-term memory persistence. + + Args: + long_term_type: Storage strategy type ('chunk', 'time', 'aggregate') + langchain_messages: List of messages to store + memory_config: Memory configuration identifier + end_user_id: User group identifier + scope: Scope parameter for chunk-based storage (default: 6) + """ from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment from app.core.memory.agent.utils.redis_tool import write_store write_store.save_session_write(end_user_id, (langchain_messages)) @@ -53,26 +66,40 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[ config_id=memory_config, # 改为整数 service_name="MemoryAgentService" ) - if long_term_type=='chunk': - '''方案一:对话窗口6轮对话''' + if long_term_type==AgentMemory_Long_Term.STRATEGY_CHUNK: + '''Strategy 1: Dialogue window with 6 rounds of conversation''' await window_dialogue(end_user_id,langchain_messages,memory_config,scope) - if long_term_type=='time': - """时间""" - await memory_long_term_storage(end_user_id, memory_config,5) - if long_term_type=='aggregate': - """方案三：聚合判断""" + if long_term_type==AgentMemory_Long_Term.STRATEGY_TIME: + """Time-based strategy""" + await memory_long_term_storage(end_user_id, memory_config,AgentMemory_Long_Term.TIME_SCOPE) + if long_term_type==AgentMemory_Long_Term.STRATEGY_AGGREGATE: + """Strategy 3: Aggregate judgment""" await aggregate_judgment(end_user_id, langchain_messages, memory_config) async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_rag_memory_id,actual_config_id): + """ + Write long-term memory with different storage types + + Handles both RAG-based storage and traditional memory storage approaches. + For traditional storage, uses chunk-based strategy with paired user-AI messages. + + Args: + storage_type: Type of storage (RAG or traditional) + end_user_id: User group identifier + message_chat: User message content + aimessages: AI response messages + user_rag_memory_id: RAG memory identifier + actual_config_id: Actual configuration ID + """ from app.core.memory.agent.langgraph_graph.routing.write_router import write_rag_agent from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save from app.core.memory.agent.langgraph_graph.tools.write_tool import agent_chat_messages if storage_type == AgentMemory_Long_Term.STORAGE_RAG: await write_rag_agent(end_user_id, message_chat, aimessages, user_rag_memory_id) else: - # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话） + # AI reply writing (user messages and AI replies paired, written as complete dialogue at once) CHUNK = AgentMemory_Long_Term.STRATEGY_CHUNK SCOPE = AgentMemory_Long_Term.DEFAULT_SCOPE long_term_messages = await agent_chat_messages(message_chat, aimessages) diff --git a/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py b/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py index 09c7ef3d..b2a594c6 100644 --- a/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py +++ b/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py @@ -1,11 +1,11 @@ """ -自我反思引擎实现 +Self-Reflection Engine Implementation -该模块实现了记忆系统的自我反思功能，包括： -1. 基于时间的反思 - 根据时间周期触发反思 -2. 基于事实的反思 - 检测记忆冲突并解决 -3. 综合反思 - 整合多种反思策略 -4. 反思结果应用 - 更新记忆库 +This module implements the self-reflection functionality of the memory system, including: +1. Time-based reflection - Triggers reflection based on time cycles +2. Fact-based reflection - Detects and resolves memory conflicts +3. Comprehensive reflection - Integrates multiple reflection strategies +4. Reflection result application - Updates memory database """ import asyncio @@ -38,7 +38,7 @@ from app.schemas.memory_storage_schema import ( ) from pydantic import BaseModel -# 配置日志 +# Configure logging _root_logger = logging.getLogger() if not _root_logger.handlers: logging.basicConfig( @@ -49,35 +49,62 @@ else: _root_logger.setLevel(logging.INFO) class TranslationResponse(BaseModel): - """翻译响应模型""" + """Translation response model for language conversion""" data: str + class ReflectionRange(str, Enum): - """反思范围枚举""" - PARTIAL = "partial" # 从检索结果中反思 - ALL = "all" # 从整个数据库中反思 + """ + Reflection range enumeration + + Defines the scope of data to be included in reflection operations. + """ + PARTIAL = "partial" # Reflect from retrieval results + ALL = "all" # Reflect from entire database class ReflectionBaseline(str, Enum): - """反思基线枚举""" - TIME = "TIME" # 基于时间的反思 - FACT = "FACT" # 基于事实的反思 - HYBRID = "HYBRID" # 混合反思 + """ + Reflection baseline enumeration + + Defines the strategy or approach used for reflection operations. + """ + TIME = "TIME" # Time-based reflection + FACT = "FACT" # Fact-based reflection + HYBRID = "HYBRID" # Hybrid reflection combining multiple strategies class ReflectionConfig(BaseModel): - """反思引擎配置""" + """ + Reflection engine configuration + + Defines all configuration parameters for the reflection engine including + operation modes, model settings, and evaluation criteria. + + Attributes: + enabled: Whether reflection engine is enabled + iteration_period: Reflection cycle period (e.g., "3" hours) + reflexion_range: Scope of reflection (PARTIAL or ALL) + baseline: Reflection strategy (TIME, FACT, or HYBRID) + model_id: LLM model identifier for reflection operations + end_user_id: User identifier for scoped operations + output_example: Example output format for guidance + memory_verify: Enable memory verification checks + quality_assessment: Enable quality assessment evaluation + violation_handling_strategy: Strategy for handling violations + language_type: Language type for output ("zh" or "en") + """ enabled: bool = False - iteration_period: str = "3" # 反思周期 + iteration_period: str = "3" # Reflection cycle period reflexion_range: ReflectionRange = ReflectionRange.PARTIAL baseline: ReflectionBaseline = ReflectionBaseline.TIME - model_id: Optional[str] = None # 模型ID + model_id: Optional[str] = None # Model ID end_user_id: Optional[str] = None - output_example: Optional[str] = None # 输出示例 + output_example: Optional[str] = None # Output example - # 评估相关字段 - memory_verify: bool = True # 记忆验证 - quality_assessment: bool = True # 质量评估 - violation_handling_strategy: str = "warn" # 违规处理策略 + # Evaluation related fields + memory_verify: bool = True # Memory verification + quality_assessment: bool = True # Quality assessment + violation_handling_strategy: str = "warn" # Violation handling strategy language_type: str = "zh" class Config: @@ -85,7 +112,21 @@ class ReflectionConfig(BaseModel): class ReflectionResult(BaseModel): - """反思结果""" + """ + Reflection operation result + + Contains comprehensive information about the outcome of a reflection operation + including success status, metrics, and execution details. + + Attributes: + success: Whether the reflection operation succeeded + message: Descriptive message about the operation result + conflicts_found: Number of conflicts detected during reflection + conflicts_resolved: Number of conflicts successfully resolved + memories_updated: Number of memory entries updated in database + execution_time: Total time taken for the reflection operation + details: Additional details about the operation (optional) + """ success: bool message: str conflicts_found: int = 0 @@ -97,9 +138,22 @@ class ReflectionResult(BaseModel): class ReflectionEngine: """ - 自我反思引擎 - - 负责执行记忆系统的自我反思，包括冲突检测、冲突解决和记忆更新。 + Self-Reflection Engine + + Responsible for executing memory system self-reflection operations including + conflict detection, conflict resolution, and memory updates. Supports multiple + reflection strategies and provides comprehensive result tracking. + + The engine can operate in different modes: + - Time-based: Reflects on memories within specific time periods + - Fact-based: Detects and resolves factual conflicts in memories + - Hybrid: Combines multiple reflection strategies + + Attributes: + config: Reflection engine configuration + neo4j_connector: Neo4j database connector + llm_client: Language model client for analysis + Various function handlers for data processing and prompt rendering """ def __init__( @@ -115,18 +169,21 @@ class ReflectionEngine: update_query: Optional[str] = None ): """ - 初始化反思引擎 + Initialize reflection engine + + Sets up the reflection engine with configuration and optional dependencies. + Uses lazy initialization to avoid circular imports and optimize startup time. Args: - config: 反思引擎配置 - neo4j_connector: Neo4j 连接器（可选） - llm_client: LLM 客户端（可选） - get_data_func: 获取数据的函数（可选） - render_evaluate_prompt_func: 渲染评估提示词的函数（可选） - render_reflexion_prompt_func: 渲染反思提示词的函数（可选） - conflict_schema: 冲突结果 Schema（可选） - reflexion_schema: 反思结果 Schema（可选） - update_query: 更新查询语句（可选） + config: Reflection engine configuration object + neo4j_connector: Neo4j connector instance (optional, will be created if not provided) + llm_client: LLM client instance (optional, will be created if not provided) + get_data_func: Function for retrieving data (optional, uses default if not provided) + render_evaluate_prompt_func: Function for rendering evaluation prompts (optional) + render_reflexion_prompt_func: Function for rendering reflection prompts (optional) + conflict_schema: Schema for conflict result validation (optional) + reflexion_schema: Schema for reflection result validation (optional) + update_query: Query string for database updates (optional) """ self.config = config self.neo4j_connector = neo4j_connector @@ -137,14 +194,20 @@ class ReflectionEngine: self.conflict_schema = conflict_schema self.reflexion_schema = reflexion_schema self.update_query = update_query - self._semaphore = asyncio.Semaphore(5) # 默认并发数为5 + self._semaphore = asyncio.Semaphore(5) # Default concurrency limit of 5 - # 延迟导入以避免循环依赖 + # Lazy import to avoid circular dependencies self._lazy_init_done = False def _lazy_init(self): - """延迟初始化，避免循环导入""" + """ + Lazy initialization to avoid circular imports + + Initializes dependencies only when needed, preventing circular import issues + and optimizing startup performance. Sets up default implementations for + any components not provided during construction. + """ if self._lazy_init_done: return @@ -158,7 +221,7 @@ class ReflectionEngine: factory = MemoryClientFactory(db) self.llm_client = factory.get_llm_client(self.config.model_id) elif isinstance(self.llm_client, str): - # 如果 llm_client 是字符串（model_id），则用它初始化客户端 + # If llm_client is a string (model_id), use it to initialize the client from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.services.memory_config_service import MemoryConfigService @@ -172,10 +235,10 @@ class ReflectionEngine: model_config = config_service.get_model_config(model_id) extra_params={ - "temperature": 0.2, # 降低温度提高响应速度和一致性 - "max_tokens": 600, # 限制最大token数 - "top_p": 0.8, # 优化采样参数 - "stream": False, # 确保非流式输出以获得最快响应 + "temperature": 0.2, # Lower temperature for faster response and consistency + "max_tokens": 600, # Limit maximum token count + "top_p": 0.8, # Optimize sampling parameters + "stream": False, # Ensure non-streaming output for fastest response } self.llm_client = OpenAIClient(RedBearModelConfig( @@ -191,7 +254,7 @@ class ReflectionEngine: if self.get_data_func is None: self.get_data_func = get_data - # 导入get_data_statement函数 + # Import get_data_statement function if not hasattr(self, 'get_data_statement'): self.get_data_statement = get_data_statement @@ -223,13 +286,20 @@ class ReflectionEngine: async def execute_reflection(self, host_id) -> ReflectionResult: """ - 执行完整的反思流程 + Execute complete reflection workflow + + Performs the full reflection process including data retrieval, conflict detection, + conflict resolution, and memory updates. This is the main entry point for + reflection operations. + Args: - host_id: 主机ID + host_id: Host identifier for scoping reflection operations + Returns: - ReflectionResult: 反思结果 + ReflectionResult: Comprehensive result of the reflection operation including + success status, conflict metrics, and execution time """ - # 延迟初始化 + # Lazy initialization self._lazy_init() if not self.config.enabled: @@ -243,7 +313,7 @@ class ReflectionEngine: print(self.config.baseline, self.config.memory_verify, self.config.quality_assessment) try: - # 1. 获取反思数据 + # 1. Get reflection data reflexion_data, statement_databasets = await self._get_reflexion_data(host_id) if not reflexion_data: return ReflectionResult( @@ -252,7 +322,7 @@ class ReflectionEngine: execution_time=asyncio.get_event_loop().time() - start_time ) - # 2. 检测冲突（基于事实的反思） + # 2. Detect conflicts (fact-based reflection) conflict_data = await self._detect_conflicts(reflexion_data, statement_databasets) conflict_list=[] for i in conflict_data: @@ -261,7 +331,7 @@ class ReflectionEngine: conflicts_found=0 - # 3. 解决冲突 + # 3. Resolve conflicts solved_data = await self._resolve_conflicts(conflict_list, statement_databasets) if not solved_data: @@ -276,7 +346,7 @@ class ReflectionEngine: logging.info(f"解决了 {conflicts_resolved} 个冲突") - # 4. 应用反思结果（更新记忆库） + # 4. Apply reflection results (update memory database) memories_updated=await self._apply_reflection_results(solved_data) execution_time = asyncio.get_event_loop().time() - start_time @@ -302,7 +372,19 @@ class ReflectionEngine: ) async def Translate(self, text): - # 翻译中文为英文 + """ + Translate Chinese text to English + + Uses the configured LLM to translate Chinese text to English with structured output. + Provides consistent translation format for reflection results. + + Args: + text: Chinese text to be translated + + Returns: + str: Translated English text + """ + # Translate Chinese to English translation_messages = [ { "role": "user", @@ -316,6 +398,19 @@ class ReflectionEngine: ) return response.data async def extract_translation(self,data): + """ + Extract and translate reflection data to English + + Processes reflection data structure and translates all Chinese content to English. + Handles nested data structures including memory verifications, quality assessments, + and reflection data while preserving the original structure. + + Args: + data: Dictionary containing reflection data with Chinese content + + Returns: + dict: Translated data structure with English content + """ end_datas={} end_datas['source_data']=await self.Translate(data['source_data']) quality_assessments = [] @@ -350,6 +445,18 @@ class ReflectionEngine: return end_datas async def reflection_run(self): + """ + Execute reflection workflow with comprehensive data processing + + Performs a complete reflection operation including conflict detection, resolution, + and result formatting. Supports both Chinese and English output based on + configuration settings. + + Returns: + dict: Comprehensive reflection results including source data, memory verifications, + quality assessments, and reflection data. Results are translated to English + if language_type is set to 'en'. + """ self._lazy_init() start_time = time.time() memory_verifies_flag = self.config.memory_verify @@ -367,7 +474,7 @@ class ReflectionEngine: result_data['source_data'] = "我是 2023 年春天去北京工作的，后来基本一直都在北京上班，也没怎么换过城市。不过后来公司调整，2024 年上半年我被调到上海待了差不多半年，那段时间每天都是在上海办公室打卡。当时入职资料用的还是我之前的身份信息，身份证号是 11010119950308123X，银行卡是 6222023847595898，这些一直没变。对了，其实我 从 2023 年开始就一直在北京生活，从来没有长期离开过北京，上海那段更多算是远程配合" # 2. 检测冲突（基于事实的反思） conflict_data = await self._detect_conflicts(databasets, source_data) - # 遍历数据提取字段 + # Traverse data to extract fields quality_assessments = [] memory_verifies = [] for item in conflict_data: @@ -375,9 +482,9 @@ class ReflectionEngine: memory_verifies.append(item['memory_verify']) result_data['memory_verifies'] = memory_verifies result_data['quality_assessments'] = quality_assessments - conflicts_found = 0 # 初始化为整数0而不是空字符串 + conflicts_found = 0 # Initialize as integer 0 instead of empty string REMOVE_KEYS = {"created_at", "expired_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"} - # Clearn conflict_data，And memory_verify和quality_assessment + # Clean conflict_data, and memory_verify and quality_assessment cleaned_conflict_data = [] for item in conflict_data: cleaned_item = { @@ -389,7 +496,7 @@ class ReflectionEngine: for item in conflict_data: cleaned_data = [] for row in item.get("data", []): - # 删除 created_at / expired_at + # Remove created_at / expired_at cleaned_row = { k: v for k, v in row.items() @@ -402,7 +509,7 @@ class ReflectionEngine: } cleaned_conflict_data_.append(cleaned_item) print(cleaned_conflict_data_) - # 3. 解决冲突 + # 3. Resolve conflicts solved_data = await self._resolve_conflicts(cleaned_conflict_data_, source_data) if not solved_data: return ReflectionResult( @@ -413,7 +520,7 @@ class ReflectionEngine: ) reflexion_data = [] - # 遍历数据提取reflexion字段 + # Traverse data to extract reflexion fields for item in solved_data: if 'results' in item: for result in item['results']: @@ -431,15 +538,24 @@ class ReflectionEngine: async def extract_fields_from_json(self): - """从example.json中提取source_data和databasets字段""" + """ + Extract source_data and databasets fields from example.json + + Reads reflection example data from the example.json file and extracts + the source data and database statements for testing and demonstration purposes. + + Returns: + tuple: (source_data, databasets) extracted from the example file + Returns empty lists if file reading fails + """ prompt_dir = os.path.join(os.path.dirname(__file__), "example") try: - # 读取JSON文件 + # Read JSON file with open(prompt_dir + '/example.json', 'r', encoding='utf-8') as f: data = json.loads(f.read()) - # 提取memory_verify下的字段 + # Extract fields under memory_verify memory_verify = data.get("memory_verify", {}) source_data = memory_verify.get("source_data", []) databasets = memory_verify.get("databasets", []) @@ -451,15 +567,17 @@ class ReflectionEngine: async def _get_reflexion_data(self, host_id: uuid.UUID) -> List[Any]: """ - 获取反思数据 - - 根据配置的反思范围获取需要反思的记忆数据。 + Get reflection data from database + + Retrieves memory data for reflection based on the configured reflection range. + Supports both partial (from retrieval results) and full (entire database) modes. Args: - host_id: 主机ID + host_id: Host UUID identifier for scoping data retrieval Returns: - List[Any]: 反思数据列表 + tuple: (reflexion_data, statement_data) containing memory data for reflection + Returns empty lists if query fails """ print("=== 获取反思数据 ===") @@ -484,26 +602,29 @@ class ReflectionEngine: async def _detect_conflicts(self, data: List[Any], statement_databasets: List[Any]) -> List[Any]: """ - 检测冲突（基于事实的反思） - - 使用 LLM 分析记忆数据，检测其中的冲突。 + Detect conflicts (fact-based reflection) + + Uses LLM to analyze memory data and detect conflicts within the memories. + Performs comprehensive conflict detection including memory verification and + quality assessment based on configuration settings. Args: - data: 待检测的记忆数据 + data: Memory data to be analyzed for conflicts + statement_databasets: Statement database records for context Returns: - List[Any]: 冲突记忆列表 + List[Any]: List of detected conflicts with detailed analysis """ if not data: return [] - # 数据预处理：如果数据量太少，直接返回无冲突 + # Data preprocessing: if data is too small, return no conflicts directly if len(data) < 2: logging.info("数据量不足，无需检测冲突") return [] - # 使用转换后的数据 - # print("转换后的数据:", data[:2] if len(data) > 2 else data) # 只打印前2条避免日志过长 + # Use converted data + # print("Converted data:", data[:2] if len(data) > 2 else data) # Only print first 2 to avoid long logs memory_verify = self.config.memory_verify logging.info("====== 冲突检测开始 ======") @@ -512,7 +633,7 @@ class ReflectionEngine: language_type=self.config.language_type try: - # 渲染冲突检测提示词 + # Render conflict detection prompt rendered_prompt = await self.render_evaluate_prompt_func( data, self.conflict_schema, @@ -526,7 +647,7 @@ class ReflectionEngine: messages = [{"role": "user", "content": rendered_prompt}] logging.info(f"提示词长度: {len(rendered_prompt)}") - # 调用 LLM 进行冲突检测 + # Call LLM for conflict detection response = await self.llm_client.response_structured( messages, self.conflict_schema @@ -539,7 +660,7 @@ class ReflectionEngine: logging.error("LLM 冲突检测输出解析失败") return [] - # 标准化返回格式 + # Standardize return format if isinstance(response, BaseModel): return [response.model_dump()] elif hasattr(response, 'dict'): @@ -553,15 +674,17 @@ class ReflectionEngine: async def _resolve_conflicts(self, conflicts: List[Any], statement_databasets: List[Any]) -> List[Any]: """ - 解决冲突 - - 使用 LLM 对检测到的冲突进行反思和解决。 + Resolve detected conflicts + + Uses LLM to perform reflection and resolution on detected conflicts. + Processes conflicts in parallel for efficiency while respecting concurrency limits. Args: - conflicts: 冲突列表 + conflicts: List of conflicts to be resolved + statement_databasets: Statement database records for context Returns: - List[Any]: 解决方案列表 + List[Any]: List of resolution solutions with reflection results """ if not conflicts: return [] @@ -570,12 +693,12 @@ class ReflectionEngine: baseline = self.config.baseline memory_verify = self.config.memory_verify - # 并行处理每个冲突 + # Process each conflict in parallel async def _resolve_one(conflict: Any) -> Optional[Dict[str, Any]]: - """解决单个冲突""" + """Resolve a single conflict""" async with self._semaphore: try: - # 渲染反思提示词 + # Render reflection prompt rendered_prompt = await self.render_reflexion_prompt_func( [conflict], self.reflexion_schema, @@ -587,7 +710,7 @@ class ReflectionEngine: messages = [{"role": "user", "content": rendered_prompt}] - # 调用 LLM 进行反思 + # Call LLM for reflection response = await self.llm_client.response_structured( messages, self.reflexion_schema @@ -596,7 +719,7 @@ class ReflectionEngine: if not response: return None - # 标准化返回格式 + # Standardize return format if isinstance(response, BaseModel): return response.model_dump() elif hasattr(response, 'dict'): @@ -610,11 +733,11 @@ class ReflectionEngine: logging.warning(f"解决单个冲突失败: {e}") return None - # 并发执行所有冲突解决任务 + # Execute all conflict resolution tasks concurrently tasks = [_resolve_one(conflict) for conflict in conflicts] results = await asyncio.gather(*tasks, return_exceptions=False) - # 过滤掉失败的结果 + # Filter out failed results solved = [r for r in results if r is not None] logging.info(f"成功解决 {len(solved)}/{len(conflicts)} 个冲突") @@ -626,15 +749,16 @@ class ReflectionEngine: solved_data: List[Dict[str, Any]] ) -> int: """ - 应用反思结果（更新记忆库） - - 将解决冲突后的记忆更新到 Neo4j 数据库中。 + Apply reflection results (update memory database) + + Updates the Neo4j database with resolved conflicts and reflection results. + Processes the solved data and applies changes to the memory storage system. Args: - solved_data: 解决方案列表 + solved_data: List of resolved conflict solutions with reflection data Returns: - int: 成功更新的记忆数量 + int: Number of successfully updated memory entries """ changes = extract_and_process_changes(solved_data) success_count = await neo4j_data(changes) @@ -642,80 +766,86 @@ class ReflectionEngine: - # 基于时间的反思方法 + # Time-based reflection methods async def time_based_reflection( self, host_id: uuid.UUID, time_period: Optional[str] = None ) -> ReflectionResult: """ - 基于时间的反思 - - 根据时间周期触发反思，检查在指定时间段内的记忆。 + Time-based reflection + + Triggers reflection based on time cycles, checking memories within + specified time periods. Uses the configured iteration period if + no specific time period is provided. Args: - host_id: 主机ID - time_period: 时间周期（如"三小时"），如果不提供则使用配置中的值 + host_id: Host UUID identifier for scoping reflection + time_period: Time period (e.g., "three hours"), uses config value if not provided Returns: - ReflectionResult: 反思结果 + ReflectionResult: Comprehensive reflection operation result """ period = time_period or self.config.iteration_period logging.info(f"执行基于时间的反思，周期: {period}") - # 使用标准反思流程 + # Use standard reflection workflow return await self.execute_reflection(host_id) - # 基于事实的反思方法 + # Fact-based reflection methods async def fact_based_reflection( self, host_id: uuid.UUID ) -> ReflectionResult: """ - 基于事实的反思 - - 检测记忆中的事实冲突并解决。 + Fact-based reflection + + Detects and resolves factual conflicts within memories. Analyzes + memory data for inconsistencies and contradictions that need resolution. Args: - host_id: 主机ID + host_id: Host UUID identifier for scoping reflection Returns: - ReflectionResult: 反思结果 + ReflectionResult: Comprehensive reflection operation result """ logging.info("执行基于事实的反思") - # 使用标准反思流程 + # Use standard reflection workflow return await self.execute_reflection(host_id) - # 综合反思方法 + # Comprehensive reflection methods async def comprehensive_reflection( self, host_id: uuid.UUID ) -> ReflectionResult: """ - 综合反思 - - 整合基于时间和基于事实的反思策略。 + Comprehensive reflection + + Integrates time-based and fact-based reflection strategies based on + the configured baseline. Supports hybrid approaches that combine + multiple reflection methodologies. Args: - host_id: 主机ID + host_id: Host UUID identifier for scoping reflection Returns: - ReflectionResult: 反思结果 + ReflectionResult: Comprehensive reflection operation result combining + multiple strategies if using hybrid baseline """ logging.info("执行综合反思") - # 根据配置的基线选择反思策略 + # Choose reflection strategy based on configured baseline if self.config.baseline == ReflectionBaseline.TIME: return await self.time_based_reflection(host_id) elif self.config.baseline == ReflectionBaseline.FACT: return await self.fact_based_reflection(host_id) elif self.config.baseline == ReflectionBaseline.HYBRID: - # 混合策略：先执行基于时间的反思，再执行基于事实的反思 + # Hybrid strategy: execute time-based reflection first, then fact-based reflection time_result = await self.time_based_reflection(host_id) fact_result = await self.fact_based_reflection(host_id) - # 合并结果 + # Merge results return ReflectionResult( success=time_result.success and fact_result.success, message=f"时间反思: {time_result.message}; 事实反思: {fact_result.message}", diff --git a/api/app/core/memory/utils/data/text_utils.py b/api/app/core/memory/utils/data/text_utils.py index 133990f7..d0b10f97 100644 --- a/api/app/core/memory/utils/data/text_utils.py +++ b/api/app/core/memory/utils/data/text_utils.py @@ -2,9 +2,17 @@ import json def escape_lucene_query(query: str) -> str: - """Escape Lucene special characters in a free-text query. - - This prevents ParseException when using Neo4j full-text procedures. + """ + Escape special characters in Lucene queries + + Prevents ParseException when using Neo4j full-text search procedures. + Escapes all Lucene reserved special characters and operators. + + Args: + query: Original query string + + Returns: + str: Escaped query string safe for Lucene search """ if query is None: return "" @@ -22,11 +30,21 @@ def escape_lucene_query(query: str) -> str: return s def extract_plain_query(query_input: str) -> str: - """Extract clean, plain-text query from various input forms. - + """ + Extract clean plain-text query from various input forms + + Handles the following cases: - Strips surrounding quotes and whitespace - If input looks like JSON, prefers the 'original' field - - Fallbacks to the raw string when parsing fails + - Falls back to raw string when parsing fails + - Handles dictionary-type input + - Best-effort unescape common escape characters + + Args: + query_input: Query input in various forms (string, dict, etc.) + + Returns: + str: Extracted plain-text query string """ if query_input is None: return "" diff --git a/api/app/core/memory/utils/data/time_utils.py b/api/app/core/memory/utils/data/time_utils.py index c6791dfc..763c642c 100644 --- a/api/app/core/memory/utils/data/time_utils.py +++ b/api/app/core/memory/utils/data/time_utils.py @@ -4,7 +4,13 @@ from datetime import datetime def validate_date_format(date_str: str) -> bool: """ - Validate if the date string is in the format YYYY-MM-DD. + Validate if date string conforms to YYYY-MM-DD format + + Args: + date_str: Date string to validate + + Returns: + bool: True if format is correct, False otherwise """ pattern = r"^\d{4}-\d{1,2}-\d{1,2}$" return bool(re.match(pattern, date_str)) @@ -41,7 +47,20 @@ def normalize_date(date_str: str) -> str: def preprocess_date_string(date_str: str) -> str: - """预处理日期字符串，处理特殊格式""" + """ + 预处理日期字符串，处理特殊格式 + + 处理以下特殊格式： + - 年份后直接跟月份没有分隔符的格式（如 "20259/28"） + - 无分隔符的纯数字格式（如 "20251028", "251028"） + - 混合分隔符，统一为 "-" + + Args: + date_str: 原始日期字符串 + + Returns: + str: 预处理后的日期字符串，格式为 "YYYY-MM-DD" 或 "YYYY-MM" + """ # 处理类似 "20259/28" 的格式（年份后直接跟月份没有分隔） match = re.match(r'^(\d{4,5})[/\.\-_]?(\d{1,2})[/\.\-_]?(\d{1,2})$', date_str) @@ -78,7 +97,23 @@ def preprocess_date_string(date_str: str) -> str: def fallback_parse(date_str: str) -> str: - """备选解析方案""" + """ + 备选日期解析方案 + + 当智能解析失败时，尝试使用预定义的日期格式进行解析。 + 支持多种常见的日期格式，包括： + - YYYY-MM-DD, YYYY/MM/DD, YYYY.MM.DD + - YYYYMMDD, YYMMDD + - MM-DD-YYYY, MM/DD/YYYY, MM.DD.YYYY + - DD-MM-YYYY, DD/MM/YYYY, DD.MM.YYYY + - YYYY-MM, YYYY/MM, YYYY.MM + + Args: + date_str: 待解析的日期字符串 + + Returns: + str: 标准化后的日期字符串（YYYY-MM-DD格式），解析失败时返回原字符串 + """ # 尝试常见的日期格式[citation:4][citation:5] formats_to_try = [ diff --git a/api/app/schemas/memory_agent_schema.py b/api/app/schemas/memory_agent_schema.py index 1a5017eb..26a7390b 100644 --- a/api/app/schemas/memory_agent_schema.py +++ b/api/app/schemas/memory_agent_schema.py @@ -25,5 +25,6 @@ class AgentMemory_Long_Term(ABC): STRATEGY_CHUNK = "chunk" STRATEGY_TIME = "time" DEFAULT_SCOPE = 6 + TIME_SCOPE=5 From d7911244fc6bbeb80df414a7462558e0b38d19b5 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Thu, 12 Mar 2026 17:00:30 +0800 Subject: [PATCH 060/196] fix(web): update i18n --- web/src/i18n/en.ts | 2 +- web/src/i18n/zh.ts | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index c93500f6..af2a3926 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1370,7 +1370,7 @@ export const en = { gotoList: 'Return to Application List', gotoDetail: 'View Details', dify: 'Dify', - pleaseUploadFile: 'Please upload workflow file', + pleaseUploadFile: 'Please upload file', }, userMemory: { userMemory: 'User Memory', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 37d70c2c..97c37771 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -754,7 +754,7 @@ export const zh = { gotoList: '返回应用列表', gotoDetail: '查看详情', dify: 'Dify', - pleaseUploadFile: '请上传工作流文件', + pleaseUploadFile: '请上传文件', }, table: { totalRecords: '共 {{total}} 条记录' From 2961ea4e44ccd41b6209b82cc7c40c5c7db356fc Mon Sep 17 00:00:00 2001 From: zhaoying Date: Thu, 12 Mar 2026 17:20:16 +0800 Subject: [PATCH 061/196] fix(web): upload cancel add refresh --- .../views/ApplicationManagement/components/UploadModal.tsx | 4 ++-- .../ApplicationManagement/components/UploadWorkflowModal.tsx | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/web/src/views/ApplicationManagement/components/UploadModal.tsx b/web/src/views/ApplicationManagement/components/UploadModal.tsx index f354c145..a7acc093 100644 --- a/web/src/views/ApplicationManagement/components/UploadModal.tsx +++ b/web/src/views/ApplicationManagement/components/UploadModal.tsx @@ -2,7 +2,7 @@ * @Author: ZhaoYing * @Date: 2026-02-28 14:08:14 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-03-06 12:05:46 + * @Last Modified time: 2026-03-12 17:19:46 */ /** * UploadModal Component @@ -63,6 +63,7 @@ const UploadModal = forwardRef(({ * Resets all states and form fields */ const handleClose = () => { + refresh() setVisible(false); form.resetFields(); setCurrent(0); @@ -211,7 +212,6 @@ const UploadModal = forwardRef(({ fileSize={100} maxCount={1} fileType={['yml']} - draggerHeight={200} /> diff --git a/web/src/views/ApplicationManagement/components/UploadWorkflowModal.tsx b/web/src/views/ApplicationManagement/components/UploadWorkflowModal.tsx index e1353843..a4adf0c5 100644 --- a/web/src/views/ApplicationManagement/components/UploadWorkflowModal.tsx +++ b/web/src/views/ApplicationManagement/components/UploadWorkflowModal.tsx @@ -2,7 +2,7 @@ * @Author: ZhaoYing * @Date: 2026-02-28 14:08:14 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-03-06 12:05:46 + * @Last Modified time: 2026-03-12 17:19:33 */ /** * UploadWorkflowModal Component @@ -72,6 +72,7 @@ const UploadWorkflowModal = forwardRef Date: Thu, 12 Mar 2026 17:36:42 +0800 Subject: [PATCH 062/196] fix(app): Bug fixes for application import and export --- api/app/services/app_dsl_service.py | 30 ++++++++++++++++++++++++++--- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/api/app/services/app_dsl_service.py b/api/app/services/app_dsl_service.py index fc071177..0c778b81 100644 --- a/api/app/services/app_dsl_service.py +++ b/api/app/services/app_dsl_service.py @@ -18,6 +18,7 @@ from app.models.tool_model import ToolConfig as ToolConfigModel from app.models.workflow_model import WorkflowConfig from app.services.workflow_service import WorkflowService from app.core.workflow.adapters.memory_bear.memory_bear_adapter import MemoryBearAdapter +from app.models.memory_config_model import MemoryConfig as MemoryConfigModel class AppDslService: @@ -220,7 +221,7 @@ class AppDslService: id=uuid.uuid4(), workspace_id=workspace_id, created_by=user_id, - name=app_meta.get("name", "导入应用"), + name=self._unique_app_name(app_meta.get("name", "导入应用"), workspace_id, app_type), description=app_meta.get("description"), icon=app_meta.get("icon"), icon_type=app_meta.get("icon_type"), @@ -296,6 +297,19 @@ class AppDslService: self.db.refresh(new_app) return new_app, warnings + def _unique_app_name(self, name: str, workspace_id: uuid.UUID, app_type: AppType) -> str: + existing = {r[0] for r in self.db.query(App.name).filter( + App.workspace_id == workspace_id, + App.type == app_type, + App.is_active.is_(True) + ).all()} + if name not in existing: + return name + counter = 1 + while f"{name}({counter})" in existing: + counter += 1 + return f"{name}({counter})" + def _resolve_model(self, ref: Optional[dict], tenant_id: uuid.UUID, warnings: list) -> Optional[uuid.UUID]: if not ref: return None @@ -398,9 +412,19 @@ class AppDslService: config_id = memory.get("memory_config_id") or memory.get("memory_content") if not config_id: return memory - from app.models.memory_config_model import MemoryConfig as MemoryConfigModel + try: + config_uuid = uuid.UUID(str(config_id)) + except (ValueError, AttributeError): + exists = self.db.query(MemoryConfigModel).filter( + MemoryConfigModel.config_id_old == int(config_id), + MemoryConfigModel.workspace_id == workspace_id + ).first() + if not exists: + warnings.append(f"记忆配置 '{config_id}' 未匹配，已置空，请导入后手动配置") + return {**memory, "memory_config_id": None, "enabled": False} + return memory exists = self.db.query(MemoryConfigModel).filter( - MemoryConfigModel.config_id == config_id, + MemoryConfigModel.config_id == config_uuid, MemoryConfigModel.workspace_id == workspace_id ).first() if not exists: From 110de0afbc08ae542e0b4fb37359e8470ae6f09a Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 18:35:09 +0800 Subject: [PATCH 063/196] [add] RAG storage displays the page effect --- .../memory_dashboard_controller.py | 7 +- api/app/services/memory_dashboard_service.py | 99 +++++++++++-------- 2 files changed, 63 insertions(+), 43 deletions(-) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 3bbb5cf7..582b759f 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -403,14 +403,15 @@ def get_current_user_rag_total_num( @router.get("/rag_content", response_model=ApiResponse) def get_rag_content( end_user_id: str = Query(..., description="宿主ID"), - limit: int = Query(15, description="返回记录数"), + page: int = Query(1, gt=0, description="页码，从1开始"), + pagesize: int = Query(15, gt=0, le=100, description="每页返回记录数"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ): """ - 获取当前宿主知识库中的chunk内容 + 获取当前宿主知识库中的chunk内容（分页） """ - data = memory_dashboard_service.get_rag_content(end_user_id, limit, db, current_user) + data = memory_dashboard_service.get_rag_content(end_user_id, page, pagesize, db, current_user) return success(data=data, msg="宿主RAGchunk数据获取成功") diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index db49c50a..a3859fe5 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -535,7 +535,8 @@ def get_users_total_chunk_batch( def get_rag_content( end_user_id: str, - limit: int, + page: int, + pagesize: int, db: Session, current_user: User ) -> dict: @@ -543,9 +544,9 @@ def get_rag_content( 先在documents表中查询file_name=='end_user_id'+'.txt'的id和kb_id, 然后调用/chunks/{kb_id}/{document_id}/chunks接口的相关代码获取所有内容， 接着对获取的内容进行提取，只要page_content的内容， - 最后返回数据 + 最后返回分页数据 """ - business_logger.info(f"获取RAG内容: end_user_id={end_user_id}, limit={limit}, 操作者: {current_user.username}") + business_logger.info(f"获取RAG内容: end_user_id={end_user_id}, page={page}, pagesize={pagesize}, 操作者: {current_user.username}") try: from app.models.document_model import Document @@ -562,63 +563,76 @@ def get_rag_content( if not documents: business_logger.warning(f"未找到文件: {file_name}") return { - "total": 0, - "contents": [] + "page": { + "page": page, + "pagesize": pagesize, + "total": 0, + "hasnext": False, + }, + "items": [] } business_logger.info(f"找到 {len(documents)} 个文档记录") - # 3. 获取所有chunks的page_content - all_contents = [] - total_chunks = 0 + # 3. 按全局偏移量计算当前页数据 + # 全局偏移范围：[offset_start, offset_end) + offset_start = (page - 1) * pagesize + offset_end = offset_start + pagesize + + global_total = 0 # 所有文档的 chunk 总数 + page_contents = [] # 当前页的内容 for document in documents: try: - # 获取知识库信息 kb = knowledge_repository.get_knowledge_by_id(db, document.kb_id) if not kb: business_logger.warning(f"知识库不存在: kb_id={document.kb_id}") continue - # 初始化向量服务 vector_service = ElasticSearchVectorFactory().init_vector(knowledge=kb) - # 获取该文档的所有chunks（分页获取） - page = 1 - pagesize = 100 # 每页100条 + # 先用 pagesize=1 获取该文档的 chunk 总数 + doc_total, _ = vector_service.search_by_segment( + document_id=str(document.id), + query=None, + pagesize=1, + page=1, + asc=True + ) - while True: - total, items = vector_service.search_by_segment( + doc_offset_start = global_total # 该文档在全局中的起始偏移 + doc_offset_end = global_total + doc_total # 该文档在全局中的结束偏移 + global_total += doc_total + + # 当前页与该文档无交集，跳过 + if doc_offset_end <= offset_start or doc_offset_start >= offset_end: + continue + + # 计算需要从该文档取的局部范围 + local_start = max(offset_start - doc_offset_start, 0) + local_end = min(offset_end - doc_offset_start, doc_total) + need_count = local_end - local_start + + # 换算成 ES 分页参数（ES page 从1开始） + es_page = (local_start // pagesize) + 1 + es_offset_in_page = local_start % pagesize + + fetched = [] + while len(fetched) < es_offset_in_page + need_count: + _, items = vector_service.search_by_segment( document_id=str(document.id), query=None, pagesize=pagesize, - page=page, + page=es_page, asc=True ) - if not items: break - - # 提取page_content - for item in items: - all_contents.append(item.page_content) - total_chunks += 1 - - # # 如果达到limit限制，直接返回 - # if limit > 0 and total_chunks >= limit: - # business_logger.info(f"已达到limit限制: {limit}") - # return { - # "total": total_chunks, - # "contents": all_contents[:limit] - # } - - # 检查是否还有下一页 - if page * pagesize >= total: - break - - page += 1 + fetched.extend(items) + es_page += 1 - business_logger.info(f"文档 {document.id} 获取了 {len(items)} 个chunks") + slice_items = fetched[es_offset_in_page: es_offset_in_page + need_count] + page_contents.extend([item.page_content for item in slice_items]) except Exception as e: business_logger.error(f"获取文档 {document.id} 的chunks失败: {str(e)}") @@ -626,11 +640,16 @@ def get_rag_content( # 4. 返回结果 result = { - "total": total_chunks, - "contents": all_contents[:limit] if limit > 0 else all_contents + "page": { + "page": page, + "pagesize": pagesize, + "total": global_total, + "hasnext": offset_end < global_total, + }, + "items": page_contents } - business_logger.info(f"成功获取RAG内容: total={total_chunks}, 返回={len(result['contents'])} 条") + business_logger.info(f"成功获取RAG内容: total={global_total}, page={page}, 返回={len(page_contents)} 条") return result except Exception as e: From 75b87955dd2e71592ad200bca53ffe1fe68cfad8 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Thu, 12 Mar 2026 18:36:49 +0800 Subject: [PATCH 064/196] feat(web): rag content add page --- web/src/api/memory.ts | 7 +- web/src/components/PageScrollList/index.tsx | 80 ++++++++++--------- web/src/views/UserMemoryDetail/Rag.tsx | 11 ++- .../components/ConversationMemory.tsx | 77 ++++++------------ 4 files changed, 75 insertions(+), 100 deletions(-) diff --git a/web/src/api/memory.ts b/web/src/api/memory.ts index 491f78ea..c40e3f30 100644 --- a/web/src/api/memory.ts +++ b/web/src/api/memory.ts @@ -2,7 +2,7 @@ * @Author: ZhaoYing * @Date: 2026-02-03 14:00:06 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-03-04 10:58:41 + * @Last Modified time: 2026-03-12 18:25:06 */ import { request } from '@/utils/request' import type { @@ -118,8 +118,9 @@ export const getChunkInsight = (end_user_id: string) => { return request.get(`/dashboard/chunk_insight`, { end_user_id }) } // RAG User Memory - Storage content -export const getRagContent = (end_user_id: string) => { - return request.get(`/dashboard/rag_content`, { end_user_id, limit: 20 }) +export const getRagContentUrl = '/dashboard/rag_content' +export const getRagContent = (end_user_id: string, page = 1, pagesize = 20) => { + return request.get(getRagContentUrl, { end_user_id, page, pagesize }) } // Emotion distribution analysis export const getWordCloud = (end_user_id: string) => { diff --git a/web/src/components/PageScrollList/index.tsx b/web/src/components/PageScrollList/index.tsx index a877a9c7..0a32dfdb 100644 --- a/web/src/components/PageScrollList/index.tsx +++ b/web/src/components/PageScrollList/index.tsx @@ -2,7 +2,7 @@ * @Author: ZhaoYing * @Date: 2026-02-02 15:18:19 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-02-03 15:44:42 + * @Last Modified time: 2026-03-12 18:36:19 */ /** * PageScrollList Component @@ -60,8 +60,8 @@ interface PageScrollListProps> { /** Infinite scroll list component with pagination support */ const PageScrollList = forwardRef(>({ - renderItem, - query, + renderItem, + query, url, column = 4, className = '', @@ -69,68 +69,70 @@ const PageScrollList = forwardRef(>({ }: PageScrollListProps, ref: React.Ref) => { /** Expose refresh method to parent component */ useImperativeHandle(ref, () => ({ - refresh, + refresh: () => { + pageRef.current = 1; + loadingRef.current = false; + setHasMore(true); + setData([]); + loadMoreData(true); + }, })); const [loading, setLoading] = useState(false); const [data, setData] = useState([]); - const [page, setPage] = useState(1); const [hasMore, setHasMore] = useState(true); const scrollRef = useRef(null); + const pageRef = useRef(1); + const loadingRef = useRef(false); + const hasMoreRef = useRef(true); /** Load more data from API with pagination */ - const loadMoreData = (flag?: boolean) => { - if (!flag && (loading || !hasMore)) { - return; - } + const loadMoreData = (reset?: boolean) => { + if (loadingRef.current || (!reset && !hasMoreRef.current)) return; + loadingRef.current = true; setLoading(true); + const currentPage = reset ? 1 : pageRef.current; request.get(url, { - page: page, + page: currentPage, pagesize: PAGE_SIZE, - ...(query||{}), + ...(query || {}), }) .then((res) => { const response = res as ApiResponse; const results = Array.isArray(response.items) ? response.items : Array.isArray(response) ? response as T[] : []; - // Replace data if flag is true, otherwise append - if (flag) { - setData(results); - } else { - setData(data.concat(results)); - } - setPage(response.page.page + 1); + pageRef.current = response.page.page + 1; + setData(prev => reset ? results : [...prev, ...results]); + hasMoreRef.current = response.page?.hasnext; setHasMore(response.page?.hasnext); - setLoading(false); - console.log(`${results.length} more items loaded!`); }) .catch(() => { - setLoading(false); + hasMoreRef.current = false; setHasMore(false); - console.error('Failed to load data'); }) .finally(() => { + loadingRef.current = false; setLoading(false); + // 内容不足以填满容器时，主动继续加载 + setTimeout(() => { + const el = scrollRef.current; + console.log(el, el?.scrollHeight, el?.clientHeight, hasMoreRef.current) + if (el && hasMoreRef.current && el.scrollHeight <= el.clientHeight) { + loadMoreData(); + } + }, 0); }); }; - /** Reset list to initial state and reload data */ - const refresh = () => { - setPage(1); + /** Reset and reload when query parameters change */ + const queryKey = JSON.stringify(query); + useEffect(() => { + pageRef.current = 1; + loadingRef.current = false; + hasMoreRef.current = true; setHasMore(true); setData([]); - } + loadMoreData(true); + }, [queryKey]); - /** Refresh when query parameters change */ - useEffect(() => { - refresh() - }, [query]); - - /** Load initial data when list is reset */ - useEffect(() => { - if (page === 1 && hasMore && data.length === 0) { - loadMoreData(true); - } - }, [page, hasMore, data]) - return ( <>
>({ > loadMoreData()} hasMore={hasMore} loader={loading && needLoading ? : false} // endMessage={It is all, nothing more 🤐} diff --git a/web/src/views/UserMemoryDetail/Rag.tsx b/web/src/views/UserMemoryDetail/Rag.tsx index 0f82ee05..3935bcc0 100644 --- a/web/src/views/UserMemoryDetail/Rag.tsx +++ b/web/src/views/UserMemoryDetail/Rag.tsx @@ -1,8 +1,8 @@ /* * @Author: ZhaoYing * @Date: 2026-02-03 17:57:11 - * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-02-03 17:57:11 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-03-12 18:00:11 */ /** * RAG User Memory Detail View @@ -150,9 +150,12 @@ const Rag: FC = () => { }) } return ( - + - +
{name?.[0]}
diff --git a/web/src/views/UserMemoryDetail/components/ConversationMemory.tsx b/web/src/views/UserMemoryDetail/components/ConversationMemory.tsx index 928b340f..2f5b4ce6 100644 --- a/web/src/views/UserMemoryDetail/components/ConversationMemory.tsx +++ b/web/src/views/UserMemoryDetail/components/ConversationMemory.tsx @@ -1,74 +1,43 @@ /* * @Author: ZhaoYing * @Date: 2026-02-03 18:34:04 - * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-02-03 18:34:04 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-03-12 18:34:52 */ -/** - * Conversation Memory Component - * Displays RAG conversation memory content list - */ - -import { type FC, useEffect, useState } from 'react' +import { type FC } from 'react' import { useTranslation } from 'react-i18next' import { useParams } from 'react-router-dom' -import { Skeleton, List } from 'antd'; import RbCard from '@/components/RbCard/Card' -import Empty from '@/components/Empty'; +import PageScrollList from '@/components/PageScrollList' import Markdown from '@/components/Markdown' -import { - getRagContent -} from '@/api/memory' +import { getRagContentUrl } from '@/api/memory' -const ConversationMemory:FC = () => { +const ConversationMemory: FC = () => { const { t } = useTranslation() const { id } = useParams() - const [loading, setLoading] = useState(true) - const [list, setList] = useState([]) - - useEffect(() => { - if (!id) return - getList() - }, [id]) - /** Fetch conversation memory list */ - const getList = () => { - if (!id) return - setLoading(true) - getRagContent(id).then((res) => { - setList((res as { contents?: [] }).contents || []) - }) - .finally(() => { - setLoading(false) - }) - } return ( - - {loading - ? - : list.length > 0 - ? ( - -
- -
- - )} - /> - : - } + + url={getRagContentUrl} + query={{ end_user_id: id }} + column={1} + renderItem={(item) => ( +
+ +
+ )} + className="rb:h-full!" + // className="rb:h-[calc(100%-24px)]!" + /> ) } -export default ConversationMemory \ No newline at end of file + +export default ConversationMemory From 572ce7f9eca43cbb5cb0f5be6d3dc0c85b14f0b9 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 19:13:24 +0800 Subject: [PATCH 065/196] [changes] Field standardization --- api/app/services/memory_dashboard_service.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index a3859fe5..7bcf15cf 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -749,8 +749,8 @@ async def generate_rag_profile( if not end_user: raise ValueError(f"end_user {end_user_id} 不存在") - rag_content = get_rag_content(end_user_id, limit, db, current_user) - chunks = rag_content.get("contents", []) + rag_content = get_rag_content(end_user_id, 1, limit, db, current_user) + chunks = rag_content.get("items", []) if not chunks: business_logger.warning(f"未找到chunk内容，无法生产RAG画像: end_user_id={end_user_id}") From e368f1c1d695803103509b5134332ce246b5c1a6 Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Thu, 12 Mar 2026 19:40:14 +0800 Subject: [PATCH 066/196] fix(mcp square): Do not obtain the mcp service when the token is empty. --- .../mcp_market_config_controller.py | 35 +++++++++++++------ 1 file changed, 25 insertions(+), 10 deletions(-) diff --git a/api/app/controllers/mcp_market_config_controller.py b/api/app/controllers/mcp_market_config_controller.py index 7f73663e..930a2aea 100644 --- a/api/app/controllers/mcp_market_config_controller.py +++ b/api/app/controllers/mcp_market_config_controller.py @@ -65,13 +65,18 @@ async def get_mcp_servers( api_logger.warning( f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, + status_code=status.HTTP_400_BAD_REQUEST, detail="The mcp market config does not exist or access is denied" ) # 3. Execute paged query - api = MCPApi() token = db_mcp_market_config.token + if not token: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="MCP market config token is not configured" + ) + api = MCPApi() api.login(token) body = { @@ -141,13 +146,18 @@ async def get_operational_mcp_servers( api_logger.warning( f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, + status_code=status.HTTP_400_BAD_REQUEST, detail="The mcp market config does not exist or access is denied" ) # 2. Execute paged query - api = MCPApi() token = db_mcp_market_config.token + if not token: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="MCP market config token is not configured" + ) + api = MCPApi() api.login(token) url = f'{api.mcp_base_url}/operational' @@ -199,13 +209,18 @@ async def get_mcp_server( api_logger.warning( f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, + status_code=status.HTTP_400_BAD_REQUEST, detail="The mcp market config does not exist or access is denied" ) # 2. Get detailed information for a specific MCP Server - api = MCPApi() token = db_mcp_market_config.token + if not token: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="MCP market config token is not configured" + ) + api = MCPApi() api.login(token) result = api.get_mcp_server(server_id=server_id) @@ -263,7 +278,7 @@ async def get_mcp_market_config( if not db_mcp_market_config: api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, + status_code=status.HTTP_400_BAD_REQUEST, detail="The mcp market config does not exist or access is denied" ) @@ -296,7 +311,7 @@ async def get_mcp_market_config_by_mcp_market_id( if not db_mcp_market_config: api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_id={mcp_market_id}") raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, + status_code=status.HTTP_400_BAD_REQUEST, detail="The mcp market config does not exist or access is denied" ) @@ -325,7 +340,7 @@ async def update_mcp_market_config( api_logger.warning( f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}") raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, + status_code=status.HTTP_400_BAD_REQUEST, detail="The mcp market config does not exist or you do not have permission to access it" ) @@ -382,7 +397,7 @@ async def delete_mcp_market_config( api_logger.warning( f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}") raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, + status_code=status.HTTP_400_BAD_REQUEST, detail="The mcp market config does not exist or you do not have permission to access it" ) From 6a67f028ce8e8bc10088d58f024686e4b99e9f89 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 19:50:32 +0800 Subject: [PATCH 067/196] [changes] Set constants --- api/app/services/memory_dashboard_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index 7bcf15cf..be656acb 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -749,7 +749,7 @@ async def generate_rag_profile( if not end_user: raise ValueError(f"end_user {end_user_id} 不存在") - rag_content = get_rag_content(end_user_id, 1, limit, db, current_user) + rag_content = get_rag_content(end_user_id, page=1, pagesize=limit, db=db, current_user=current_user) chunks = rag_content.get("items", []) if not chunks: From 7b8f101824055122631dd590f8f9b873211ec136 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 20:27:50 +0800 Subject: [PATCH 068/196] [add] Create the attribute values of the community nodes --- .../core/memory/agent/utils/write_tools.py | 4 +- .../clustering_engine/label_propagation.py | 83 ++++++++++++++++++- .../neo4j/community_repository.py | 24 ++++++ api/app/repositories/neo4j/cypher_queries.py | 9 ++ api/app/repositories/neo4j/graph_saver.py | 20 +++-- 5 files changed, 132 insertions(+), 8 deletions(-) diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index 22030278..b3707083 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -165,7 +165,9 @@ async def write( statement_chunk_edges=all_statement_chunk_edges, statement_entity_edges=all_statement_entity_edges, entity_edges=all_entity_entity_edges, - connector=neo4j_connector + connector=neo4j_connector, + config_id=config_id, + llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None, ) if success: logger.info("Successfully saved all data to Neo4j") diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index cb6e5804..251d4fea 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -19,6 +19,8 @@ logger = logging.getLogger(__name__) # 全量迭代最大轮数，防止不收敛 MAX_ITERATIONS = 10 +# 社区摘要核心实体数量 +CORE_ENTITY_LIMIT = 5 def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float: @@ -62,9 +64,16 @@ def _weighted_vote( class LabelPropagationEngine: """标签传播聚类引擎""" - def __init__(self, connector: Neo4jConnector): + def __init__( + self, + connector: Neo4jConnector, + config_id: Optional[str] = None, + llm_model_id: Optional[str] = None, + ): self.connector = connector self.repo = CommunityRepository(connector) + self.config_id = config_id + self.llm_model_id = llm_model_id # ────────────────────────────────────────────────────────────────────────── # 公开接口 @@ -155,6 +164,10 @@ class LabelPropagationEngine: f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，" f"{len(labels)} 个实体" ) + # 为所有社区生成元数据 + unique_communities = list(set(labels.values())) + for cid in unique_communities: + await self._generate_community_metadata(cid, end_user_id) async def incremental_update( self, new_entity_ids: List[str], end_user_id: str @@ -211,6 +224,7 @@ class LabelPropagationEngine: logger.debug( f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}" ) + await self._generate_community_metadata(new_cid, end_user_id) else: # 加入得票最多的社区 await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id) @@ -222,6 +236,7 @@ class LabelPropagationEngine: await self._evaluate_merge( list(community_ids_in_neighbors), end_user_id ) + await self._generate_community_metadata(target_cid, end_user_id) async def _evaluate_merge( self, community_ids: List[str], end_user_id: str @@ -354,6 +369,72 @@ class LabelPropagationEngine: except Exception: return None + async def _generate_community_metadata( + self, community_id: str, end_user_id: str + ) -> None: + """ + 为社区生成并写入元数据：名称、摘要、核心实体。 + + - core_entities：按 activation_value 排序取 top-N 实体名称列表（无需 LLM） + - name / summary：若有 llm_model_id 则调用 LLM 生成，否则用实体名称拼接兜底 + """ + try: + members = await self.repo.get_community_members(community_id, end_user_id) + if not members: + return + + # 核心实体：按 activation_value 降序取 top-N + sorted_members = sorted( + members, + key=lambda m: m.get("activation_value") or 0, + reverse=True, + ) + core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")] + all_names = [m["name"] for m in members if m.get("name")] + + name = "、".join(core_entities[:3]) if core_entities else community_id[:8] + summary = f"包含实体：{', '.join(all_names)}" + + # 若有 LLM 配置，调用 LLM 生成更好的名称和摘要 + if self.llm_model_id: + try: + from app.db import get_db_context + from app.core.memory.utils.llm.llm_utils import MemoryClientFactory + + entity_list_str = "、".join(all_names) + prompt = ( + f"以下是一组语义相关的实体：{entity_list_str}\n\n" + f"请为这组实体所代表的主题：\n" + f"1. 起一个简洁的中文名称（不超过10个字）\n" + f"2. 写一句话摘要（不超过50个字）\n\n" + f"严格按以下格式输出，不要有其他内容：\n" + f"名称：<名称>\n摘要：<摘要>" + ) + with get_db_context() as db: + factory = MemoryClientFactory(db) + llm_client = factory.get_llm_client(self.llm_model_id) + response = await llm_client.chat([{"role": "user", "content": prompt}]) + text = response.content if hasattr(response, "content") else str(response) + + for line in text.strip().splitlines(): + if line.startswith("名称："): + name = line[3:].strip() + elif line.startswith("摘要："): + summary = line[3:].strip() + except Exception as e: + logger.warning(f"[Clustering] LLM 生成社区元数据失败，使用兜底值: {e}") + + await self.repo.update_community_metadata( + community_id=community_id, + end_user_id=end_user_id, + name=name, + summary=summary, + core_entities=core_entities, + ) + logger.debug(f"[Clustering] 社区 {community_id} 元数据已更新: name={name}") + except Exception as e: + logger.error(f"[Clustering] _generate_community_metadata failed for {community_id}: {e}") + @staticmethod def _new_community_id() -> str: return str(uuid.uuid4()) diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 2a1f4f2b..6c5c7618 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/api/app/repositories/neo4j/community_repository.py @@ -17,6 +17,7 @@ from app.repositories.neo4j.cypher_queries import ( GET_ALL_COMMUNITY_MEMBERS_BATCH, CHECK_USER_HAS_COMMUNITIES, UPDATE_COMMUNITY_MEMBER_COUNT, + UPDATE_COMMUNITY_METADATA, ) logger = logging.getLogger(__name__) @@ -147,3 +148,26 @@ class CommunityRepository: except Exception as e: logger.error(f"refresh_member_count failed: {e}") return 0 + + async def update_community_metadata( + self, + community_id: str, + end_user_id: str, + name: str, + summary: str, + core_entities: List[str], + ) -> bool: + """更新社区的名称、摘要和核心实体列表。""" + try: + result = await self.connector.execute_query( + UPDATE_COMMUNITY_METADATA, + community_id=community_id, + end_user_id=end_user_id, + name=name, + summary=summary, + core_entities=core_entities, + ) + return bool(result) + except Exception as e: + logger.error(f"update_community_metadata failed: {e}") + return False diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 84889d65..b270ed64 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1150,3 +1150,12 @@ WITH c, count(e) AS cnt SET c.member_count = cnt RETURN c.community_id AS community_id, cnt AS member_count """ + +UPDATE_COMMUNITY_METADATA = """ +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +SET c.name = $name, + c.summary = $summary, + c.core_entities = $core_entities, + c.updated_at = datetime() +RETURN c.community_id AS community_id +""" diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index a94bc23b..2ef9bafc 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -1,5 +1,6 @@ import asyncio -from typing import List +import os +from typing import List, Optional # 使用新的仓储层 from app.repositories.neo4j.neo4j_connector import Neo4jConnector @@ -156,7 +157,9 @@ async def save_dialog_and_statements_to_neo4j( entity_edges: List[EntityEntityEdge], statement_chunk_edges: List[StatementChunkEdge], statement_entity_edges: List[StatementEntityEdge], - connector: Neo4jConnector + connector: Neo4jConnector, + config_id: Optional[str] = None, + llm_model_id: Optional[str] = None, ) -> bool: """Save dialogue nodes, chunk nodes, statement nodes, entities, and all relationships to Neo4j using graph models. @@ -290,12 +293,15 @@ async def save_dialog_and_statements_to_neo4j( logger.info("Transaction completed. Summary: %s", summary) logger.debug("Full transaction results: %r", results) - # 写入成功后，触发聚类 - if entity_nodes: + # 写入成功后，触发聚类（可通过环境变量 CLUSTERING_ENABLED=false 禁用，用于基准测试对比） + clustering_enabled = os.getenv("CLUSTERING_ENABLED", "true").lower() != "false" + if entity_nodes and clustering_enabled: end_user_id = entity_nodes[0].end_user_id new_entity_ids = [e.id for e in entity_nodes] logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}") - await _trigger_clustering(new_entity_ids, end_user_id) + asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, config_id=config_id, llm_model_id=llm_model_id)) + elif entity_nodes and not clustering_enabled: + logger.info("[Clustering] 聚类已禁用（CLUSTERING_ENABLED=false），跳过聚类触发") return True @@ -309,6 +315,8 @@ async def save_dialog_and_statements_to_neo4j( async def _trigger_clustering( new_entity_ids: List[str], end_user_id: str, + config_id: Optional[str] = None, + llm_model_id: Optional[str] = None, ) -> None: """ 聚类触发函数，自动判断全量初始化还是增量更新。 @@ -318,7 +326,7 @@ async def _trigger_clustering( from app.core.memory.storage_services.clustering_engine import LabelPropagationEngine logger.info(f"[Clustering] 开始聚类，end_user_id={end_user_id}, 实体数={len(new_entity_ids)}") connector = Neo4jConnector() - engine = LabelPropagationEngine(connector) + engine = LabelPropagationEngine(connector, config_id=config_id, llm_model_id=llm_model_id) await engine.run(end_user_id=end_user_id, new_entity_ids=new_entity_ids) logger.info(f"[Clustering] 聚类完成，end_user_id={end_user_id}") except Exception as e: From d66b9dd8cbc2928a1f60c38933ff23cefd1ea73c Mon Sep 17 00:00:00 2001 From: wxy Date: Fri, 13 Mar 2026 10:26:59 +0800 Subject: [PATCH 069/196] feat(app): add cross-workspace app sharing backend --- api/alembic.ini | 7 +- api/app/controllers/app_controller.py | 66 +- api/app/models/appshare_model.py | 3 +- api/app/schemas/app_schema.py | 36 +- api/app/services/app_service.py | 207 ++++++- api/app/services/draft_run_service.py | 50 +- api/uv.lock | 847 ++++++++++++-------------- 7 files changed, 726 insertions(+), 490 deletions(-) diff --git a/api/alembic.ini b/api/alembic.ini index 71a6553f..7cb2e801 100644 --- a/api/alembic.ini +++ b/api/alembic.ini @@ -60,7 +60,12 @@ version_path_separator = os # Use os.pathsep. Default configuration used for ne # are written from script.py.mako # output_encoding = utf-8 -sqlalchemy.url = postgresql://user:password@localhost/dbname +# Database connection URL - DO NOT hardcode credentials here! +# Connection string is set dynamically from environment variables in migrations/env.py +# Required env vars: DB_USER, DB_PASSWORD, DB_HOST, DB_PORT, DB_NAME +# Example: postgresql://user:password@localhost:5432/dbname +; sqlalchemy.url = postgresql://user:password@host:port/dbname +sqlalchemy.url = driver://user:password@host:port/dbname [post_write_hooks] diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index ee957f28..5d1551d8 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -93,6 +93,20 @@ def list_apps( return success(data=PageData(page=meta, items=items)) +@router.get("/my-shared-out", summary="列出本工作空间主动分享出去的记录") +@cur_workspace_access_guard() +def list_my_shared_out( + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """列出本工作空间主动分享给其他工作空间的所有记录（我的共享）""" + workspace_id = current_user.current_workspace_id + service = app_service.AppService(db) + shares = service.list_my_shared_out(workspace_id=workspace_id) + data = [app_schema.AppShare.model_validate(s) for s in shares] + return success(data=data) + + @router.get("/{app_id}", summary="获取应用详情") @cur_workspace_access_guard() def get_app( @@ -302,7 +316,8 @@ def share_app( app_id=app_id, target_workspace_ids=payload.target_workspace_ids, user_id=current_user.id, - workspace_id=workspace_id + workspace_id=workspace_id, + permission=payload.permission ) data = [app_schema.AppShare.model_validate(s) for s in shares] @@ -333,6 +348,32 @@ def unshare_app( return success(msg="应用分享已取消") +@router.patch("/{app_id}/share/{target_workspace_id}", summary="更新共享权限") +@cur_workspace_access_guard() +def update_share_permission( + app_id: uuid.UUID, + target_workspace_id: uuid.UUID, + payload: app_schema.UpdateSharePermissionRequest, + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """更新共享权限（readonly <-> editable） + + - 只能修改自己工作空间应用的共享权限 + """ + workspace_id = current_user.current_workspace_id + + service = app_service.AppService(db) + share = service.update_share_permission( + app_id=app_id, + target_workspace_id=target_workspace_id, + permission=payload.permission, + workspace_id=workspace_id + ) + + return success(data=app_schema.AppShare.model_validate(share)) + + @router.get("/{app_id}/shares", summary="列出应用的分享记录") @cur_workspace_access_guard() def list_app_shares( @@ -356,6 +397,29 @@ def list_app_shares( return success(data=data) +@router.delete("/{app_id}/shared", summary="移除共享给我的应用") +@cur_workspace_access_guard() +def remove_shared_app( + app_id: uuid.UUID, + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """被共享者从自己的工作空间移除共享应用 + + - 不会删除源应用，只删除共享记录 + - 只能移除共享给自己工作空间的应用 + """ + workspace_id = current_user.current_workspace_id + + service = app_service.AppService(db) + service.remove_shared_app( + app_id=app_id, + workspace_id=workspace_id + ) + + return success(msg="已移除共享应用") + + @router.post("/{app_id}/draft/run", summary="试运行 Agent（使用当前草稿配置）") @cur_workspace_access_guard() async def draft_run( diff --git a/api/app/models/appshare_model.py b/api/app/models/appshare_model.py index 57ea59bc..643b0e11 100644 --- a/api/app/models/appshare_model.py +++ b/api/app/models/appshare_model.py @@ -1,6 +1,6 @@ import datetime import uuid -from sqlalchemy import Column, DateTime, ForeignKey +from sqlalchemy import Column, DateTime, ForeignKey, String from sqlalchemy.dialects.postgresql import UUID from app.db import Base from sqlalchemy.orm import relationship @@ -18,6 +18,7 @@ class AppShare(Base): source_workspace_id = Column(UUID(as_uuid=True), ForeignKey('workspaces.id'), nullable=False, comment="源工作空间ID") target_workspace_id = Column(UUID(as_uuid=True), ForeignKey('workspaces.id'), nullable=False, comment="目标工作空间ID") shared_by = Column(UUID(as_uuid=True), ForeignKey('users.id'), nullable=False, comment="分享者用户ID") + permission = Column(String, default="readonly", nullable=False, comment="权限模式: readonly | editable") created_at = Column(DateTime, default=datetime.datetime.now) updated_at = Column(DateTime, default=datetime.datetime.now) diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index c0482ec3..8e7e4bd2 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -277,7 +277,12 @@ class App(BaseModel): tags: List[str] = [] current_release_id: Optional[uuid.UUID] = None is_active: bool - is_shared: bool = False # 是否是共享应用（从其他工作空间共享来的） + is_shared: bool = False + share_permission: Optional[str] = None + source_workspace_name: Optional[str] = None # 共享来源工作空间名称（仅共享应用有值） + source_workspace_icon: Optional[str] = None # 共享来源工作空间图标 + source_app_version: Optional[str] = None # 应用版本号 + source_app_is_active: Optional[bool] = None # 应用是否生效 created_at: datetime.datetime updated_at: datetime.datetime @@ -422,6 +427,12 @@ class AppRelease(BaseModel): class AppShareCreate(BaseModel): """应用分享请求""" target_workspace_ids: List[uuid.UUID] = Field(..., description="目标工作空间ID列表") + permission: str = Field(default="readonly", description="权限模式: readonly | editable") + + +class UpdateSharePermissionRequest(BaseModel): + """更新共享权限请求""" + permission: str = Field(..., description="新权限值: readonly | editable") class AppShare(BaseModel): @@ -433,9 +444,32 @@ class AppShare(BaseModel): source_workspace_id: uuid.UUID target_workspace_id: uuid.UUID shared_by: uuid.UUID + permission: str = "readonly" created_at: datetime.datetime updated_at: datetime.datetime + # 关联名称（从 relationship 读取） + source_app_name: Optional[str] = None + source_app_type: Optional[str] = None + source_app_version: Optional[str] = None + source_app_is_active: Optional[bool] = None + target_workspace_name: Optional[str] = None + target_workspace_icon: Optional[str] = None + + @classmethod + def model_validate(cls, obj, **kwargs): + instance = super().model_validate(obj, **kwargs) + if hasattr(obj, 'source_app') and obj.source_app: + instance.source_app_name = obj.source_app.name + instance.source_app_type = obj.source_app.type + instance.source_app_is_active = obj.source_app.is_active + release = obj.source_app.current_release + instance.source_app_version = release.version_name if release else None + if hasattr(obj, 'target_workspace') and obj.target_workspace: + instance.target_workspace_name = obj.target_workspace.name + instance.target_workspace_icon = obj.target_workspace.icon + return instance + @field_serializer("created_at", when_used="json") def _serialize_created_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None diff --git a/api/app/services/app_service.py b/api/app/services/app_service.py index 88582cee..2326be6e 100644 --- a/api/app/services/app_service.py +++ b/api/app/services/app_service.py @@ -125,6 +125,49 @@ class AppService: ) raise BusinessException("应用不可访问", BizCode.WORKSPACE_NO_ACCESS) + def _get_share_permission(self, app: App, workspace_id: Optional[uuid.UUID]) -> Optional[str]: + """获取共享应用的权限 + + Returns: + None: 不是共享应用（是本工作空间的应用） + 'readonly': 只读共享 + 'editable': 可编辑共享 + """ + from app.models import AppShare + + if workspace_id is None or app.workspace_id == workspace_id: + return None # 本工作空间的应用，不是共享的 + + stmt = select(AppShare).where( + AppShare.source_app_id == app.id, + AppShare.target_workspace_id == workspace_id + ) + share = self.db.scalars(stmt).first() + return share.permission if share else None + + def _validate_app_writable(self, app: App, workspace_id: Optional[uuid.UUID]) -> None: + """Validate that the app config is writable (owner only). + + Shared apps (both readonly and editable) cannot modify config. + - Own workspace app: allowed + - Any shared app: denied + + Raises: + BusinessException: when app is not writable + """ + if workspace_id is None: + return + + # Own workspace app, allow + if app.workspace_id == workspace_id: + return + + logger.warning( + "应用写操作被拒", + extra={"app_id": str(app.id), "workspace_id": str(workspace_id)} + ) + raise BusinessException("共享应用不可修改配置", BizCode.WORKSPACE_NO_ACCESS) + def _get_app_or_404(self, app_id: uuid.UUID) -> App: """获取应用或抛出404异常 @@ -454,6 +497,32 @@ class AppService: Returns: app_schema.App: 应用 Schema """ + is_shared = app.workspace_id != current_workspace_id + share_permission = None + source_workspace_name = None + source_workspace_icon = None + source_app_version = None + source_app_is_active = None + + if is_shared: + # 查询共享权限和来源工作空间名称 + from app.models import AppShare + stmt = select(AppShare).where( + AppShare.source_app_id == app.id, + AppShare.target_workspace_id == current_workspace_id + ) + share = self.db.scalars(stmt).first() + if share: + share_permission = share.permission + if share.source_workspace: + source_workspace_name = share.source_workspace.name + source_workspace_icon = share.source_workspace.icon + + # 版本号和生效状态 + if app.current_release: + source_app_version = app.current_release.version_name + source_app_is_active = app.is_active + app_dict = { "id": app.id, "workspace_id": app.workspace_id, @@ -468,7 +537,12 @@ class AppService: "tags": app.tags or [], "current_release_id": app.current_release_id, "is_active": app.is_active, - "is_shared": app.workspace_id != current_workspace_id, # 判断是否是共享应用 + "is_shared": is_shared, + "share_permission": share_permission, + "source_workspace_name": source_workspace_name, + "source_workspace_icon": source_workspace_icon, + "source_app_version": source_app_version, + "source_app_is_active": source_app_is_active, "created_at": app.created_at, "updated_at": app.updated_at } @@ -594,7 +668,7 @@ class AppService: logger.info("更新应用", extra={"app_id": str(app_id)}) app = self._get_app_or_404(app_id) - self._validate_workspace_access(app, workspace_id) + self._validate_app_writable(app, workspace_id) changed = False for field in ["name", "description", "icon", "icon_type", "visibility", "status", "tags"]: @@ -952,7 +1026,7 @@ class AppService: if app.type != "agent": raise BusinessException("只有 Agent 类型应用支持 Agent 配置", BizCode.APP_TYPE_NOT_SUPPORTED) - self._validate_workspace_access(app, workspace_id) + self._validate_app_writable(app, workspace_id) stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active.is_(True)).order_by( AgentConfig.updated_at.desc()) @@ -1163,7 +1237,7 @@ class AppService: if app.type != AppType.WORKFLOW: raise BusinessException("只有 Workflow 类型应用支持 Workflow 配置", BizCode.APP_TYPE_NOT_SUPPORTED) - self._validate_workspace_access(app, workspace_id) + self._validate_app_writable(app, workspace_id) # 获取现有配置 repo = WorkflowConfigRepository(self.db) @@ -1654,7 +1728,8 @@ class AppService: app_id: uuid.UUID, target_workspace_ids: List[uuid.UUID], user_id: uuid.UUID, - workspace_id: Optional[uuid.UUID] = None + workspace_id: Optional[uuid.UUID] = None, + permission: str = "readonly" ) -> list[AppShare]: """分享应用到其他工作空间 @@ -1685,6 +1760,14 @@ class AppService: app = self._get_app_or_404(app_id) self._validate_workspace_access(app, workspace_id) + # 仅允许 agent 和 workflow 类型共享，multi_agent 不支持 + from app.models.app_model import AppType + if app.type == AppType.MULTI_AGENT: + raise BusinessException( + "集群 Agent 不支持共享应用功能", + BizCode.INVALID_PARAMETER + ) + # 2. 验证目标工作空间 for target_ws_id in target_workspace_ids: target_ws = self.db.get(Workspace, target_ws_id) @@ -1725,6 +1808,7 @@ class AppService: source_workspace_id=app.workspace_id, target_workspace_id=target_ws_id, shared_by=user_id, + permission=permission, created_at=now, updated_at=now ) @@ -1848,6 +1932,119 @@ class AppService: return shares + def remove_shared_app( + self, + *, + app_id: uuid.UUID, + workspace_id: uuid.UUID + ) -> None: + """被共享者从自己的工作空间移除共享应用 + + 只删除共享记录，不影响源应用。 + + Args: + app_id: 应用ID + workspace_id: 当前工作空间ID（被共享的目标工作空间） + + Raises: + ResourceNotFoundException: 当共享记录不存在时 + """ + from app.models import AppShare + + logger.info( + "移除共享应用", + extra={"app_id": str(app_id), "workspace_id": str(workspace_id)} + ) + + stmt = select(AppShare).where( + AppShare.source_app_id == app_id, + AppShare.target_workspace_id == workspace_id + ) + share = self.db.scalars(stmt).first() + + if not share: + raise ResourceNotFoundException( + "共享记录", + f"app_id={app_id}, workspace_id={workspace_id}" + ) + + self.db.delete(share) + self.db.commit() + + logger.info( + "共享应用已移除", + extra={"app_id": str(app_id), "workspace_id": str(workspace_id)} + ) + + def list_my_shared_out( + self, + *, + workspace_id: uuid.UUID + ) -> List[AppShare]: + """列出本工作空间主动分享出去的所有记录（我的共享） + + Returns: + List[AppShare]: 分享记录列表，含源应用信息 + """ + from app.models import AppShare + + stmt = ( + select(AppShare) + .where(AppShare.source_workspace_id == workspace_id) + .order_by(AppShare.created_at.desc()) + ) + return list(self.db.scalars(stmt).all()) + def update_share_permission( + self, + *, + app_id: uuid.UUID, + target_workspace_id: uuid.UUID, + permission: str, + workspace_id: Optional[uuid.UUID] = None + ) -> "AppShare": + """更新共享权限（readonly <-> editable） + + Args: + app_id: 应用ID + target_workspace_id: 目标工作空间ID + permission: 新权限值 readonly | editable + workspace_id: 当前工作空间ID（用于权限验证） + + Returns: + AppShare: 更新后的共享记录 + """ + from app.models import AppShare + + if permission not in ("readonly", "editable"): + raise BusinessException("权限值无效，只允许 readonly 或 editable", BizCode.INVALID_PARAMETER) + + app = self._get_app_or_404(app_id) + self._validate_workspace_access(app, workspace_id) + + stmt = select(AppShare).where( + AppShare.source_app_id == app_id, + AppShare.target_workspace_id == target_workspace_id + ) + share = self.db.scalars(stmt).first() + + if not share: + raise ResourceNotFoundException( + "共享记录", + f"app_id={app_id}, target_workspace_id={target_workspace_id}" + ) + + share.permission = permission + share.updated_at = datetime.datetime.now() + self.db.commit() + self.db.refresh(share) + + logger.info( + "共享权限已更新", + extra={"app_id": str(app_id), "target_workspace_id": str(target_workspace_id), "permission": permission} + ) + return share + + # ==================== 向后兼容的函数接口 ==================== # 保留函数接口以兼容现有代码，但内部使用服务类 diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 5026bf27..d7914db5 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -688,7 +688,8 @@ class AgentRunService: conversation_id=conversation_id, app_id=agent_config.app_id, workspace_id=workspace_id, - user_id=user_id + user_id=user_id, + sub_agent=sub_agent ) # 6. 加载历史消息 @@ -848,7 +849,8 @@ class AgentRunService: conversation_id: Optional[str], app_id: uuid.UUID, workspace_id: uuid.UUID, - user_id: Optional[str] + user_id: Optional[str], + sub_agent: bool = False ) -> str: """确保会话存在（创建或验证） @@ -909,20 +911,36 @@ class AgentRunService: conv_uuid = uuid.UUID(conversation_id) conversation = conversation_service.get_conversation(conv_uuid) - # 验证会话属于当前工作空间 - if conversation.workspace_id != workspace_id: - logger.warning( - "会话不属于当前工作空间", - extra={ - "conversation_id": conversation_id, - "conversation_workspace_id": str(conversation.workspace_id), - "current_workspace_id": str(workspace_id) - } - ) - raise BusinessException( - "会话不属于当前工作空间", - BizCode.PERMISSION_DENIED - ) + # 验证会话属于当前工作空间（或属于共享应用的源工作空间） + # sub_agent 内部调用时跳过校验，已在上层验证过 + if not sub_agent and conversation.workspace_id != workspace_id: + # 检查是否是共享应用的会话（被共享者 workspace 访问源应用） + from app.models import AppShare + from sqlalchemy import select as sa_select + share = self.db.scalars( + sa_select(AppShare).where( + AppShare.source_app_id == app_id, + AppShare.target_workspace_id == workspace_id + ) + ).first() + + # 情况2：sub_agent 内部调用时，workspace_id 是源应用的 workspace， + # 而会话是被共享者创建的，只要会话属于同一个 app 即可放行 + same_app = (conversation.app_id == app_id) + + if not share and not same_app: + logger.warning( + "会话不属于当前工作空间", + extra={ + "conversation_id": conversation_id, + "conversation_workspace_id": str(conversation.workspace_id), + "current_workspace_id": str(workspace_id) + } + ) + raise BusinessException( + "会话不属于当前工作空间", + BizCode.PERMISSION_DENIED + ) logger.debug( "使用现有会话", diff --git a/api/uv.lock b/api/uv.lock index a9bde1ed..e040f78b 100644 --- a/api/uv.lock +++ b/api/uv.lock @@ -7,6 +7,18 @@ resolution-markers = [ "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')", ] +[[package]] +name = "aiofile" +version = "3.9.0" +source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } +dependencies = [ + { name = "caio" }, +] +sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/67/e2/d7cb819de8df6b5c1968a2756c3cb4122d4fa2b8fc768b53b7c9e5edb646/aiofile-3.9.0.tar.gz", hash = "sha256:e5ad718bb148b265b6df1b3752c4d1d83024b93da9bd599df74b9d9ffcf7919b", size = 17943, upload-time = "2024-10-08T10:39:35.846Z" } +wheels = [ + { url = "https://pypi.tuna.tsinghua.edu.cn/packages/50/25/da1f0b4dd970e52bf5a36c204c107e11a0c6d3ed195eba0bfbc664c312b2/aiofile-3.9.0-py3-none-any.whl", hash = "sha256:ce2f6c1571538cbdfa0143b04e16b208ecb0e9cb4148e528af8a640ed51cc8aa", size = 19539, upload-time = "2024-10-08T10:39:32.955Z" }, +] + [[package]] name = "aiofiles" version = "25.1.0" @@ -172,14 +184,14 @@ wheels = [ [[package]] name = "authlib" -version = "1.6.6" +version = "1.6.9" source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } dependencies = [ { name = "cryptography" }, ] -sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/bb/9b/b1661026ff24bc641b76b78c5222d614776b0c085bcfdac9bd15a1cb4b35/authlib-1.6.6.tar.gz", hash = "sha256:45770e8e056d0f283451d9996fbb59b70d45722b45d854d58f32878d0a40c38e", size = 164894, upload-time = "2025-12-12T08:01:41.464Z" } 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--git a/api/migrations/versions/01587a13522f_202603131028.py b/api/migrations/versions/01587a13522f_202603131028.py new file mode 100644 index 00000000..6412dedd --- /dev/null +++ b/api/migrations/versions/01587a13522f_202603131028.py @@ -0,0 +1,38 @@ +"""202603131028 + +Revision ID: 01587a13522f +Revises: fb834419b18f +Create Date: 2026-03-13 10:28:43.601370 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '01587a13522f' +down_revision: Union[str, None] = 'fb834419b18f' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.add_column('tenants', sa.Column('default_language', sa.String(length=10), server_default='zh', nullable=False)) + op.add_column('tenants', sa.Column('supported_languages', postgresql.ARRAY(sa.String(length=10)), server_default=sa.text("'{zh,en}'"), nullable=False)) + op.create_index(op.f('ix_tenants_default_language'), 'tenants', ['default_language'], unique=False) + op.add_column('users', sa.Column('preferred_language', sa.String(length=10), server_default=sa.text("'zh'"), nullable=False)) + op.create_index(op.f('ix_users_preferred_language'), 'users', ['preferred_language'], unique=False) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_index(op.f('ix_users_preferred_language'), table_name='users') + op.drop_column('users', 'preferred_language') + op.drop_index(op.f('ix_tenants_default_language'), table_name='tenants') + op.drop_column('tenants', 'supported_languages') + op.drop_column('tenants', 'default_language') + # ### end Alembic commands ### From 6c691812909ac154b94a9efb6a73155f50dceb87 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Fri, 13 Mar 2026 10:33:11 +0800 Subject: [PATCH 071/196] fix:reset --- web/src/views/ToolManagement/Market.tsx | 80 +++++++++++++++++++------ 1 file changed, 62 insertions(+), 18 deletions(-) diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index 9bcf5f67..c1f25100 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -9,7 +9,6 @@ import type { McpServiceModalRef } from './types'; import pageEmptyIcon from '@/assets/images/empty/pageEmpty.png' import Empty from '@/components/Empty/index' import { getMarketTools, getMarketConfig, getMarketMCPs, getMarketMCPDetail, getMarketMCPsActivated, getTools } from '@/api/tools'; -import BodyWrapper from '@/components/Empty/BodyWrapper'; interface MarketSource { id: string; name: string; @@ -75,6 +74,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => const [activatedMcps, setActivatedMcps] = useState([]); const [currentPage, setCurrentPage] = useState(1); const pageSize = 20; + const searchTimerRef = useRef(null); // 获取市场数据 useEffect(() => { @@ -109,7 +109,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => fetchMarketData(); }, [message]); - const fetchMcpList = async (sourceId: string, page = 1, append = false) => { + const fetchMcpList = async (sourceId: string, page = 1, append = false, keywords = '') => { setLoading(true); try { let configId = configIdMap[sourceId]; @@ -139,7 +139,12 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => const allTools: any = await getTools({ tool_type: 'mcp' }); const toolsList = Array.isArray(allTools) ? allTools : []; - const res: any = await getMarketMCPs({ mcp_market_config_id: configId, page, pagesize: pageSize }); + const res: any = await getMarketMCPs({ + mcp_market_config_id: configId, + page, + pagesize: pageSize, + ...(keywords ? { keywords } : {}) + }); if (res?.items && Array.isArray(res.items)) { // 标记已激活和已入库的 MCP const mcpsWithActivated = res.items.map((item: MarketMcp) => { @@ -176,8 +181,46 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => const loadMore = useCallback(() => { if (!selectedSource || loading) return; - fetchMcpList(selectedSource, currentPage + 1, true); - }, [selectedSource, currentPage, loading]); + fetchMcpList(selectedSource, currentPage + 1, true, searchKeyword); + }, [selectedSource, currentPage, loading, searchKeyword]); + + const handleSearchChange = (value: string) => { + setSearchKeyword(value); + + // 清除之前的定时器 + if (searchTimerRef.current) { + clearTimeout(searchTimerRef.current); + } + + // 如果清空搜索框，恢复原始列表 + if (!value.trim()) { + if (selectedSource) { + // 清除缓存，重新加载原始列表 + setMcpCache(prev => { + const next = { ...prev }; + delete next[selectedSource]; + return next; + }); + setCurrentPage(1); + fetchMcpList(selectedSource, 1, false, ''); + } + return; + } + + // 设置新的定时器，500ms 后执行搜索 + searchTimerRef.current = setTimeout(() => { + if (selectedSource) { + // 清除缓存，重新搜索 + setMcpCache(prev => { + const next = { ...prev }; + delete next[selectedSource]; + return next; + }); + setCurrentPage(1); + fetchMcpList(selectedSource, 1, false, value); + } + }, 500); + }; const handleSelectSource = async (sourceId: string) => { setSelectedSource(sourceId); @@ -313,12 +356,6 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => if (!source) return null; const mcpList = mcpCache[selectedSource] || []; - const filteredList = mcpList.filter(mcp => { - const name = getLocaleField(mcp, 'name'); - const desc = getLocaleField(mcp, 'description'); - return name.toLowerCase().includes(searchKeyword.toLowerCase()) || - desc.toLowerCase().includes(searchKeyword.toLowerCase()); - }); return ( <> @@ -358,15 +395,14 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => {t('tool.marketRefresh')} )} - {mcpList.length > 0 && ( } placeholder={t('tool.marketSearchPlaceholder')} value={searchKeyword} - onChange={(e) => setSearchKeyword(e.target.value)} + onChange={(e) => handleSearchChange(e.target.value)} + allowClear style={{ width: 200 }} /> - )}
-
+ {!loading && mcpList.length === 0 ? ( + + ) : ( } @@ -394,7 +438,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => gridTemplateColumns: 'repeat(auto-fill, minmax(300px, 1fr))' }} > - {filteredList.map(mcp => ( + {mcpList.map(mcp => (
ReactNode }> = () => ))}
+ )}
-
); From b88e9c5f5e7c1254200904bcdfcc0f33131b44f6 Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Fri, 13 Mar 2026 11:07:32 +0800 Subject: [PATCH 072/196] fix(mcp): The MCP Square can obtain a maximum of 100 MCP services. --- api/app/controllers/mcp_market_config_controller.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/api/app/controllers/mcp_market_config_controller.py b/api/app/controllers/mcp_market_config_controller.py index 930a2aea..b1c22975 100644 --- a/api/app/controllers/mcp_market_config_controller.py +++ b/api/app/controllers/mcp_market_config_controller.py @@ -55,6 +55,12 @@ async def get_mcp_servers( status_code=status.HTTP_400_BAD_REQUEST, detail="The paging parameter must be greater than 0" ) + if page * pagesize > 100: + api_logger.warning(f"Paging parameters exceed ModelScope limit: page={page}, pagesize={pagesize}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"page × pagesize must not exceed 100 (got {page} × {pagesize} = {page * pagesize})" + ) # 2. Query mcp market config information from the database api_logger.debug(f"Query mcp market config: {mcp_market_config_id}") From f6d929ab7a48e9a5417f9f45f376de02a796058a Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 12:59:36 +0800 Subject: [PATCH 073/196] [add] Community node interface development --- .../controllers/user_memory_controllers.py | 37 ++++ api/app/services/user_memory_service.py | 160 ++++++++++++++++++ 2 files changed, 197 insertions(+) diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index d3fe7d83..be796ff9 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -17,6 +17,7 @@ from app.services.user_memory_service import ( UserMemoryService, analytics_memory_types, analytics_graph_data, + analytics_community_graph_data, ) from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction from app.schemas.response_schema import ApiResponse @@ -295,6 +296,42 @@ async def get_graph_data_api( return fail(BizCode.INTERNAL_ERROR, "图数据查询失败", str(e)) +@router.get("/analytics/community_graph", response_model=ApiResponse) +async def get_community_graph_data_api( + end_user_id: str, + current_user: User = Depends(get_current_user), + db: Session = Depends(get_db), +) -> dict: + workspace_id = current_user.current_workspace_id + + if workspace_id is None: + api_logger.warning(f"用户 {current_user.username} 尝试查询社区图谱但未选择工作空间") + return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") + + api_logger.info( + f"社区图谱查询请求: end_user_id={end_user_id}, user={current_user.username}, " + f"workspace={workspace_id}" + ) + + try: + result = await analytics_community_graph_data(db=db, end_user_id=end_user_id) + + if "message" in result and result["statistics"]["total_nodes"] == 0: + api_logger.warning(f"社区图谱查询返回空结果: {result.get('message')}") + return success(data=result, msg=result.get("message", "查询成功")) + + api_logger.info( + f"成功获取社区图谱: end_user_id={end_user_id}, " + f"nodes={result['statistics']['total_nodes']}, " + f"edges={result['statistics']['total_edges']}" + ) + return success(data=result, msg="查询成功") + + except Exception as e: + api_logger.error(f"社区图谱查询失败: end_user_id={end_user_id}, error={str(e)}") + return fail(BizCode.INTERNAL_ERROR, "社区图谱查询失败", str(e)) + + @router.get("/read_end_user/profile", response_model=ApiResponse) async def get_end_user_profile( end_user_id: str, diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 8bacc112..d21df064 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1727,6 +1727,166 @@ async def analytics_graph_data( # 辅助函数 +async def analytics_community_graph_data( + db: Session, + end_user_id: str, +) -> Dict[str, Any]: + """ + 获取社区图谱数据，包含 Community 节点、ExtractedEntity 节点及其关系。 + + Returns: + 包含 nodes、edges、statistics 的字典，格式与 analytics_graph_data 一致 + """ + try: + user_uuid = uuid.UUID(end_user_id) + repo = EndUserRepository(db) + end_user = repo.get_by_id(user_uuid) + if not end_user: + return { + "nodes": [], "edges": [], + "statistics": {"total_nodes": 0, "total_edges": 0, "node_types": {}, "edge_types": {}}, + "message": "用户不存在" + } + + # 查询社区节点、实体节点、BELONGS_TO_COMMUNITY 边、实体间关系 + cypher = """ + MATCH (c:Community {end_user_id: $end_user_id}) + MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[b:BELONGS_TO_COMMUNITY]->(c) + OPTIONAL MATCH (e)-[r:EXTRACTED_RELATIONSHIP]-(e2:ExtractedEntity {end_user_id: $end_user_id}) + RETURN + elementId(c) AS c_id, + properties(c) AS c_props, + elementId(e) AS e_id, + properties(e) AS e_props, + elementId(b) AS b_id, + elementId(e2) AS e2_id, + properties(e2) AS e2_props, + elementId(r) AS r_id, + type(r) AS r_type, + properties(r) AS r_props, + startNode(r) = e AS r_from_e + """ + rows = await _neo4j_connector.execute_query(cypher, end_user_id=end_user_id) + + nodes_map: Dict[str, dict] = {} + edges_map: Dict[str, dict] = {} + # 记录每个 Community 对应的实体 id 列表 + community_members: Dict[str, list] = {} + + for row in rows: + # Community 节点 + c_id = row["c_id"] + if c_id and c_id not in nodes_map: + raw = row["c_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "community_id", "end_user_id", "member_count", "updated_at", + "name", "summary", "core_entities", + ) if k in raw} + nodes_map[c_id] = { + "id": c_id, + "label": "Community", + "properties": props, + } + + # ExtractedEntity 节点 (e) + e_id = row["e_id"] + if e_id and e_id not in nodes_map: + raw = row["e_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "name", "end_user_id", "description", "created_at", "entity_type", + ) if k in raw} + # 注入所属社区名称（c 是 e 直接归属的社区） + c_raw = row["c_props"] or {} + props["community_name"] = _clean_neo4j_value(c_raw.get("name")) or "" + nodes_map[e_id] = { + "id": e_id, + "label": "ExtractedEntity", + "properties": props, + } + + # ExtractedEntity 节点 (e2，可选) + e2_id = row.get("e2_id") + if e2_id and e2_id not in nodes_map: + raw = row["e2_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "name", "end_user_id", "description", "created_at", "entity_type", + ) if k in raw} + # e2 的社区归属在后处理阶段通过 community_members 补充 + props["community_name"] = "" + nodes_map[e2_id] = { + "id": e2_id, + "label": "ExtractedEntity", + "properties": props, + } + + # BELONGS_TO_COMMUNITY 边 + b_id = row["b_id"] + if b_id and b_id not in edges_map: + edges_map[b_id] = { + "id": b_id, + "source": e_id, + "target": c_id, + } + # 收集社区成员 id + if c_id and e_id: + community_members.setdefault(c_id, []) + if e_id not in community_members[c_id]: + community_members[c_id].append(e_id) + + # EXTRACTED_RELATIONSHIP 边（可选） + r_id = row.get("r_id") + if r_id and r_id not in edges_map and e2_id: + r_props = {k: _clean_neo4j_value(v) for k, v in (row["r_props"] or {}).items()} + source = e_id if row.get("r_from_e") else e2_id + target = e2_id if row.get("r_from_e") else e_id + edges_map[r_id] = { + "id": r_id, + "source": source, + "target": target, + } + + nodes = list(nodes_map.values()) + edges = list(edges_map.values()) + + # 为每个 Community 节点注入 member_entity_ids，同时补全 e2 节点的 community_name + for c_id, member_ids in community_members.items(): + c_node = nodes_map.get(c_id) + if c_node: + c_node["properties"]["member_entity_ids"] = member_ids + c_name = c_node["properties"].get("name") or "" + # 补全属于该社区但 community_name 为空的实体（即 e2 节点） + for eid in member_ids: + e_node = nodes_map.get(eid) + if e_node and e_node["label"] == "ExtractedEntity": + if not e_node["properties"].get("community_name"): + e_node["properties"]["community_name"] = c_name + + node_type_counts: Dict[str, int] = {} + for n in nodes: + node_type_counts[n["label"]] = node_type_counts.get(n["label"], 0) + 1 + + return { + "nodes": nodes, + "edges": edges, + "statistics": { + "total_nodes": len(nodes), + "total_edges": len(edges), + "node_types": node_type_counts, + } + } + + except ValueError: + logger.error(f"无效的 end_user_id 格式: {end_user_id}") + return { + "nodes": [], "edges": [], + "statistics": {"total_nodes": 0, "total_edges": 0, "node_types": {}, "edge_types": {}}, + "message": "无效的用户ID格式" + } + except Exception as e: + logger.error(f"获取社区图谱数据失败: {str(e)}", exc_info=True) + raise + + async def _extract_node_properties(label: str, properties: Dict[str, Any],node_id: str) -> Dict[str, Any]: """ 根据节点类型提取需要的属性字段 From dac1c01a2c923a10001ffde800d43b200b768cd0 Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Fri, 13 Mar 2026 14:05:27 +0800 Subject: [PATCH 074/196] fix(mcp): bug fix --- .../mcp_market_config_controller.py | 35 ++++--------------- api/app/core/tools/mcp/client.py | 1 + 2 files changed, 8 insertions(+), 28 deletions(-) diff --git a/api/app/controllers/mcp_market_config_controller.py b/api/app/controllers/mcp_market_config_controller.py index b1c22975..95449310 100644 --- a/api/app/controllers/mcp_market_config_controller.py +++ b/api/app/controllers/mcp_market_config_controller.py @@ -70,10 +70,7 @@ async def get_mcp_servers( if not db_mcp_market_config: api_logger.warning( f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="The mcp market config does not exist or access is denied" - ) + return success(msg='The mcp market config does not exist or access is denied') # 3. Execute paged query token = db_mcp_market_config.token @@ -151,10 +148,7 @@ async def get_operational_mcp_servers( if not db_mcp_market_config: api_logger.warning( f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="The mcp market config does not exist or access is denied" - ) + return success(msg='The mcp market config does not exist or access is denied') # 2. Execute paged query token = db_mcp_market_config.token @@ -214,10 +208,7 @@ async def get_mcp_server( if not db_mcp_market_config: api_logger.warning( f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="The mcp market config does not exist or access is denied" - ) + return success(msg='The mcp market config does not exist or access is denied') # 2. Get detailed information for a specific MCP Server token = db_mcp_market_config.token @@ -283,10 +274,7 @@ async def get_mcp_market_config( db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user) if not db_mcp_market_config: api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}") - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="The mcp market config does not exist or access is denied" - ) + return success(msg='The mcp market config does not exist or access is denied') api_logger.info(f"mcp market config query successful: (ID: {db_mcp_market_config.id})") return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)), @@ -316,10 +304,7 @@ async def get_mcp_market_config_by_mcp_market_id( db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_mcp_market_id(db, mcp_market_id=mcp_market_id, current_user=current_user) if not db_mcp_market_config: api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_id={mcp_market_id}") - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="The mcp market config does not exist or access is denied" - ) + return success(msg='The mcp market config does not exist or access is denied') api_logger.info(f"mcp market config query successful: (ID: {db_mcp_market_config.id})") return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)), @@ -345,10 +330,7 @@ async def update_mcp_market_config( if not db_mcp_market_config: api_logger.warning( f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}") - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="The mcp market config does not exist or you do not have permission to access it" - ) + return success(msg='The mcp market config does not exist or access is denied') # 2. Update fields (only update non-null fields) api_logger.debug(f"Start updating the mcp market config fields: {mcp_market_config_id}") @@ -402,10 +384,7 @@ async def delete_mcp_market_config( if not db_mcp_market_config: api_logger.warning( f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}") - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="The mcp market config does not exist or you do not have permission to access it" - ) + return success(msg='The mcp market config does not exist or access is denied') # 2. Deleting mcp market config mcp_market_config_service.delete_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user) diff --git a/api/app/core/tools/mcp/client.py b/api/app/core/tools/mcp/client.py index c082b314..f19902a2 100644 --- a/api/app/core/tools/mcp/client.py +++ b/api/app/core/tools/mcp/client.py @@ -53,6 +53,7 @@ class SimpleMCPClient: else: await self._connect_http() except Exception as e: + await self.disconnect() logger.error(f"MCP连接失败: {self.server_url}, 错误: {e}") raise MCPConnectionError(f"连接失败: {e}") From d03473da10a1bfe23632885664441254b4be3b19 Mon Sep 17 00:00:00 2001 From: yujiangping Date: Fri, 13 Mar 2026 14:07:41 +0800 Subject: [PATCH 075/196] fix:input disable --- .../views/ToolManagement/components/MarketConfigModal.tsx | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/web/src/views/ToolManagement/components/MarketConfigModal.tsx b/web/src/views/ToolManagement/components/MarketConfigModal.tsx index 2b4496fa..280512de 100644 --- a/web/src/views/ToolManagement/components/MarketConfigModal.tsx +++ b/web/src/views/ToolManagement/components/MarketConfigModal.tsx @@ -37,6 +37,7 @@ const MarketConfigModal = forwardRef(null); const [showApiKey, setShowApiKey] = useState(false); + const formValues = Form.useWatch([], form); const handleClose = () => { setVisible(false); @@ -101,6 +102,9 @@ const MarketConfigModal = forwardRef 0; + useImperativeHandle(ref, () => ({ handleOpen, handleClose @@ -116,6 +120,7 @@ const MarketConfigModal = forwardRef
@@ -169,7 +174,7 @@ const MarketConfigModal = forwardRef - API Key ({t('tool.marketApiKeyOptional')}) + API Key } extra={{t('tool.marketApiKeyExtra')}} From cbc8714414ade556402c5e7ab5f3918d05b09386 Mon Sep 17 00:00:00 2001 From: Mark Date: Fri, 13 Mar 2026 14:36:54 +0800 Subject: [PATCH 076/196] [fix] i18n import error --- api/app/i18n/__init__.py | 11 +++++++++++ api/app/i18n/serializers.py | 4 +++- 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/api/app/i18n/__init__.py b/api/app/i18n/__init__.py index 23561bec..38d2b5bd 100644 --- a/api/app/i18n/__init__.py +++ b/api/app/i18n/__init__.py @@ -61,6 +61,12 @@ from app.i18n.logger import ( log_translation_error, ) from app.i18n.middleware import LanguageMiddleware +from app.i18n.serializers import ( + I18nResponseMixin, + WorkspaceSerializer, + WorkspaceMemberSerializer, + WorkspaceInviteSerializer, +) from app.i18n.service import ( TranslationService, get_translation_service, @@ -86,6 +92,11 @@ __all__ = [ "get_translation_logger", "log_missing_translation", "log_translation_error", + # Serializers + "I18nResponseMixin", + "WorkspaceSerializer", + "WorkspaceMemberSerializer", + "WorkspaceInviteSerializer", # Exception classes "I18nException", "BadRequestError", diff --git a/api/app/i18n/serializers.py b/api/app/i18n/serializers.py index 9381b8f0..15ba4de5 100644 --- a/api/app/i18n/serializers.py +++ b/api/app/i18n/serializers.py @@ -72,7 +72,9 @@ class I18nResponseMixin: Returns: 添加了 _display 字段的字典 """ - from app.i18n.service import translation_service + from app.i18n.service import get_translation_service + + translation_service = get_translation_service() result = data.copy() From 6a0ee22d8145d17654b010656e52f7ead22bc92c Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 14:43:29 +0800 Subject: [PATCH 077/196] [add] Create trigger events for the purpose of completing the existing data --- api/app/celery_app.py | 1 + .../memory_dashboard_controller.py | 14 +- .../clustering_engine/label_propagation.py | 70 +++++++-- api/app/tasks.py | 138 ++++++++++++++++++ 4 files changed, 206 insertions(+), 17 deletions(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 0319e079..dbfa9d51 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -113,6 +113,7 @@ celery_app.conf.update( 'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'}, 'app.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'}, 'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'}, + 'app.tasks.init_community_clustering_for_users': {'queue': 'periodic_tasks'}, }, ) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 1b5b45fb..f01445d3 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -177,7 +177,19 @@ async def get_workspace_end_users( await aio_redis_set(cache_key, json.dumps(result), expire=30) except Exception as e: api_logger.warning(f"Redis 缓存写入失败: {str(e)}") - + + # 触发社区聚类补全任务（异步，不阻塞接口响应） + # 对有 ExtractedEntity 但无 Community 节点的存量用户自动补跑全量聚类 + try: + from app.tasks import init_community_clustering_for_users + init_community_clustering_for_users.apply_async( + kwargs={"end_user_ids": end_user_ids}, + queue="periodic_tasks", + ) + api_logger.info(f"已触发社区聚类补全任务，候选用户数: {len(end_user_ids)}") + except Exception as e: + api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {str(e)}") + api_logger.info(f"成功获取 {len(end_users)} 个宿主记录") return success(data=result, msg="宿主列表获取成功") diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 251d4fea..4491b416 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -165,8 +165,15 @@ class LabelPropagationEngine: f"{len(labels)} 个实体" ) # 为所有社区生成元数据 - unique_communities = list(set(labels.values())) - for cid in unique_communities: + # 注意：_evaluate_merge 后部分社区已被合并消解，需重新从 Neo4j 查询实际存活的社区 + # 不能复用 labels.values()，那里包含已被 dissolve 的旧社区 ID + surviving_communities = await self.repo.get_all_entities(end_user_id) + surviving_community_ids = list({ + e.get("community_id") for e in surviving_communities + if e.get("community_id") + }) + logger.info(f"[Clustering] 合并后实际存活社区数: {len(surviving_community_ids)}") + for cid in surviving_community_ids: await self._generate_community_metadata(cid, end_user_id) async def incremental_update( @@ -249,7 +256,7 @@ class LabelPropagationEngine: 全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。 """ - MERGE_THRESHOLD = 0.75 + MERGE_THRESHOLD = 0.85 BATCH_THRESHOLD = 20 # 超过此数量走批量查询 community_embeddings: Dict[str, Optional[List[float]]] = {} @@ -305,34 +312,65 @@ class LabelPropagationEngine: logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区") - # 执行合并：用 union-find 思路避免重复迁移已被合并的社区 - # 维护一个 canonical 映射，确保链式合并正确收敛 - canonical: Dict[str, str] = {cid: cid for cid in cids} + # 执行合并：逐对处理，每次合并后重新计算合并社区的平均向量 + # 避免 union-find 链式传递导致语义不相关的社区被间接合并 + # （A≈B、B≈C 不代表 A≈C，不能因传递性把 A/B/C 全部合并） + merged_into: Dict[str, str] = {} # dissolve → keep 的最终映射 - def find(x: str) -> str: - while canonical[x] != x: - canonical[x] = canonical[canonical[x]] - x = canonical[x] + def get_root(x: str) -> str: + """路径压缩，找到 x 当前所属的根社区。""" + while x in merged_into: + merged_into[x] = merged_into.get(merged_into[x], merged_into[x]) + x = merged_into[x] return x for c1, c2 in to_merge: - root1, root2 = find(c1), find(c2) + root1, root2 = get_root(c1), get_root(c2) if root1 == root2: - continue # 已经在同一社区，跳过 + continue + + # 用合并后的最新平均向量重新验证相似度 + # 防止链式传递：A≈B 合并后 B 的向量已更新，C 必须和新 B 相似才能合并 + current_sim = _cosine_similarity( + community_embeddings.get(root1), + community_embeddings.get(root2), + ) + if current_sim <= MERGE_THRESHOLD: + # 合并后向量已漂移，不再满足阈值，跳过 + logger.debug( + f"[Clustering] 跳过合并 {root1} ↔ {root2}，" + f"当前相似度 {current_sim:.3f} ≤ {MERGE_THRESHOLD}" + ) + continue + keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2 dissolve = root2 if keep == root1 else root1 - canonical[dissolve] = keep + merged_into[dissolve] = keep members = await self.repo.get_community_members(dissolve, end_user_id) for m in members: await self.repo.assign_entity_to_community(m["id"], keep, end_user_id) - # 更新 sizes 以便后续合并决策准确 - community_sizes[keep] = community_sizes.get(keep, 0) + len(members) + + # 合并后重新计算 keep 的平均向量（加权平均） + keep_emb = community_embeddings.get(keep) + dissolve_emb = community_embeddings.get(dissolve) + keep_size = community_sizes.get(keep, 0) + dissolve_size = community_sizes.get(dissolve, 0) + total_size = keep_size + dissolve_size + if keep_emb and dissolve_emb and total_size > 0: + dim = len(keep_emb) + community_embeddings[keep] = [ + (keep_emb[i] * keep_size + dissolve_emb[i] * dissolve_size) / total_size + for i in range(dim) + ] + community_embeddings[dissolve] = None + + community_sizes[keep] = total_size community_sizes[dissolve] = 0 await self.repo.refresh_member_count(keep, end_user_id) logger.info( f"[Clustering] 社区合并: {dissolve} → {keep}，" - f"迁移 {len(members)} 个成员" + f"相似度={current_sim:.3f}，迁移 {len(members)} 个成员" ) async def _flush_labels( diff --git a/api/app/tasks.py b/api/app/tasks.py index a6ebbb8e..134d4744 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2416,3 +2416,141 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: "elapsed_time": elapsed_time, "task_id": self.request.id } + + +# ============================================================================= +# 社区聚类补全任务（触发型） +# ============================================================================= + +@celery_app.task( + name="app.tasks.init_community_clustering_for_users", + bind=True, + ignore_result=False, + max_retries=0, + acks_late=False, + time_limit=7200, # 2小时硬超时 + soft_time_limit=6900, +) +def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]: + """触发型任务：检查指定用户列表，对有 ExtractedEntity 但无 Community 节点的用户执行全量聚类。 + + 由 /dashboard/end_users 接口触发，已有社区节点的用户直接跳过。 + + Args: + end_user_ids: 需要检查的用户 ID 列表 + + Returns: + 包含任务执行结果的字典 + """ + start_time = time.time() + + async def _run() -> Dict[str, Any]: + from app.core.logging_config import get_logger + from app.repositories.neo4j.community_repository import CommunityRepository + from app.repositories.neo4j.neo4j_connector import Neo4jConnector + from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine + + logger = get_logger(__name__) + logger.info(f"[CommunityCluster] 开始社区聚类补全任务，候选用户数: {len(end_user_ids)}") + + initialized = 0 + skipped = 0 + failed = 0 + + connector = Neo4jConnector() + try: + repo = CommunityRepository(connector) + + # 获取 llm_model_id（从第一个用户的配置中读取，作为全局兜底） + llm_model_id = None + try: + with get_db_context() as db: + from app.services.memory_agent_service import get_end_user_connected_config + from app.services.memory_config_service import MemoryConfigService + for uid in end_user_ids: + try: + connected = get_end_user_connected_config(uid, db) + config_id = connected.get("memory_config_id") + workspace_id = connected.get("workspace_id") + if config_id or workspace_id: + cfg = MemoryConfigService(db).load_memory_config( + config_id=config_id, workspace_id=workspace_id + ) + llm_model_id = str(cfg.llm_model_id) + break + except Exception: + continue + except Exception as e: + logger.warning(f"[CommunityCluster] 获取 LLM 配置失败，将使用兜底值: {e}") + + engine = LabelPropagationEngine( + connector=connector, + llm_model_id=llm_model_id, + ) + + for end_user_id in end_user_ids: + try: + # 已有社区节点则跳过 + has_communities = await repo.has_communities(end_user_id) + if has_communities: + skipped += 1 + logger.debug(f"[CommunityCluster] 用户 {end_user_id} 已有社区节点，跳过") + continue + + # 检查是否有 ExtractedEntity 节点 + entities = await repo.get_all_entities(end_user_id) + if not entities: + skipped += 1 + logger.debug(f"[CommunityCluster] 用户 {end_user_id} 无实体节点，跳过") + continue + + logger.info(f"[CommunityCluster] 用户 {end_user_id} 有 {len(entities)} 个实体，开始全量聚类") + await engine.full_clustering(end_user_id) + initialized += 1 + logger.info(f"[CommunityCluster] 用户 {end_user_id} 聚类完成") + + except Exception as e: + failed += 1 + logger.error(f"[CommunityCluster] 用户 {end_user_id} 聚类失败: {e}") + + finally: + await connector.close() + + logger.info( + f"[CommunityCluster] 任务完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}" + ) + return { + "status": "SUCCESS", + "initialized": initialized, + "skipped": skipped, + "failed": failed, + } + + try: + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) + result["elapsed_time"] = time.time() - start_time + result["task_id"] = self.request.id + return result + + except Exception as e: + return { + "status": "FAILURE", + "error": str(e), + "elapsed_time": time.time() - start_time, + "task_id": self.request.id, + } From b71bc1f875f1b50d5fca46da0dabf1be7b3c1e5c Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Fri, 13 Mar 2026 13:33:58 +0800 Subject: [PATCH 078/196] feat(multimodel): support multimodal memory display and improve code style --- api/app/aioRedis.py | 3 +- api/app/celery_app.py | 23 +- api/app/celery_worker.py | 2 +- api/app/controllers/app_controller.py | 15 +- .../agent/langgraph_graph/nodes/data_nodes.py | 15 +- .../agent/langgraph_graph/routing/routers.py | 13 +- .../langgraph_graph/routing/write_router.py | 115 ++--- .../agent/langgraph_graph/tools/tool.py | 108 ++--- .../agent/langgraph_graph/tools/write_tool.py | 45 +- .../agent/langgraph_graph/write_graph.py | 37 +- api/app/core/memory/agent/utils/llm_tools.py | 16 +- .../triplet_extraction.py | 18 +- .../memory/utils/prompt/template_render.py | 16 +- api/app/core/models/base.py | 32 +- api/app/core/workflow/nodes/base_node.py | 11 +- api/app/core/workflow/nodes/if_else/node.py | 22 +- api/app/core/workflow/nodes/knowledge/node.py | 6 + api/app/core/workflow/nodes/llm/node.py | 59 ++- .../nodes/parameter_extractor/node.py | 8 + api/app/models/memory_perceptual_model.py | 11 + .../memory_perceptual_repository.py | 13 +- api/app/schemas/memory_perceptual_schema.py | 2 - api/app/schemas/model_schema.py | 11 + api/app/services/app_chat_service.py | 36 +- api/app/services/draft_run_service.py | 32 +- api/app/services/memory_agent_service.py | 2 +- api/app/services/memory_perceptual_service.py | 114 ++++- api/app/services/multimodal_service.py | 129 +++--- .../prompt/perceptual_summary_system.jinja2 | 53 +++ api/app/tasks.py | 392 ++++++++---------- api/app/utils/redis_lock.py | 61 +++ 31 files changed, 877 insertions(+), 543 deletions(-) create mode 100644 api/app/services/prompt/perceptual_summary_system.jinja2 create mode 100644 api/app/utils/redis_lock.py diff --git a/api/app/aioRedis.py b/api/app/aioRedis.py index f758dd15..aac2aa84 100644 --- a/api/app/aioRedis.py +++ b/api/app/aioRedis.py @@ -1,10 +1,11 @@ -import os import asyncio import json import logging from typing import Dict, Any, Optional + import redis.asyncio as redis from redis.asyncio import ConnectionPool + from app.core.config import settings # 设置日志记录器 diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 21ee291d..60c22855 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -62,7 +62,7 @@ celery_app.conf.update( task_serializer='json', accept_content=['json'], result_serializer='json', - + # 时区 timezone='Asia/Shanghai', enable_utc=False, @@ -70,43 +70,44 @@ celery_app.conf.update( # 任务追踪 task_track_started=True, task_ignore_result=False, - + # 超时设置 task_time_limit=3600, # 60分钟硬超时 task_soft_time_limit=3000, # 50分钟软超时 - + # Worker 设置 (per-worker settings are in docker-compose command line) worker_prefetch_multiplier=1, # Don't hoard tasks, fairer distribution - + # 结果过期时间 result_expires=3600, # 结果保存1小时 - + # 任务确认设置 task_acks_late=True, task_reject_on_worker_lost=True, worker_disable_rate_limits=True, - + # FLower setting worker_send_task_events=True, task_send_sent_event=True, - + # task routing task_routes={ # Memory tasks → memory_tasks queue (threads worker) 'app.core.memory.agent.read_message_priority': {'queue': 'memory_tasks'}, 'app.core.memory.agent.read_message': {'queue': 'memory_tasks'}, 'app.core.memory.agent.write_message': {'queue': 'memory_tasks'}, - + 'app.tasks.write_perceptual_memory': {'queue': 'memory_tasks'}, + # Long-term storage tasks → memory_tasks queue (batched write strategies) 'app.core.memory.agent.long_term_storage.window': {'queue': 'memory_tasks'}, 'app.core.memory.agent.long_term_storage.time': {'queue': 'memory_tasks'}, 'app.core.memory.agent.long_term_storage.aggregate': {'queue': 'memory_tasks'}, - + # Document tasks → document_tasks queue (prefork worker) 'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'}, 'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'}, 'app.core.rag.tasks.sync_knowledge_for_kb': {'queue': 'document_tasks'}, - + # Beat/periodic tasks → periodic_tasks queue (dedicated periodic worker) 'app.tasks.workspace_reflection_task': {'queue': 'periodic_tasks'}, 'app.tasks.regenerate_memory_cache': {'queue': 'periodic_tasks'}, @@ -131,7 +132,7 @@ implicit_emotions_update_schedule = crontab( minute=settings.IMPLICIT_EMOTIONS_UPDATE_MINUTE, ) -#构建定时任务配置 +# 构建定时任务配置 beat_schedule_config = { "run-workspace-reflection": { "task": "app.tasks.workspace_reflection_task", diff --git a/api/app/celery_worker.py b/api/app/celery_worker.py index 7d3ee686..4ea4fee1 100644 --- a/api/app/celery_worker.py +++ b/api/app/celery_worker.py @@ -13,4 +13,4 @@ logger.info("Celery worker logging initialized") # 导入任务模块以注册任务 import app.tasks -__all__ = ['celery_app'] \ No newline at end of file +__all__ = ['celery_app'] diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index 5d1551d8..31451a7d 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -808,6 +808,15 @@ async def draft_run_compare( raise BusinessException("只有 Agent 类型应用支持试运行", BizCode.APP_TYPE_NOT_SUPPORTED) service._validate_app_accessible(app, workspace_id) + if payload.user_id is None: + end_user_repo = EndUserRepository(db) + new_end_user = end_user_repo.get_or_create_end_user( + app_id=app_id, + other_id=str(current_user.id), + original_user_id=str(current_user.id) # Save original user_id to other_id + ) + payload.user_id = str(new_end_user.id) + # 2. 获取 Agent 配置 from sqlalchemy import select from app.models import AgentConfig @@ -853,6 +862,8 @@ async def draft_run_compare( "conversation_id": model_item.conversation_id # 传递每个模型的 conversation_id }) + + # 流式返回 if payload.stream: async def event_generator(): @@ -864,7 +875,7 @@ async def draft_run_compare( message=payload.message, workspace_id=workspace_id, conversation_id=payload.conversation_id, - user_id=payload.user_id or str(current_user.id), + user_id=payload.user_id, variables=payload.variables, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, @@ -895,7 +906,7 @@ async def draft_run_compare( message=payload.message, workspace_id=workspace_id, conversation_id=payload.conversation_id, - user_id=payload.user_id or str(current_user.id), + user_id=payload.user_id, variables=payload.variables, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py index 829f26c4..ca08db76 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py @@ -4,13 +4,13 @@ from app.core.memory.agent.utils.llm_tools import ReadState, WriteState def content_input_node(state: ReadState) -> ReadState: """ Start node - Extract content and maintain state information - + Extracts the content from the first message in the state and returns it as the data field while preserving all other state information. - + Args: state: ReadState containing messages and other state data - + Returns: ReadState: Updated state with extracted content in data field """ @@ -19,19 +19,20 @@ def content_input_node(state: ReadState) -> ReadState: # Return content and maintain all state information return {"data": content} + def content_input_write(state: WriteState) -> WriteState: """ Start node - Extract content and maintain state information for write operations - + Extracts the content from the first message in the state for write operations. - + Args: state: WriteState containing messages and other state data - + Returns: WriteState: Updated state with extracted content in data field """ content = state['messages'][0].content if state.get('messages') else '' # Return content and maintain all state information - return {"data": content} \ No newline at end of file + return {"data": content} diff --git a/api/app/core/memory/agent/langgraph_graph/routing/routers.py b/api/app/core/memory/agent/langgraph_graph/routing/routers.py index 004e03b3..d6ca3333 100644 --- a/api/app/core/memory/agent/langgraph_graph/routing/routers.py +++ b/api/app/core/memory/agent/langgraph_graph/routing/routers.py @@ -1,13 +1,13 @@ - from typing import Literal from app.core.logging_config import get_agent_logger from app.core.memory.agent.utils.llm_tools import ReadState, COUNTState - logger = get_agent_logger(__name__) counter = COUNTState(limit=3) -def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summary"]: + + +def Split_continue(state: ReadState) -> Literal["Split_The_Problem", "Input_Summary"]: """ Determine routing based on search_switch value. @@ -25,6 +25,7 @@ def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summa return 'Input_Summary' return 'Split_The_Problem' # 默认情况 + def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]: """ Determine routing based on search_switch value. @@ -43,8 +44,10 @@ def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]: elif search_switch == '1': return 'Retrieve_Summary' return 'Retrieve_Summary' # Default based on business logic + + def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]: - status=state.get('verify', '')['status'] + status = state.get('verify', '')['status'] # loop_count = counter.get_total() if "success" in status: # counter.reset() @@ -53,7 +56,7 @@ def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "co # if loop_count < 2: # Maximum loop count is 3 # return "content_input" # else: - # counter.reset() + # counter.reset() return "Summary_fails" else: # Add default return value to avoid returning None diff --git a/api/app/core/memory/agent/langgraph_graph/routing/write_router.py b/api/app/core/memory/agent/langgraph_graph/routing/write_router.py index ddb6ca3e..6176caf5 100644 --- a/api/app/core/memory/agent/langgraph_graph/routing/write_router.py +++ b/api/app/core/memory/agent/langgraph_graph/routing/write_router.py @@ -2,32 +2,32 @@ import json import os from app.core.logging_config import get_agent_logger -from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, messages_parse -from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph, long_term_storage - +from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, messages_parse from app.core.memory.agent.models.write_aggregate_model import WriteAggregateModel from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_ -from app.core.memory.agent.utils.redis_tool import write_store from app.core.memory.agent.utils.redis_tool import count_store +from app.core.memory.agent.utils.redis_tool import write_store from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.db import get_db_context, get_db +from app.db import get_db_context from app.repositories.memory_short_repository import LongTermMemoryRepository from app.schemas.memory_agent_schema import AgentMemory_Long_Term from app.services.memory_konwledges_server import write_rag from app.services.task_service import get_task_memory_write_result from app.tasks import write_message_task from app.utils.config_utils import resolve_config_id + logger = get_agent_logger(__name__) template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') + async def write_rag_agent(end_user_id, user_message, ai_message, user_rag_memory_id): """ Write messages to RAG storage system - + Combines user and AI messages into a single string format and stores them in the RAG (Retrieval-Augmented Generation) knowledge base for future retrieval. - + Args: end_user_id: User identifier for the conversation user_message: User's input message content @@ -38,14 +38,24 @@ async def write_rag_agent(end_user_id, user_message, ai_message, user_rag_memory combined_message = f"user: {user_message}\nassistant: {ai_message}" await write_rag(end_user_id, combined_message, user_rag_memory_id) logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}') -async def write(storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, - actual_config_id, long_term_messages=[]): + + +async def write( + storage_type, + end_user_id, + user_message, + ai_message, + user_rag_memory_id, + actual_end_user_id, + actual_config_id, + long_term_messages=None +): """ Write memory with structured message support - + Handles memory writing operations for different storage types (Neo4j/RAG). Supports both individual message pairs and batch long-term message processing. - + Args: storage_type: Storage type identifier ("neo4j" or "rag") end_user_id: Terminal user identifier @@ -55,7 +65,7 @@ async def write(storage_type, end_user_id, user_message, ai_message, user_rag_me actual_end_user_id: Actual user identifier for storage actual_config_id: Configuration identifier long_term_messages: Optional list of structured messages for batch processing - + Logic explanation: - RAG mode: Combines user_message and ai_message into string format, maintains original logic - Neo4j mode: Uses structured message lists @@ -64,8 +74,9 @@ async def write(storage_type, end_user_id, user_message, ai_message, user_rag_me 3. Each message is converted to independent Chunk, preserving speaker field """ - db = next(get_db()) - try: + if long_term_messages is None: + long_term_messages = [] + with get_db_context() as db: actual_config_id = resolve_config_id(actual_config_id, db) # Neo4j mode: Use structured message lists structured_messages = [] @@ -105,17 +116,16 @@ async def write(storage_type, end_user_id, user_message, ai_message, user_rag_me logger.info(f"[WRITE] Celery task submitted - task_id={write_id}") write_status = get_task_memory_write_result(str(write_id)) logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}') - finally: - db.close() -async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type,scope): + +async def term_memory_save(long_term_messages, actual_config_id, end_user_id, type, scope): """ Save long-term memory data to database - + Handles the storage of long-term memory data based on different strategies (chunk-based or aggregate-based) and manages the transition from short-term to long-term memory storage. - + Args: long_term_messages: Long-term message data to be saved actual_config_id: Configuration identifier for memory settings @@ -126,13 +136,12 @@ async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type, with get_db_context() as db_session: repo = LongTermMemoryRepository(db_session) - from app.core.memory.agent.utils.redis_tool import write_store result = write_store.get_session_by_userid(end_user_id) - if type==AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE: + if type == AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE: data = await format_parsing(result, "dict") chunk_data = data[:scope] - if len(chunk_data)==scope: + if len(chunk_data) == scope: repo.upsert(end_user_id, chunk_data) logger.info(f'---------写入短长期-----------') else: @@ -142,22 +151,23 @@ async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type, logger.info(f'写入短长期：') - """Window-based dialogue processing""" -async def window_dialogue(end_user_id,langchain_messages,memory_config,scope): + + +async def window_dialogue(end_user_id, langchain_messages, memory_config, scope): """ Process dialogue based on window size and write to Neo4j - + Manages conversation data based on a sliding window approach. When the window reaches the specified scope size, it triggers long-term memory storage to Neo4j. - + Args: end_user_id: Terminal user identifier memory_config: Memory configuration object containing settings langchain_messages: Original message data list scope: Window size determining when to trigger long-term storage """ - scope=scope + scope = scope is_end_user_id = count_store.get_sessions_count(end_user_id) if is_end_user_id is not False: is_end_user_id = count_store.get_sessions_count(end_user_id)[0] @@ -174,42 +184,53 @@ async def window_dialogue(end_user_id,langchain_messages,memory_config,scope): config_id = memory_config.config_id else: config_id = memory_config - - await write(AgentMemory_Long_Term.STORAGE_NEO4J, end_user_id, "", "", None, end_user_id, - config_id, formatted_messages) + + await write( + AgentMemory_Long_Term.STORAGE_NEO4J, + end_user_id, + "", + "", + None, + end_user_id, + config_id, + formatted_messages + ) count_store.update_sessions_count(end_user_id, 1, langchain_messages) else: count_store.save_sessions_count(end_user_id, 1, langchain_messages) """Time-based memory processing""" -async def memory_long_term_storage(end_user_id,memory_config,time): + + +async def memory_long_term_storage(end_user_id, memory_config, time): """ Process memory storage based on time intervals and write to Neo4j - + Retrieves Redis data based on time intervals and writes it to Neo4j for long-term storage. This function handles time-based memory consolidation. - + Args: end_user_id: Terminal user identifier memory_config: Memory configuration object containing settings time: Time interval for data retrieval """ long_time_data = write_store.find_user_recent_sessions(end_user_id, time) - format_messages = (long_time_data) - messages=[] - memory_config=memory_config.config_id + format_messages = long_time_data + messages = [] + memory_config = memory_config.config_id for i in format_messages: - message=json.loads(i['Query']) - messages+= message - if format_messages!=[]: + message = json.loads(i['Query']) + messages += message + if format_messages: await write(AgentMemory_Long_Term.STORAGE_NEO4J, end_user_id, "", "", None, end_user_id, memory_config, messages) -"""Aggregation judgment processing""" + + async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config) -> dict: """ Aggregation judgment function: determine if input sentence and historical messages describe the same event - + Uses LLM-based analysis to determine whether new messages should be aggregated with existing historical data or stored as separate events. This helps optimize memory storage and retrieval. @@ -217,11 +238,11 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config end_user_id: Terminal user identifier ori_messages: Original message list, format like [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] memory_config: Memory configuration object containing LLM settings - + Returns: dict: Aggregation judgment result containing is_same_event flag and processed output """ - + history = None try: # 1. Get historical session data (using new method) result = write_store.get_all_sessions_by_end_user_id(end_user_id) @@ -255,7 +276,7 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config output_value = structured.output if isinstance(output_value, list): output_value = [ - {"role": msg.role, "content": msg.content} + {"role": msg.role, "content": msg.content} for msg in output_value ] @@ -268,16 +289,16 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config await write("neo4j", end_user_id, "", "", None, end_user_id, memory_config.config_id, output_value) return result_dict - + except Exception as e: print(f"[aggregate_judgment] 发生错误: {e}") import traceback traceback.print_exc() - + return { "is_same_event": False, "output": ori_messages, "messages": ori_messages, "history": history if 'history' in locals() else [], "error": str(e) - } \ No newline at end of file + } diff --git a/api/app/core/memory/agent/langgraph_graph/tools/tool.py b/api/app/core/memory/agent/langgraph_graph/tools/tool.py index bee77ddf..9bd2b2cf 100644 --- a/api/app/core/memory/agent/langgraph_graph/tools/tool.py +++ b/api/app/core/memory/agent/langgraph_graph/tools/tool.py @@ -2,26 +2,25 @@ import asyncio import json from datetime import datetime, timedelta - from langchain.tools import tool from pydantic import BaseModel, Field - from app.core.memory.src.search import ( search_by_temporal, search_by_keyword_temporal, ) + def extract_tool_message_content(response): """ Extract ToolMessage content and tool names from agent response - + Parses agent response messages to extract tool execution results and metadata. Handles JSON parsing and provides structured access to tool output data. - + Args: response: Agent response dictionary containing messages - + Returns: dict: Dictionary containing tool_name and parsed content, or None if no tool message found - tool_name: Name of the executed tool @@ -61,10 +60,10 @@ def extract_tool_message_content(response): class TimeRetrievalInput(BaseModel): """ Input schema for time retrieval tool - + Defines the expected input parameters for time-based retrieval operations. Used for validation and documentation of tool parameters. - + Attributes: context: User input query content for search end_user_id: Group ID for filtering search results, defaults to test user @@ -72,25 +71,26 @@ class TimeRetrievalInput(BaseModel): context: str = Field(description="用户输入的查询内容") end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") + def create_time_retrieval_tool(end_user_id: str): """ Create a TimeRetrieval tool with specific end_user_id (synchronous version) for searching statements by time range - + Creates a specialized time-based retrieval tool that searches for statements within specified time ranges. Includes field cleaning functionality to remove unnecessary metadata from search results. - + Args: end_user_id: User identifier for scoping search results - + Returns: function: Configured TimeRetrievalWithGroupId tool function """ - + def clean_temporal_result_fields(data): """ Clean unnecessary fields from temporal search results and modify structure - + Removes metadata fields that are not needed for end-user consumption and restructures the response format for better usability. @@ -102,10 +102,10 @@ def create_time_retrieval_tool(end_user_id: str): """ # List of fields to filter out fields_to_remove = { - 'id', 'apply_id', 'user_id', 'chunk_id', 'created_at', + 'id', 'apply_id', 'user_id', 'chunk_id', 'created_at', 'valid_at', 'invalid_at', 'statement_ids' } - + if isinstance(data, dict): cleaned = {} for key, value in data.items(): @@ -126,15 +126,16 @@ def create_time_retrieval_tool(end_user_id: str): return [clean_temporal_result_fields(item) for item in data] else: return data - + @tool - def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str: + def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, + end_user_id_param: str = None, clean_output: bool = True) -> str: """ Optimized time retrieval tool, combines time range search only (synchronous version), automatically filters unnecessary metadata fields - + Performs time-based search operations with automatic metadata filtering. Supports flexible date range specification and provides clean, user-friendly output. - + Explicit parameters: - context: Query context content - start_date: Start time (optional, format: YYYY-MM-DD) @@ -142,10 +143,11 @@ def create_time_retrieval_tool(end_user_id: str): - end_user_id_param: Group ID (optional, overrides default group ID) - clean_output: Whether to clean metadata fields from output - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d") - + Returns: str: JSON formatted search results with temporal data """ + async def _async_search(): # Use passed parameters or default values actual_end_user_id = end_user_id_param or end_user_id @@ -167,18 +169,19 @@ def create_time_retrieval_tool(end_user_id: str): cleaned_results = results return json.dumps(cleaned_results, ensure_ascii=False, indent=2) - + return asyncio.run(_async_search()) @tool - def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None, clean_output: bool = True) -> str: + def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None, + clean_output: bool = True) -> str: """ Optimized keyword time retrieval tool, combines keyword and time range search (synchronous version), automatically filters unnecessary metadata fields - + Performs combined keyword and temporal search operations with automatic metadata filtering. Provides more targeted search results by combining content relevance with time-based filtering. - + Explicit parameters: - context: Query content for keyword matching - days_back: Number of days to search backwards, default 7 days @@ -186,10 +189,11 @@ def create_time_retrieval_tool(end_user_id: str): - end_date: End time (optional, format: YYYY-MM-DD) - clean_output: Whether to clean metadata fields from output - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d") - + Returns: str: JSON formatted search results combining keyword and temporal data """ + async def _async_search(): actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d") actual_start_date = start_date or (datetime.now() - timedelta(days=days_back)).strftime("%Y-%m-%d") @@ -212,29 +216,29 @@ def create_time_retrieval_tool(end_user_id: str): return json.dumps(cleaned_results, ensure_ascii=False, indent=2) return asyncio.run(_async_search()) - + return TimeRetrievalWithGroupId def create_hybrid_retrieval_tool_async(memory_config, **search_params): """ Create hybrid retrieval tool using run_hybrid_search for hybrid retrieval, optimize output format and filter unnecessary fields - + Creates an advanced hybrid search tool that combines multiple search strategies (keyword, vector, hybrid) with automatic result cleaning and formatting. Args: memory_config: Memory configuration object containing LLM and search settings **search_params: Search parameters including end_user_id, limit, include, etc. - + Returns: function: Configured HybridSearch tool function with async capabilities """ - + def clean_result_fields(data): """ Recursively clean unnecessary fields from results - + Removes metadata fields that are not needed for end-user consumption, improving readability and reducing response size. @@ -247,11 +251,11 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): # List of fields to filter out # TODO: fact_summary functionality temporarily disabled, will be enabled after future development fields_to_remove = { - 'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids', - 'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id', - 'user_id', 'statement_ids', 'updated_at',"chunk_ids" ,"fact_summary" + 'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids', + 'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id', + 'user_id', 'statement_ids', 'updated_at', "chunk_ids", "fact_summary" } - + if isinstance(data, dict): # Clean dictionary cleaned = {} @@ -265,7 +269,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): else: # Return other types directly return data - + @tool async def HybridSearch( context: str, @@ -279,7 +283,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): ) -> str: """ Optimized hybrid retrieval tool, supports keyword, vector and hybrid search, automatically filters unnecessary metadata fields - + Provides comprehensive search capabilities combining multiple search strategies with intelligent result ranking and automatic metadata filtering for clean output. @@ -292,7 +296,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): use_forgetting_rerank: Whether to use forgetting-based reranking use_llm_rerank: Whether to use LLM-based reranking clean_output: Whether to clean metadata fields from output - + Returns: str: JSON formatted comprehensive search results """ @@ -329,9 +333,9 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): "search_type": search_type, "results": cleaned_results } - + return json.dumps(formatted_results, ensure_ascii=False, indent=2, default=str) - + except Exception as e: error_result = { "error": f"混合检索失败: {str(e)}", @@ -340,35 +344,36 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): "timestamp": datetime.now().isoformat() } return json.dumps(error_result, ensure_ascii=False, indent=2) - + return HybridSearch def create_hybrid_retrieval_tool_sync(memory_config, **search_params): """ Create synchronous version of hybrid retrieval tool, optimize output format and filter unnecessary fields - + Creates a synchronous wrapper around the async hybrid search functionality, making it compatible with synchronous tool execution environments. Args: memory_config: Memory configuration object containing search settings **search_params: Search parameters for configuration - + Returns: function: Configured HybridSearchSync tool function """ + @tool def HybridSearchSync( - context: str, - search_type: str = "hybrid", - limit: int = 10, - end_user_id: str = None, - clean_output: bool = True + context: str, + search_type: str = "hybrid", + limit: int = 10, + end_user_id: str = None, + clean_output: bool = True ) -> str: """ Optimized hybrid retrieval tool (synchronous version), automatically filters unnecessary metadata fields - + Provides the same hybrid search capabilities as the async version but in a synchronous execution context. Automatically handles async-to-sync conversion. @@ -378,10 +383,11 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): limit: Result quantity limit end_user_id: Group ID for filtering search results clean_output: Whether to clean metadata fields from output - + Returns: str: JSON formatted search results """ + async def _async_search(): # Create async tool and execute async_tool = create_hybrid_retrieval_tool_async(memory_config, **search_params) @@ -392,7 +398,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): "end_user_id": end_user_id, "clean_output": clean_output }) - + return asyncio.run(_async_search()) - - return HybridSearchSync \ No newline at end of file + + return HybridSearchSync diff --git a/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py b/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py index aa5e09a6..e11a2085 100644 --- a/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py +++ b/api/app/core/memory/agent/langgraph_graph/tools/write_tool.py @@ -1,10 +1,12 @@ import json from langchain_core.messages import HumanMessage, AIMessage -async def format_parsing(messages: list,type:str='string'): + + +async def format_parsing(messages: list, type: str = 'string'): """ Format and parse message lists into different output types - + Processes message lists from storage and converts them into either string format or dictionary format based on the specified type parameter. Handles JSON parsing and role-based message organization. @@ -19,8 +21,8 @@ async def format_parsing(messages: list,type:str='string'): - 'dict': List of dictionaries mapping user messages to AI responses """ result = [] - user=[] - ai=[] + user = [] + ai = [] for message in messages: hstory_messages = message['messages'] @@ -30,37 +32,38 @@ async def format_parsing(messages: list,type:str='string'): role = content['role'] content = content['content'] if type == "string": - if role == 'human' or role=="user": + if role == 'human' or role == "user": content = '用户:' + content else: content = 'AI:' + content result.append(content) - if type == "dict" : - if role == 'human' or role=="user": - user.append( content) + if type == "dict": + if role == 'human' or role == "user": + user.append(content) else: ai.append(content) if type == "dict": - for key,values in zip(user,ai): - result.append({key:values}) + for key, values in zip(user, ai): + result.append({key: values}) return result + async def messages_parse(messages: list | dict): """ Parse messages from storage format into user-AI conversation pairs - + Extracts and organizes conversation data from stored message format, separating user and AI messages and pairing them for database storage. - + Args: messages: List or dictionary containing stored message data with Query fields - + Returns: list: List of dictionaries containing user-AI message pairs for database storage """ - user=[] - ai=[] - database=[] + user = [] + ai = [] + database = [] for message in messages: Query = message['Query'] Query = json.loads(Query) @@ -72,20 +75,20 @@ async def messages_parse(messages: list | dict): ai.append(data['content']) for key, values in zip(user, ai): database.append({key, values}) - return database + return database -async def agent_chat_messages(user_content,ai_content): +async def agent_chat_messages(user_content, ai_content): """ Create structured chat message format for agent conversations - + Formats user and AI content into a standardized message structure suitable for agent processing and storage. Creates role-based message objects. - + Args: user_content: User's message content string ai_content: AI's response content string - + Returns: list: List of structured message dictionaries with role and content fields """ diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py index 15009955..bf3c6597 100644 --- a/api/app/core/memory/agent/langgraph_graph/write_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py @@ -13,7 +13,6 @@ from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node from app.schemas.memory_agent_schema import AgentMemory_Long_Term from app.services.memory_config_service import MemoryConfigService - warnings.filterwarnings("ignore", category=RuntimeWarning) logger = get_agent_logger(__name__) @@ -42,13 +41,15 @@ async def make_write_graph(): yield graph -async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[],memory_config:str='',end_user_id:str='',scope:int=6): + +async def long_term_storage(long_term_type: str = "chunk", langchain_messages: list = [], memory_config: str = '', + end_user_id: str = '', scope: int = 6): """ Handle long-term memory storage with different strategies - - Supports multiple storage strategies including chunk-based, time-based, + + Supports multiple storage strategies including chunk-based, time-based, and aggregate judgment approaches for long-term memory persistence. - + Args: long_term_type: Storage strategy type ('chunk', 'time', 'aggregate') langchain_messages: List of messages to store @@ -56,9 +57,10 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[ end_user_id: User group identifier scope: Scope parameter for chunk-based storage (default: 6) """ - from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment + from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue, \ + aggregate_judgment from app.core.memory.agent.utils.redis_tool import write_store - write_store.save_session_write(end_user_id, (langchain_messages)) + write_store.save_session_write(end_user_id, langchain_messages) # 获取数据库会话 with get_db_context() as db_session: config_service = MemoryConfigService(db_session) @@ -66,25 +68,24 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[ config_id=memory_config, # 改为整数 service_name="MemoryAgentService" ) - if long_term_type==AgentMemory_Long_Term.STRATEGY_CHUNK: + if long_term_type == AgentMemory_Long_Term.STRATEGY_CHUNK: '''Strategy 1: Dialogue window with 6 rounds of conversation''' - await window_dialogue(end_user_id,langchain_messages,memory_config,scope) - if long_term_type==AgentMemory_Long_Term.STRATEGY_TIME: + await window_dialogue(end_user_id, langchain_messages, memory_config, scope) + if long_term_type == AgentMemory_Long_Term.STRATEGY_TIME: """Time-based strategy""" - await memory_long_term_storage(end_user_id, memory_config,AgentMemory_Long_Term.TIME_SCOPE) - if long_term_type==AgentMemory_Long_Term.STRATEGY_AGGREGATE: + await memory_long_term_storage(end_user_id, memory_config, AgentMemory_Long_Term.TIME_SCOPE) + if long_term_type == AgentMemory_Long_Term.STRATEGY_AGGREGATE: """Strategy 3: Aggregate judgment""" await aggregate_judgment(end_user_id, langchain_messages, memory_config) - -async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_rag_memory_id,actual_config_id): +async def write_long_term(storage_type, end_user_id, message_chat, aimessages, user_rag_memory_id, actual_config_id): """ Write long-term memory with different storage types - + Handles both RAG-based storage and traditional memory storage approaches. For traditional storage, uses chunk-based strategy with paired user-AI messages. - + Args: storage_type: Type of storage (RAG or traditional) end_user_id: User group identifier @@ -95,7 +96,7 @@ async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_ """ from app.core.memory.agent.langgraph_graph.routing.write_router import write_rag_agent from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save - from app.core.memory.agent.langgraph_graph.tools.write_tool import agent_chat_messages + from app.core.memory.agent.langgraph_graph.tools.write_tool import agent_chat_messages if storage_type == AgentMemory_Long_Term.STORAGE_RAG: await write_rag_agent(end_user_id, message_chat, aimessages, user_rag_memory_id) else: @@ -128,4 +129,4 @@ async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_ # # if __name__ == "__main__": # import asyncio -# asyncio.run(main()) \ No newline at end of file +# asyncio.run(main()) diff --git a/api/app/core/memory/agent/utils/llm_tools.py b/api/app/core/memory/agent/utils/llm_tools.py index 1c183422..ea8add48 100644 --- a/api/app/core/memory/agent/utils/llm_tools.py +++ b/api/app/core/memory/agent/utils/llm_tools.py @@ -8,10 +8,11 @@ from langgraph.graph import add_messages PROJECT_ROOT_ = str(Path(__file__).resolve().parents[3]) + class WriteState(TypedDict): - ''' + """ Langgrapg Writing TypedDict - ''' + """ messages: Annotated[list[AnyMessage], add_messages] end_user_id: str errors: list[dict] # Track errors: [{"tool": "tool_name", "error": "message"}] @@ -20,6 +21,7 @@ class WriteState(TypedDict): data: str language: str # 语言类型 ("zh" 中文, "en" 英文) + class ReadState(TypedDict): """ LangGraph 工作流状态定义 @@ -43,18 +45,20 @@ class ReadState(TypedDict): config_id: str data: str # 新增字段用于传递内容 spit_data: dict # 新增字段用于传递问题分解结果 - problem_extension:dict + problem_extension: dict storage_type: str user_rag_memory_id: str llm_id: str embedding_id: str memory_config: object # 新增字段用于传递内存配置对象 - retrieve:dict + retrieve: dict RetrieveSummary: dict InputSummary: dict verify: dict SummaryFails: dict summary: dict + + class COUNTState: """ 工作流对话检索内容计数器 @@ -99,6 +103,7 @@ class COUNTState: self.total = 0 print("[COUNTState] 已重置为 0") + def deduplicate_entries(entries): seen = set() deduped = [] @@ -109,6 +114,7 @@ def deduplicate_entries(entries): deduped.append(entry) return deduped + def merge_to_key_value_pairs(data, query_key, result_key): grouped = defaultdict(list) for item in data: @@ -142,4 +148,4 @@ def convert_extended_question_to_question(data): return [convert_extended_question_to_question(item) for item in data] else: # 其他类型直接返回 - return data \ No newline at end of file + return data diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py index 024e320a..147ed777 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py @@ -5,7 +5,7 @@ from typing import List, Dict, Optional from app.core.logging_config import get_memory_logger from app.core.memory.llm_tools.openai_client import OpenAIClient from app.core.memory.utils.prompt.prompt_utils import render_triplet_extraction_prompt -from app.core.memory.utils.data.ontology import PREDICATE_DEFINITIONS, Predicate # 引入枚举 Predicate 白名单过滤 +from app.core.memory.utils.data.ontology import PREDICATE_DEFINITIONS, Predicate # 引入枚举 Predicate 白名单过滤 from app.core.memory.models.triplet_models import TripletExtractionResponse from app.core.memory.models.message_models import DialogData, Statement from app.core.memory.models.ontology_extraction_models import OntologyTypeList @@ -14,15 +14,15 @@ from app.core.memory.utils.log.logging_utils import prompt_logger logger = get_memory_logger(__name__) - class TripletExtractor: """Extracts knowledge triplets and entities from statements using LLM""" def __init__( - self, - llm_client: OpenAIClient, - ontology_types: Optional[OntologyTypeList] = None, - language: str = "zh"): + self, + llm_client: OpenAIClient, + ontology_types: Optional[OntologyTypeList] = None, + language: str = "zh" + ): """Initialize the TripletExtractor with an LLM client Args: @@ -65,7 +65,8 @@ class TripletExtractor: # Create messages for LLM messages = [ - {"role": "system", "content": "You are an expert at extracting knowledge triplets and entities from text. Follow the provided instructions carefully and return valid JSON."}, + {"role": "system", + "content": "You are an expert at extracting knowledge triplets and entities from text. Follow the provided instructions carefully and return valid JSON."}, {"role": "user", "content": prompt_content} ] @@ -116,7 +117,8 @@ class TripletExtractor: logger.error(f"Error processing statement: {e}", exc_info=True) return TripletExtractionResponse(triplets=[], entities=[]) - async def extract_triplets_from_statements(self, dialog_data: DialogData, limit_chunks: int = None) -> Dict[str, TripletExtractionResponse]: + async def extract_triplets_from_statements(self, dialog_data: DialogData, limit_chunks: int = None) -> Dict[ + str, TripletExtractionResponse]: """Extract triplets and entities from statements Args: diff --git a/api/app/core/memory/utils/prompt/template_render.py b/api/app/core/memory/utils/prompt/template_render.py index 68e0ffe4..4df8d55b 100644 --- a/api/app/core/memory/utils/prompt/template_render.py +++ b/api/app/core/memory/utils/prompt/template_render.py @@ -2,15 +2,15 @@ import os from jinja2 import Environment, FileSystemLoader from typing import List, Dict, Any - # Setup Jinja2 environment prompt_dir = os.path.join(os.path.dirname(__file__), "prompts") prompt_env = Environment(loader=FileSystemLoader(prompt_dir)) + async def render_evaluate_prompt(evaluate_data: List[Any], schema: Any, baseline: str = "TIME", - memory_verify: bool = False,quality_assessment:bool = False, - statement_databasets: List[str] = [],language_type:str = "zh") -> str: + memory_verify: bool = False, quality_assessment: bool = False, + statement_databasets=None, language_type: str = "zh") -> str: """ Renders the evaluate prompt using the evaluate_optimized.jinja2 template. @@ -23,6 +23,8 @@ async def render_evaluate_prompt(evaluate_data: List[Any], schema: Any, Returns: Rendered prompt content as string """ + if statement_databasets is None: + statement_databasets = [] template = prompt_env.get_template("evaluate.jinja2") # Convert Pydantic model to JSON schema if needed @@ -46,7 +48,7 @@ async def render_evaluate_prompt(evaluate_data: List[Any], schema: Any, async def render_reflexion_prompt(data: Dict[str, Any], schema: Any, baseline: str, memory_verify: bool = False, - statement_databasets: List[str] = [],language_type:str = "zh") -> str: + statement_databasets=None, language_type: str = "zh") -> str: """ Renders the reflexion prompt using the reflexion_optimized.jinja2 template. @@ -58,6 +60,8 @@ async def render_reflexion_prompt(data: Dict[str, Any], schema: Any, baseline: s Returns: Rendered prompt content as a string. """ + if statement_databasets is None: + statement_databasets = [] template = prompt_env.get_template("reflexion.jinja2") # Convert Pydantic model to JSON schema if needed @@ -69,7 +73,7 @@ async def render_reflexion_prompt(data: Dict[str, Any], schema: Any, baseline: s json_schema = schema rendered_prompt = template.render(data=data, json_schema=json_schema, - baseline=baseline,memory_verify=memory_verify, - statement_databasets=statement_databasets,language_type=language_type) + baseline=baseline, memory_verify=memory_verify, + statement_databasets=statement_databasets, language_type=language_type) return rendered_prompt diff --git a/api/app/core/models/base.py b/api/app/core/models/base.py index dba6717d..4a453c6b 100644 --- a/api/app/core/models/base.py +++ b/api/app/core/models/base.py @@ -1,23 +1,19 @@ from __future__ import annotations -import asyncio import os -import time -from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, List, Optional, TypeVar +from typing import Any, Dict, Optional, TypeVar + +from langchain_aws import ChatBedrock +from langchain_community.chat_models import ChatTongyi +from langchain_core.embeddings import Embeddings +from langchain_core.language_models import BaseLLM +from langchain_ollama import OllamaLLM +from langchain_openai import ChatOpenAI, OpenAI +from pydantic import BaseModel, Field -import httpx from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.models.models_model import ModelProvider, ModelType -from langchain_community.document_compressors import JinaRerank -from langchain_core.callbacks import CallbackManagerForLLMRun -from langchain_core.embeddings import Embeddings -from langchain_core.language_models import BaseLanguageModel, BaseLLM -from langchain_core.outputs import Generation, LLMResult -from langchain_core.retrievers import BaseRetriever -from langchain_core.runnables import RunnableSerializable -from pydantic import BaseModel, Field T = TypeVar("T") @@ -163,25 +159,17 @@ def get_provider_llm_class(config: RedBearModelConfig, type: ModelType = ModelTy # dashscope 的 omni 模型使用 OpenAI 兼容模式 if provider == ModelProvider.DASHSCOPE and config.is_omni: - from langchain_openai import ChatOpenAI return ChatOpenAI - - if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK] : + if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]: if type == ModelType.LLM: - from langchain_openai import OpenAI return OpenAI elif type == ModelType.CHAT: - from langchain_openai import ChatOpenAI return ChatOpenAI elif provider == ModelProvider.DASHSCOPE: - from langchain_community.chat_models import ChatTongyi return ChatTongyi elif provider == ModelProvider.OLLAMA: - from langchain_ollama import OllamaLLM return OllamaLLM elif provider == ModelProvider.BEDROCK: - from langchain_aws import ChatBedrock, ChatBedrockConverse - return ChatBedrock else: raise BusinessException(f"不支持的模型提供商: {provider}", code=BizCode.PROVIDER_NOT_SUPPORTED) diff --git a/api/app/core/workflow/nodes/base_node.py b/api/app/core/workflow/nodes/base_node.py index 39c7887b..0e3fecee 100644 --- a/api/app/core/workflow/nodes/base_node.py +++ b/api/app/core/workflow/nodes/base_node.py @@ -16,6 +16,7 @@ from app.core.workflow.variable.base_variable import VariableType, FileObject from app.db import get_db_read from app.models import ModelConfig, ModelApiKey, LoadBalanceStrategy from app.schemas import FileInput +from app.schemas.model_schema import ModelInfo from app.services.multimodal_service import MultimodalService logger = logging.getLogger(__name__) @@ -620,11 +621,12 @@ class BaseNode(ABC): @staticmethod async def process_message( - provider: str, - is_omni: bool, + api_config: ModelInfo, content: str | dict | FileObject, + end_user_id: str, enable_file=False ) -> list | str | None: + provider = api_config.provider if isinstance(content, dict): content = FileObject( type=content.get("type"), @@ -643,7 +645,7 @@ class BaseNode(ABC): if content.content_cache.get(provider): return content.content_cache[provider] with get_db_read() as db: - multimodel_service = MultimodalService(db, provider, is_omni=is_omni) + multimodel_service = MultimodalService(db, api_config=api_config) file_obj = FileInput( type=content.type, url=content.url, @@ -653,7 +655,8 @@ class BaseNode(ABC): ) file_obj.set_content(content.get_content()) message = await multimodel_service.process_files( - [file_obj] + end_user_id, + [file_obj], ) content.set_content(file_obj.get_content()) if message: diff --git a/api/app/core/workflow/nodes/if_else/node.py b/api/app/core/workflow/nodes/if_else/node.py index 29f7085b..7e98efab 100644 --- a/api/app/core/workflow/nodes/if_else/node.py +++ b/api/app/core/workflow/nodes/if_else/node.py @@ -5,7 +5,7 @@ from typing import Any from app.core.workflow.engine.state_manager import WorkflowState from app.core.workflow.engine.variable_pool import VariablePool from app.core.workflow.nodes.base_node import BaseNode -from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator +from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator, ValueInputType from app.core.workflow.nodes.if_else import IfElseNodeConfig from app.core.workflow.nodes.operators import ConditionExpressionResolver, CompareOperatorInstance from app.core.workflow.variable.base_variable import VariableType @@ -23,6 +23,26 @@ class IfElseNode(BaseNode): "output": VariableType.STRING } + def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]: + result = [] + for case in self.typed_config.cases: + expressions = [] + for expression in case.expressions: + expressions.append({ + "left": self.get_variable(expression.left, variable_pool, strict=False), + "right": expression.right + if expression.input_type == ValueInputType.CONSTANT + else self.get_variable(expression.right, variable_pool, strict=False), + "operator": expression.operator, + }) + result.append({ + "expressions": expressions, + "logical_operator": case.logical_operator, + }) + return { + "cases": result + } + @staticmethod def _evaluate(operator, instance: CompareOperatorInstance) -> Any: match operator: diff --git a/api/app/core/workflow/nodes/knowledge/node.py b/api/app/core/workflow/nodes/knowledge/node.py index 696298eb..14f789a9 100644 --- a/api/app/core/workflow/nodes/knowledge/node.py +++ b/api/app/core/workflow/nodes/knowledge/node.py @@ -30,6 +30,12 @@ class KnowledgeRetrievalNode(BaseNode): "output": VariableType.ARRAY_STRING } + def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]: + return { + "query": self._render_template(self.typed_config.query, variable_pool), + "knowledge_bases": [kb_config.model_dump(mode="json") for kb_config in self.typed_config.knowledge_bases], + } + @staticmethod def _build_kb_filter(kb_ids: list[uuid.UUID], permission: knowledge_model.PermissionType): """ diff --git a/api/app/core/workflow/nodes/llm/node.py b/api/app/core/workflow/nodes/llm/node.py index 186c204f..b293d1f4 100644 --- a/api/app/core/workflow/nodes/llm/node.py +++ b/api/app/core/workflow/nodes/llm/node.py @@ -20,6 +20,7 @@ from app.core.workflow.nodes.llm.config import LLMNodeConfig from app.core.workflow.variable.base_variable import VariableType from app.db import get_db_context from app.models import ModelType +from app.schemas.model_schema import ModelInfo from app.services.model_service import ModelConfigService logger = logging.getLogger(__name__) @@ -113,12 +114,15 @@ class LLMNode(BaseNode): # 在 Session 关闭前提取所有需要的数据 api_config = self.model_balance(config) - model_name = api_config.model_name - provider = api_config.provider - api_key = api_config.api_key - api_base = api_config.api_base - is_omni = api_config.is_omni - model_type = config.type + model_info = ModelInfo( + model_name=api_config.model_name, + model_type=ModelType(config.type), + api_key=api_config.api_key, + api_base=api_config.api_base, + provider=api_config.provider, + is_omni=api_config.is_omni, + capability=api_config.capability + ) # 4. 创建 LLM 实例（使用已提取的数据） # 注意：对于流式输出，需要在模型初始化时设置 streaming=True @@ -126,17 +130,18 @@ class LLMNode(BaseNode): llm = RedBearLLM( RedBearModelConfig( - model_name=model_name, - provider=provider, - api_key=api_key, - base_url=api_base, + model_name=model_info.model_name, + provider=model_info.provider, + api_key=model_info.api_key, + base_url=model_info.api_base, extra_params=extra_params, - is_omni=is_omni + is_omni=model_info.is_omni ), - type=ModelType(model_type) + type=model_info.model_type ) - logger.debug(f"创建 LLM 实例: provider={provider}, model={model_name}, streaming={stream}") + logger.debug( + f"创建 LLM 实例: provider={model_info.provider}, model={model_info.model_name}, streaming={stream}") messages_config = self.typed_config.messages @@ -148,35 +153,40 @@ class LLMNode(BaseNode): content_template = msg_config.content content_template = self._render_context(content_template, variable_pool) content = self._render_template(content_template, variable_pool) - + user_id = self.get_variable("sys.user_id", variable_pool) # 根据角色创建对应的消息对象 if role == "system": messages.append({ "role": "system", - "content": await self.process_message(provider, is_omni, content, self.typed_config.vision) + "content": await self.process_message( + model_info, + content, + user_id, + self.typed_config.vision, + ) }) elif role in ["user", "human"]: messages.append({ "role": "user", - "content": await self.process_message(provider, is_omni, content, self.typed_config.vision) + "content": await self.process_message(model_info, content, user_id, self.typed_config.vision) }) elif role in ["ai", "assistant"]: messages.append({ "role": "assistant", - "content": await self.process_message(provider, is_omni, content, self.typed_config.vision) + "content": await self.process_message(model_info, content, user_id, self.typed_config.vision) }) else: logger.warning(f"未知的消息角色: {role}，默认使用 user") messages.append({ "role": "user", - "content": await self.process_message(provider, is_omni, content, self.typed_config.vision) + "content": await self.process_message(model_info, content, user_id, self.typed_config.vision) }) if self.typed_config.vision_input and self.typed_config.vision: file_content = [] files = variable_pool.get_instance(self.typed_config.vision_input) for file in files.value: - content = await self.process_message(provider, is_omni, file.value, self.typed_config.vision) + content = await self.process_message(model_info, file.value, user_id, self.typed_config.vision) if content: file_content.extend(content) if messages and messages[-1]["role"] == 'user': @@ -190,14 +200,19 @@ class LLMNode(BaseNode): if isinstance(message["content"], list): file_content = [] for file in message["content"]: - content = await self.process_message(provider, is_omni, file, self.typed_config.vision) + content = await self.process_message(model_info, file, user_id, self.typed_config.vision) if content: file_content.extend(content) history_message.append( {"role": message["role"], "content": file_content} ) else: - message["content"] = await self.process_message(provider, is_omni, message["content"], self.typed_config.vision) + message["content"] = await self.process_message( + model_info, + message["content"], + user_id, + self.typed_config.vision + ) history_message.append(message) messages = messages[:-1] + history_message + messages[-1:] self.messages = messages @@ -293,7 +308,7 @@ class LLMNode(BaseNode): # 调用 LLM（流式，支持字符串或消息列表） last_meta_data = {} - async for chunk in llm.astream(self.messages, stream_usage=True): + async for chunk in llm.astream(self.messages): # 提取内容 if hasattr(chunk, 'content'): content = self.process_model_output(chunk.content) diff --git a/api/app/core/workflow/nodes/parameter_extractor/node.py b/api/app/core/workflow/nodes/parameter_extractor/node.py index 700ed85f..acac09e4 100644 --- a/api/app/core/workflow/nodes/parameter_extractor/node.py +++ b/api/app/core/workflow/nodes/parameter_extractor/node.py @@ -37,6 +37,14 @@ class ParameterExtractorNode(BaseNode): } return None + def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]: + return { + "text": self._render_template(self.typed_config.text, variable_pool), + "prompt": self._render_template(self.typed_config.prompt, variable_pool), + "params": [param.model_dump(mode="json") for param in self.typed_config.params], + "model_id": str(self.typed_config.model_id), + } + def _output_types(self) -> dict[str, VariableType]: outputs = {} for param in self.typed_config.params: diff --git a/api/app/models/memory_perceptual_model.py b/api/app/models/memory_perceptual_model.py index cafb18d4..9fed7c5d 100644 --- a/api/app/models/memory_perceptual_model.py +++ b/api/app/models/memory_perceptual_model.py @@ -7,6 +7,7 @@ from sqlalchemy.dialects.postgresql import UUID from sqlalchemy.dialects.postgresql import JSONB from app.db import Base +from app.schemas import FileType class PerceptualType(IntEnum): @@ -15,6 +16,16 @@ class PerceptualType(IntEnum): TEXT = 3 CONVERSATION = 4 + @staticmethod + def trans_from_file_type(file_type: FileType | str): + type_map = { + FileType.IMAGE: PerceptualType.VISION, + FileType.AUDIO: PerceptualType.AUDIO, + FileType.VIDEO: PerceptualType.VISION, + FileType.DOCUMENT: PerceptualType.TEXT + } + return type_map.get(file_type, PerceptualType.TEXT) + class FileStorageService(IntEnum): LOCAL = 1 diff --git a/api/app/repositories/memory_perceptual_repository.py b/api/app/repositories/memory_perceptual_repository.py index 9fa9536e..9077af03 100644 --- a/api/app/repositories/memory_perceptual_repository.py +++ b/api/app/repositories/memory_perceptual_repository.py @@ -2,7 +2,7 @@ import uuid from datetime import datetime from typing import List, Tuple, Optional -from sqlalchemy import and_, desc +from sqlalchemy import and_, desc, select from sqlalchemy.orm import Session from app.core.logging_config import get_db_logger @@ -127,6 +127,17 @@ class MemoryPerceptualRepository: db_logger.error(f"Failed to query perceptual memory timeline: end_user_id={end_user_id} - {str(e)}") raise + def get_by_url( + self, + file_url: str + ) -> list[MemoryPerceptualModel]: + try: + stmt = select(MemoryPerceptualModel).where(MemoryPerceptualModel.file_path == file_url) + return list(self.db.execute(stmt).scalars()) + except Exception: + db_logger.error(f"Failed to query perceptual memories by file_url: file_url={file_url}") + raise + def get_by_type( self, end_user_id: uuid.UUID, diff --git a/api/app/schemas/memory_perceptual_schema.py b/api/app/schemas/memory_perceptual_schema.py index 7dfefe01..c9b741ef 100644 --- a/api/app/schemas/memory_perceptual_schema.py +++ b/api/app/schemas/memory_perceptual_schema.py @@ -1,5 +1,4 @@ import uuid -from datetime import datetime from typing import Optional from pydantic import BaseModel, Field @@ -85,7 +84,6 @@ class Semantic(BaseModel): class Content(BaseModel): - summary: str keywords: list[str] topic: str domain: str diff --git a/api/app/schemas/model_schema.py b/api/app/schemas/model_schema.py index 4f3878ce..058f082d 100644 --- a/api/app/schemas/model_schema.py +++ b/api/app/schemas/model_schema.py @@ -326,3 +326,14 @@ class ModelBaseQuery(BaseModel): is_official: Optional[bool] = Field(None, description="是否官方模型") is_deprecated: Optional[bool] = Field(None, description="是否弃用") search: Optional[str] = Field(None, description="搜索关键词", max_length=255) + +class ModelInfo(BaseModel): + """模型信息Schema""" + model_name: str = Field(..., description="模型名称") + provider: str = Field(..., description="模型提供商") + api_key: str = Field(..., description="API密钥") + api_base: str = Field(..., description="API基础URL") + is_omni: bool = Field(default=False, description="是否为omni模型") + model_type: ModelType = Field(..., description="模型类型") + capability: List[str] = Field(default_factory=list, description="模型能力列表") + diff --git a/api/app/services/app_chat_service.py b/api/app/services/app_chat_service.py index f3cdde2a..9b2b2a77 100644 --- a/api/app/services/app_chat_service.py +++ b/api/app/services/app_chat_service.py @@ -8,25 +8,21 @@ from typing import Optional, Dict, Any, AsyncGenerator, Annotated, List from fastapi import Depends from sqlalchemy.orm import Session -from app.core.agent.agent_middleware import AgentMiddleware from app.core.agent.langchain_agent import LangChainAgent -from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger from app.db import get_db -from app.models import MultiAgentConfig, AgentConfig +from app.models import MultiAgentConfig, AgentConfig, ModelType from app.models import WorkflowConfig from app.repositories.tool_repository import ToolRepository from app.schemas import DraftRunRequest from app.schemas.app_schema import FileInput +from app.schemas.model_schema import ModelInfo from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole from app.services.conversation_service import ConversationService -from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool, \ - AgentRunService -from app.services.draft_run_service import create_web_search_tool +from app.services.draft_run_service import AgentRunService from app.services.model_service import ModelApiKeyService from app.services.multi_agent_orchestrator import MultiAgentOrchestrator from app.services.multimodal_service import MultimodalService -from app.services.tool_service import ToolService from app.services.workflow_service import WorkflowService logger = get_business_logger() @@ -126,8 +122,17 @@ class AppChatService: # 处理多模态文件 processed_files = None if files: - multimodal_service = MultimodalService(self.db, api_key_obj.provider, is_omni=api_key_obj.is_omni) - processed_files = await multimodal_service.process_files(files) + model_info = ModelInfo( + model_name=api_key_obj.model_name, + provider=api_key_obj.provider, + api_key=api_key_obj.api_key, + api_base=api_key_obj.api_base, + capability=api_key_obj.capability, + is_omni=api_key_obj.is_omni, + model_type=ModelType.LLM + ) + multimodal_service = MultimodalService(self.db, model_info) + processed_files = await multimodal_service.process_files(user_id, files) logger.info(f"处理了 {len(processed_files)} 个文件") # 调用 Agent（支持多模态） @@ -266,8 +271,17 @@ class AppChatService: # 处理多模态文件 processed_files = None if files: - multimodal_service = MultimodalService(self.db, api_key_obj.provider, is_omni=api_key_obj.is_omni) - processed_files = await multimodal_service.process_files(files) + model_info = ModelInfo( + model_name=api_key_obj.model_name, + provider=api_key_obj.provider, + api_key=api_key_obj.api_key, + api_base=api_key_obj.api_base, + capability=api_key_obj.capability, + is_omni=api_key_obj.is_omni, + model_type=ModelType.LLM + ) + multimodal_service = MultimodalService(self.db, model_info) + processed_files = await multimodal_service.process_files(user_id, files) logger.info(f"处理了 {len(processed_files)} 个文件") # 流式调用 Agent（支持多模态） diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index d7914db5..b3b136a1 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -23,9 +23,10 @@ from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger from app.core.rag.nlp.search import knowledge_retrieval from app.db import get_db_context -from app.models import AgentConfig, ModelConfig +from app.models import AgentConfig, ModelConfig, ModelType from app.repositories.tool_repository import ToolRepository from app.schemas.app_schema import FileInput +from app.schemas.model_schema import ModelInfo from app.schemas.prompt_schema import PromptMessageRole, render_prompt_message from app.services import task_service from app.services.conversation_service import ConversationService @@ -501,9 +502,18 @@ class AgentRunService: processed_files = None if files: # 获取 provider 信息 + model_info = ModelInfo( + model_name=api_key_config["model_name"], + provider=api_key_config["provider"], + api_key=api_key_config["api_key"], + api_base=api_key_config["api_base"], + capability=api_key_config["capability"], + is_omni=api_key_config["is_omni"], + model_type=ModelType.LLM + ) provider = api_key_config.get("provider", "openai") - multimodal_service = MultimodalService(self.db, provider=provider, is_omni=api_key_config.get("is_omni", False)) - processed_files = await multimodal_service.process_files(files) + multimodal_service = MultimodalService(self.db, model_info) + processed_files = await multimodal_service.process_files(user_id, files) logger.info(f"处理了 {len(processed_files)} 个文件，provider={provider}") # 7. 知识库检索 @@ -704,9 +714,18 @@ class AgentRunService: processed_files = None if files: # 获取 provider 信息 + model_info = ModelInfo( + model_name=api_key_config["model_name"], + provider=api_key_config["provider"], + api_key=api_key_config["api_key"], + api_base=api_key_config["api_base"], + capability=api_key_config["capability"], + is_omni=api_key_config["is_omni"], + model_type=ModelType.LLM + ) provider = api_key_config.get("provider", "openai") - multimodal_service = MultimodalService(self.db, provider=provider, is_omni=api_key_config.get("is_omni", False)) - processed_files = await multimodal_service.process_files(files) + multimodal_service = MultimodalService(self.db, model_info) + processed_files = await multimodal_service.process_files(user_id, files) logger.info(f"处理了 {len(processed_files)} 个文件，provider={provider}") # 7. 知识库检索 @@ -841,7 +860,8 @@ class AgentRunService: "api_key": api_key.api_key, "api_base": api_key.api_base, "api_key_id": api_key.id, - "is_omni": api_key.is_omni + "is_omni": api_key.is_omni, + "capability": api_key.capability } async def _ensure_conversation( diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index a20b968a..1e1d9e45 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -274,7 +274,7 @@ class MemoryAgentService: Args: end_user_id: Group identifier (also used as end_user_id) - message: Message to write + messages: Message to write config_id: Configuration ID from database db: SQLAlchemy database session storage_type: Storage type (neo4j or rag) diff --git a/api/app/services/memory_perceptual_service.py b/api/app/services/memory_perceptual_service.py index b9d96a0b..53d935fe 100644 --- a/api/app/services/memory_perceptual_service.py +++ b/api/app/services/memory_perceptual_service.py @@ -1,19 +1,27 @@ +import os import uuid from typing import Dict, Any, Optional +from urllib.parse import urlparse, unquote +import json_repair +from jinja2 import Template from sqlalchemy.orm import Session from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger +from app.core.models import RedBearLLM, RedBearModelConfig from app.models.memory_perceptual_model import PerceptualType, FileStorageService +from app.models.prompt_optimizer_model import RoleType from app.repositories.memory_perceptual_repository import MemoryPerceptualRepository +from app.schemas import FileType from app.schemas.memory_perceptual_schema import ( PerceptualQuerySchema, PerceptualTimelineResponse, PerceptualMemoryItem, AudioModal, Content, VideoModal, TextModal ) +from app.schemas.model_schema import ModelInfo business_logger = get_business_logger() @@ -99,7 +107,7 @@ class MemoryPerceptualService: "keywords": content.keywords, "topic": content.topic, "domain": content.domain, - "created_time": int(memory.created_time.timestamp()*1000), + "created_time": int(memory.created_time.timestamp() * 1000), **detail } @@ -108,7 +116,8 @@ class MemoryPerceptualService: return result except Exception as e: - business_logger.error(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}") + business_logger.error(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}", + exc_info=True) raise BusinessException(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}", BizCode.DB_ERROR) @@ -138,7 +147,7 @@ class MemoryPerceptualService: for memory in memories: meta_data = memory.meta_data or {} content = meta_data.get("content", {}) - + # 安全地提取 content 字段，提供默认值 if content: content_obj = Content(**content) @@ -149,7 +158,7 @@ class MemoryPerceptualService: topic = "Unknown" domain = "Unknown" keywords = [] - + memory_item = PerceptualMemoryItem( id=memory.id, perceptual_type=PerceptualType(memory.perceptual_type), @@ -161,7 +170,7 @@ class MemoryPerceptualService: topic=topic, domain=domain, keywords=keywords, - created_time=int(memory.created_time.timestamp()*1000), + created_time=int(memory.created_time.timestamp() * 1000), storage_service=FileStorageService(memory.storage_service), ) memory_items.append(memory_item) @@ -183,3 +192,98 @@ class MemoryPerceptualService: except Exception as e: business_logger.error(f"Failed to fetch perceptual memory timeline: {str(e)}") raise BusinessException(f"Failed to fetch perceptual memory timeline: {str(e)}", BizCode.DB_ERROR) + + async def generate_perceptual_memory( + self, + end_user_id: str, + model_config: ModelInfo, + file_type: str, + file_url: str, + file_message: dict, + ): + memories = self.repository.get_by_url(file_url) + if memories: + business_logger.info(f"Perceptual memory already exists: {file_url}") + if end_user_id not in [memory.end_user_id for memory in memories]: + business_logger.info(f"Copy perceptual memory end_user_id: {end_user_id}") + memory_cache = memories[0] + self.repository.create_perceptual_memory( + end_user_id=uuid.UUID(end_user_id), + perceptual_type=PerceptualType(memory_cache.perceptual_type), + file_path=memory_cache.file_path, + file_name=memory_cache.file_name, + file_ext=memory_cache.file_ext, + summary=memory_cache.summary, + meta_data=memory_cache.meta_data + ) + self.db.commit() + + return + llm = RedBearLLM(RedBearModelConfig( + model_name=model_config.model_name, + provider=model_config.provider, + api_key=model_config.api_key, + base_url=model_config.api_base, + is_omni=model_config.is_omni + ), type=model_config.model_type) + try: + prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt') + with open(os.path.join(prompt_path, 'perceptual_summary_system.jinja2'), 'r', encoding='utf-8') as f: + opt_system_prompt = f.read() + rendered_system_message = Template(opt_system_prompt).render(file_type=file_type, language='zh') + except FileNotFoundError: + raise BusinessException(message="System prompt template not found", code=BizCode.NOT_FOUND) + messages = [ + {"role": RoleType.SYSTEM.value, "content": [{"type": "text", "text": rendered_system_message}]}, + {"role": RoleType.USER.value, "content": [ + {"type": "text", "text": "Summarize the following file"}, file_message + ]} + ] + result = await llm.ainvoke(messages) + content = json_repair.repair_json(result.content, return_objects=True) + path = urlparse(file_url).path + filename = os.path.basename(path) + filename = unquote(filename) + file_ext = os.path.splitext(filename)[1] + if not file_ext: + if file_type == FileType.AUDIO: + file_ext = ".mp3" + elif file_type == FileType.VIDEO: + file_ext = ".mp4" + elif file_type == FileType.DOCUMENT: + file_ext = ".txt" + elif file_type == FileType.IMAGE: + file_ext = ".jpg" + filename += file_ext + file_content = { + "keywords": content.get("keywords", []), + "topic": content.get("topic"), + "domain": content.get("domain") + } + if file_type in [FileType.IMAGE, FileType.VIDEO]: + file_modalities = { + "scene": content.get("scene") + } + elif file_type in [FileType.DOCUMENT]: + file_modalities = { + "section_count": content.get("section_count"), + "title": content.get("title"), + "first_line": content.get("first_line") + } + else: + file_modalities = { + "speaker_count": content.get("speaker_count") + } + self.repository.create_perceptual_memory( + end_user_id=uuid.UUID(end_user_id), + perceptual_type=PerceptualType.trans_from_file_type(file_type), + file_path=file_url, + file_name=filename, + file_ext=file_ext, + summary=content.get('summary'), + meta_data={ + "content": file_content, + "modalities": file_modalities + } + ) + self.db.commit() diff --git a/api/app/services/multimodal_service.py b/api/app/services/multimodal_service.py index fffca2e5..935efafe 100644 --- a/api/app/services/multimodal_service.py +++ b/api/app/services/multimodal_service.py @@ -10,6 +10,7 @@ """ import base64 import io +import uuid from abc import ABC, abstractmethod from typing import List, Dict, Any, Optional @@ -23,9 +24,12 @@ from app.core.config import settings from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger +from app.models import ModelApiKey from app.models.file_metadata_model import FileMetadata from app.schemas.app_schema import FileInput, FileType, TransferMethod +from app.schemas.model_schema import ModelInfo from app.services.audio_transcription_service import AudioTranscriptionService +from app.tasks import write_perceptual_memory logger = get_business_logger() @@ -39,6 +43,7 @@ DOC_MIME = [ class MultimodalFormatStrategy(ABC): """多模态格式策略基类""" + def __init__(self, file: FileInput): self.file = file @@ -95,7 +100,7 @@ class DashScopeFormatStrategy(MultimodalFormatStrategy): if transcription: return { "type": "text", - "text": f"" + "text": f"" } # 通义千问音频格式：{"type": "audio", "audio": "url"} return { @@ -284,34 +289,56 @@ PROVIDER_STRATEGIES = { class MultimodalService: - """多模态文件处理服务""" + """ + Service for handling multimodal file processing. - def __init__(self, db: Session, provider: str = "dashscope", api_key: Optional[str] = None, - enable_audio_transcription: bool = False, is_omni: bool = False): + Attributes: + db (Session): Database session. + model_api_key (str): API key for the model provider. + provider (str): Name of the model provider. + is_omni (bool): Indicates whether the model supports full multimodal capability. + capability (list): Capability configuration of the model. + audio_api_key (str | None): API key used for audio transcription. + enable_audio_transcription (bool): Whether audio transcription is enabled. + """ + + def __init__( + self, + db: Session, + api_config: ModelInfo | None = None, + audio_api_key: Optional[str] = None, + enable_audio_transcription: bool = False, + ): """ - 初始化多模态服务 - + Initialize the multimodal service. + Args: - db: 数据库会话 - provider: 模型提供商（dashscope, bedrock, anthropic, openai 等） - api_key: API 密钥（用于音频转文本） - enable_audio_transcription: 是否启用音频转文本 - is_omni: 是否为 Omni 模型（dashscope 的 omni 模型需要使用 OpenAI 兼容格式） + db (Session): Database session. + api_config (ModelApiKey | None): Model API configuration. + audio_api_key (str | None): API key for audio transcription. + enable_audio_transcription (bool): Enable audio transcription. """ self.db = db - self.provider = provider.lower() - self.api_key = api_key + self.api_config = api_config + if self.api_config is not None: + self.model_api_key = api_config.api_key + self.provider = api_config.provider.lower() + self.is_omni = api_config.is_omni + self.capability = api_config.capability + self.audio_api_key = audio_api_key self.enable_audio_transcription = enable_audio_transcription - self.is_omni = is_omni async def process_files( self, - files: Optional[List[FileInput]] + end_user_id: uuid.UUID | str, + files: Optional[List[FileInput]], + ) -> List[Dict[str, Any]]: """ 处理文件列表，返回 LLM 可用的格式 Args: + end_user_id: 用户ID files: 文件输入列表 Returns: @@ -319,6 +346,8 @@ class MultimodalService: """ if not files: return [] + if isinstance(end_user_id, uuid.UUID): + end_user_id = str(end_user_id) # 获取对应的策略 # dashscope 的 omni 模型使用 OpenAI 兼容格式 @@ -333,19 +362,25 @@ class MultimodalService: result = [] for idx, file in enumerate(files): strategy = strategy_class(file) + if not file.url: + file.url = await self.get_file_url(file) try: - if file.type == FileType.IMAGE: + if file.type == FileType.IMAGE and "vision" in self.capability: content = await self._process_image(file, strategy) result.append(content) + self.write_perceptual_memory(end_user_id, file.type, file.url, content) elif file.type == FileType.DOCUMENT: content = await self._process_document(file, strategy) result.append(content) - elif file.type == FileType.AUDIO: + self.write_perceptual_memory(end_user_id, file.type, file.url, content) + elif file.type == FileType.AUDIO and "audio" in self.capability: content = await self._process_audio(file, strategy) result.append(content) - elif file.type == FileType.VIDEO: + self.write_perceptual_memory(end_user_id, file.type, file.url, content) + elif file.type == FileType.VIDEO and "video" in self.capability: content = await self._process_video(file, strategy) result.append(content) + self.write_perceptual_memory(end_user_id, file.type, file.url, content) else: logger.warning(f"不支持的文件类型: {file.type}") except Exception as e: @@ -355,7 +390,8 @@ class MultimodalService: "file_index": idx, "file_type": file.type, "error": str(e) - } + }, + exc_info=True ) # 继续处理其他文件，不中断整个流程 result.append({ @@ -366,6 +402,17 @@ class MultimodalService: logger.info(f"成功处理 {len(result)}/{len(files)} 个文件，provider={self.provider}") return result + def write_perceptual_memory( + self, + end_user_id: str, + file_type: str, + file_url: str, + file_message: dict + ): + """写入感知记忆""" + if end_user_id and self.api_config: + write_perceptual_memory.delay(end_user_id, self.api_config.model_dump(), file_type, file_url, file_message) + async def _process_image(self, file: FileInput, strategy) -> Dict[str, Any]: """ 处理图片文件 @@ -387,43 +434,6 @@ class MultimodalService: "text": f"[图片处理失败: {str(e)}]" } - @staticmethod - async def _download_and_encode_image(url: str) -> tuple[str, str]: - """ - 下载图片并转换为 base64 - - Args: - url: 图片 URL - - Returns: - tuple: (base64_data, media_type) - """ - from mimetypes import guess_type - - # 下载图片 - async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.get(url) - response.raise_for_status() - - # 获取图片数据 - image_data = response.content - - # 确定 media type - content_type = response.headers.get("content-type") - if content_type and content_type.startswith("image/"): - media_type = content_type - else: - # 从 URL 推断 - guessed_type, _ = guess_type(url) - media_type = guessed_type if guessed_type and guessed_type.startswith("image/") else "image/jpeg" - - # 转换为 base64 - base64_data = base64.b64encode(image_data).decode("utf-8") - - logger.debug(f"图片编码完成: media_type={media_type}, size={len(base64_data)}") - - return base64_data, media_type - async def _process_document(self, file: FileInput, strategy) -> Dict[str, Any]: """ 处理文档文件（PDF、Word 等） @@ -436,7 +446,6 @@ class MultimodalService: Dict: 根据 provider 返回不同格式的文档内容 """ if file.transfer_method == TransferMethod.REMOTE_URL: - # 远程文档暂不支持提取 return { "type": "text", "text": f"\n{await self._extract_document_text(file)}\n" @@ -471,12 +480,12 @@ class MultimodalService: # 如果启用音频转文本且有 API Key transcription = None - if self.enable_audio_transcription and self.api_key: + if self.enable_audio_transcription and self.audio_api_key: logger.info(f"开始音频转文本: {url}") if self.provider == "dashscope": - transcription = await AudioTranscriptionService.transcribe_dashscope(url, self.api_key) + transcription = await AudioTranscriptionService.transcribe_dashscope(url, self.audio_api_key) elif self.provider == "openai": - transcription = await AudioTranscriptionService.transcribe_openai(url, self.api_key) + transcription = await AudioTranscriptionService.transcribe_openai(url, self.audio_api_key) else: logger.warning(f"Provider {self.provider} 不支持音频转文本") diff --git a/api/app/services/prompt/perceptual_summary_system.jinja2 b/api/app/services/prompt/perceptual_summary_system.jinja2 new file mode 100644 index 00000000..ee5d3eb5 --- /dev/null +++ b/api/app/services/prompt/perceptual_summary_system.jinja2 @@ -0,0 +1,53 @@ +{% raw %}You are a professional information extraction system. + +Your task is to analyze the provided document content and generate structured metadata. + +Extract the following fields: + +* **summary**: A concise summary of the document in 2–4 sentences. +* **keywords**: 5–10 important keywords or key phrases that best represent the document. This field MUST be a JSON array of strings. +* **topic**: The primary topic of the document expressed as a short phrase (3–8 words). +* **domain**: The broader knowledge domain or field the document belongs to (e.g., Artificial Intelligence, Computer Science, Finance, Healthcare, Education, Law, etc.). + +STRICT RULES: + +1. Output MUST be valid JSON. +2. Do NOT output markdown. +3. Do NOT output explanations. +4. Do NOT output any text before or after the JSON. +5. The JSON MUST contain EXACTLY these four keys: + * summary + * keywords + * topic + * domain{% endraw %} +{% if file_type == 'image' or file_type == 'video' %} * scene {% endif %} +{% if file_type == 'audio' %} * speaker_count {% endif %} +{% if file_type == 'document' %} * section_count + * title + * first_line +{% endif %} +{% raw %} +6. `keywords` MUST be a JSON array of strings. +7. If the document content is insufficient, infer the best possible answer based on context. +8. Ensure the JSON is syntactically correct. +{% endraw %} +9. Output using the language {{ language }} +{% raw %} +Required JSON format: + +{ +"summary": "string", +"keywords": ["keyword1", "keyword2", "keyword3", "keyword4", "keyword5"], +"topic": "string", +"domain": "string", +{% endraw %} +{% if file_type == 'image' or file_type == 'video' %} "scene": ["string", "string"] {% endif %} +{% if file_type == 'document' %} "section_count": integer +"title": "string", +"first_line": "string" +{% endif %} +{% if file_type == 'audio' %} "speaker_count": integer {% endif %} +{% raw %} +} + +Now analyze the following document and return the JSON result.{% endraw %} diff --git a/api/app/tasks.py b/api/app/tasks.py index 6fd9c954..5e1550bd 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -1,6 +1,5 @@ import asyncio -import json -import logging +import hashlib import os import re import shutil @@ -11,20 +10,48 @@ from datetime import datetime, timezone from math import ceil from pathlib import Path from typing import Any, Dict, List, Optional -from uuid import UUID import redis -import requests from redis.exceptions import RedisError -logger = logging.getLogger(__name__) +# Import a unified Celery instance +from app.celery_app import celery_app +from app.core.config import settings +from app.core.logging_config import get_logger +from app.core.rag.crawler.web_crawler import WebCrawler +from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb +from app.core.rag.graphrag.utils import get_llm_cache, set_llm_cache +from app.core.rag.integrations.feishu.client import FeishuAPIClient +from app.core.rag.integrations.feishu.models import FileInfo +from app.core.rag.integrations.yuque.client import YuqueAPIClient +from app.core.rag.integrations.yuque.models import YuqueDocInfo +from app.core.rag.llm.chat_model import Base +from app.core.rag.llm.cv_model import QWenCV +from app.core.rag.llm.embedding_model import OpenAIEmbed +from app.core.rag.llm.sequence2txt_model import QWenSeq2txt +from app.core.rag.models.chunk import DocumentChunk +from app.core.rag.prompts.generator import question_proposal +from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ( + ElasticSearchVectorFactory, +) +from app.db import get_db, get_db_context +from app.models import Document, File, Knowledge +from app.schemas import document_schema, file_schema +from app.schemas.model_schema import ModelInfo +from app.services.memory_agent_service import MemoryAgentService +from app.services.memory_perceptual_service import MemoryPerceptualService +from app.utils.config_utils import resolve_config_id +from app.utils.redis_lock import RedisLock + +logger = get_logger(__name__) # 模块级同步 Redis 连接池，供 Celery 任务共享使用 # 连接 CELERY_BACKEND DB，与 write_message:last_done 时间戳写入保持一致 # 使用连接池而非单例客户端，提供更好的并发性能和自动重连 -_sync_redis_pool: redis.ConnectionPool = None +_sync_redis_pool: redis.ConnectionPool | None = None -def _get_or_create_redis_pool() -> redis.ConnectionPool: + +def _get_or_create_redis_pool() -> redis.ConnectionPool | None: """获取或创建 Redis 连接池（懒初始化）""" global _sync_redis_pool if _sync_redis_pool is None: @@ -47,6 +74,7 @@ def _get_or_create_redis_pool() -> redis.ConnectionPool: return None return _sync_redis_pool + def get_sync_redis_client() -> Optional[redis.StrictRedis]: """获取同步 Redis 客户端（使用连接池） @@ -60,7 +88,7 @@ def get_sync_redis_client() -> Optional[redis.StrictRedis]: pool = _get_or_create_redis_pool() if pool is None: return None - + client = redis.StrictRedis(connection_pool=pool) # 验证连接可用性 client.ping() @@ -72,32 +100,18 @@ def get_sync_redis_client() -> Optional[redis.StrictRedis]: logger.error(f"Unexpected error getting Redis client: {e}", exc_info=True) return None -# Import a unified Celery instance -from app.celery_app import celery_app -from app.core.config import settings -from app.core.rag.crawler.web_crawler import WebCrawler -from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb -from app.core.rag.graphrag.utils import get_llm_cache, set_llm_cache -from app.core.rag.integrations.feishu.client import FeishuAPIClient -from app.core.rag.integrations.feishu.models import FileInfo -from app.core.rag.integrations.yuque.client import YuqueAPIClient -from app.core.rag.integrations.yuque.models import YuqueDocInfo -from app.core.rag.llm.chat_model import Base -from app.core.rag.llm.cv_model import QWenCV -from app.core.rag.llm.embedding_model import OpenAIEmbed -from app.core.rag.llm.sequence2txt_model import QWenSeq2txt -from app.core.rag.models.chunk import DocumentChunk -from app.core.rag.prompts.generator import question_proposal -from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ( - ElasticSearchVectorFactory, -) -from app.db import get_db, get_db_context -from app.models.document_model import Document -from app.models.file_model import File -from app.models.knowledge_model import Knowledge -from app.schemas import document_schema, file_schema -from app.services.memory_agent_service import MemoryAgentService -from app.utils.config_utils import resolve_config_id + +def set_asyncio_event_loop(): + """Set the asyncio event loop for the current thread.""" + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + return loop @celery_app.task(name="tasks.process_item") @@ -294,9 +308,18 @@ def parse_document(file_path: str, document_id: uuid.UUID): vector_size = len(vts[0]) init_graphrag(task, vector_size) - async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service, - chat_model, embedding_model, callback, with_resolution: bool = True, - with_community: bool = True, ) -> dict: + async def _run( + row: dict, + document_ids: list[str], + language: str, + parser_config: dict, + vector_service, + chat_model, + embedding_model, + callback, + with_resolution: bool = True, + with_community: bool = True + ) -> dict: await trio.sleep(5) # Delay for 10 seconds nonlocal progress_msg # Declare the use of an external progress_msg variable result = await run_graphrag_for_kb( @@ -329,6 +352,7 @@ def parse_document(file_path: str, document_id: uuid.UUID): with_community=with_community, ) ) + try: with ThreadPoolExecutor(max_workers=1) as executor: future = executor.submit(sync_task) @@ -448,6 +472,7 @@ def build_graphrag_for_kb(kb_id: uuid.UUID): with_community=with_community, ) ) + try: with ThreadPoolExecutor(max_workers=1) as executor: future = executor.submit(sync_task) @@ -1002,29 +1027,21 @@ def read_message_task(self, end_user_id: str, message: str, history: List[Dict[s # Log but continue - will fail later with proper error pass - async def _run() -> str: + async def _run() -> dict: with get_db_context() as db: service = MemoryAgentService() - return await service.read_memory(end_user_id, message, history, search_switch, actual_config_id, db, - storage_type, user_rag_memory_id) + return await service.read_memory( + end_user_id, + message, + history, + search_switch, + actual_config_id, db, + storage_type, user_rag_memory_id + ) try: - # 使用 nest_asyncio 来避免事件循环冲突 - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - # 尝试获取现有事件循环，如果不存在则创建新的 - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time @@ -1056,7 +1073,8 @@ def read_message_task(self, end_user_id: str, message: str, history: List[Dict[s @celery_app.task(name="app.core.memory.agent.write_message", bind=True) -def write_message_task(self, end_user_id: str, message: list[dict], config_id: str | int, storage_type: str, user_rag_memory_id: str, +def write_message_task(self, end_user_id: str, message: list[dict], config_id: str | int, storage_type: str, + user_rag_memory_id: str, language: str = "zh") -> Dict[str, Any]: """Celery task to process a write message via MemoryAgentService. Args: @@ -1073,10 +1091,11 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s Raises: Exception on failure """ - from app.core.logging_config import get_logger - logger = get_logger(__name__) - logger.info(f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, config_id={config_id} (type: {type(config_id).__name__}), storage_type={storage_type}, language={language}") + logger.info( + f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, " + f"config_id={config_id} (type: {type(config_id).__name__}), " + f"storage_type={storage_type}, language={language}") start_time = time.time() # Convert config_id to UUID @@ -1086,13 +1105,14 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s try: with get_db_context() as db: actual_config_id = resolve_config_id(config_id, db) - print(100*'-') + print(100 * '-') print(actual_config_id) - print(100*'-') + print(100 * '-') logger.info( f"[CELERY WRITE] Converted config_id to UUID: {actual_config_id} (type: {type(actual_config_id).__name__})") except (ValueError, AttributeError) as e: - logger.error(f"[CELERY WRITE] Invalid config_id format: {config_id} (type: {type(config_id).__name__}), error: {e}") + logger.error( + f"[CELERY WRITE] Invalid config_id format: {config_id} (type: {type(config_id).__name__}), error: {e}") return { "status": "FAILURE", "error": f"Invalid config_id format: {config_id} - {str(e)}", @@ -1116,7 +1136,8 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s async def _run() -> str: with get_db_context() as db: logger.info( - f"[CELERY WRITE] Executing MemoryAgentService.write_memory with config_id={actual_config_id} (type: {type(actual_config_id).__name__}), language={language}") + f"[CELERY WRITE] Executing MemoryAgentService.write_memory " + f"with config_id={actual_config_id} (type: {type(actual_config_id).__name__}), language={language}") service = MemoryAgentService() result = await service.write_memory(end_user_id, message, actual_config_id, db, storage_type, user_rag_memory_id, language) @@ -1124,22 +1145,8 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s return result try: - # 使用 nest_asyncio 来避免事件循环冲突 - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - # 尝试获取现有事件循环，如果不存在则创建新的 - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time @@ -1193,28 +1200,6 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s } -def reflection_engine() -> None: - """Empty function placeholder for timed background reflection. - - Intentionally left blank; replace with real reflection logic later. - """ - import asyncio - - from app.core.memory.utils.self_reflexion_utils.self_reflexion import self_reflexion - - host_id = uuid.UUID("2f6ff1eb-50c7-4765-8e89-e4566be19122") - asyncio.run(self_reflexion(host_id)) - - -@celery_app.task(name="app.core.memory.agent.reflection.timer") -def reflection_timer_task() -> None: - """Periodic Celery task that invokes reflection_engine. - - Raises an exception on failure. - """ - reflection_engine() - - # unused task # @celery_app.task(name="app.core.memory.agent.health.check_read_service") # def check_read_service_task() -> Dict[str, str]: @@ -1368,6 +1353,8 @@ def write_total_memory_task(workspace_id: str) -> Dict[str, Any]: "workspace_id": workspace_id, "elapsed_time": elapsed_time, } + + @celery_app.task( name="app.tasks.write_all_workspaces_memory_task", bind=True, @@ -1391,15 +1378,12 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: start_time = time.time() async def _run() -> Dict[str, Any]: - from app.core.logging_config import get_api_logger from app.models.app_model import App from app.models.end_user_model import EndUser from app.models.workspace_model import Workspace from app.repositories.memory_increment_repository import write_memory_increment from app.services.memory_storage_service import search_all - api_logger = get_api_logger() - with get_db_context() as db: try: # 获取所有活跃的工作空间 @@ -1408,7 +1392,7 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: ).all() if not workspaces: - api_logger.warning("没有找到活跃的工作空间") + logger.warning("没有找到活跃的工作空间") return { "status": "SUCCESS", "message": "没有找到活跃的工作空间", @@ -1416,13 +1400,13 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: "workspace_results": [] } - api_logger.info(f"开始统计 {len(workspaces)} 个工作空间的记忆增量") + logger.info(f"开始统计 {len(workspaces)} 个工作空间的记忆增量") all_workspace_results = [] # 遍历每个工作空间 for workspace in workspaces: workspace_id = workspace.id - api_logger.info(f"开始处理工作空间: {workspace.name} (ID: {workspace_id})") + logger.info(f"开始处理工作空间: {workspace.name} (ID: {workspace_id})") try: # 1. 查询当前workspace下的所有app（仅未删除的） @@ -1447,7 +1431,7 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: "memory_increment_id": str(memory_increment.id), "created_at": memory_increment.created_at.isoformat(), }) - api_logger.info(f"工作空间 {workspace.name} 没有应用，记录总量为0") + logger.info(f"工作空间 {workspace.name} 没有应用，记录总量为0") continue # 2. 查询所有app下的end_user_id（去重） @@ -1472,7 +1456,7 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: }) except Exception as e: # 记录单个用户查询失败，但继续处理其他用户 - api_logger.warning(f"查询用户 {end_user_id} 记忆失败: {str(e)}") + logger.warning(f"查询用户 {end_user_id} 记忆失败: {str(e)}") end_user_details.append({ "end_user_id": str(end_user_id), "total": 0, @@ -1496,13 +1480,13 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: "created_at": memory_increment.created_at.isoformat(), }) - api_logger.info( + logger.info( f"工作空间 {workspace.name} 统计完成: 总量={total_num}, 用户数={len(end_users)}" ) except Exception as e: db.rollback() # 回滚失败的事务，允许继续处理下一个工作空间 - api_logger.error(f"处理工作空间 {workspace.name} (ID: {workspace_id}) 失败: {str(e)}") + logger.error(f"处理工作空间 {workspace.name} (ID: {workspace_id}) 失败: {str(e)}") all_workspace_results.append({ "workspace_id": str(workspace_id), "workspace_name": workspace.name, @@ -1525,7 +1509,7 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: } except Exception as e: - api_logger.error(f"记忆增量统计任务执行失败: {str(e)}") + logger.error(f"记忆增量统计任务执行失败: {str(e)}") return { "status": "FAILURE", "error": str(e), @@ -1534,22 +1518,8 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]: } try: - # 使用 nest_asyncio 来避免事件循环冲突 - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - # 尝试获取现有事件循环，如果不存在则创建新的 - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time @@ -1597,11 +1567,9 @@ def regenerate_memory_cache(self) -> Dict[str, Any]: start_time = time.time() async def _run() -> Dict[str, Any]: - from app.core.logging_config import get_logger from app.repositories.end_user_repository import EndUserRepository from app.services.user_memory_service import UserMemoryService - logger = get_logger(__name__) logger.info("开始执行记忆缓存重新生成定时任务") service = UserMemoryService() @@ -1734,22 +1702,8 @@ def regenerate_memory_cache(self) -> Dict[str, Any]: } try: - # 使用 nest_asyncio 来避免事件循环冲突 - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - # 尝试获取现有事件循环，如果不存在则创建新的 - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time @@ -1785,15 +1739,12 @@ def workspace_reflection_task(self) -> Dict[str, Any]: start_time = time.time() async def _run() -> Dict[str, Any]: - from app.core.logging_config import get_api_logger from app.models.workspace_model import Workspace from app.services.memory_reflection_service import ( MemoryReflectionService, WorkspaceAppService, ) - api_logger = get_api_logger() - with get_db_context() as db: try: # 获取所有工作空间 @@ -1812,7 +1763,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: # 遍历每个工作空间 for workspace in workspaces: workspace_id = workspace.id - api_logger.info(f"开始处理工作空间反思，workspace_id: {workspace_id}") + logger.info(f"开始处理工作空间反思，workspace_id: {workspace_id}") try: reflection_service = MemoryReflectionService(db) @@ -1824,7 +1775,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: workspace_reflection_results = [] for data in result['apps_detailed_info']: - if data['memory_configs'] == []: + if not data['memory_configs']: continue releases = data['releases'] @@ -1835,7 +1786,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: if str(base['config']) == str(config['config_id']) and str(base['app_id']) == str( user['app_id']): # 调用反思服务 - api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}") + logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}") reflection_result = await reflection_service.start_reflection_from_data( config_data=config, @@ -1855,12 +1806,12 @@ def workspace_reflection_task(self) -> Dict[str, Any]: "reflection_results": workspace_reflection_results }) - api_logger.info( + logger.info( f"工作空间 {workspace_id} 反思处理完成，处理了 {len(workspace_reflection_results)} 个任务") except Exception as e: db.rollback() # Rollback failed transaction to allow next query - api_logger.error(f"处理工作空间 {workspace_id} 反思失败: {str(e)}") + logger.error(f"处理工作空间 {workspace_id} 反思失败: {str(e)}") all_reflection_results.append({ "workspace_id": str(workspace_id), "error": str(e), @@ -1879,7 +1830,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: } except Exception as e: - api_logger.error(f"工作空间反思任务执行失败: {str(e)}") + logger.error(f"工作空间反思任务执行失败: {str(e)}") return { "status": "FAILURE", "error": str(e), @@ -1888,22 +1839,8 @@ def workspace_reflection_task(self) -> Dict[str, Any]: } try: - # 使用 nest_asyncio 来避免事件循环冲突 - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - # 尝试获取现有事件循环，如果不存在则创建新的 - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time @@ -1944,18 +1881,16 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di start_time = time.time() async def _run() -> Dict[str, Any]: - from app.core.logging_config import get_api_logger from app.services.memory_forget_service import MemoryForgetService - api_logger = get_api_logger() - with get_db_context() as db: try: - api_logger.info(f"开始执行遗忘周期定时任务，config_id: {config_id}") + logger.info(f"开始执行遗忘周期定时任务，config_id: {config_id}") forget_service = MemoryForgetService() # 运行遗忘周期 + # FIXME: MemeoryForgetService report = await forget_service.trigger_forgetting( db=db, end_user_id=None, # 处理所有组 @@ -1964,7 +1899,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di duration = time.time() - start_time - api_logger.info( + logger.info( f"遗忘周期定时任务完成: " f"融合 {report['merged_count']} 对节点, " f"失败 {report['failed_count']} 对, " @@ -1980,7 +1915,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di except Exception as e: duration = time.time() - start_time - api_logger.error(f"遗忘周期定时任务失败: {str(e)}", exc_info=True) + logger.error(f"遗忘周期定时任务失败: {str(e)}", exc_info=True) return { "status": "FAILED", @@ -1997,6 +1932,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di finally: loop.close() + # ============================================================================= # Long-term Memory Storage Tasks (Batched Write Strategies) # ============================================================================= @@ -2222,9 +2158,8 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: start_time = time.time() async def _run() -> Dict[str, Any]: - from sqlalchemy import func, select + from sqlalchemy import select - from app.core.logging_config import get_logger from app.models.implicit_emotions_storage_model import ImplicitEmotionsStorage from app.repositories.implicit_emotions_storage_repository import ( ImplicitEmotionsStorageRepository, @@ -2233,7 +2168,6 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: from app.services.emotion_analytics_service import EmotionAnalyticsService from app.services.implicit_memory_service import ImplicitMemoryService - logger = get_logger(__name__) logger.info("开始执行隐性记忆和情绪数据更新定时任务") total_users = 0 @@ -2267,7 +2201,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: for end_user_id in refresh_iter: logger.info(f"开始处理用户: {end_user_id}") user_start_time = time.time() - + implicit_success = False emotion_success = False errors = [] @@ -2318,7 +2252,7 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: failed += 1 user_elapsed = time.time() - user_start_time - + # 记录用户处理结果 user_result = { "end_user_id": end_user_id, @@ -2460,22 +2394,8 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: } try: - # 使用 nest_asyncio 来避免事件循环冲突 - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - # 尝试获取现有事件循环，如果不存在则创建新的 - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time @@ -2521,14 +2441,12 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, start_time = time.time() async def _run() -> Dict[str, Any]: - from app.core.logging_config import get_logger from app.repositories.implicit_emotions_storage_repository import ( ImplicitEmotionsStorageRepository, ) from app.services.emotion_analytics_service import EmotionAnalyticsService from app.services.implicit_memory_service import ImplicitMemoryService - logger = get_logger(__name__) logger.info(f"开始按需初始化隐性记忆/情绪数据，候选用户数: {len(end_user_ids)}") initialized = 0 @@ -2587,20 +2505,7 @@ def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, } try: - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) result["elapsed_time"] = time.time() - start_time @@ -2633,6 +2538,7 @@ def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[ 默认生成中文（zh）兴趣分布数据。 Args: + self: task object end_user_ids: 需要检查的用户ID列表 Returns: @@ -2641,11 +2547,9 @@ def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[ start_time = time.time() async def _run() -> Dict[str, Any]: - from app.core.logging_config import get_logger from app.cache.memory.interest_memory import InterestMemoryCache, INTEREST_CACHE_EXPIRE from app.services.memory_agent_service import MemoryAgentService - logger = get_logger(__name__) logger.info(f"开始按需初始化兴趣分布缓存，候选用户数: {len(end_user_ids)}") initialized = 0 @@ -2694,20 +2598,7 @@ def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[ } try: - try: - import nest_asyncio - nest_asyncio.apply() - except ImportError: - pass - - try: - loop = asyncio.get_event_loop() - if loop.is_closed(): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) + loop = set_asyncio_event_loop() result = loop.run_until_complete(_run()) result["elapsed_time"] = time.time() - start_time @@ -2720,3 +2611,54 @@ def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[ "elapsed_time": time.time() - start_time, "task_id": self.request.id, } + + +@celery_app.task( + name="app.tasks.write_perceptual_memory", + bind=True, + ignore_result=True, + max_retries=0, + acks_late=False, + time_limit=3600, + soft_time_limit=3300, +) +def write_perceptual_memory( + self, + end_user_id: str, + model_api_config: dict, + file_type: str, + file_url: str, + file_message: dict +): + """ + Write perceptual memory for a user into PostgreSQL and Neo4j. + + This task generates or updates the user's perceptual memory + in the backend databases. It is intended to be executed asynchronously + via Celery. + + Args: + end_user_id (uuid.UUID): The unique identifier of the end user. + model_api_config (ModelInfo): API configuration for the model + used to generate perceptual memory. + file_type (str): The file type + file_url (url): The url of file + file_message (dict): The file message containing details about the file + to be processed. + + Returns: + None + """ + file_url_md5 = hashlib.md5(file_url.encode("utf-8")).hexdigest() + set_asyncio_event_loop() + with RedisLock(f"perceptual:{file_url_md5}", redis_client=get_sync_redis_client()): + model_info = ModelInfo(**model_api_config) + with get_db_context() as db: + memory_perceptual_service = MemoryPerceptualService(db) + return asyncio.run(memory_perceptual_service.generate_perceptual_memory( + end_user_id, + model_info, + file_type, + file_url, + file_message, + )) diff --git a/api/app/utils/redis_lock.py b/api/app/utils/redis_lock.py new file mode 100644 index 00000000..99f62d84 --- /dev/null +++ b/api/app/utils/redis_lock.py @@ -0,0 +1,61 @@ +import redis +import uuid +import time + +UNLOCK_SCRIPT = """ +if redis.call("get", KEYS[1]) == ARGV[1] then + return redis.call("del", KEYS[1]) +else + return 0 +end +""" + + +class RedisLock: + def __init__( + self, + key: str, + redis_client: redis.StrictRedis, + expire: int = 60, + retry_interval: float = 0.1, + timeout: float = 30 + + ): + self.key = key + self.expire = expire + self.value = str(uuid.uuid4()) + self._locked = False + self.retry_interval = retry_interval + self.timeout = timeout + self.redis_client = redis_client + + def acquire(self) -> bool: + start = time.time() + while True: + ok = self.redis_client.set(self.key, self.value, ex=self.expire, nx=True) + if ok: + self._locked = True + return True + if time.time() - start >= self.timeout: + return False + time.sleep(self.retry_interval) + + def release(self): + if not self._locked: + return + self.redis_client.eval( + UNLOCK_SCRIPT, + 1, + self.key, + self.value + ) + self._locked = False + + def __enter__(self): + ok = self.acquire() + if not ok: + raise RuntimeError(f"Get redis lock timeout: {self.key}") + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.release() From 01a1e8eab1ee97dbb04b6c79d1d3fd085b491b0f Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 14:50:21 +0800 Subject: [PATCH 079/196] [changes] Update the pointers in the main repository to point to the submodules --- redbear-mem-benchmark | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/redbear-mem-benchmark b/redbear-mem-benchmark index 8494e824..b4ddbe9b 160000 --- a/redbear-mem-benchmark +++ b/redbear-mem-benchmark @@ -1 +1 @@ -Subproject commit 8494e82498cb99c70ac67a64a544ff872432363a +Subproject commit b4ddbe9b19014bb8d2d20f1b41eb656d03a5e5ed From 05c2a093c06bfc6c2afd768d031771e1a2365e87 Mon Sep 17 00:00:00 2001 From: Mark Date: Fri, 13 Mar 2026 14:54:06 +0800 Subject: [PATCH 080/196] [add] migration script --- .../versions/ea31b4e347d8_202603131452.py | 30 +++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 api/migrations/versions/ea31b4e347d8_202603131452.py diff --git a/api/migrations/versions/ea31b4e347d8_202603131452.py b/api/migrations/versions/ea31b4e347d8_202603131452.py new file mode 100644 index 00000000..12716fd9 --- /dev/null +++ b/api/migrations/versions/ea31b4e347d8_202603131452.py @@ -0,0 +1,30 @@ +"""202603131452 + +Revision ID: ea31b4e347d8 +Revises: 01587a13522f +Create Date: 2026-03-13 14:53:20.587580 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = 'ea31b4e347d8' +down_revision: Union[str, None] = '01587a13522f' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.add_column('app_shares', sa.Column('permission', sa.String(), nullable=False, comment='权限模式: readonly | editable')) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('app_shares', 'permission') + # ### end Alembic commands ### From fd7e77eff8c6000c0d44193269dda547067ed9d2 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Fri, 13 Mar 2026 15:17:06 +0800 Subject: [PATCH 081/196] feat(web): add community network --- web/package.json | 2 + web/src/api/memory.ts | 11 +- web/src/components/D3Graph/CommunityGraph.tsx | 67 +++ web/src/components/D3Graph/hooks.ts | 24 + web/src/components/D3Graph/types.ts | 102 ++++ web/src/components/D3Graph/utils.ts | 547 ++++++++++++++++++ web/src/i18n/en.ts | 27 + web/src/i18n/zh.ts | 27 + .../components/CommunityNetwork.tsx | 72 +++ .../components/RelationshipNetwork.tsx | 222 +++---- web/src/views/UserMemoryDetail/types.ts | 56 +- 11 files changed, 1054 insertions(+), 103 deletions(-) create mode 100644 web/src/components/D3Graph/CommunityGraph.tsx create mode 100644 web/src/components/D3Graph/hooks.ts create mode 100644 web/src/components/D3Graph/types.ts create mode 100644 web/src/components/D3Graph/utils.ts create mode 100644 web/src/views/UserMemoryDetail/components/CommunityNetwork.tsx diff --git a/web/package.json b/web/package.json index e2d5c898..2799a631 100644 --- a/web/package.json +++ b/web/package.json @@ -36,6 +36,7 @@ "codemirror": "^6.0.2", "copy-to-clipboard": "^3.3.3", "crypto-js": "^4.2.0", + "d3": "^7.9.0", "dayjs": "^1.11.18", "echarts": "^5.6.0", "echarts-for-react": "^3.0.2", @@ -67,6 +68,7 @@ "@tailwindcss/vite": "^4.1.14", "@types/codemirror": "^5.60.17", "@types/crypto-js": "^4.2.2", + "@types/d3": "^7.4.3", "@types/js-yaml": "^4.0.9", "@types/node": "^24.6.0", "@types/react": "^18.2.0", diff --git a/web/src/api/memory.ts b/web/src/api/memory.ts index 491f78ea..b8bfac32 100644 --- a/web/src/api/memory.ts +++ b/web/src/api/memory.ts @@ -2,9 +2,10 @@ * @Author: ZhaoYing * @Date: 2026-02-03 14:00:06 * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-03-04 10:58:41 + * @Last Modified time: 2026-03-13 10:48:41 */ import { request } from '@/utils/request' +import type { AxiosRequestConfig } from 'axios' import type { MemoryFormData, } from '@/views/MemoryManagement/types' @@ -94,8 +95,12 @@ export const updatedEndUserProfile = (values: EndUser) => { return request.post(`/memory-storage/updated_end_user/profile`, values) } // User Memory - Relationship network -export const getMemorySearchEdges = (end_user_id: string) => { - return request.get(`/memory-storage/analytics/graph_data`, { end_user_id }) +export const getMemorySearchEdges = (end_user_id: string, config?: AxiosRequestConfig) => { + return request.get(`/memory-storage/analytics/graph_data`, { end_user_id }, config) +} +// User Memory - Community graph +export const getMemoryCommunityGraph = (end_user_id: string, config?: AxiosRequestConfig) => { + return request.get(`/memory-storage/analytics/community_graph`, { end_user_id }, config) } // User Memory - User interest distribution export const getInterestDistributionByUser = (end_user_id: string) => { diff --git a/web/src/components/D3Graph/CommunityGraph.tsx b/web/src/components/D3Graph/CommunityGraph.tsx new file mode 100644 index 00000000..549d69f3 --- /dev/null +++ b/web/src/components/D3Graph/CommunityGraph.tsx @@ -0,0 +1,67 @@ +import React, { useState, useRef, useMemo, useEffect, type FC } from 'react' +import Empty from '@/components/Empty' +import { GRAPH_COLORS, initCommunityGraph } from './utils' +import { useD3Graph } from './hooks' +import type { CommunityD3Node, D3Link, CommunityGraphProps } from './types' + +// ─── Component ──────────────────────────────────────────────────────────────── +// Renders a D3-powered community graph with optional tooltip and legend. + +const CommunityGraph: FC = ({ + data, + empty: emptyProp, + colors = GRAPH_COLORS, + renderTooltip, + showLegend = true, + onCommunityClick, + onNodeClick, + defaultZoom = 1, +}) => { + // Tooltip position and hovered node state + const [tooltip, setTooltip] = useState<{ x: number; y: number; node: CommunityD3Node } | null>(null) + + // Keep callback refs stable to avoid re-initializing the graph on every render + const onCommunityClickRef = useRef(onCommunityClick) + const onNodeClickRef = useRef(onNodeClick) + const renderTooltipRef = useRef(renderTooltip) + useEffect(() => { onCommunityClickRef.current = onCommunityClick }, [onCommunityClick]) + useEffect(() => { onNodeClickRef.current = onNodeClick }, [onNodeClick]) + useEffect(() => { renderTooltipRef.current = renderTooltip }, [renderTooltip]) + + const graphState = useMemo(() => data, [data]) + // Show empty state when explicitly flagged or when there are no nodes + const isEmpty = emptyProp ?? !data?.nodes.length + + // Initialize (or re-initialize) the D3 graph whenever relevant state changes + const containerRef = useD3Graph((container) => { + if (!graphState) return + return initCommunityGraph( + container, + graphState.nodes, + graphState.links as D3Link[], + graphState.communityMap, + graphState.communityCaption, + graphState.communityNodeMap, + { colors, showLegend, defaultZoom, setTooltip: renderTooltip ? setTooltip : () => {}, onCommunityClickRef, onNodeClickRef } + ) + }, [graphState, showLegend, defaultZoom]) + + // Resolve tooltip content: use custom renderer if provided, otherwise fall back to DefaultTooltip + const tooltipNode = tooltip && renderTooltipRef.current + ? renderTooltipRef.current(tooltip.node) + : null + + if (isEmpty) return + return ( +
+
+ {tooltipNode ? ( +
+ {tooltipNode} +
+ ) : undefined} +
+ ) +} + +export default React.memo(CommunityGraph) diff --git a/web/src/components/D3Graph/hooks.ts b/web/src/components/D3Graph/hooks.ts new file mode 100644 index 00000000..93355718 --- /dev/null +++ b/web/src/components/D3Graph/hooks.ts @@ -0,0 +1,24 @@ +import { useRef, useEffect } from 'react' +import * as d3 from 'd3' + +/** + * Generic hook that mounts a D3 graph inside a div container. + * Clears any existing SVG before calling initFn, and runs cleanup on unmount or dep change. + */ +export function useD3Graph( + initFn: (container: HTMLDivElement) => (() => void) | void, + deps: T[] +) { + const containerRef = useRef(null) + useEffect(() => { + const container = containerRef.current + if (!container) return + d3.select(container).selectAll('svg').remove() + const cleanup = initFn(container) + return () => { + cleanup?.() + d3.select(container).selectAll('svg').remove() + } + }, deps) + return containerRef +} diff --git a/web/src/components/D3Graph/types.ts b/web/src/components/D3Graph/types.ts new file mode 100644 index 00000000..88bbbfeb --- /dev/null +++ b/web/src/components/D3Graph/types.ts @@ -0,0 +1,102 @@ +import type { ReactNode, RefObject } from 'react' +import type * as d3 from 'd3' + +// ─── Raw input types (mirror of API response, no external dependency) ───────── +// These interfaces map 1-to-1 with the graph API response shape. + +export interface RawCommunityNode { + id: string + label: 'Community' + properties: { + name: string + summary: string + member_entity_ids: string[] + member_count: number + core_entities: string[] + community_id: string + end_user_id?: string + updated_at?: string + } +} + +export interface RawEntityNode { + id: string + label: 'ExtractedEntity' + properties: { + name: string + description: string + entity_type: string + community_name?: string + [key: string]: unknown + } +} + +export interface RawEdge { + id: string + source: string + target: string +} + +export interface RawCommunityGraphData { + nodes: (RawCommunityNode | RawEntityNode)[] + edges: RawEdge[] +} + +// ─── D3 graph types ─────────────────────────────────────────────────────────── +// Runtime node shape used by D3 simulations; extends SimulationNodeDatum for x/y/vx/vy. + +export interface CommunityD3Node extends d3.SimulationNodeDatum { + id: string + name: string + community: string + label: string + symbolSize: number + color: string + properties?: RawEntityNode['properties'] +} + +export interface D3Link extends d3.SimulationLinkDatum { + isCross: boolean +} + +// Convex-hull shape rendered behind each community cluster. +export interface HullDatum { + id: string + path: string + color: string + labelX: number + labelY: number + dashed: boolean + caption: string +} + +// Fully transformed graph data ready to be passed into initCommunityGraph. +export interface CommunityGraphData { + nodes: CommunityD3Node[] + links: Array<{ source: string; target: string; isCross: boolean }> + communityMap: Map + communityCaption: Map + communityNodeMap: Map +} + +// Props accepted by the CommunityGraph React component. +export interface CommunityGraphProps { + data: CommunityGraphData | null + empty?: boolean + colors?: string[] + renderTooltip?: (node: CommunityD3Node) => ReactNode + showLegend?: boolean + onCommunityClick?: (node: RawCommunityNode) => void + onNodeClick?: (node: CommunityD3Node) => void + defaultZoom?: number +} + +// Options forwarded from the React component into the D3 initializer. +export interface InitOptions { + colors: string[] + showLegend: boolean + defaultZoom: number + setTooltip: (s: { x: number; y: number; node: CommunityD3Node } | null) => void + onCommunityClickRef: RefObject<((node: RawCommunityNode) => void) | undefined> + onNodeClickRef: RefObject<((node: CommunityD3Node) => void) | undefined> +} diff --git a/web/src/components/D3Graph/utils.ts b/web/src/components/D3Graph/utils.ts new file mode 100644 index 00000000..87d888ac --- /dev/null +++ b/web/src/components/D3Graph/utils.ts @@ -0,0 +1,547 @@ +import * as d3 from 'd3' +import type { CommunityD3Node, D3Link, HullDatum, CommunityGraphData, RawCommunityGraphData, RawCommunityNode, RawEntityNode, InitOptions } from './types' + +// ─── Colors ─────────────────────────────────────────────────────────────────── + +export const GRAPH_COLORS = ['#155EEF', '#369F21', '#4DA8FF', '#FF5D34', '#9C6FFF', '#FF8A4C', '#8BAEF7', '#FFB048'] +export const colorAt = (i: number) => GRAPH_COLORS[i % GRAPH_COLORS.length] + +export function connectionToRadius(connections: number): number { + if (connections <= 1) return 5 + if (connections <= 10) return 8 + if (connections <= 15) return 11 + if (connections <= 20) return 16 + return 22 +} + +// ─── Arrow markers ──────────────────────────────────────────────────────────── + +export function addArrowMarkers( + defs: d3.Selection, + markers: { id: string; color: string }[] +) { + markers.forEach(({ id, color }) => { + defs.append('marker') + .attr('id', id) + .attr('viewBox', '0 -4 8 8') + .attr('refX', 8).attr('refY', 0) + .attr('markerWidth', 6).attr('markerHeight', 6) + .attr('orient', 'auto') + .append('path').attr('d', 'M0,-4L8,0L0,4').attr('fill', color) + }) +} + +// ─── Zoom ───────────────────────────────────────────────────────────────────── + +export function addZoom( + svg: d3.Selection, + g: d3.Selection +) { + svg.call( + d3.zoom().scaleExtent([0.2, 4]) + .on('zoom', e => g.attr('transform', e.transform)) + ) +} + +// ─── Node drag ──────────────────────────────────────────────────────────────── + +export function makeNodeDrag( + simulation: d3.Simulation> +) { + return d3.drag() + .on('start', (e, d) => { if (!e.active) simulation.alphaTarget(0.3).restart(); d.fx = d.x; d.fy = d.y }) + .on('drag', (e, d) => { d.fx = e.x; d.fy = e.y }) + .on('end', (e, d) => { if (!e.active) simulation.alphaTarget(0); d.fx = e.x; d.fy = e.y }) +} + +// ─── Cluster force ──────────────────────────────────────────────────────────── +// Works for both string and number group keys. + +export function makeClusterForce( + nodes: N[], + getGroup: (d: N) => string | number, + centers: Record, + width: number, + height: number, + opts: { pullStrength?: number; minSepRatio?: number; pushStrength?: number } = {} +) { + const { pullStrength = 0.45, minSepRatio = 0.68, pushStrength = 1.0 } = opts + return (alpha: number) => { + // pre-group nodes by key to avoid repeated filter() in hot path + const groups = new Map() + nodes.forEach(d => { + const k = String(getGroup(d)) + if (!groups.has(k)) groups.set(k, []) + groups.get(k)!.push(d) + }) + // pull toward group center + nodes.forEach(d => { + const c = centers[getGroup(d)] + if (!c) return + d.vx = (d.vx ?? 0) + (c.x - (d.x ?? 0)) * pullStrength * alpha + d.vy = (d.vy ?? 0) + (c.y - (d.y ?? 0)) * pullStrength * alpha + }) + // live centroids + const centroids: Record = {} + nodes.forEach(d => { + const g = String(getGroup(d)) + if (!centroids[g]) centroids[g] = { x: 0, y: 0, n: 0 } + centroids[g].x += d.x ?? 0 + centroids[g].y += d.y ?? 0 + centroids[g].n++ + }) + Object.values(centroids).forEach(c => { c.x /= c.n; c.y /= c.n }) + // push groups apart + const keys = Object.keys(centroids) + const minSep = Math.min(width, height) * minSepRatio + for (let i = 0; i < keys.length; i++) { + for (let j = i + 1; j < keys.length; j++) { + const ci = centroids[keys[i]], cj = centroids[keys[j]] + const dx = cj.x - ci.x, dy = cj.y - ci.y + const dist = Math.sqrt(dx * dx + dy * dy) || 1 + if (dist >= minSep) continue + const push = ((minSep - dist) / dist) * pushStrength * alpha + const fx = dx * push, fy = dy * push + groups.get(keys[i])?.forEach(d => { d.vx = (d.vx ?? 0) - fx; d.vy = (d.vy ?? 0) - fy }) + groups.get(keys[j])?.forEach(d => { d.vx = (d.vx ?? 0) + fx; d.vy = (d.vy ?? 0) + fy }) + } + } + } +} + +// ─── Group centers ──────────────────────────────────────────────────────────── + +export function buildGroupCenters( + keys: (string | number)[], + width: number, + height: number, + radiusRatio = 0.4 +): Record { + const centers: Record = {} + const r = Math.min(width, height) * radiusRatio + keys.forEach((key, i) => { + const angle = (i / keys.length) * 2 * Math.PI - Math.PI / 2 + centers[key] = { x: width / 2 + r * Math.cos(angle), y: height / 2 + r * Math.sin(angle) } + }) + return centers +} + +// ─── Community graph data transform ───────────────────────────────────────── + +export function buildCommunityGraphData(raw: RawCommunityGraphData, colors: string[] = GRAPH_COLORS): CommunityGraphData | null { + const getColor = (i: number) => colors[i % colors.length] + + const communityNodes = raw.nodes.filter(n => n.label === 'Community') as RawCommunityNode[] + const communityCaption = new Map() + const communityMap = new Map() + + communityNodes.forEach(n => { + communityCaption.set(n.id, n.properties.name) + communityMap.set(n.id, n.properties.member_entity_ids) + }) + + const entityToCommunity = new Map() + communityMap.forEach((members, commId) => members.forEach(eid => entityToCommunity.set(eid, commId))) + + const commKeys = Array.from(communityMap.keys()) + const commIndex = new Map(commKeys.map((k, i) => [k, i])) + + const entityNodes = raw.nodes.filter(n => n.label === 'ExtractedEntity') as RawEntityNode[] + const entityNodeSet = new Set(entityNodes.map(n => n.id)) + + const connectionCount: Record = {} + raw.edges.forEach(e => { + if (entityNodeSet.has(e.source)) connectionCount[e.source] = (connectionCount[e.source] || 0) + 1 + if (entityNodeSet.has(e.target)) connectionCount[e.target] = (connectionCount[e.target] || 0) + 1 + }) + + const nodes: CommunityD3Node[] = entityNodes.map(n => { + const commId = entityToCommunity.get(n.id) ?? commKeys[0] + return { + id: n.id, + name: n.properties.name, + community: commId, + label: n.label, + symbolSize: connectionToRadius(connectionCount[n.id] || 0), + color: getColor(commIndex.get(commId) ?? 0), + properties: n.properties, + } + }) + + if (!nodes.length) return null + + const links = raw.edges + .filter(e => entityNodeSet.has(e.source) && entityNodeSet.has(e.target)) + .map(e => ({ + source: e.source, + target: e.target, + isCross: entityToCommunity.get(e.source) !== entityToCommunity.get(e.target), + })) + + const communityNodeMap = new Map( + communityNodes.map(n => [n.id, n]) + ) + return { nodes, links, communityMap, communityCaption, communityNodeMap } +} + +// ─── Hull helpers ───────────────────────────────────────────────────────────── + +const smoothLine = d3.line<[number, number]>() + .x(d => d[0]).y(d => d[1]) + .curve(d3.curveCatmullRomClosed.alpha(0.5)) + +function expandPoints(pts: [number, number][], pad: number): [number, number][] { + const cx = pts.reduce((s, p) => s + p[0], 0) / pts.length + const cy = pts.reduce((s, p) => s + p[1], 0) / pts.length + return pts.map(([x, y]) => { + const dx = x - cx, dy = y - cy + const len = Math.sqrt(dx * dx + dy * dy) || 1 + return [x + (dx / len) * pad, y + (dy / len) * pad] + }) +} + +function toHullPoints(pts: [number, number][]): [number, number][] { + if (pts.length === 1) { + const [x, y] = pts[0] + return [[x - 1, y - 1], [x + 1, y - 1], [x, y + 1]] + } + if (pts.length === 2) { + const [[x1, y1], [x2, y2]] = pts + return [[x1, y1], [x2, y2], [(x1 + x2) / 2, (y1 + y2) / 2 - 1]] + } + return d3.polygonHull(pts) ?? pts +} + +const CIRCLE_THRESHOLD = 4 // 节点数 < 此值时使用圆形 +const CIRCLE_SEGMENTS = 32 + +function circlePoints(cx: number, cy: number, r: number): [number, number][] { + return Array.from({ length: CIRCLE_SEGMENTS }, (_, i) => { + const a = (i / CIRCLE_SEGMENTS) * 2 * Math.PI + return [cx + r * Math.cos(a), cy + r * Math.sin(a)] as [number, number] + }) +} + +export function buildHullData( + nodes: CommunityD3Node[], + communityMap: Map, + communityCaption: Map, + colors: string[] +): HullDatum[] { + const getColor = (i: number) => colors[i % colors.length] + const byComm = new Map() + communityMap.forEach((_, id) => byComm.set(id, [])) + nodes.forEach(d => { + if (d.x != null && d.y != null) byComm.get(d.community)?.push([d.x, d.y]) + }) + + const hulls: HullDatum[] = [] + let ci = 0 + byComm.forEach((pts, id) => { + const color = getColor(ci++) + if (!pts.length) return + let pathPoints: [number, number][] + if (pts.length < CIRCLE_THRESHOLD) { + const cx = pts.reduce((s, p) => s + p[0], 0) / pts.length + const cy = pts.reduce((s, p) => s + p[1], 0) / pts.length + pathPoints = circlePoints(cx, cy, 60) + } else { + pathPoints = expandPoints(toHullPoints(pts), 60) as [number, number][] + } + const path = smoothLine(pathPoints) + if (!path) return + hulls.push({ + id, path, color, + labelX: pathPoints.reduce((s, p) => s + p[0], 0) / pathPoints.length, + labelY: Math.min(...pathPoints.map(p => p[1])) - 10, + dashed: pts.length <= 2, + caption: communityCaption.get(id) ?? id, + }) + }) + return hulls +} + +// ─── Hull render ────────────────────────────────────────────────────────────── + +export function renderHulls( + hullG: d3.Selection, + hulls: HullDatum[], + hiddenCommunities: Set, + nodes: CommunityD3Node[], + simulation: d3.Simulation, + onCommunityClick?: (node: RawCommunityNode) => void, + communityNodeMap?: Map +) { + let dragNodes: CommunityD3Node[] = [] + let dragStart = { x: 0, y: 0 } + const communityDrag = d3.drag() + .on('start', (event, d) => { + if (!event.active) simulation.alphaTarget(0.3).restart() + dragNodes = nodes.filter(n => n.community === d.id) + dragStart = { x: event.x, y: event.y } + dragNodes.forEach(n => { n.fx = n.x; n.fy = n.y }) + }) + .on('drag', (event) => { + const dx = event.x - dragStart.x, dy = event.y - dragStart.y + dragStart = { x: event.x, y: event.y } + dragNodes.forEach(n => { n.fx = (n.fx ?? n.x ?? 0) + dx; n.fy = (n.fy ?? n.y ?? 0) + dy }) + }) + .on('end', (event) => { if (!event.active) simulation.alphaTarget(0) }) + + const pathSel = hullG.selectAll('path.hull').data(hulls, d => d.id) + pathSel.enter().append('path').attr('class', 'hull').style('cursor', 'grab') + .merge(pathSel) + .call(communityDrag) + .attr('d', d => d.path) + .attr('fill', d => d.color).attr('fill-opacity', 0.08) + .attr('stroke', d => d.color).attr('stroke-opacity', 0.5).attr('stroke-width', 1.5) + .attr('stroke-dasharray', 'none') + .style('display', d => hiddenCommunities.has(d.id) ? 'none' : null) + .on('click', (event, d) => { + if ((event as MouseEvent).defaultPrevented) return + const node = communityNodeMap?.get(d.id) + if (node) onCommunityClick?.(node) + }) + pathSel.exit().remove() + + const labelSel = hullG.selectAll('text.hull-label').data(hulls, d => d.id) + labelSel.enter().append('text').attr('class', 'hull-label') + .attr('text-anchor', 'middle').attr('font-size', '12px').attr('font-weight', '500') + .style('pointer-events', 'none') + .merge(labelSel) + .attr('x', d => d.labelX).attr('y', d => d.labelY) + .attr('fill', d => d.color) + .style('display', d => hiddenCommunities.has(d.id) ? 'none' : null) + .text(d => d.caption) + labelSel.exit().remove() +} + +// ─── Community graph init ───────────────────────────────────────────────────── + +export function initCommunityGraph( + container: HTMLDivElement, + nodes: CommunityD3Node[], + links: D3Link[], + communityMap: Map, + communityCaption: Map, + communityNodeMap: Map, + opts: InitOptions +) { + const { colors, showLegend, defaultZoom, setTooltip, onCommunityClickRef, onNodeClickRef } = opts + const getColor = (i: number) => colors[i % colors.length] + + const width = container.clientWidth || 600 + const height = container.clientHeight || 518 + + const svg = d3.select(container).append('svg') + .attr('width', width).attr('height', height) + .style('width', '100%').style('height', '100%') + .style('background', '#F6F8FC') + + const g = svg.append('g') + + const zoom = d3.zoom() + .scaleExtent([0.2, 4]) + .on('zoom', e => g.attr('transform', e.transform)) + svg.call(zoom) + if (defaultZoom !== 1) { + svg.call(zoom.transform, d3.zoomIdentity + .translate(width / 2 * (1 - defaultZoom), height / 2 * (1 - defaultZoom)) + .scale(defaultZoom) + ) + } + + const defs = svg.append('defs') + addArrowMarkers(defs, [{ id: 'arrow', color: 'rgba(91, 97, 103, 0.7)' }]) + + const commKeys = Array.from(communityMap.keys()) + const centers = buildGroupCenters(commKeys, width, height, 0.45) + const linkedIds = new Set(links.flatMap(l => [l.source as string, l.target as string])) + + const simulation = d3.forceSimulation(nodes) + .force('link', d3.forceLink(links).id(d => d.id).distance(60)) + .force('charge', d3.forceManyBody().strength(-120)) + .force('center', d3.forceCenter(width / 2, height / 2).strength(0.02)) + .force('collision', d3.forceCollide(d => d.symbolSize + 16)) + .force('cluster', makeClusterForce(nodes, d => d.community, centers, width, height, { + pullStrength: 0.45, minSepRatio: 0.68, pushStrength: 1.0, + })) + .force('isolatedPull', (alpha: number) => { + nodes.forEach(d => { + if (linkedIds.has(d.id)) return + const c = centers[d.community] + if (!c) return + d.vx = (d.vx ?? 0) + (c.x - (d.x ?? 0)) * 0.4 * alpha + d.vy = (d.vy ?? 0) + (c.y - (d.y ?? 0)) * 0.4 * alpha + }) + }) + + const hullG = g.append('g').attr('class', 'hulls') + const hiddenCommunities = new Set() + + const linkSel = g.append('g').selectAll('line') + .data(links).enter().append('line') + .attr('stroke', '#5B6167') + .attr('stroke-opacity', d => d.isCross ? 0.3 : 0.5) + .attr('stroke-width', d => d.isCross ? 1 : 1.2) + .attr('marker-end', 'url(#arrow)') + + const nodeSel = g.append('g').selectAll('g') + .data(nodes).enter().append('g') + .call(makeNodeDrag(simulation)) + + nodeSel.append('circle') + .attr('r', d => d.symbolSize) + .attr('fill', d => d.color).attr('fill-opacity', 0.85) + .attr('stroke', '#fff').attr('stroke-width', 1.5) + .style('cursor', 'pointer') + .on('mouseenter', (event: MouseEvent, d: CommunityD3Node) => { + const { left, top } = container.getBoundingClientRect() + setTooltip({ x: event.clientX - left, y: event.clientY - top, node: d }) + }) + .on('mousemove', (event: MouseEvent) => { + const { left, top } = container.getBoundingClientRect() + const nd = d3.select(event.target as SVGCircleElement).datum() + setTooltip({ x: event.clientX - left, y: event.clientY - top, node: nd }) + }) + .on('mouseleave', () => setTooltip(null)) + .on('click', (_event: MouseEvent, d: CommunityD3Node) => onNodeClickRef.current?.(d)) + + nodeSel.append('text') + .text(d => d.name) + .attr('x', 0).attr('dy', d => -(d.symbolSize + 5)) + .attr('text-anchor', 'middle').attr('font-size', '11px').attr('fill', '#444') + .style('pointer-events', 'none') + + if (showLegend) { + renderLegend( + svg, + commKeys.map((cid, i) => ({ key: cid, label: communityCaption.get(cid) ?? cid, color: getColor(i) })), + width, height, + (key, hidden) => { + const cid = key as string + if (hidden) hiddenCommunities.add(cid) + else hiddenCommunities.delete(cid) + nodeSel.style('display', d => hiddenCommunities.has(d.community) ? 'none' : null) + linkSel.style('display', d => { + const s = d.source as CommunityD3Node, t = d.target as CommunityD3Node + return hiddenCommunities.has(s.community) || hiddenCommunities.has(t.community) ? 'none' : null + }) + hullG.selectAll('path.hull').style('display', d => hiddenCommunities.has(d.id) ? 'none' : null) + hullG.selectAll('text.hull-label').style('display', d => hiddenCommunities.has(d.id) ? 'none' : null) + } + ) + } + + simulation.on('tick', () => { + linkSel + .attr('x1', d => (d.source as CommunityD3Node).x ?? 0) + .attr('y1', d => (d.source as CommunityD3Node).y ?? 0) + .attr('x2', d => { + const s = d.source as CommunityD3Node, t = d.target as CommunityD3Node + const dx = (t.x ?? 0) - (s.x ?? 0), dy = (t.y ?? 0) - (s.y ?? 0) + const dist = Math.sqrt(dx * dx + dy * dy) || 1 + return (t.x ?? 0) - (dx / dist) * (t.symbolSize + 2) + }) + .attr('y2', d => { + const s = d.source as CommunityD3Node, t = d.target as CommunityD3Node + const dx = (t.x ?? 0) - (s.x ?? 0), dy = (t.y ?? 0) - (s.y ?? 0) + const dist = Math.sqrt(dx * dx + dy * dy) || 1 + return (t.y ?? 0) - (dy / dist) * (t.symbolSize + 2) + }) + nodeSel.attr('transform', d => `translate(${d.x ?? 0},${d.y ?? 0})`) + renderHulls(hullG, buildHullData(nodes, communityMap, communityCaption, colors), hiddenCommunities, nodes, simulation, (n) => onCommunityClickRef.current?.(n), communityNodeMap) + }) + + return () => { simulation.stop(); d3.select(container).selectAll('svg').remove() } +} + +// ─── Legend ─────────────────────────────────────────────────────────────────── + +export interface LegendItem { + key: string | number + label: string + color: string +} + +const LEGEND_GAP = 12 +const LEGEND_RECT_W = 20 +const LEGEND_RECT_H = 10 +const LEGEND_TEXT_OFFSET = 24 +const LEGEND_FONT_SIZE = 11 +const LEGEND_ROW_H = 24 +const LEGEND_BOTTOM_PAD = 8 + +// Approximate text width using canvas measureText if available, else char-based estimate +function measureText(text: string, fontSize: number): number { + try { + const ctx = document.createElement('canvas').getContext('2d') + if (ctx) { ctx.font = `${fontSize}px sans-serif`; return ctx.measureText(text).width } + } catch { /* noop */ } + return text.length * fontSize * 0.6 +} + +export function renderLegend( + svg: d3.Selection, + items: LegendItem[], + width: number, + height: number, + onToggle: (key: string | number, hidden: boolean) => void +) { + // Compute per-item width: rect + text-offset + textW + const itemWidths = items.map(item => + LEGEND_RECT_W + LEGEND_TEXT_OFFSET + measureText(item.label, LEGEND_FONT_SIZE) + ) + + // Layout items into rows + const rows: { item: LegendItem; w: number; x: number; row: number }[] = [] + let rowIdx = 0, curX = 0 + itemWidths.forEach((w, i) => { + const slotW = w + LEGEND_GAP + if (curX > 0 && curX + w > width - LEGEND_GAP * 2) { rowIdx++; curX = 0 } + rows.push({ item: items[i], w, x: curX, row: rowIdx }) + curX += slotW + }) + + const totalRows = rowIdx + 1 + const totalH = totalRows * LEGEND_ROW_H + const baseY = height - totalH - LEGEND_BOTTOM_PAD + + // Center each row + const rowWidths: number[] = Array(totalRows).fill(0) + rows.forEach(({ w, row }, i) => { + rowWidths[row] += w + (i > 0 && rows[i - 1].row === row ? LEGEND_GAP : 0) + }) + // Recalculate row widths properly + const rowTotals: number[] = Array(totalRows).fill(0) + const rowCounts: number[] = Array(totalRows).fill(0) + rows.forEach(r => { rowCounts[r.row]++; rowTotals[r.row] += r.w }) + rowTotals.forEach((_, ri) => { rowTotals[ri] += Math.max(0, rowCounts[ri] - 1) * LEGEND_GAP }) + + const legendG = svg.append('g') + + rows.forEach(({ item, x, row }) => { + const rowOffsetX = (width - rowTotals[row]) / 2 + const g = legendG.append('g') + .attr('transform', `translate(${rowOffsetX + x},${baseY + row * LEGEND_ROW_H + LEGEND_ROW_H / 2})`) + .style('cursor', 'pointer') + + const rect = g.append('rect') + .attr('x', 0).attr('y', -LEGEND_RECT_H / 2) + .attr('width', LEGEND_RECT_W).attr('height', LEGEND_RECT_H).attr('rx', 2) + .attr('fill', item.color) + + const text = g.append('text') + .text(item.label) + .attr('x', LEGEND_TEXT_OFFSET).attr('dy', '0.35em') + .attr('font-size', `${LEGEND_FONT_SIZE}px`).attr('fill', '#5B6167') + + let hidden = false + g.on('click', () => { + hidden = !hidden + rect.attr('fill', hidden ? '#ccc' : item.color) + text.attr('fill', hidden ? '#bbb' : '#5B6167') + onToggle(item.key, hidden) + }) + }) +} diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 62f404aa..baeb5848 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1482,6 +1482,33 @@ export const en = { memoryNum: 'memories', memory_config_name: 'Memory Engine', searchPlaceholder: 'Search memory store name', + + communityNetwork: 'Community Graph', + community: 'Community', + "Person": "Person Entity Node", + "Organization": "Organization Entity Node", + "ORG": "Organization Entity Node", + "Location": "Location Entity Node", + "LOC": "Location Entity Node", + "Event": "Event Entity Node", + "Concept": "Concept Entity Node", + "Time": "Time Entity Node", + "Position": "Position Entity Node", + "WorkRole": "Work Role Entity Node", + "System": "System Entity Node", + "Policy": "Policy Entity Node", + "HistoricalPeriod": "Historical Period Entity Node", + "HistoricalState": "Historical State Entity Node", + "HistoricalEvent": "Historical Event Entity Node", + "EconomicFactor": "Economic Factor Entity Node", + "Condition": "Condition Entity Node", + "Numeric": "Numeric Entity Node", + "Work": "Work / Output", + member_count: 'Member Count', + member_count_desc: 'entities', + summary: 'Summary', + core_entities: 'Core Entities', + communityDetailEmptyDesc: 'Click on a community in the chart on the left to view details', }, space: { createSpace: 'Create Space', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 387c67c3..e8aa7e5a 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1480,6 +1480,33 @@ export const zh = { memoryNum: '条记忆', memory_config_name: '记忆引擎', searchPlaceholder: '搜索记忆库名称', + + communityNetwork: '社区图谱', + community: '社区', + "Person": "人物实体节点", + "Organization": "组织实体节点", + "ORG": "组织实体节点", + "Location": "地点实体节点", + "LOC": "地点实体节点", + "Event": "事件实体节点", + "Concept": "概念实体节点", + "Time": "时间实体节点", + "Position": "职位实体节点", + "WorkRole": "职业实体节点", + "System": "系统实体节点", + "Policy": "政策实体节点", + "HistoricalPeriod": "历史时期实体节点", + "HistoricalState": "历史国家实体节点", + "HistoricalEvent": "历史事件实体节点", + "EconomicFactor": "经济因素实体节点", + "Condition": "条件实体节点", + "Numeric": "数值实体节点", + "Work": "作品/工作成果", + member_count: '成员数', + member_count_desc: '个实体', + summary: '摘要', + core_entities: '核心实体', + communityDetailEmptyDesc: '点击左侧图表中的社区查看详情', }, space: { createSpace: '创建空间', diff --git a/web/src/views/UserMemoryDetail/components/CommunityNetwork.tsx b/web/src/views/UserMemoryDetail/components/CommunityNetwork.tsx new file mode 100644 index 00000000..2757498d --- /dev/null +++ b/web/src/views/UserMemoryDetail/components/CommunityNetwork.tsx @@ -0,0 +1,72 @@ +import React, { useState, type FC, useEffect } from 'react' +import { useParams } from 'react-router-dom' +import { useTranslation } from 'react-i18next' +import type { CommunityD3Node, CommunityGraphData, RawCommunityGraphData, RawCommunityNode } from '@/components/D3Graph/types' +import { buildCommunityGraphData } from '@/components/D3Graph/utils' +import CommunityGraph from '@/components/D3Graph/CommunityGraph' +import { getMemoryCommunityGraph } from '@/api/memory' + +// ─── Tooltip ────────────────────────────────────────────────────────────────── + +const NodeTooltip: FC<{ node: CommunityD3Node }> = ({ node }) => { + const { t } = useTranslation() + return ( +
+
+ {node.properties?.name ?? node.name} +
+ {node.properties?.description && ( +
+ {node.properties.description} +
+ )} +
+ {t('userMemory.type')}： + {t(`userMemory.${node.properties?.entity_type}`)} +
+
+ {t('userMemory.community')}： + {node.properties?.community_name} +
+
+ ) +} + +// ─── Component ──────────────────────────────────────────────────────────────── + +const CommunityNetwork: FC<{ onSelectCommunity?: (node: RawCommunityNode) => void }> = ({ onSelectCommunity }) => { + const { id } = useParams() + const [graphData, setGraphData] = useState(null) + const [empty, setEmpty] = useState(false) + + useEffect(() => { + if (!id) return + const controller = new AbortController() + setEmpty(false) + setGraphData(null) + getMemoryCommunityGraph(id, { signal: controller.signal }).then(res => { + const raw = res as RawCommunityGraphData + if (!raw.nodes?.length) { setEmpty(true); return } + const built = buildCommunityGraphData(raw) + if (!built) { setEmpty(true); return } + setGraphData(built) + }).catch((e) => { if (e?.code !== 'ERR_CANCELED') setEmpty(true) }) + return () => controller.abort() + }, [id]) + + return ( + } + /> + ) +} + +export default React.memo(CommunityNetwork) diff --git a/web/src/views/UserMemoryDetail/components/RelationshipNetwork.tsx b/web/src/views/UserMemoryDetail/components/RelationshipNetwork.tsx index aa8b9d7c..66e37a45 100644 --- a/web/src/views/UserMemoryDetail/components/RelationshipNetwork.tsx +++ b/web/src/views/UserMemoryDetail/components/RelationshipNetwork.tsx @@ -1,8 +1,8 @@ /* * @Author: ZhaoYing * @Date: 2026-02-03 18:32:00 - * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-02-03 18:32:00 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-03-13 14:51:17 */ /** * Relationship Network Component @@ -13,18 +13,20 @@ import React, { type FC, useEffect, useState, useRef, useCallback } from 'react' import { useTranslation } from 'react-i18next' import { useParams, useNavigate } from 'react-router-dom' -import { Col, Row, Space, Button } from 'antd' +import { Col, Row, Space, Button, Tabs, Flex, Divider } from 'antd' import dayjs from 'dayjs' import ReactEcharts from 'echarts-for-react' import RbCard from '@/components/RbCard/Card' import detailEmpty from '@/assets/images/userMemory/detail_empty.png' import type { Node, Edge, GraphData, StatementNodeProperties, ExtractedEntityNodeProperties } from '../types' +import type { RawCommunityNode } from '@/components/D3Graph/types' import { getMemorySearchEdges, } from '@/api/memory' import Empty from '@/components/Empty' import Tag from '@/components/Tag' +import CommunityNetwork from './CommunityNetwork' /** Node color palette */ const colors = ['#155EEF', '#369F21', '#4DA8FF', '#FF5D34', '#9C6FFF', '#FF8A4C', '#8BAEF7', '#FFB048'] @@ -36,16 +38,21 @@ const RelationshipNetwork:FC = () => { const [nodes, setNodes] = useState([]) const [links, setLinks] = useState([]) const [categories, setCategories] = useState<{ name: string }[]>([]) - const [selectedNode, setSelectedNode] = useState(null) + const [selectedNode, setSelectedNode] = useState(null) // const [fullScreen, setFullScreen] = useState(false) const navigate = useNavigate() + const [activeTab, setActiveTab] = useState('relationshipNetwork') console.log('categories', categories) + const edgeAbortRef = useRef(null) + /** Fetch relationship network data */ const getEdgeData = useCallback(() => { if (!id) return + edgeAbortRef.current?.abort() + edgeAbortRef.current = new AbortController() setSelectedNode(null) - getMemorySearchEdges(id).then((res) => { + getMemorySearchEdges(id, { signal: edgeAbortRef.current.signal }).then((res) => { const { nodes, edges, statistics } = res as GraphData const curNodes: Node[] = [] const curEdges: Edge[] = [] @@ -123,6 +130,7 @@ const RelationshipNetwork:FC = () => { useEffect(() => { if (!id) return getEdgeData() + return () => { edgeAbortRef.current?.abort() } }, [id]) useEffect(() => { @@ -153,34 +161,36 @@ const RelationshipNetwork:FC = () => { const params = new URLSearchParams({ nodeId: selectedNode.id, nodeLabel: selectedNode.label, - nodeName: selectedNode.name || '' + nodeName: (selectedNode as Node).name || '' }) navigate(`/user-memory/detail/${id}/GRAPH?${params.toString()}`) } + const handleChangeTab = (tab: string) => { + if (tab === 'communityNetwork') { + edgeAbortRef.current?.abort() + } else { + getEdgeData() + } + setActiveTab(tab) + setSelectedNode(null) + } return ( {/* Relationship Network */} - - //
- // {t('userMemory.fullScreen')} - //
- // } - > + + ({ key, label: t(`userMemory.${key}`) }))} + activeKey={activeTab} + onChange={handleChangeTab} + />
- {nodes.length === 0 ? ( - - ) : ( - setSelectedNode(community)} /> + : nodes.length === 0 + ? + : { } }} /> - )} + }
{/* Memory Details */} - -
- {t('userMemory.completeMemory')} - } + bodyClassName="rb:p-0!" + extra={selectedNode && !(selectedNode as RawCommunityNode).properties.community_id && ( + + )} >
{!selectedNode - ? - : <> - {selectedNode.name &&
{selectedNode.name}
} -
- <> + ? + : (selectedNode as RawCommunityNode).properties.community_id + ?
+
+ {(selectedNode as RawCommunityNode).properties.name} +
+
{t('userMemory.summary')}
+
+ {(selectedNode as RawCommunityNode).properties.summary} +
+ + {t('userMemory.member_count')} + {(selectedNode as RawCommunityNode).properties.member_count}{t('userMemory.member_count_desc')} + + + +
{t('userMemory.core_entities')}
+
    + {(selectedNode as RawCommunityNode).properties.core_entities.map((entity, index) =>
  • {entity}
    • )} +
+
+ : <> + {(selectedNode as Node).name && ( +
+ {(selectedNode as Node).name} +
+ )} +
{t('userMemory.memoryContent')}
{['Chunk', 'Dialogue', 'MemorySummary'].includes(selectedNode.label) && 'content' in selectedNode.properties ? selectedNode.properties.content : selectedNode.label === 'ExtractedEntity' && 'description' in selectedNode.properties - ? selectedNode.properties.description - : selectedNode.label === 'Statement' && 'statement' in selectedNode.properties - ? selectedNode.properties.statement - : '' - } -
- -
-
{t('userMemory.created_at')}
-
- {dayjs(selectedNode?.properties.created_at).format('YYYY-MM-DD HH:mm:ss')} + ? selectedNode.properties.description + : selectedNode.label === 'Statement' && 'statement' in selectedNode.properties + ? selectedNode.properties.statement + : ''}
- {selectedNode?.properties.associative_memory > 0 &&
-
{t('userMemory.associative_memory')}
+
+
{t('userMemory.created_at')}
- {selectedNode?.properties.associative_memory} {t('userMemory.unix')}{t('userMemory.associative_memory')} + {dayjs((selectedNode as Node).properties.created_at).format('YYYY-MM-DD HH:mm:ss')}
-
} - {selectedNode.label === 'Statement' && <> - {(['emotion_keywords', 'emotion_type', 'emotion_subject', 'importance_score'] as const).map(key => { - const statementProps = selectedNode.properties as StatementNodeProperties; - if ((key === 'emotion_keywords' && statementProps[key]?.length > 0) || typeof statementProps[key] === 'string') { - console.log('statementProps[key]', statementProps[key]) - return ( -
- {t(`userMemory.Statement_${key}`)} -
- {key === 'emotion_keywords' - ? {statementProps.emotion_keywords.map((vo, index) => {vo})} - : statementProps[key] - } + {(selectedNode as Node).properties.associative_memory > 0 && ( +
+
{t('userMemory.associative_memory')}
+
+ {(selectedNode as Node).properties.associative_memory} + {' '}{t('userMemory.unix')}{t('userMemory.associative_memory')} +
+
+ )} + + {selectedNode.label === 'Statement' && ( + (['emotion_keywords', 'emotion_type', 'emotion_subject', 'importance_score'] as const).map(key => { + const p = selectedNode.properties as StatementNodeProperties + if ((key === 'emotion_keywords' && p[key]?.length > 0) || typeof p[key] === 'string') { + return ( +
+ {t(`userMemory.Statement_${key}`)} +
+ {key === 'emotion_keywords' + ? {p.emotion_keywords.map((v, i) => {v})} + : p[key]} +
-
- ) - } - return null - })} - } - {selectedNode.label === 'ExtractedEntity' && <> - {(['name', 'entity_type', 'aliases', 'connect_strngth', 'importance_score'] as const).map(key => { - const entityProps = selectedNode.properties as ExtractedEntityNodeProperties; - if (entityProps[key]) { - return ( -
- {t(`userMemory.ExtractedEntity_${key}`)} -
- {Array.isArray(entityProps[key]) && entityProps[key].length > 0 - ? entityProps[key].map((vo, index) =>
- {vo}
) - : entityProps[key] - } + ) + } + return null + }) + )} + + {selectedNode.label === 'ExtractedEntity' && ( + (['name', 'entity_type', 'aliases', 'connect_strngth', 'importance_score'] as const).map(key => { + const p = selectedNode.properties as ExtractedEntityNodeProperties + if (p[key]) { + return ( +
+ {t(`userMemory.ExtractedEntity_${key}`)} +
+ {Array.isArray(p[key]) && p[key].length > 0 + ? p[key].map((v, i) =>
- {v}
) + : p[key]} +
-
- ) - } - return null - })} - } + ) + } + return null + }) + )} +
-
- + }
diff --git a/web/src/views/UserMemoryDetail/types.ts b/web/src/views/UserMemoryDetail/types.ts index 8333cb2c..72e896ad 100644 --- a/web/src/views/UserMemoryDetail/types.ts +++ b/web/src/views/UserMemoryDetail/types.ts @@ -1,8 +1,8 @@ /* * @Author: ZhaoYing * @Date: 2026-02-03 17:57:15 - * @Last Modified by: ZhaoYing - * @Last Modified time: 2026-02-03 17:57:15 + * @Last Modified by: ZhaoYing + * @Last Modified time: 2026-03-13 11:49:52 */ /** * User Memory Detail Types @@ -90,6 +90,7 @@ export interface ExtractedEntityNodeProperties { connect_strngth: string; importance_score: number; associative_memory: number; + community_name?: string; } /** * Memory summary node @@ -246,4 +247,53 @@ export interface ForgetData { */ export interface GraphDetailRef { handleOpen: (vo: Node) => void -} \ No newline at end of file +} +// Community +export type CommunityNodeType = 'Community' | 'ExtractedEntity'; +export type CommunityEdgeType = 'BELONGS_TO_COMMUNITY' | 'EXTRACTED_RELATIONSHIP'; +export type CommunityEntityType = "Person" | "Organization" | "ORG" | "Location" | "LOC" | "Event" | "Concept" | "Time" | "Position" | "WorkRole" | "System" | "Policy" | "HistoricalPeriod" | "HistoricalState" | "HistoricalEvent" | "EconomicFactor" | "Condition" | "Numeric" | "Work"; +// 社区节点 +export interface CommunityTypeNode { + id: string; + label: 'Community'; + properties: { + community_id: string; + end_user_id: string; + member_count: number; + updated_at: string; + name: string; + summary: string; + core_entities: string[]; + member_entity_ids: string[]; + }; +} +// 核心实体 +export interface ExtractedEntityTypeNode { + id: string; + label: 'ExtractedEntity'; + properties: { + name: string; + end_user_id: string; + description: string; + created_at: string; + entity_type: CommunityEntityType; + community_name: string; + }; +} +// 社区图谱连线 +export interface CommunityEdge { + id: string; + target: string; + source: string; +} +export interface CommunityStatistics { + total_nodes: number; + total_edges: number; + node_types: Record; + edge_types: Record; +} +export interface CommunityGraphData { + nodes: (CommunityTypeNode | ExtractedEntityTypeNode)[]; + edges: CommunityEdge[]; + statistics: CommunityStatistics; +} From db8257b67ae526e3bf2cd42dd8f1bfc012c0411e Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 17:06:43 +0800 Subject: [PATCH 082/196] [add] Create community nodes --- .../clustering_engine/__init__.py | 3 + .../clustering_engine/label_propagation.py | 311 ++++++++++++++++++ .../neo4j/community_repository.py | 129 ++++++++ api/app/repositories/neo4j/cypher_queries.py | 93 +++++- api/app/repositories/neo4j/graph_saver.py | 34 ++ 5 files changed, 569 insertions(+), 1 deletion(-) create mode 100644 api/app/core/memory/storage_services/clustering_engine/__init__.py create mode 100644 api/app/core/memory/storage_services/clustering_engine/label_propagation.py create mode 100644 api/app/repositories/neo4j/community_repository.py diff --git a/api/app/core/memory/storage_services/clustering_engine/__init__.py b/api/app/core/memory/storage_services/clustering_engine/__init__.py new file mode 100644 index 00000000..992d8bff --- /dev/null +++ b/api/app/core/memory/storage_services/clustering_engine/__init__.py @@ -0,0 +1,3 @@ +from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine + +__all__ = ["LabelPropagationEngine"] diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py new file mode 100644 index 00000000..80e238fd --- /dev/null +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -0,0 +1,311 @@ +"""标签传播聚类引擎 + +基于 ZEP 论文的动态标签传播算法，对 Neo4j 中的 ExtractedEntity 节点进行社区聚类。 + +支持两种模式： +- 全量初始化（full_clustering）：首次运行，对所有实体做完整 LPA 迭代 +- 增量更新（incremental_update）：新实体到达时，只处理新实体及其邻居 +""" + +import logging +import uuid +from math import sqrt +from typing import Dict, List, Optional + +from app.repositories.neo4j.community_repository import CommunityRepository +from app.repositories.neo4j.neo4j_connector import Neo4jConnector + +logger = logging.getLogger(__name__) + +# 全量迭代最大轮数，防止不收敛 +MAX_ITERATIONS = 10 + + +def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float: + """计算两个向量的余弦相似度，任一为空则返回 0。""" + if not v1 or not v2 or len(v1) != len(v2): + return 0.0 + dot = sum(a * b for a, b in zip(v1, v2)) + norm1 = sqrt(sum(a * a for a in v1)) + norm2 = sqrt(sum(b * b for b in v2)) + if norm1 == 0 or norm2 == 0: + return 0.0 + return dot / (norm1 * norm2) + + +def _weighted_vote( + neighbors: List[Dict], + self_embedding: Optional[List[float]], +) -> Optional[str]: + """ + 加权多数投票，选出得票最高的社区。 + + 权重 = 语义相似度（name_embedding 余弦）* activation_value 加成 + 没有 community_id 的邻居不参与投票。 + """ + votes: Dict[str, float] = {} + for nb in neighbors: + cid = nb.get("community_id") + if not cid: + continue + sem = _cosine_similarity(self_embedding, nb.get("name_embedding")) + act = nb.get("activation_value") or 0.5 + # 语义相似度权重 0.6，激活值权重 0.4 + weight = 0.6 * sem + 0.4 * act + votes[cid] = votes.get(cid, 0.0) + weight + + if not votes: + return None + return max(votes, key=votes.__getitem__) + + +class LabelPropagationEngine: + """标签传播聚类引擎""" + + def __init__(self, connector: Neo4jConnector): + self.connector = connector + self.repo = CommunityRepository(connector) + + # ────────────────────────────────────────────────────────────────────────── + # 公开接口 + # ────────────────────────────────────────────────────────────────────────── + + async def run( + self, + end_user_id: str, + new_entity_ids: Optional[List[str]] = None, + ) -> None: + """ + 统一入口：自动判断全量还是增量。 + + - 若该用户尚无 Community 节点 → 全量初始化 + - 否则 → 增量更新（仅处理 new_entity_ids） + """ + has_communities = await self.repo.has_communities(end_user_id) + if not has_communities: + logger.info(f"[Clustering] 用户 {end_user_id} 首次聚类，执行全量初始化") + await self.full_clustering(end_user_id) + else: + if new_entity_ids: + logger.info( + f"[Clustering] 增量更新，新实体数: {len(new_entity_ids)}" + ) + await self.incremental_update(new_entity_ids, end_user_id) + + async def full_clustering(self, end_user_id: str) -> None: + """ + 全量标签传播初始化。 + + 1. 拉取所有实体，初始化每个实体为独立社区 + 2. 迭代：每轮对所有实体做邻居投票，更新社区标签 + 3. 直到标签不再变化或达到 MAX_ITERATIONS + 4. 将最终标签写入 Neo4j + """ + entities = await self.repo.get_all_entities(end_user_id) + if not entities: + logger.info(f"[Clustering] 用户 {end_user_id} 无实体，跳过全量聚类") + return + + # 初始化：每个实体持有自己 id 作为社区标签 + labels: Dict[str, str] = {e["id"]: e["id"] for e in entities} + embeddings: Dict[str, Optional[List[float]]] = { + e["id"]: e.get("name_embedding") for e in entities + } + + for iteration in range(MAX_ITERATIONS): + changed = 0 + # 随机顺序（Python dict 在 3.7+ 保持插入顺序，这里直接遍历） + for entity in entities: + eid = entity["id"] + neighbors = await self.repo.get_entity_neighbors(eid, end_user_id) + + # 将邻居的当前内存标签注入（覆盖 Neo4j 中的旧值） + enriched = [] + for nb in neighbors: + nb_copy = dict(nb) + nb_copy["community_id"] = labels.get(nb["id"], nb.get("community_id")) + enriched.append(nb_copy) + + new_label = _weighted_vote(enriched, embeddings.get(eid)) + if new_label and new_label != labels[eid]: + labels[eid] = new_label + changed += 1 + + logger.info( + f"[Clustering] 全量迭代 {iteration + 1}/{MAX_ITERATIONS}，" + f"标签变化数: {changed}" + ) + if changed == 0: + logger.info("[Clustering] 标签已收敛，提前结束迭代") + break + + # 将最终标签写入 Neo4j + await self._flush_labels(labels, end_user_id) + logger.info( + f"[Clustering] 全量聚类完成，共 {len(set(labels.values()))} 个社区，" + f"{len(labels)} 个实体" + ) + + async def incremental_update( + self, new_entity_ids: List[str], end_user_id: str + ) -> None: + """ + 增量更新：只处理新实体及其邻居，不重跑全图。 + + 1. 对每个新实体查询邻居 + 2. 加权多数投票决定社区归属 + 3. 若邻居无社区 → 创建新社区 + 4. 若邻居分属多个社区 → 评估是否合并 + """ + for entity_id in new_entity_ids: + await self._process_single_entity(entity_id, end_user_id) + + # ────────────────────────────────────────────────────────────────────────── + # 内部方法 + # ────────────────────────────────────────────────────────────────────────── + + async def _process_single_entity( + self, entity_id: str, end_user_id: str + ) -> None: + """处理单个新实体的社区分配。""" + neighbors = await self.repo.get_entity_neighbors(entity_id, end_user_id) + + # 查询自身 embedding（从邻居查询结果中无法获取，需单独查） + self_embedding = await self._get_entity_embedding(entity_id, end_user_id) + + if not neighbors: + # 孤立实体：创建单成员社区 + new_cid = self._new_community_id() + await self.repo.upsert_community(new_cid, end_user_id, member_count=1) + await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id) + logger.debug(f"[Clustering] 孤立实体 {entity_id} → 新社区 {new_cid}") + return + + # 统计邻居社区分布 + community_ids_in_neighbors = set( + nb["community_id"] for nb in neighbors if nb.get("community_id") + ) + + target_cid = _weighted_vote(neighbors, self_embedding) + + if target_cid is None: + # 邻居都没有社区，连同新实体一起创建新社区 + new_cid = self._new_community_id() + await self.repo.upsert_community(new_cid, end_user_id) + await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id) + for nb in neighbors: + await self.repo.assign_entity_to_community( + nb["id"], new_cid, end_user_id + ) + await self.repo.refresh_member_count(new_cid, end_user_id) + logger.debug( + f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}" + ) + else: + # 加入得票最多的社区 + await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id) + await self.repo.refresh_member_count(target_cid, end_user_id) + logger.debug(f"[Clustering] 新实体 {entity_id} → 社区 {target_cid}") + + # 若邻居分属多个社区，评估合并 + if len(community_ids_in_neighbors) > 1: + await self._evaluate_merge( + list(community_ids_in_neighbors), end_user_id + ) + + async def _evaluate_merge( + self, community_ids: List[str], end_user_id: str + ) -> None: + """ + 评估多个社区是否应合并。 + + 策略：计算各社区成员 embedding 的平均向量，若两两余弦相似度 > 0.75 则合并。 + 合并时保留成员数最多的社区，其余成员迁移过来。 + """ + MERGE_THRESHOLD = 0.75 + + community_embeddings: Dict[str, Optional[List[float]]] = {} + community_sizes: Dict[str, int] = {} + + for cid in community_ids: + members = await self.repo.get_community_members(cid, end_user_id) + community_sizes[cid] = len(members) + # 计算社区成员 embedding 的平均向量 + valid_embeddings = [ + m["name_embedding"] + for m in members + if m.get("name_embedding") + ] + if valid_embeddings: + dim = len(valid_embeddings[0]) + avg = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + community_embeddings[cid] = avg + else: + community_embeddings[cid] = None + + # 找出应合并的社区对 + to_merge: List[tuple] = [] + cids = list(community_ids) + for i in range(len(cids)): + for j in range(i + 1, len(cids)): + sim = _cosine_similarity( + community_embeddings[cids[i]], + community_embeddings[cids[j]], + ) + if sim > MERGE_THRESHOLD: + to_merge.append((cids[i], cids[j])) + + for c1, c2 in to_merge: + keep = c1 if community_sizes.get(c1, 0) >= community_sizes.get(c2, 0) else c2 + dissolve = c2 if keep == c1 else c1 + members = await self.repo.get_community_members(dissolve, end_user_id) + for m in members: + await self.repo.assign_entity_to_community( + m["id"], keep, end_user_id + ) + await self.repo.refresh_member_count(keep, end_user_id) + logger.info( + f"[Clustering] 社区合并: {dissolve} → {keep}，" + f"迁移 {len(members)} 个成员" + ) + + async def _flush_labels( + self, labels: Dict[str, str], end_user_id: str + ) -> None: + """将内存中的标签批量写入 Neo4j。""" + # 先创建所有唯一社区节点 + unique_communities = set(labels.values()) + for cid in unique_communities: + await self.repo.upsert_community(cid, end_user_id) + + # 再批量分配实体 + for entity_id, community_id in labels.items(): + await self.repo.assign_entity_to_community( + entity_id, community_id, end_user_id + ) + + # 刷新成员数 + for cid in unique_communities: + await self.repo.refresh_member_count(cid, end_user_id) + + async def _get_entity_embedding( + self, entity_id: str, end_user_id: str + ) -> Optional[List[float]]: + """查询单个实体的 name_embedding。""" + try: + result = await self.connector.execute_query( + "MATCH (e:ExtractedEntity {id: $eid, end_user_id: $uid}) " + "RETURN e.name_embedding AS name_embedding", + eid=entity_id, + uid=end_user_id, + ) + return result[0]["name_embedding"] if result else None + except Exception: + return None + + @staticmethod + def _new_community_id() -> str: + return str(uuid.uuid4()) diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py new file mode 100644 index 00000000..16e30a10 --- /dev/null +++ b/api/app/repositories/neo4j/community_repository.py @@ -0,0 +1,129 @@ +"""Community 节点仓库 + +管理 Neo4j 中 Community 节点及 BELONGS_TO_COMMUNITY 边的 CRUD 操作。 +""" + +import logging +from typing import Dict, List, Optional + +from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.repositories.neo4j.cypher_queries import ( + COMMUNITY_NODE_UPSERT, + ENTITY_JOIN_COMMUNITY, + ENTITY_LEAVE_ALL_COMMUNITIES, + GET_ENTITY_NEIGHBORS, + GET_ALL_ENTITIES_FOR_USER, + GET_COMMUNITY_MEMBERS, + CHECK_USER_HAS_COMMUNITIES, + UPDATE_COMMUNITY_MEMBER_COUNT, +) + +logger = logging.getLogger(__name__) + + +class CommunityRepository: + def __init__(self, connector: Neo4jConnector): + self.connector = connector + + async def upsert_community( + self, community_id: str, end_user_id: str, member_count: int = 0 + ) -> Optional[str]: + """创建或更新 Community 节点，返回 community_id。""" + try: + result = await self.connector.execute_query( + COMMUNITY_NODE_UPSERT, + community_id=community_id, + end_user_id=end_user_id, + member_count=member_count, + ) + return result[0]["community_id"] if result else None + except Exception as e: + logger.error(f"upsert_community failed: {e}") + return None + + async def assign_entity_to_community( + self, entity_id: str, community_id: str, end_user_id: str + ) -> bool: + """将实体关联到社区（先解除旧关联，再建立新关联）。""" + try: + await self.connector.execute_query( + ENTITY_LEAVE_ALL_COMMUNITIES, + entity_id=entity_id, + end_user_id=end_user_id, + ) + result = await self.connector.execute_query( + ENTITY_JOIN_COMMUNITY, + entity_id=entity_id, + community_id=community_id, + end_user_id=end_user_id, + ) + return bool(result) + except Exception as e: + logger.error(f"assign_entity_to_community failed: {e}") + return False + + async def get_entity_neighbors( + self, entity_id: str, end_user_id: str + ) -> List[Dict]: + """查询实体的直接邻居及其社区归属。""" + try: + return await self.connector.execute_query( + GET_ENTITY_NEIGHBORS, + entity_id=entity_id, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_entity_neighbors failed: {e}") + return [] + + async def get_all_entities(self, end_user_id: str) -> List[Dict]: + """拉取某用户下所有实体及其当前社区归属。""" + try: + return await self.connector.execute_query( + GET_ALL_ENTITIES_FOR_USER, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_all_entities failed: {e}") + return [] + + async def get_community_members( + self, community_id: str, end_user_id: str + ) -> List[Dict]: + """查询社区成员列表。""" + try: + return await self.connector.execute_query( + GET_COMMUNITY_MEMBERS, + community_id=community_id, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_community_members failed: {e}") + return [] + + async def has_communities(self, end_user_id: str) -> bool: + """检查该用户是否已有 Community 节点（用于判断全量 vs 增量）。""" + try: + result = await self.connector.execute_query( + CHECK_USER_HAS_COMMUNITIES, + end_user_id=end_user_id, + ) + return result[0]["community_count"] > 0 if result else False + except Exception as e: + logger.error(f"has_communities failed: {e}") + return False + + async def refresh_member_count( + self, community_id: str, end_user_id: str + ) -> int: + """重新统计并更新社区成员数，返回最新数量。""" + try: + result = await self.connector.execute_query( + UPDATE_COMMUNITY_MEMBER_COUNT, + community_id=community_id, + end_user_id=end_user_id, + ) + return result[0]["member_count"] if result else 0 + except Exception as e: + logger.error(f"refresh_member_count failed: {e}") + return 0 diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 651c513f..947097a2 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1058,4 +1058,95 @@ Graph_Node_query = """ 3 AS priority LIMIT $limit - """ \ No newline at end of file + """ + + +# ============================================================ +# Community 节点 & BELONGS_TO_COMMUNITY 边 +# ============================================================ + +COMMUNITY_NODE_SAVE = """ +MERGE (c:Community {community_id: $community_id}) +SET c.end_user_id = $end_user_id, + c.formed_at = $formed_at, + c.updated_at = datetime(), + c.status = $status, + c.member_count = $member_count +RETURN c.community_id AS community_id +""" + +COMMUNITY_ADD_MEMBER = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) +SET c.updated_at = datetime(), + c.member_count = $member_count +""" + + + +# ─── Community 聚类相关 Cypher 模板 ─────────────────────────────────────────── + +COMMUNITY_NODE_UPSERT = """ +MERGE (c:Community {community_id: $community_id}) +SET c.end_user_id = $end_user_id, + c.member_count = $member_count, + c.updated_at = datetime() +RETURN c.community_id AS community_id +""" + +ENTITY_JOIN_COMMUNITY = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) +SET c.updated_at = datetime() +RETURN e.id AS entity_id, c.community_id AS community_id +""" + +ENTITY_LEAVE_ALL_COMMUNITIES = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (e)-[r:BELONGS_TO_COMMUNITY]->(:Community) +DELETE r +""" + +GET_ENTITY_NEIGHBORS = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb:ExtractedEntity {end_user_id: $end_user_id}) +OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community) +RETURN DISTINCT + nb.id AS id, + nb.name AS name, + nb.name_embedding AS name_embedding, + nb.activation_value AS activation_value, + CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id +""" + +GET_ALL_ENTITIES_FOR_USER = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id}) +OPTIONAL MATCH (e)-[:BELONGS_TO_COMMUNITY]->(c:Community) +RETURN e.id AS id, + e.name AS name, + e.name_embedding AS name_embedding, + e.activation_value AS activation_value, + CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id +""" + +GET_COMMUNITY_MEMBERS = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id}) +RETURN e.id AS id, e.name AS name, e.entity_type AS entity_type, + e.importance_score AS importance_score, e.activation_value AS activation_value, + e.name_embedding AS name_embedding +ORDER BY coalesce(e.activation_value, 0) DESC +""" + +CHECK_USER_HAS_COMMUNITIES = """ +MATCH (c:Community {end_user_id: $end_user_id}) +RETURN count(c) AS community_count +""" + +UPDATE_COMMUNITY_MEMBER_COUNT = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id}) +WITH c, count(e) AS cnt +SET c.member_count = cnt +RETURN c.community_id AS community_id, cnt AS member_count +""" diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index 526d16ec..a94bc23b 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -1,3 +1,4 @@ +import asyncio from typing import List # 使用新的仓储层 @@ -288,6 +289,14 @@ async def save_dialog_and_statements_to_neo4j( } logger.info("Transaction completed. Summary: %s", summary) logger.debug("Full transaction results: %r", results) + + # 写入成功后，触发聚类 + if entity_nodes: + end_user_id = entity_nodes[0].end_user_id + new_entity_ids = [e.id for e in entity_nodes] + logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}") + await _trigger_clustering(new_entity_ids, end_user_id) + return True except Exception as e: @@ -295,3 +304,28 @@ async def save_dialog_and_statements_to_neo4j( print(f"Neo4j integration error: {e}") print("Continuing without database storage...") return False + + +async def _trigger_clustering( + new_entity_ids: List[str], + end_user_id: str, +) -> None: + """ + 聚类触发函数，自动判断全量初始化还是增量更新。 + """ + connector = None + try: + from app.core.memory.storage_services.clustering_engine import LabelPropagationEngine + logger.info(f"[Clustering] 开始聚类，end_user_id={end_user_id}, 实体数={len(new_entity_ids)}") + connector = Neo4jConnector() + engine = LabelPropagationEngine(connector) + await engine.run(end_user_id=end_user_id, new_entity_ids=new_entity_ids) + logger.info(f"[Clustering] 聚类完成，end_user_id={end_user_id}") + except Exception as e: + logger.error(f"[Clustering] 聚类触发失败: {e}", exc_info=True) + finally: + if connector: + try: + await connector.close() + except Exception: + pass From 744ba31ba6072b1e3f9f9af26a87949d6a1d6313 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Wed, 11 Mar 2026 18:04:04 +0800 Subject: [PATCH 083/196] [changes] Initial stage of community integration --- .../clustering_engine/label_propagation.py | 94 ++++++++++++++----- .../neo4j/community_repository.py | 20 ++++ api/app/repositories/neo4j/cypher_queries.py | 48 +++++----- 3 files changed, 115 insertions(+), 47 deletions(-) diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 80e238fd..cb6e5804 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -141,8 +141,18 @@ class LabelPropagationEngine: # 将最终标签写入 Neo4j await self._flush_labels(labels, end_user_id) + pre_merge_count = len(set(labels.values())) logger.info( - f"[Clustering] 全量聚类完成，共 {len(set(labels.values()))} 个社区，" + f"[Clustering] 全量迭代完成，共 {pre_merge_count} 个社区，" + f"{len(labels)} 个实体，开始后处理合并" + ) + + # 全量初始化后做一轮社区合并（基于 name_embedding 余弦相似度） + all_community_ids = list(set(labels.values())) + await self._evaluate_merge(all_community_ids, end_user_id) + + logger.info( + f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，" f"{len(labels)} 个实体" ) @@ -221,30 +231,50 @@ class LabelPropagationEngine: 策略：计算各社区成员 embedding 的平均向量，若两两余弦相似度 > 0.75 则合并。 合并时保留成员数最多的社区，其余成员迁移过来。 + + 全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。 """ MERGE_THRESHOLD = 0.75 + BATCH_THRESHOLD = 20 # 超过此数量走批量查询 community_embeddings: Dict[str, Optional[List[float]]] = {} community_sizes: Dict[str, int] = {} - for cid in community_ids: - members = await self.repo.get_community_members(cid, end_user_id) - community_sizes[cid] = len(members) - # 计算社区成员 embedding 的平均向量 - valid_embeddings = [ - m["name_embedding"] - for m in members - if m.get("name_embedding") - ] - if valid_embeddings: - dim = len(valid_embeddings[0]) - avg = [ - sum(e[i] for e in valid_embeddings) / len(valid_embeddings) - for i in range(dim) + if len(community_ids) > BATCH_THRESHOLD: + # 批量查询：一次拉取所有社区成员 + all_members = await self.repo.get_all_community_members_batch( + community_ids, end_user_id + ) + for cid in community_ids: + members = all_members.get(cid, []) + community_sizes[cid] = len(members) + valid_embeddings = [ + m["name_embedding"] for m in members if m.get("name_embedding") ] - community_embeddings[cid] = avg - else: - community_embeddings[cid] = None + if valid_embeddings: + dim = len(valid_embeddings[0]) + community_embeddings[cid] = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + else: + community_embeddings[cid] = None + else: + # 增量场景：逐个查询 + for cid in community_ids: + members = await self.repo.get_community_members(cid, end_user_id) + community_sizes[cid] = len(members) + valid_embeddings = [ + m["name_embedding"] for m in members if m.get("name_embedding") + ] + if valid_embeddings: + dim = len(valid_embeddings[0]) + community_embeddings[cid] = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + else: + community_embeddings[cid] = None # 找出应合并的社区对 to_merge: List[tuple] = [] @@ -258,14 +288,32 @@ class LabelPropagationEngine: if sim > MERGE_THRESHOLD: to_merge.append((cids[i], cids[j])) + logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区") + + # 执行合并：用 union-find 思路避免重复迁移已被合并的社区 + # 维护一个 canonical 映射，确保链式合并正确收敛 + canonical: Dict[str, str] = {cid: cid for cid in cids} + + def find(x: str) -> str: + while canonical[x] != x: + canonical[x] = canonical[canonical[x]] + x = canonical[x] + return x + for c1, c2 in to_merge: - keep = c1 if community_sizes.get(c1, 0) >= community_sizes.get(c2, 0) else c2 - dissolve = c2 if keep == c1 else c1 + root1, root2 = find(c1), find(c2) + if root1 == root2: + continue # 已经在同一社区，跳过 + keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2 + dissolve = root2 if keep == root1 else root1 + canonical[dissolve] = keep + members = await self.repo.get_community_members(dissolve, end_user_id) for m in members: - await self.repo.assign_entity_to_community( - m["id"], keep, end_user_id - ) + await self.repo.assign_entity_to_community(m["id"], keep, end_user_id) + # 更新 sizes 以便后续合并决策准确 + community_sizes[keep] = community_sizes.get(keep, 0) + len(members) + community_sizes[dissolve] = 0 await self.repo.refresh_member_count(keep, end_user_id) logger.info( f"[Clustering] 社区合并: {dissolve} → {keep}，" diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 16e30a10..2a1f4f2b 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/api/app/repositories/neo4j/community_repository.py @@ -14,6 +14,7 @@ from app.repositories.neo4j.cypher_queries import ( GET_ENTITY_NEIGHBORS, GET_ALL_ENTITIES_FOR_USER, GET_COMMUNITY_MEMBERS, + GET_ALL_COMMUNITY_MEMBERS_BATCH, CHECK_USER_HAS_COMMUNITIES, UPDATE_COMMUNITY_MEMBER_COUNT, ) @@ -101,6 +102,25 @@ class CommunityRepository: logger.error(f"get_community_members failed: {e}") return [] + async def get_all_community_members_batch( + self, community_ids: List[str], end_user_id: str + ) -> Dict[str, List[Dict]]: + """批量查询多个社区的成员，返回 {community_id: [members]} 字典。""" + try: + rows = await self.connector.execute_query( + GET_ALL_COMMUNITY_MEMBERS_BATCH, + community_ids=community_ids, + end_user_id=end_user_id, + ) + result: Dict[str, List[Dict]] = {} + for row in rows: + cid = row["community_id"] + result.setdefault(cid, []).append(row) + return result + except Exception as e: + logger.error(f"get_all_community_members_batch failed: {e}") + return {} + async def has_communities(self, end_user_id: str) -> bool: """检查该用户是否已有 Community 节点（用于判断全量 vs 增量）。""" try: diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 947097a2..84889d65 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1065,26 +1065,6 @@ Graph_Node_query = """ # Community 节点 & BELONGS_TO_COMMUNITY 边 # ============================================================ -COMMUNITY_NODE_SAVE = """ -MERGE (c:Community {community_id: $community_id}) -SET c.end_user_id = $end_user_id, - c.formed_at = $formed_at, - c.updated_at = datetime(), - c.status = $status, - c.member_count = $member_count -RETURN c.community_id AS community_id -""" - -COMMUNITY_ADD_MEMBER = """ -MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) -MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) -MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) -SET c.updated_at = datetime(), - c.member_count = $member_count -""" - - - # ─── Community 聚类相关 Cypher 模板 ─────────────────────────────────────────── COMMUNITY_NODE_UPSERT = """ @@ -1111,12 +1091,23 @@ DELETE r GET_ENTITY_NEIGHBORS = """ MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) -OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源一：直接关系邻居（EXTRACTED_RELATIONSHIP 边） +OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb1:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源二：同 Statement 共现邻居（REFERENCES_ENTITY 边） +OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e) +OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(nb2:ExtractedEntity {end_user_id: $end_user_id}) +WHERE nb2.id <> e.id + +WITH collect(DISTINCT nb1) + collect(DISTINCT nb2) AS all_neighbors +UNWIND all_neighbors AS nb +WITH nb WHERE nb IS NOT NULL OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community) RETURN DISTINCT - nb.id AS id, - nb.name AS name, - nb.name_embedding AS name_embedding, + nb.id AS id, + nb.name AS name, + nb.name_embedding AS name_embedding, nb.activation_value AS activation_value, CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id """ @@ -1139,6 +1130,15 @@ RETURN e.id AS id, e.name AS name, e.entity_type AS entity_type, ORDER BY coalesce(e.activation_value, 0) DESC """ +GET_ALL_COMMUNITY_MEMBERS_BATCH = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community) +WHERE c.community_id IN $community_ids +RETURN c.community_id AS community_id, + e.id AS id, + e.name_embedding AS name_embedding, + e.activation_value AS activation_value +""" + CHECK_USER_HAS_COMMUNITIES = """ MATCH (c:Community {end_user_id: $end_user_id}) RETURN count(c) AS community_count From 6d8b1aede481b53b4f4fbe7b1c686dad532f59df Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 20:27:50 +0800 Subject: [PATCH 084/196] [add] Create the attribute values of the community nodes --- .../core/memory/agent/utils/write_tools.py | 4 +- .../clustering_engine/label_propagation.py | 83 ++++++++++++++++++- .../neo4j/community_repository.py | 24 ++++++ api/app/repositories/neo4j/cypher_queries.py | 9 ++ api/app/repositories/neo4j/graph_saver.py | 20 +++-- 5 files changed, 132 insertions(+), 8 deletions(-) diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index 22030278..b3707083 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -165,7 +165,9 @@ async def write( statement_chunk_edges=all_statement_chunk_edges, statement_entity_edges=all_statement_entity_edges, entity_edges=all_entity_entity_edges, - connector=neo4j_connector + connector=neo4j_connector, + config_id=config_id, + llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None, ) if success: logger.info("Successfully saved all data to Neo4j") diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index cb6e5804..251d4fea 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -19,6 +19,8 @@ logger = logging.getLogger(__name__) # 全量迭代最大轮数，防止不收敛 MAX_ITERATIONS = 10 +# 社区摘要核心实体数量 +CORE_ENTITY_LIMIT = 5 def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float: @@ -62,9 +64,16 @@ def _weighted_vote( class LabelPropagationEngine: """标签传播聚类引擎""" - def __init__(self, connector: Neo4jConnector): + def __init__( + self, + connector: Neo4jConnector, + config_id: Optional[str] = None, + llm_model_id: Optional[str] = None, + ): self.connector = connector self.repo = CommunityRepository(connector) + self.config_id = config_id + self.llm_model_id = llm_model_id # ────────────────────────────────────────────────────────────────────────── # 公开接口 @@ -155,6 +164,10 @@ class LabelPropagationEngine: f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，" f"{len(labels)} 个实体" ) + # 为所有社区生成元数据 + unique_communities = list(set(labels.values())) + for cid in unique_communities: + await self._generate_community_metadata(cid, end_user_id) async def incremental_update( self, new_entity_ids: List[str], end_user_id: str @@ -211,6 +224,7 @@ class LabelPropagationEngine: logger.debug( f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}" ) + await self._generate_community_metadata(new_cid, end_user_id) else: # 加入得票最多的社区 await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id) @@ -222,6 +236,7 @@ class LabelPropagationEngine: await self._evaluate_merge( list(community_ids_in_neighbors), end_user_id ) + await self._generate_community_metadata(target_cid, end_user_id) async def _evaluate_merge( self, community_ids: List[str], end_user_id: str @@ -354,6 +369,72 @@ class LabelPropagationEngine: except Exception: return None + async def _generate_community_metadata( + self, community_id: str, end_user_id: str + ) -> None: + """ + 为社区生成并写入元数据：名称、摘要、核心实体。 + + - core_entities：按 activation_value 排序取 top-N 实体名称列表（无需 LLM） + - name / summary：若有 llm_model_id 则调用 LLM 生成，否则用实体名称拼接兜底 + """ + try: + members = await self.repo.get_community_members(community_id, end_user_id) + if not members: + return + + # 核心实体：按 activation_value 降序取 top-N + sorted_members = sorted( + members, + key=lambda m: m.get("activation_value") or 0, + reverse=True, + ) + core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")] + all_names = [m["name"] for m in members if m.get("name")] + + name = "、".join(core_entities[:3]) if core_entities else community_id[:8] + summary = f"包含实体：{', '.join(all_names)}" + + # 若有 LLM 配置，调用 LLM 生成更好的名称和摘要 + if self.llm_model_id: + try: + from app.db import get_db_context + from app.core.memory.utils.llm.llm_utils import MemoryClientFactory + + entity_list_str = "、".join(all_names) + prompt = ( + f"以下是一组语义相关的实体：{entity_list_str}\n\n" + f"请为这组实体所代表的主题：\n" + f"1. 起一个简洁的中文名称（不超过10个字）\n" + f"2. 写一句话摘要（不超过50个字）\n\n" + f"严格按以下格式输出，不要有其他内容：\n" + f"名称：<名称>\n摘要：<摘要>" + ) + with get_db_context() as db: + factory = MemoryClientFactory(db) + llm_client = factory.get_llm_client(self.llm_model_id) + response = await llm_client.chat([{"role": "user", "content": prompt}]) + text = response.content if hasattr(response, "content") else str(response) + + for line in text.strip().splitlines(): + if line.startswith("名称："): + name = line[3:].strip() + elif line.startswith("摘要："): + summary = line[3:].strip() + except Exception as e: + logger.warning(f"[Clustering] LLM 生成社区元数据失败，使用兜底值: {e}") + + await self.repo.update_community_metadata( + community_id=community_id, + end_user_id=end_user_id, + name=name, + summary=summary, + core_entities=core_entities, + ) + logger.debug(f"[Clustering] 社区 {community_id} 元数据已更新: name={name}") + except Exception as e: + logger.error(f"[Clustering] _generate_community_metadata failed for {community_id}: {e}") + @staticmethod def _new_community_id() -> str: return str(uuid.uuid4()) diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 2a1f4f2b..6c5c7618 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/api/app/repositories/neo4j/community_repository.py @@ -17,6 +17,7 @@ from app.repositories.neo4j.cypher_queries import ( GET_ALL_COMMUNITY_MEMBERS_BATCH, CHECK_USER_HAS_COMMUNITIES, UPDATE_COMMUNITY_MEMBER_COUNT, + UPDATE_COMMUNITY_METADATA, ) logger = logging.getLogger(__name__) @@ -147,3 +148,26 @@ class CommunityRepository: except Exception as e: logger.error(f"refresh_member_count failed: {e}") return 0 + + async def update_community_metadata( + self, + community_id: str, + end_user_id: str, + name: str, + summary: str, + core_entities: List[str], + ) -> bool: + """更新社区的名称、摘要和核心实体列表。""" + try: + result = await self.connector.execute_query( + UPDATE_COMMUNITY_METADATA, + community_id=community_id, + end_user_id=end_user_id, + name=name, + summary=summary, + core_entities=core_entities, + ) + return bool(result) + except Exception as e: + logger.error(f"update_community_metadata failed: {e}") + return False diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 84889d65..b270ed64 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1150,3 +1150,12 @@ WITH c, count(e) AS cnt SET c.member_count = cnt RETURN c.community_id AS community_id, cnt AS member_count """ + +UPDATE_COMMUNITY_METADATA = """ +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +SET c.name = $name, + c.summary = $summary, + c.core_entities = $core_entities, + c.updated_at = datetime() +RETURN c.community_id AS community_id +""" diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index a94bc23b..2ef9bafc 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -1,5 +1,6 @@ import asyncio -from typing import List +import os +from typing import List, Optional # 使用新的仓储层 from app.repositories.neo4j.neo4j_connector import Neo4jConnector @@ -156,7 +157,9 @@ async def save_dialog_and_statements_to_neo4j( entity_edges: List[EntityEntityEdge], statement_chunk_edges: List[StatementChunkEdge], statement_entity_edges: List[StatementEntityEdge], - connector: Neo4jConnector + connector: Neo4jConnector, + config_id: Optional[str] = None, + llm_model_id: Optional[str] = None, ) -> bool: """Save dialogue nodes, chunk nodes, statement nodes, entities, and all relationships to Neo4j using graph models. @@ -290,12 +293,15 @@ async def save_dialog_and_statements_to_neo4j( logger.info("Transaction completed. Summary: %s", summary) logger.debug("Full transaction results: %r", results) - # 写入成功后，触发聚类 - if entity_nodes: + # 写入成功后，触发聚类（可通过环境变量 CLUSTERING_ENABLED=false 禁用，用于基准测试对比） + clustering_enabled = os.getenv("CLUSTERING_ENABLED", "true").lower() != "false" + if entity_nodes and clustering_enabled: end_user_id = entity_nodes[0].end_user_id new_entity_ids = [e.id for e in entity_nodes] logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}") - await _trigger_clustering(new_entity_ids, end_user_id) + asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, config_id=config_id, llm_model_id=llm_model_id)) + elif entity_nodes and not clustering_enabled: + logger.info("[Clustering] 聚类已禁用（CLUSTERING_ENABLED=false），跳过聚类触发") return True @@ -309,6 +315,8 @@ async def save_dialog_and_statements_to_neo4j( async def _trigger_clustering( new_entity_ids: List[str], end_user_id: str, + config_id: Optional[str] = None, + llm_model_id: Optional[str] = None, ) -> None: """ 聚类触发函数，自动判断全量初始化还是增量更新。 @@ -318,7 +326,7 @@ async def _trigger_clustering( from app.core.memory.storage_services.clustering_engine import LabelPropagationEngine logger.info(f"[Clustering] 开始聚类，end_user_id={end_user_id}, 实体数={len(new_entity_ids)}") connector = Neo4jConnector() - engine = LabelPropagationEngine(connector) + engine = LabelPropagationEngine(connector, config_id=config_id, llm_model_id=llm_model_id) await engine.run(end_user_id=end_user_id, new_entity_ids=new_entity_ids) logger.info(f"[Clustering] 聚类完成，end_user_id={end_user_id}") except Exception as e: From 967139cea4e6527f5d823b18c49702b40b187a8b Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 12:59:36 +0800 Subject: [PATCH 085/196] [add] Community node interface development --- .../controllers/user_memory_controllers.py | 37 ++++ api/app/services/user_memory_service.py | 160 ++++++++++++++++++ 2 files changed, 197 insertions(+) diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index d3fe7d83..be796ff9 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -17,6 +17,7 @@ from app.services.user_memory_service import ( UserMemoryService, analytics_memory_types, analytics_graph_data, + analytics_community_graph_data, ) from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction from app.schemas.response_schema import ApiResponse @@ -295,6 +296,42 @@ async def get_graph_data_api( return fail(BizCode.INTERNAL_ERROR, "图数据查询失败", str(e)) +@router.get("/analytics/community_graph", response_model=ApiResponse) +async def get_community_graph_data_api( + end_user_id: str, + current_user: User = Depends(get_current_user), + db: Session = Depends(get_db), +) -> dict: + workspace_id = current_user.current_workspace_id + + if workspace_id is None: + api_logger.warning(f"用户 {current_user.username} 尝试查询社区图谱但未选择工作空间") + return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") + + api_logger.info( + f"社区图谱查询请求: end_user_id={end_user_id}, user={current_user.username}, " + f"workspace={workspace_id}" + ) + + try: + result = await analytics_community_graph_data(db=db, end_user_id=end_user_id) + + if "message" in result and result["statistics"]["total_nodes"] == 0: + api_logger.warning(f"社区图谱查询返回空结果: {result.get('message')}") + return success(data=result, msg=result.get("message", "查询成功")) + + api_logger.info( + f"成功获取社区图谱: end_user_id={end_user_id}, " + f"nodes={result['statistics']['total_nodes']}, " + f"edges={result['statistics']['total_edges']}" + ) + return success(data=result, msg="查询成功") + + except Exception as e: + api_logger.error(f"社区图谱查询失败: end_user_id={end_user_id}, error={str(e)}") + return fail(BizCode.INTERNAL_ERROR, "社区图谱查询失败", str(e)) + + @router.get("/read_end_user/profile", response_model=ApiResponse) async def get_end_user_profile( end_user_id: str, diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 8bacc112..d21df064 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1727,6 +1727,166 @@ async def analytics_graph_data( # 辅助函数 +async def analytics_community_graph_data( + db: Session, + end_user_id: str, +) -> Dict[str, Any]: + """ + 获取社区图谱数据，包含 Community 节点、ExtractedEntity 节点及其关系。 + + Returns: + 包含 nodes、edges、statistics 的字典，格式与 analytics_graph_data 一致 + """ + try: + user_uuid = uuid.UUID(end_user_id) + repo = EndUserRepository(db) + end_user = repo.get_by_id(user_uuid) + if not end_user: + return { + "nodes": [], "edges": [], + "statistics": {"total_nodes": 0, "total_edges": 0, "node_types": {}, "edge_types": {}}, + "message": "用户不存在" + } + + # 查询社区节点、实体节点、BELONGS_TO_COMMUNITY 边、实体间关系 + cypher = """ + MATCH (c:Community {end_user_id: $end_user_id}) + MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[b:BELONGS_TO_COMMUNITY]->(c) + OPTIONAL MATCH (e)-[r:EXTRACTED_RELATIONSHIP]-(e2:ExtractedEntity {end_user_id: $end_user_id}) + RETURN + elementId(c) AS c_id, + properties(c) AS c_props, + elementId(e) AS e_id, + properties(e) AS e_props, + elementId(b) AS b_id, + elementId(e2) AS e2_id, + properties(e2) AS e2_props, + elementId(r) AS r_id, + type(r) AS r_type, + properties(r) AS r_props, + startNode(r) = e AS r_from_e + """ + rows = await _neo4j_connector.execute_query(cypher, end_user_id=end_user_id) + + nodes_map: Dict[str, dict] = {} + edges_map: Dict[str, dict] = {} + # 记录每个 Community 对应的实体 id 列表 + community_members: Dict[str, list] = {} + + for row in rows: + # Community 节点 + c_id = row["c_id"] + if c_id and c_id not in nodes_map: + raw = row["c_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "community_id", "end_user_id", "member_count", "updated_at", + "name", "summary", "core_entities", + ) if k in raw} + nodes_map[c_id] = { + "id": c_id, + "label": "Community", + "properties": props, + } + + # ExtractedEntity 节点 (e) + e_id = row["e_id"] + if e_id and e_id not in nodes_map: + raw = row["e_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "name", "end_user_id", "description", "created_at", "entity_type", + ) if k in raw} + # 注入所属社区名称（c 是 e 直接归属的社区） + c_raw = row["c_props"] or {} + props["community_name"] = _clean_neo4j_value(c_raw.get("name")) or "" + nodes_map[e_id] = { + "id": e_id, + "label": "ExtractedEntity", + "properties": props, + } + + # ExtractedEntity 节点 (e2，可选) + e2_id = row.get("e2_id") + if e2_id and e2_id not in nodes_map: + raw = row["e2_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "name", "end_user_id", "description", "created_at", "entity_type", + ) if k in raw} + # e2 的社区归属在后处理阶段通过 community_members 补充 + props["community_name"] = "" + nodes_map[e2_id] = { + "id": e2_id, + "label": "ExtractedEntity", + "properties": props, + } + + # BELONGS_TO_COMMUNITY 边 + b_id = row["b_id"] + if b_id and b_id not in edges_map: + edges_map[b_id] = { + "id": b_id, + "source": e_id, + "target": c_id, + } + # 收集社区成员 id + if c_id and e_id: + community_members.setdefault(c_id, []) + if e_id not in community_members[c_id]: + community_members[c_id].append(e_id) + + # EXTRACTED_RELATIONSHIP 边（可选） + r_id = row.get("r_id") + if r_id and r_id not in edges_map and e2_id: + r_props = {k: _clean_neo4j_value(v) for k, v in (row["r_props"] or {}).items()} + source = e_id if row.get("r_from_e") else e2_id + target = e2_id if row.get("r_from_e") else e_id + edges_map[r_id] = { + "id": r_id, + "source": source, + "target": target, + } + + nodes = list(nodes_map.values()) + edges = list(edges_map.values()) + + # 为每个 Community 节点注入 member_entity_ids，同时补全 e2 节点的 community_name + for c_id, member_ids in community_members.items(): + c_node = nodes_map.get(c_id) + if c_node: + c_node["properties"]["member_entity_ids"] = member_ids + c_name = c_node["properties"].get("name") or "" + # 补全属于该社区但 community_name 为空的实体（即 e2 节点） + for eid in member_ids: + e_node = nodes_map.get(eid) + if e_node and e_node["label"] == "ExtractedEntity": + if not e_node["properties"].get("community_name"): + e_node["properties"]["community_name"] = c_name + + node_type_counts: Dict[str, int] = {} + for n in nodes: + node_type_counts[n["label"]] = node_type_counts.get(n["label"], 0) + 1 + + return { + "nodes": nodes, + "edges": edges, + "statistics": { + "total_nodes": len(nodes), + "total_edges": len(edges), + "node_types": node_type_counts, + } + } + + except ValueError: + logger.error(f"无效的 end_user_id 格式: {end_user_id}") + return { + "nodes": [], "edges": [], + "statistics": {"total_nodes": 0, "total_edges": 0, "node_types": {}, "edge_types": {}}, + "message": "无效的用户ID格式" + } + except Exception as e: + logger.error(f"获取社区图谱数据失败: {str(e)}", exc_info=True) + raise + + async def _extract_node_properties(label: str, properties: Dict[str, Any],node_id: str) -> Dict[str, Any]: """ 根据节点类型提取需要的属性字段 From 668539e737c43f79330ca5652181ba5de16f1fa9 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 15:47:53 +0800 Subject: [PATCH 086/196] [add] Selective merge submission --- api/app/celery_app.py | 1 + .../memory_dashboard_controller.py | 14 +- .../clustering_engine/label_propagation.py | 70 +++++++-- api/app/tasks.py | 138 ++++++++++++++++++ 4 files changed, 206 insertions(+), 17 deletions(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 60c22855..e77ed683 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -116,6 +116,7 @@ celery_app.conf.update( 'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'}, 'app.tasks.init_implicit_emotions_for_users': {'queue': 'periodic_tasks'}, 'app.tasks.init_interest_distribution_for_users': {'queue': 'periodic_tasks'}, + 'app.tasks.init_community_clustering_for_users': {'queue': 'periodic_tasks'}, }, ) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 3bbb5cf7..ce32a519 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -193,7 +193,19 @@ async def get_workspace_end_users( await aio_redis_set(cache_key, json.dumps(result), expire=30) except Exception as e: api_logger.warning(f"Redis 缓存写入失败: {str(e)}") - + + # 触发社区聚类补全任务（异步，不阻塞接口响应） + # 对有 ExtractedEntity 但无 Community 节点的存量用户自动补跑全量聚类 + try: + from app.tasks import init_community_clustering_for_users + init_community_clustering_for_users.apply_async( + kwargs={"end_user_ids": end_user_ids}, + queue="periodic_tasks", + ) + api_logger.info(f"已触发社区聚类补全任务，候选用户数: {len(end_user_ids)}") + except Exception as e: + api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {str(e)}") + api_logger.info(f"成功获取 {len(end_users)} 个宿主记录") return success(data=result, msg="宿主列表获取成功") diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 251d4fea..4491b416 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -165,8 +165,15 @@ class LabelPropagationEngine: f"{len(labels)} 个实体" ) # 为所有社区生成元数据 - unique_communities = list(set(labels.values())) - for cid in unique_communities: + # 注意：_evaluate_merge 后部分社区已被合并消解，需重新从 Neo4j 查询实际存活的社区 + # 不能复用 labels.values()，那里包含已被 dissolve 的旧社区 ID + surviving_communities = await self.repo.get_all_entities(end_user_id) + surviving_community_ids = list({ + e.get("community_id") for e in surviving_communities + if e.get("community_id") + }) + logger.info(f"[Clustering] 合并后实际存活社区数: {len(surviving_community_ids)}") + for cid in surviving_community_ids: await self._generate_community_metadata(cid, end_user_id) async def incremental_update( @@ -249,7 +256,7 @@ class LabelPropagationEngine: 全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。 """ - MERGE_THRESHOLD = 0.75 + MERGE_THRESHOLD = 0.85 BATCH_THRESHOLD = 20 # 超过此数量走批量查询 community_embeddings: Dict[str, Optional[List[float]]] = {} @@ -305,34 +312,65 @@ class LabelPropagationEngine: logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区") - # 执行合并：用 union-find 思路避免重复迁移已被合并的社区 - # 维护一个 canonical 映射，确保链式合并正确收敛 - canonical: Dict[str, str] = {cid: cid for cid in cids} + # 执行合并：逐对处理，每次合并后重新计算合并社区的平均向量 + # 避免 union-find 链式传递导致语义不相关的社区被间接合并 + # （A≈B、B≈C 不代表 A≈C，不能因传递性把 A/B/C 全部合并） + merged_into: Dict[str, str] = {} # dissolve → keep 的最终映射 - def find(x: str) -> str: - while canonical[x] != x: - canonical[x] = canonical[canonical[x]] - x = canonical[x] + def get_root(x: str) -> str: + """路径压缩，找到 x 当前所属的根社区。""" + while x in merged_into: + merged_into[x] = merged_into.get(merged_into[x], merged_into[x]) + x = merged_into[x] return x for c1, c2 in to_merge: - root1, root2 = find(c1), find(c2) + root1, root2 = get_root(c1), get_root(c2) if root1 == root2: - continue # 已经在同一社区，跳过 + continue + + # 用合并后的最新平均向量重新验证相似度 + # 防止链式传递：A≈B 合并后 B 的向量已更新，C 必须和新 B 相似才能合并 + current_sim = _cosine_similarity( + community_embeddings.get(root1), + community_embeddings.get(root2), + ) + if current_sim <= MERGE_THRESHOLD: + # 合并后向量已漂移，不再满足阈值，跳过 + logger.debug( + f"[Clustering] 跳过合并 {root1} ↔ {root2}，" + f"当前相似度 {current_sim:.3f} ≤ {MERGE_THRESHOLD}" + ) + continue + keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2 dissolve = root2 if keep == root1 else root1 - canonical[dissolve] = keep + merged_into[dissolve] = keep members = await self.repo.get_community_members(dissolve, end_user_id) for m in members: await self.repo.assign_entity_to_community(m["id"], keep, end_user_id) - # 更新 sizes 以便后续合并决策准确 - community_sizes[keep] = community_sizes.get(keep, 0) + len(members) + + # 合并后重新计算 keep 的平均向量（加权平均） + keep_emb = community_embeddings.get(keep) + dissolve_emb = community_embeddings.get(dissolve) + keep_size = community_sizes.get(keep, 0) + dissolve_size = community_sizes.get(dissolve, 0) + total_size = keep_size + dissolve_size + if keep_emb and dissolve_emb and total_size > 0: + dim = len(keep_emb) + community_embeddings[keep] = [ + (keep_emb[i] * keep_size + dissolve_emb[i] * dissolve_size) / total_size + for i in range(dim) + ] + community_embeddings[dissolve] = None + + community_sizes[keep] = total_size community_sizes[dissolve] = 0 await self.repo.refresh_member_count(keep, end_user_id) logger.info( f"[Clustering] 社区合并: {dissolve} → {keep}，" - f"迁移 {len(members)} 个成员" + f"相似度={current_sim:.3f}，迁移 {len(members)} 个成员" ) async def _flush_labels( diff --git a/api/app/tasks.py b/api/app/tasks.py index 5e1550bd..f737bb48 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2662,3 +2662,141 @@ def write_perceptual_memory( file_url, file_message, )) + + +# ============================================================================= +# 社区聚类补全任务（触发型） +# ============================================================================= + +@celery_app.task( + name="app.tasks.init_community_clustering_for_users", + bind=True, + ignore_result=False, + max_retries=0, + acks_late=False, + time_limit=7200, # 2小时硬超时 + soft_time_limit=6900, +) +def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]: + """触发型任务：检查指定用户列表，对有 ExtractedEntity 但无 Community 节点的用户执行全量聚类。 + + 由 /dashboard/end_users 接口触发，已有社区节点的用户直接跳过。 + + Args: + end_user_ids: 需要检查的用户 ID 列表 + + Returns: + 包含任务执行结果的字典 + """ + start_time = time.time() + + async def _run() -> Dict[str, Any]: + from app.core.logging_config import get_logger + from app.repositories.neo4j.community_repository import CommunityRepository + from app.repositories.neo4j.neo4j_connector import Neo4jConnector + from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine + + logger = get_logger(__name__) + logger.info(f"[CommunityCluster] 开始社区聚类补全任务，候选用户数: {len(end_user_ids)}") + + initialized = 0 + skipped = 0 + failed = 0 + + connector = Neo4jConnector() + try: + repo = CommunityRepository(connector) + + # 获取 llm_model_id（从第一个用户的配置中读取，作为全局兜底） + llm_model_id = None + try: + with get_db_context() as db: + from app.services.memory_agent_service import get_end_user_connected_config + from app.services.memory_config_service import MemoryConfigService + for uid in end_user_ids: + try: + connected = get_end_user_connected_config(uid, db) + config_id = connected.get("memory_config_id") + workspace_id = connected.get("workspace_id") + if config_id or workspace_id: + cfg = MemoryConfigService(db).load_memory_config( + config_id=config_id, workspace_id=workspace_id + ) + llm_model_id = str(cfg.llm_model_id) + break + except Exception: + continue + except Exception as e: + logger.warning(f"[CommunityCluster] 获取 LLM 配置失败，将使用兜底值: {e}") + + engine = LabelPropagationEngine( + connector=connector, + llm_model_id=llm_model_id, + ) + + for end_user_id in end_user_ids: + try: + # 已有社区节点则跳过 + has_communities = await repo.has_communities(end_user_id) + if has_communities: + skipped += 1 + logger.debug(f"[CommunityCluster] 用户 {end_user_id} 已有社区节点，跳过") + continue + + # 检查是否有 ExtractedEntity 节点 + entities = await repo.get_all_entities(end_user_id) + if not entities: + skipped += 1 + logger.debug(f"[CommunityCluster] 用户 {end_user_id} 无实体节点，跳过") + continue + + logger.info(f"[CommunityCluster] 用户 {end_user_id} 有 {len(entities)} 个实体，开始全量聚类") + await engine.full_clustering(end_user_id) + initialized += 1 + logger.info(f"[CommunityCluster] 用户 {end_user_id} 聚类完成") + + except Exception as e: + failed += 1 + logger.error(f"[CommunityCluster] 用户 {end_user_id} 聚类失败: {e}") + + finally: + await connector.close() + + logger.info( + f"[CommunityCluster] 任务完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}" + ) + return { + "status": "SUCCESS", + "initialized": initialized, + "skipped": skipped, + "failed": failed, + } + + try: + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) + result["elapsed_time"] = time.time() - start_time + result["task_id"] = self.request.id + return result + + except Exception as e: + return { + "status": "FAILURE", + "error": str(e), + "elapsed_time": time.time() - start_time, + "task_id": self.request.id, + } From 5141a9104187f4c6b29ded29ed0ec9646a78ef0f Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 15:52:38 +0800 Subject: [PATCH 087/196] [changes] --- .../clustering_engine/label_propagation.py | 8 +++++- .../neo4j/community_repository.py | 21 ++++++++++++++++ api/app/repositories/neo4j/cypher_queries.py | 25 +++++++++++++++++++ 3 files changed, 53 insertions(+), 1 deletion(-) diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 4491b416..cbc303b1 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -121,12 +121,18 @@ class LabelPropagationEngine: e["id"]: e.get("name_embedding") for e in entities } + # 预加载所有实体的邻居，避免迭代内 O(iterations * |E|) 次 Neo4j 往返 + logger.info(f"[Clustering] 预加载 {len(entities)} 个实体的邻居图...") + neighbors_cache: Dict[str, List[Dict]] = await self.repo.get_all_entity_neighbors_batch(end_user_id) + logger.info(f"[Clustering] 邻居预加载完成，覆盖实体数: {len(neighbors_cache)}") + for iteration in range(MAX_ITERATIONS): changed = 0 # 随机顺序（Python dict 在 3.7+ 保持插入顺序，这里直接遍历） for entity in entities: eid = entity["id"] - neighbors = await self.repo.get_entity_neighbors(eid, end_user_id) + # 直接从缓存取邻居，不再发起 Neo4j 查询 + neighbors = neighbors_cache.get(eid, []) # 将邻居的当前内存标签注入（覆盖 Neo4j 中的旧值） enriched = [] diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 6c5c7618..f2f11f76 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/api/app/repositories/neo4j/community_repository.py @@ -15,6 +15,7 @@ from app.repositories.neo4j.cypher_queries import ( GET_ALL_ENTITIES_FOR_USER, GET_COMMUNITY_MEMBERS, GET_ALL_COMMUNITY_MEMBERS_BATCH, + GET_ALL_ENTITY_NEIGHBORS_BATCH, CHECK_USER_HAS_COMMUNITIES, UPDATE_COMMUNITY_MEMBER_COUNT, UPDATE_COMMUNITY_METADATA, @@ -78,6 +79,26 @@ class CommunityRepository: logger.error(f"get_entity_neighbors failed: {e}") return [] + async def get_all_entity_neighbors_batch( + self, end_user_id: str + ) -> Dict[str, List[Dict]]: + """一次性批量拉取该用户下所有实体的邻居，返回 {entity_id: [neighbors]} 字典。 + 用于全量聚类预加载，避免每个实体单独查询。""" + try: + rows = await self.connector.execute_query( + GET_ALL_ENTITY_NEIGHBORS_BATCH, + end_user_id=end_user_id, + ) + result: Dict[str, List[Dict]] = {} + for row in rows: + eid = row["entity_id"] + neighbor = {k: v for k, v in row.items() if k != "entity_id"} + result.setdefault(eid, []).append(neighbor) + return result + except Exception as e: + logger.error(f"get_all_entity_neighbors_batch failed: {e}") + return {} + async def get_all_entities(self, end_user_id: str) -> List[Dict]: """拉取某用户下所有实体及其当前社区归属。""" try: diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index b270ed64..3487e55e 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1159,3 +1159,28 @@ SET c.name = $name, c.updated_at = datetime() RETURN c.community_id AS community_id """ + +GET_ALL_ENTITY_NEIGHBORS_BATCH = """ +// 批量拉取某用户下所有实体的邻居（用于全量聚类预加载） +MATCH (e:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源一：直接关系邻居 +OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb1:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源二：同 Statement 共现邻居 +OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e) +OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(nb2:ExtractedEntity {end_user_id: $end_user_id}) +WHERE nb2.id <> e.id + +WITH e, collect(DISTINCT nb1) + collect(DISTINCT nb2) AS all_neighbors +UNWIND all_neighbors AS nb +WITH e, nb WHERE nb IS NOT NULL +OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community) +RETURN DISTINCT + e.id AS entity_id, + nb.id AS id, + nb.name AS name, + nb.name_embedding AS name_embedding, + nb.activation_value AS activation_value, + CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id +""" From c354618e2004407f875f46db53be6f97cecd1508 Mon Sep 17 00:00:00 2001 From: wxy Date: Fri, 13 Mar 2026 15:57:23 +0800 Subject: [PATCH 088/196] feat(app): add shared_only filter and batch unshare endpoints --- api/app/controllers/app_controller.py | 36 ++++++++++++ api/app/services/app_service.py | 80 ++++++++++++++++++++++++++- 2 files changed, 114 insertions(+), 2 deletions(-) diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index 5d1551d8..73ff7279 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -53,6 +53,7 @@ def list_apps( status: str | None = None, search: str | None = None, include_shared: bool = True, + shared_only: bool = False, page: int = 1, pagesize: int = 10, ids: Optional[str] = None, @@ -84,6 +85,7 @@ def list_apps( status=status, search=search, include_shared=include_shared, + shared_only=shared_only, page=page, pagesize=pagesize, ) @@ -107,6 +109,23 @@ def list_my_shared_out( return success(data=data) +@router.delete("/share/{target_workspace_id}", summary="取消对某工作空间的所有应用分享") +@cur_workspace_access_guard() +def unshare_all_apps_to_workspace( + target_workspace_id: uuid.UUID, + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """Cancel all app shares from current workspace to a target workspace.""" + workspace_id = current_user.current_workspace_id + service = app_service.AppService(db) + count = service.unshare_all_apps_to_workspace( + target_workspace_id=target_workspace_id, + workspace_id=workspace_id + ) + return success(msg=f"已取消 {count} 个应用的分享") + + @router.get("/{app_id}", summary="获取应用详情") @cur_workspace_access_guard() def get_app( @@ -397,6 +416,23 @@ def list_app_shares( return success(data=data) +@router.delete("/shared/{source_workspace_id}", summary="批量移除某来源工作空间的所有共享应用") +@cur_workspace_access_guard() +def remove_all_shared_apps_from_workspace( + source_workspace_id: uuid.UUID, + db: Session = Depends(get_db), + current_user=Depends(get_current_user), +): + """Remove all shared apps from a specific source workspace (recipient operation).""" + workspace_id = current_user.current_workspace_id + service = app_service.AppService(db) + count = service.remove_all_shared_apps_from_workspace( + source_workspace_id=source_workspace_id, + workspace_id=workspace_id + ) + return success(msg=f"已移除 {count} 个共享应用") + + @router.delete("/{app_id}/shared", summary="移除共享给我的应用") @cur_workspace_access_guard() def remove_shared_app( diff --git a/api/app/services/app_service.py b/api/app/services/app_service.py index 2326be6e..d83f88d5 100644 --- a/api/app/services/app_service.py +++ b/api/app/services/app_service.py @@ -12,7 +12,7 @@ import uuid from typing import Annotated, Any, Dict, List, Optional, Tuple from fastapi import Depends -from sqlalchemy import and_, func, or_, select +from sqlalchemy import and_, delete, func, or_, select from sqlalchemy.orm import Session from app.core.error_codes import BizCode @@ -878,6 +878,7 @@ class AppService: status: Optional[str] = None, search: Optional[str] = None, include_shared: bool = True, + shared_only: bool = False, page: int = 1, pagesize: int = 10, ) -> Tuple[List[App], int]: @@ -924,7 +925,14 @@ class AppService: filters.append(func.lower(App.name).like(f"%{search.lower()}%")) # 基础查询：本工作空间的应用 - if include_shared: + if shared_only: + # 只返回共享给本工作空间的应用，不含自有应用 + shared_app_ids_stmt = ( + select(AppShare.source_app_id) + .where(AppShare.target_workspace_id == workspace_id) + ) + stmt = select(App).where(App.id.in_(shared_app_ids_stmt)) + elif include_shared: # 查询本工作空间的应用 + 分享给本工作空间的应用 # 使用 OR 条件：workspace_id = current OR app_id IN (shared apps) @@ -1891,6 +1899,39 @@ class AppService: extra={"app_id": str(app_id), "target_workspace_id": str(target_workspace_id)} ) + def unshare_all_apps_to_workspace( + self, + *, + target_workspace_id: uuid.UUID, + workspace_id: uuid.UUID + ) -> int: + """Cancel all app shares from current workspace to a target workspace. + + Args: + target_workspace_id: Target workspace ID to cancel all shares to + workspace_id: Current workspace ID (source) + + Returns: + Number of share records deleted + """ + from app.models import AppShare + + logger.info( + "取消对目标工作空间的所有应用分享", + extra={"target_workspace_id": str(target_workspace_id), "workspace_id": str(workspace_id)} + ) + + stmt = delete(AppShare).where( + AppShare.source_workspace_id == workspace_id, + AppShare.target_workspace_id == target_workspace_id + ) + result = self.db.execute(stmt) + self.db.commit() + + count = result.rowcount + logger.info("已取消分享记录数", extra={"count": count}) + return count + def list_app_shares( self, *, @@ -1976,6 +2017,39 @@ class AppService: extra={"app_id": str(app_id), "workspace_id": str(workspace_id)} ) + def remove_all_shared_apps_from_workspace( + self, + *, + source_workspace_id: uuid.UUID, + workspace_id: uuid.UUID + ) -> int: + """Remove all shared apps from a specific source workspace. + + Args: + source_workspace_id: The workspace that shared the apps + workspace_id: Current workspace ID (recipient) + + Returns: + Number of share records deleted + """ + from app.models import AppShare + + logger.info( + "批量移除来源工作空间的共享应用", + extra={"source_workspace_id": str(source_workspace_id), "workspace_id": str(workspace_id)} + ) + + stmt = delete(AppShare).where( + AppShare.source_workspace_id == source_workspace_id, + AppShare.target_workspace_id == workspace_id + ) + result = self.db.execute(stmt) + self.db.commit() + + count = result.rowcount + logger.info("已移除共享记录数", extra={"count": count}) + return count + def list_my_shared_out( self, *, @@ -2139,6 +2213,7 @@ def list_apps( status: Optional[str] = None, search: Optional[str] = None, include_shared: bool = True, + shared_only: bool = False, page: int = 1, pagesize: int = 10, ) -> Tuple[List[App], int]: @@ -2151,6 +2226,7 @@ def list_apps( status=status, search=search, include_shared=include_shared, + shared_only=shared_only, page=page, pagesize=pagesize, ) From d4f2094ee01b7d795e57fd245b65d7e95cad9189 Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Fri, 13 Mar 2026 16:18:46 +0800 Subject: [PATCH 089/196] fix(mcp): The token configuration modification of MCP Market needs to be verified. --- .../mcp_market_config_controller.py | 43 +++++++++++++++++-- 1 file changed, 39 insertions(+), 4 deletions(-) diff --git a/api/app/controllers/mcp_market_config_controller.py b/api/app/controllers/mcp_market_config_controller.py index 95449310..ab4c0768 100644 --- a/api/app/controllers/mcp_market_config_controller.py +++ b/api/app/controllers/mcp_market_config_controller.py @@ -59,7 +59,7 @@ async def get_mcp_servers( api_logger.warning(f"Paging parameters exceed ModelScope limit: page={page}, pagesize={pagesize}") raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail=f"page × pagesize must not exceed 100 (got {page} × {pagesize} = {page * pagesize})" + detail=f"The maximum number of MCP services can view is 100. Please visit the ModelScope MCP Plaza." ) # 2. Query mcp market config information from the database @@ -238,7 +238,26 @@ async def create_mcp_market_config( try: api_logger.debug(f"Start creating the mcp market config: {create_data.mcp_market_id}") - # 1. Check if the mcp market name already exists + # 1. Validate token can access ModelScope MCP market + if not create_data.token: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="Token is required to access ModelScope MCP market" + ) + try: + api = MCPApi() + api.login(create_data.token) + body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None} + cookies = api.get_cookies(create_data.token) + r = api.session.put(url=api.mcp_base_url, headers=api.builder_headers(api.headers), json=body, cookies=cookies) + raise_for_http_status(r) + except Exception as e: + api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Unable to access ModelScope MCP market with the provided token: {str(e)}" + ) + # 2. Check if the mcp market name already exists db_mcp_market_config_exist = mcp_market_config_service.get_mcp_market_config_by_mcp_market_id(db, mcp_market_id=create_data.mcp_market_id, current_user=current_user) if db_mcp_market_config_exist: api_logger.warning(f"The mcp market id already exists: {create_data.mcp_market_id}") @@ -332,7 +351,23 @@ async def update_mcp_market_config( f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}") return success(msg='The mcp market config does not exist or access is denied') - # 2. Update fields (only update non-null fields) + # 2. Validate new token if provided + if update_data.token is not None: + try: + api = MCPApi() + api.login(update_data.token) + body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None} + cookies = api.get_cookies(update_data.token) + r = api.session.put(url=api.mcp_base_url, headers=api.builder_headers(api.headers), json=body, cookies=cookies) + raise_for_http_status(r) + except Exception as e: + api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Unable to access ModelScope MCP market with the provided token: {str(e)}" + ) + + # 3. Update fields (only update non-null fields) api_logger.debug(f"Start updating the mcp market config fields: {mcp_market_config_id}") update_dict = update_data.dict(exclude_unset=True) updated_fields = [] @@ -347,7 +382,7 @@ async def update_mcp_market_config( if updated_fields: api_logger.debug(f"updated fields: {', '.join(updated_fields)}") - # 3. Save to database + # 4. Save to database try: db.commit() db.refresh(db_mcp_market_config) From aab54ca1a8d6c21bfb99a341233f2c461a63a590 Mon Sep 17 00:00:00 2001 From: wxy Date: Fri, 13 Mar 2026 16:19:35 +0800 Subject: [PATCH 090/196] refactor(app): address AI review suggestions on sharing endpoints --- api/app/controllers/app_controller.py | 4 ++-- api/app/services/app_service.py | 28 +++++++++++++++++++-------- 2 files changed, 22 insertions(+), 10 deletions(-) diff --git a/api/app/controllers/app_controller.py b/api/app/controllers/app_controller.py index 73ff7279..63f484ca 100644 --- a/api/app/controllers/app_controller.py +++ b/api/app/controllers/app_controller.py @@ -123,7 +123,7 @@ def unshare_all_apps_to_workspace( target_workspace_id=target_workspace_id, workspace_id=workspace_id ) - return success(msg=f"已取消 {count} 个应用的分享") + return success(msg=f"已取消 {count} 个应用的分享", data={"count": count}) @router.get("/{app_id}", summary="获取应用详情") @@ -430,7 +430,7 @@ def remove_all_shared_apps_from_workspace( source_workspace_id=source_workspace_id, workspace_id=workspace_id ) - return success(msg=f"已移除 {count} 个共享应用") + return success(msg=f"已移除 {count} 个共享应用", data={"count": count}) @router.delete("/{app_id}/shared", summary="移除共享给我的应用") diff --git a/api/app/services/app_service.py b/api/app/services/app_service.py index d83f88d5..ca9a3c4a 100644 --- a/api/app/services/app_service.py +++ b/api/app/services/app_service.py @@ -924,6 +924,10 @@ class AppService: if search: filters.append(func.lower(App.name).like(f"%{search.lower()}%")) + # shared_only implies include_shared; enforce to avoid confusing API usage + if shared_only: + include_shared = True + # 基础查询：本工作空间的应用 if shared_only: # 只返回共享给本工作空间的应用，不含自有应用 @@ -1921,14 +1925,18 @@ class AppService: extra={"target_workspace_id": str(target_workspace_id), "workspace_id": str(workspace_id)} ) - stmt = delete(AppShare).where( + # Query IDs first to get a reliable count, avoiding rowcount driver inconsistencies + id_stmt = select(AppShare.id).where( AppShare.source_workspace_id == workspace_id, AppShare.target_workspace_id == target_workspace_id ) - result = self.db.execute(stmt) - self.db.commit() + ids = list(self.db.scalars(id_stmt).all()) + count = len(ids) + + if ids: + self.db.execute(delete(AppShare).where(AppShare.id.in_(ids))) + self.db.commit() - count = result.rowcount logger.info("已取消分享记录数", extra={"count": count}) return count @@ -2039,14 +2047,18 @@ class AppService: extra={"source_workspace_id": str(source_workspace_id), "workspace_id": str(workspace_id)} ) - stmt = delete(AppShare).where( + # Query IDs first to get a reliable count, avoiding rowcount driver inconsistencies + id_stmt = select(AppShare.id).where( AppShare.source_workspace_id == source_workspace_id, AppShare.target_workspace_id == workspace_id ) - result = self.db.execute(stmt) - self.db.commit() + ids = list(self.db.scalars(id_stmt).all()) + count = len(ids) + + if ids: + self.db.execute(delete(AppShare).where(AppShare.id.in_(ids))) + self.db.commit() - count = result.rowcount logger.info("已移除共享记录数", extra={"count": count}) return count From 098a2e54ae5b379cd155ee00c35791605031dd7d Mon Sep 17 00:00:00 2001 From: yujiangping Date: Fri, 13 Mar 2026 16:41:55 +0800 Subject: [PATCH 091/196] fix:loading --- web/src/i18n/en.ts | 1 + web/src/i18n/zh.ts | 1 + web/src/views/ToolManagement/Market.tsx | 3 +- .../components/MarketConfigModal.tsx | 53 +++++++++++++------ 4 files changed, 42 insertions(+), 16 deletions(-) diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index b470ce07..ce357e74 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1990,6 +1990,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re marketUrlPlaceholder: 'Market URL', marketCopy: 'Copy', marketApiKeyOptional: 'Optional', + marketApiKeyRequired: 'API Key is required', marketApiKeyExtra: 'Some markets require an API Key to access the full service list', marketApiKeyPlaceholder: 'Enter API Key to access more services', marketConnectionStatus: 'Connection Status', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 1151014b..01b1ab9e 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1986,6 +1986,7 @@ export const zh = { marketUrlPlaceholder: '市场地址', marketCopy: '复制', marketApiKeyOptional: '可选', + marketApiKeyRequired: '请输入 API Key', marketApiKeyExtra: '部分市场需要 API Key 才能获取完整的服务列表', marketApiKeyPlaceholder: '输入 API Key 以获取更多服务', marketConnectionStatus: '连接状态', diff --git a/web/src/views/ToolManagement/Market.tsx b/web/src/views/ToolManagement/Market.tsx index 351ae8b7..e6250e71 100644 --- a/web/src/views/ToolManagement/Market.tsx +++ b/web/src/views/ToolManagement/Market.tsx @@ -255,6 +255,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => if (!source) return; try { const config: any = await getMarketConfig(sourceId); + console.log('获取到的配置数据:', config); marketConfigModalRef.current?.handleOpen({ ...source, connected: config?.status === 1, @@ -431,7 +432,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () => dataLength={mcpList.length} next={loadMore} hasMore={hasMore} - loader={} + loader={null} scrollableTarget="mcpScrollableDiv" >
(null); const [showApiKey, setShowApiKey] = useState(false); + const [initialValues, setInitialValues] = useState<{ token: string }>({ token: '' }); const formValues = Form.useWatch([], form); const handleClose = () => { @@ -45,16 +46,29 @@ const MarketConfigModal = forwardRef { + console.log('Modal 接收到的数据:', source); setCurrentSource(source); - form.setFieldsValue({ - token: source.token || '', - }); + setInitialValues({ token: source.token || '' }); setVisible(true); }; + const handleAfterOpenChange = (open: boolean) => { + if (open && currentSource) { + // Modal 完全打开后再设置表单值，使用 setTimeout 确保在下一个事件循环 + setTimeout(() => { + form.setFieldsValue({ + token: currentSource.token || '', + }); + console.log('Modal 打开后设置表单值:', { token: currentSource.token || '' }); + console.log('当前表单所有值:', form.getFieldsValue()); + }, 100); + } + }; + const handleSave = () => { form .validateFields() @@ -103,7 +117,7 @@ const MarketConfigModal = forwardRef 0; + const canSave = formValues?.token?.trim().length > 0; useImperativeHandle(ref, () => ({ handleOpen, @@ -117,6 +131,7 @@ const MarketConfigModal = forwardRef
@@ -177,19 +194,25 @@ const MarketConfigModal = forwardRef } + rules={[ + { required: true, message: t('tool.marketApiKeyRequired') }, + { whitespace: true, message: t('tool.marketApiKeyRequired') } + ]} extra={{t('tool.marketApiKeyExtra')}} > - - - +
+ } + type="info" + showIcon + /> +
+ ); + } + + if (!isPreviewable()) { return ( @@ -230,23 +273,26 @@ const DocumentPreview: FC = ({ message="预览失败" description={
-

无法加载文档预览，可能的原因：

-
    -
  • 文件需要认证访问，Office 预览服务无法访问
    • -
  • 文件 URL 无法公开访问（需要配置公开访问或临时签名 URL）
    • -
  • 文件大小超过限制（Office 预览通常限制 10MB）
    • -
  • 预览服务暂时不可用
    • +

    无法加载文档预览

    + {errorMessage && ( +

    + 错误详情：{errorMessage} +

    + )} +

    可能的原因：

    +
      +
    • 文件 URL 无法访问（401/403/404）
      • +
    • 认证 token 已过期
      • +
    • 文件格式损坏或不匹配
      • +
    • 网络连接问题
    -

    建议：请下载文件到本地查看

    - {showModeSwitch && !isPdfFile() && ( - - )} +
} @@ -256,26 +302,23 @@ const DocumentPreview: FC = ({
)} - {/* 图片文件预览 */} {isImageFile() && !error && !loading && (
{fileName setError(true)} + onError={() => handleError('图片加载失败')} />
)} - {/* Markdown 文件预览 */} {isMarkdownFile() && !error && !loading && (
)} - {/* 文本文件预览 */} {isTextFile() && !error && !loading && ( 
@@ -284,44 +327,52 @@ const DocumentPreview: FC = ({
)} - {/* PDF 文件预览（使用浏览器内置预览） */} + {isWordFile() && !error && !loading && ( +
+
+
+ )} + + {isExcelFile() && !error && !loading && ( +
+ {excelData.map((sheet, index) => ( +
+

{sheet.sheetName}

+ {sheet.data.length > 0 && ( + ({ key: idx, ...row }))} + columns={sheet.data[0]?.map((header: any, colIdx: number) => ({ + title: header || `列 ${colIdx + 1}`, + dataIndex: colIdx, + key: colIdx, + width: 150, + })) || []} + pagination={false} + scroll={{ x: 'max-content' }} + size="small" + bordered + /> + )} + + ))} + + )} + {isPdfFile() && !error && !loading && (