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Merge pull request #779 from SuanmoSuanyangTechnology/develop

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Develop
This commit is contained in:
yingzhao
2026-04-02 16:01:08 +08:00
committed by GitHub
parent 9bce14be4e 377f79773d
commit a52e2137b7
54 changed files with 1578 additions and 585 deletions
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36
.github/workflows/sync-to-gitee.yml vendored Normal file
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@@ -0,0 +1,36 @@
name: Sync to Gitee
on:
push:
branches:
- main # Production
- develop # Integration
- 'release/*' # Release preparation
- 'hotfix/*' # Urgent fixes
tags:
- '*' # All version tags (v1.0.0, etc.)
jobs:
sync:
runs-on: ubuntu-latest
steps:
- name: Checkout Source Code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Sync to Gitee
run: |
GITEE_URL="https://${{ secrets.GITEE_USERNAME }}:${{ secrets.GITEE_TOKEN }}@gitee.com/hangzhou-hongxiong-intelligent_1/MemoryBear.git"
git remote add gitee "$GITEE_URL"
# 遍历并推送所有分支
for branch in $(git branch -r | grep -v HEAD | sed 's/origin\///'); do
echo "Syncing branch: $branch"
git push -f gitee "origin/$branch:refs/heads/$branch"
done
# 推送所有标签
echo "Syncing tags..."
git push gitee --tags --force

1
.gitignore vendored
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@@ -18,6 +18,7 @@ examples/
.kiro
.vscode
.idea
.claude
# Temporary outputs
.DS_Store

4
README.md
View File

@@ -2,6 +2,10 @@
# MemoryBear empowers AI with human-like memory capabilities
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
[![Python](https://img.shields.io/badge/Python-3.12+-green?logo=python&logoColor=white)](https://www.python.org/)
[![Gitee Sync](https://img.shields.io/github/actions/workflow/status/SuanmoSuanyangTechnology/MemoryBear/sync-to-gitee.yml?label=Gitee%20Sync&logo=gitee&logoColor=white)](https://github.com/SuanmoSuanyangTechnology/MemoryBear/actions/workflows/sync-to-gitee.yml)
[中文](./README_CN.md) | English
### [Installation Guide](#memorybear-installation-guide)

4
README_CN.md
View File

@@ -2,6 +2,10 @@
# MemoryBear 让AI拥有如同人类一样的记忆
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
[![Python](https://img.shields.io/badge/Python-3.12+-green?logo=python&logoColor=white)](https://www.python.org/)
[![Gitee Sync](https://img.shields.io/github/actions/workflow/status/SuanmoSuanyangTechnology/MemoryBear/sync-to-gitee.yml?label=Gitee%20Sync&logo=gitee&logoColor=white)](https://github.com/SuanmoSuanyangTechnology/MemoryBear/actions/workflows/sync-to-gitee.yml)
中文 | [English](./README.md)
### [安装教程](#memorybear安装教程)

32
api/app/controllers/home_page_controller.py
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@@ -3,9 +3,10 @@ from sqlalchemy.orm import Session
from app.core.config import settings
from app.core.response_utils import success
from app.db import get_db
from app.db import get_db, SessionLocal
from app.dependencies import get_current_user
from app.models.user_model import User
from app.repositories.home_page_repository import HomePageRepository
from app.schemas.response_schema import ApiResponse
from app.services.home_page_service import HomePageService
@@ -31,9 +32,32 @@ def get_workspace_list(
@router.get("/version", response_model=ApiResponse)
def get_system_version():
"""获取系统版本号+说明"""
current_version = settings.SYSTEM_VERSION
version_info = HomePageService.load_version_introduction(current_version)
"""获取系统版本号 + 说明"""
current_version = None
version_info = None
# 1️⃣ 优先从数据库获取最新已发布的版本
try:
db = SessionLocal()
try:
current_version, version_info = HomePageRepository.get_latest_version_introduction(db)
finally:
db.close()
except Exception as e:
pass
# 2️⃣ 降级：使用环境变量中的版本号
if not current_version:
current_version = settings.SYSTEM_VERSION
version_info = HomePageService.load_version_introduction(current_version)
# 3️⃣ 如果数据库和 JSON 都没有，返回基本信息
if not version_info:
version_info = {
"introduction": {"codeName": "", "releaseDate": "", "upgradePosition": "", "coreUpgrades": []},
"introduction_en": {"codeName": "", "releaseDate": "", "upgradePosition": "", "coreUpgrades": []}
}
return success(
data={
"version": current_version,

1
api/app/controllers/knowledge_controller.py
View File

@@ -352,6 +352,7 @@ async def delete_knowledge(
# 2. Soft-delete knowledge base
api_logger.debug(f"Perform a soft delete: {db_knowledge.name} (ID: {knowledge_id})")
db_knowledge.status = 2
db_knowledge.updated_at = datetime.datetime.now()
db.commit()
api_logger.info(f"The knowledge base has been successfully deleted: {db_knowledge.name} (ID: {knowledge_id})")
return success(msg="The knowledge base has been successfully deleted")

115
api/app/controllers/memory_dashboard_controller.py
View File

@@ -591,7 +591,7 @@ async def dashboard_data(
"total_api_call": None
}
# 1. 获取记忆总量（total_memory）
# 1. 获取记忆总量（total_memory）—— neo4j 独有逻辑：查询 neo4j 存储节点
try:
total_memory_data = await memory_dashboard_service.get_workspace_total_memory_count(
db=db,
@@ -600,49 +600,33 @@ async def dashboard_data(
end_user_id=end_user_id
)
neo4j_data["total_memory"] = total_memory_data.get("total_memory_count", 0)
# total_app: 统计当前空间下的所有app数量
# 包含自有app + 被分享给本工作空间的app
from app.services import app_service as _app_svc
_, total_app = _app_svc.AppService(db).list_apps(
workspace_id=workspace_id, include_shared=True, pagesize=1
)
neo4j_data["total_app"] = total_app
api_logger.info(f"成功获取记忆总量: {neo4j_data['total_memory']}, 应用数量: {neo4j_data['total_app']}")
api_logger.info(f"成功获取记忆总量: {neo4j_data['total_memory']}")
except Exception as e:
api_logger.warning(f"获取记忆总量失败: {str(e)}")
# 2. 获取知识库类型统计（total_knowledge）
try:
from app.services.memory_agent_service import MemoryAgentService
memory_agent_service = MemoryAgentService()
knowledge_stats = await memory_agent_service.get_knowledge_type_stats(
end_user_id=end_user_id,
only_active=True,
current_workspace_id=workspace_id,
db=db
)
neo4j_data["total_knowledge"] = knowledge_stats.get("total", 0)
api_logger.info(f"成功获取知识库类型统计total: {neo4j_data['total_knowledge']}")
except Exception as e:
api_logger.warning(f"获取知识库类型统计失败: {str(e)}")
# 2. 获取共享统计数据（total_app、total_knowledge、total_api_call）
common_stats = memory_dashboard_service.get_dashboard_common_stats(db, workspace_id)
neo4j_data.update(common_stats)
api_logger.info(f"成功获取共享统计: app={common_stats['total_app']}, knowledge={common_stats['total_knowledge']}, api_call={common_stats['total_api_call']}")
# 3. 获取API调用统计（total_api_call）
# 计算昨日对比
try:
# 使用 AppStatisticsService 获取真实的API调用统计
app_stats_service = AppStatisticsService(db)
api_stats = app_stats_service.get_workspace_api_statistics(
changes = memory_dashboard_service.get_dashboard_yesterday_changes(
db=db,
workspace_id=workspace_id,
start_date=start_date,
end_date=end_date
storage_type=storage_type,
today_data=neo4j_data
)
# 计算总调用次数
total_api_calls = sum(item.get("total_calls", 0) for item in api_stats)
neo4j_data["total_api_call"] = total_api_calls
api_logger.info(f"成功获取API调用统计: {neo4j_data['total_api_call']}")
neo4j_data.update(changes)
except Exception as e:
api_logger.error(f"获取API调用统计失败: {str(e)}")
neo4j_data["total_api_call"] = 0
api_logger.warning(f"计算neo4j昨日对比失败: {str(e)}")
neo4j_data.update({
"total_memory_change": None,
"total_app_change": None,
"total_knowledge_change": None,
"total_api_call_change": None,
})
result["neo4j_data"] = neo4j_data
api_logger.info("成功获取neo4j_data")
@@ -655,44 +639,37 @@ async def dashboard_data(
"total_api_call": None
}
# 获取RAG相关数据
# 1. 获取记忆总量（total_memory）—— rag 独有逻辑：查询 document 表的 chunk_num
try:
# total_memory: 只统计用户知识库（permission_id='Memory'）的chunk数
total_chunk = memory_dashboard_service.get_rag_user_kb_total_chunk(db, current_user)
rag_data["total_memory"] = total_chunk
# total_app: 统计当前空间下的所有app数量
# 包含自有app + 被分享给本工作空间的app
from app.services import app_service as _app_svc
_, total_app = _app_svc.AppService(db).list_apps(
workspace_id=workspace_id, include_shared=True, pagesize=1
)
rag_data["total_app"] = total_app
# total_knowledge: 使用 total_kb（总知识库数）
total_kb = memory_dashboard_service.get_rag_total_kb(db, current_user)
rag_data["total_knowledge"] = total_kb
# total_api_call: 使用 AppStatisticsService 获取真实的API调用统计
try:
app_stats_service = AppStatisticsService(db)
api_stats = app_stats_service.get_workspace_api_statistics(
workspace_id=workspace_id,
start_date=start_date,
end_date=end_date
)
# 计算总调用次数
total_api_calls = sum(item.get("total_calls", 0) for item in api_stats)
rag_data["total_api_call"] = total_api_calls
api_logger.info(f"成功获取RAG模式API调用统计: {rag_data['total_api_call']}")
except Exception as e:
api_logger.warning(f"获取RAG模式API调用统计失败，使用默认值: {str(e)}")
rag_data["total_api_call"] = 0
api_logger.info(f"成功获取RAG相关数据: memory={total_chunk}, app={total_app}, knowledge={total_kb}, api_calls={rag_data['total_api_call']}")
api_logger.info(f"成功获取RAG记忆总量: {total_chunk}")
except Exception as e:
api_logger.warning(f"获取RAG相关数据失败: {str(e)}")
api_logger.warning(f"获取RAG记忆总量失败: {str(e)}")
# 2. 获取共享统计数据（total_app、total_knowledge、total_api_call）
common_stats = memory_dashboard_service.get_dashboard_common_stats(db, workspace_id)
rag_data.update(common_stats)
api_logger.info(f"成功获取共享统计: app={common_stats['total_app']}, knowledge={common_stats['total_knowledge']}, api_call={common_stats['total_api_call']}")
# 计算昨日对比
try:
changes = memory_dashboard_service.get_dashboard_yesterday_changes(
db=db,
workspace_id=workspace_id,
storage_type=storage_type,
today_data=rag_data
)
rag_data.update(changes)
except Exception as e:
api_logger.warning(f"计算RAG昨日对比失败: {str(e)}")
rag_data.update({
"total_memory_change": None,
"total_app_change": None,
"total_knowledge_change": None,
"total_api_call_change": None,
})
result["rag_data"] = rag_data
api_logger.info("成功获取rag_data")

7
api/app/controllers/memory_storage_controller.py
View File

@@ -26,7 +26,7 @@ from app.services.memory_storage_service import (
analytics_hot_memory_tags,
analytics_recent_activity_stats,
kb_type_distribution,
search_all,
search_all_batch,
search_chunk,
search_detials,
search_dialogue,
@@ -409,7 +409,10 @@ async def search_all_num(
) -> dict:
api_logger.info(f"Search all requested for end_user_id: {end_user_id}")
try:
result = await search_all(end_user_id)
if not end_user_id:
return success(data={"total": 0}, msg="查询成功")
batch_result = await search_all_batch([end_user_id])
result = {"total": batch_result.get(end_user_id, 0)}
return success(data=result, msg="查询成功")
except Exception as e:
api_logger.error(f"Search all failed: {str(e)}")

1
api/app/controllers/ontology_controller.py
View File

@@ -163,6 +163,7 @@ def _get_ontology_service(
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
max_retries=3,
timeout=60.0
)

6
api/app/controllers/public_share_controller.py
View File

@@ -453,6 +453,9 @@ async def chat(
# 流式返回
agent_config = agent_config_4_app_release(release)
if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
agent_config.model_parameters["deep_thinking"] = False
if payload.stream:
async def event_generator():
async for event in app_chat_service.agnet_chat_stream(
@@ -634,7 +637,8 @@ async def config_query(
"app_type": release.app.type,
"variables": release.config.get("variables"),
"memory": release.config.get("memory", {}).get("enabled"),
"features": release.config.get("features")
"features": release.config.get("features"),
"model_parameters": release.config.get("model_parameters")
}
elif release.app.type == AppType.MULTI_AGENT:
content = {

3
api/app/controllers/service/__init__.py
View File

@@ -4,7 +4,7 @@
认证方式: API Key
"""
from fastapi import APIRouter
from . import app_api_controller, rag_api_knowledge_controller, rag_api_document_controller, rag_api_file_controller, rag_api_chunk_controller, memory_api_controller
from . import app_api_controller, rag_api_knowledge_controller, rag_api_document_controller, rag_api_file_controller, rag_api_chunk_controller, memory_api_controller, end_user_api_controller
# 创建 V1 API 路由器
service_router = APIRouter()
@@ -16,5 +16,6 @@ service_router.include_router(rag_api_document_controller.router)
service_router.include_router(rag_api_file_controller.router)
service_router.include_router(rag_api_chunk_controller.router)
service_router.include_router(memory_api_controller.router)
service_router.include_router(end_user_api_controller.router)
__all__ = ["service_router"]

5
api/app/controllers/service/app_api_controller.py
View File

@@ -144,6 +144,11 @@ async def chat(
# print(app.current_release.default_model_config_id)
agent_config = agent_config_4_app_release(app.current_release)
# print(agent_config.default_model_config_id)
# thinking 开关：仅当 agent 配置了 deep_thinking 且请求 thinking=True 时才启用
if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
agent_config.model_parameters["deep_thinking"] = False
# 流式返回
if payload.stream:
async def event_generator():

92
api/app/controllers/service/end_user_api_controller.py Normal file
View File

@@ -0,0 +1,92 @@
"""End User 服务接口 - 基于 API Key 认证"""
import uuid
from fastapi import APIRouter, Body, Depends, Request
from sqlalchemy.orm import Session
from app.core.api_key_auth import require_api_key
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.response_utils import success
from app.db import get_db
from app.repositories.end_user_repository import EndUserRepository
from app.schemas.api_key_schema import ApiKeyAuth
from app.schemas.memory_api_schema import CreateEndUserRequest, CreateEndUserResponse
from app.services.memory_config_service import MemoryConfigService
router = APIRouter(prefix="/end_user", tags=["V1 - End User API"])
logger = get_business_logger()
@router.post("/create")
@require_api_key(scopes=["memory"])
async def create_end_user(
request: Request,
api_key_auth: ApiKeyAuth = None,
db: Session = Depends(get_db),
message: str = Body(..., description="Request body"),
):
"""
Create or retrieve an end user for the workspace.
Creates a new end user and connects it to a memory configuration.
If an end user with the same other_id already exists in the workspace,
returns the existing one.
Optionally accepts a memory_config_id to connect the end user to a specific
memory configuration. If not provided, falls back to the workspace default config.
"""
body = await request.json()
payload = CreateEndUserRequest(**body)
workspace_id = api_key_auth.workspace_id
logger.info("Create end user request - other_id: %s, workspace_id: %s", payload.other_id, workspace_id)
# Resolve memory_config_id: explicit > workspace default
memory_config_id = None
config_service = MemoryConfigService(db)
if payload.memory_config_id:
try:
memory_config_id = uuid.UUID(payload.memory_config_id)
except ValueError:
raise BusinessException(
f"Invalid memory_config_id format: {payload.memory_config_id}",
BizCode.INVALID_PARAMETER
)
config = config_service.get_config_with_fallback(memory_config_id, workspace_id)
if not config:
raise BusinessException(
f"Memory config not found: {payload.memory_config_id}",
BizCode.MEMORY_CONFIG_NOT_FOUND
)
memory_config_id = config.config_id
else:
default_config = config_service.get_workspace_default_config(workspace_id)
if default_config:
memory_config_id = default_config.config_id
logger.info(f"Using workspace default memory config: {memory_config_id}")
else:
logger.warning(f"No default memory config found for workspace: {workspace_id}")
end_user_repo = EndUserRepository(db)
end_user = end_user_repo.get_or_create_end_user_with_config(
app_id=api_key_auth.resource_id,
workspace_id=workspace_id,
other_id=payload.other_id,
memory_config_id=memory_config_id,
)
logger.info(f"End user ready: {end_user.id}")
result = {
"id": str(end_user.id),
"other_id": end_user.other_id or "",
"other_name": end_user.other_name or "",
"workspace_id": str(end_user.workspace_id),
"memory_config_id": str(end_user.memory_config_id) if end_user.memory_config_id else None,
}
return success(data=CreateEndUserResponse(**result).model_dump(), msg="End user created successfully")

91
api/app/core/agent/langchain_agent.py
View File

@@ -37,7 +37,10 @@ class LangChainAgent:
tools: Optional[Sequence[BaseTool]] = None,
streaming: bool = False,
max_iterations: Optional[int] = None, # 最大迭代次数（None 表示自动计算）
max_tool_consecutive_calls: int = 3 # 单个工具最大连续调用次数
max_tool_consecutive_calls: int = 3, # 单个工具最大连续调用次数
deep_thinking: bool = False, # 是否启用深度思考模式
thinking_budget_tokens: Optional[int] = None, # 深度思考 token 预算
capability: Optional[List[str]] = None # 模型能力列表，用于校验是否支持深度思考
):
"""初始化 LangChain Agent
@@ -60,6 +63,7 @@ class LangChainAgent:
self.streaming = streaming
self.is_omni = is_omni
self.max_tool_consecutive_calls = max_tool_consecutive_calls
self.deep_thinking = deep_thinking and ("thinking" in (capability or []))
# 工具调用计数器：记录每个工具的连续调用次数
self.tool_call_counter: Dict[str, int] = {}
@@ -82,6 +86,13 @@ class LangChainAgent:
f"auto_calculated={max_iterations is None}"
)
# 根据 capability 校验是否真正支持深度思考
actual_deep_thinking = self.deep_thinking
if deep_thinking and not actual_deep_thinking:
logger.warning(
f"模型 {model_name} 不支持深度思考（capability 中无 'thinking'），已自动关闭 deep_thinking"
)
# 创建 RedBearLLM（支持多提供商）
model_config = RedBearModelConfig(
model_name=model_name,
@@ -89,10 +100,13 @@ class LangChainAgent:
api_key=api_key,
base_url=api_base,
is_omni=is_omni,
deep_thinking=actual_deep_thinking,
thinking_budget_tokens=thinking_budget_tokens if actual_deep_thinking else None,
support_thinking="thinking" in (capability or []),
extra_params={
"temperature": temperature,
"max_tokens": max_tokens,
"streaming": streaming # 使用参数控制流式
"streaming": streaming
}
)
@@ -249,6 +263,33 @@ class LangChainAgent:
return messages
@staticmethod
def _extract_tokens_from_message(msg) -> int:
"""从 AIMessage 或类似对象中提取 total_tokens，兼容多种 provider 格式
支持的格式：
- response_metadata.token_usage.total_tokens (OpenAI/ChatOpenAI)
- response_metadata.usage.total_tokens (部分 provider)
- usage_metadata.total_tokens (LangChain 新版)
"""
total = 0
# 1. response_metadata
response_meta = getattr(msg, "response_metadata", None)
if response_meta and isinstance(response_meta, dict):
# 尝试 token_usage 路径
token_usage = response_meta.get("token_usage") or response_meta.get("usage", {})
if isinstance(token_usage, dict):
total = token_usage.get("total_tokens", 0)
# 2. usage_metadata（LangChain 新版 AIMessage 属性）
if not total:
usage_meta = getattr(msg, "usage_metadata", None)
if usage_meta:
if isinstance(usage_meta, dict):
total = usage_meta.get("total_tokens", 0)
else:
total = getattr(usage_meta, "total_tokens", 0)
return total or 0
def _build_multimodal_content(self, text: str, files: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
构建多模态消息内容
@@ -283,6 +324,17 @@ class LangChainAgent:
return content_parts
@staticmethod
def _extract_reasoning_content(msg) -> str:
"""从 AIMessage 中提取深度思考内容（reasoning_content）
所有 provider 统一通过 additional_kwargs.reasoning_content 传递：
- DeepSeek-R1 / QwQ: 原生字段
- Volcano (Doubao-thinking): 由 VolcanoChatOpenAI 从 delta.reasoning_content 注入
"""
additional = getattr(msg, "additional_kwargs", None) or {}
return additional.get("reasoning_content") or additional.get("reasoning", "")
async def chat(
self,
message: str,
@@ -348,6 +400,7 @@ class LangChainAgent:
logger.debug(f"输出消息数量: {len(output_messages)}")
total_tokens = 0
reasoning_content = ""
for msg in reversed(output_messages):
if isinstance(msg, AIMessage):
logger.debug(f"找到 AI 消息，content 类型: {type(msg.content)}")
@@ -382,8 +435,8 @@ class LangChainAgent:
else:
content = str(msg.content)
logger.debug(f"转换为字符串: {content[:100]}...")
response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0
total_tokens = self._extract_tokens_from_message(msg)
reasoning_content = self._extract_reasoning_content(msg) if self.deep_thinking else ""
break
logger.info(f"最终提取的内容长度: {len(content)}")
@@ -399,6 +452,8 @@ class LangChainAgent:
"total_tokens": total_tokens
}
}
if reasoning_content:
response["reasoning_content"] = reasoning_content
logger.debug(
"Agent 调用完成",
@@ -420,7 +475,7 @@ class LangChainAgent:
history: Optional[List[Dict[str, str]]] = None,
context: Optional[str] = None,
files: Optional[List[Dict[str, Any]]] = None
) -> AsyncGenerator[str, None]:
) -> AsyncGenerator[str | int, None]:
"""执行流式对话
Args:
@@ -431,6 +486,8 @@ class LangChainAgent:
Yields:
str: 消息内容块
int: token 统计
Dict: 深度思考内容 {"type": "reasoning", "content": "..."}
"""
logger.info("=" * 80)
logger.info(" chat_stream 方法开始执行")
@@ -451,6 +508,7 @@ class LangChainAgent:
# 统一使用 agent 的 astream_events 实现流式输出
logger.debug("使用 Agent astream_events 实现流式输出")
full_content = ''
full_reasoning = ''
try:
last_event = {}
async for event in self.agent.astream_events(
@@ -467,6 +525,13 @@ class LangChainAgent:
# LLM 流式输出
chunk = event.get("data", {}).get("chunk")
if chunk and hasattr(chunk, "content"):
# 提取深度思考内容（仅在启用深度思考时）
if self.deep_thinking:
reasoning_chunk = self._extract_reasoning_content(chunk)
if reasoning_chunk:
full_reasoning += reasoning_chunk
yield {"type": "reasoning", "content": reasoning_chunk}
# 处理多模态响应：content 可能是字符串或列表
chunk_content = chunk.content
if isinstance(chunk_content, str) and chunk_content:
@@ -497,6 +562,13 @@ class LangChainAgent:
chunk = event.get("data", {}).get("chunk")
if chunk:
if hasattr(chunk, "content"):
# 提取深度思考内容（仅在启用深度思考时）
if self.deep_thinking:
reasoning_chunk = self._extract_reasoning_content(chunk)
if reasoning_chunk:
full_reasoning += reasoning_chunk
yield {"type": "reasoning", "content": reasoning_chunk}
chunk_content = chunk.content
if isinstance(chunk_content, str) and chunk_content:
full_content += chunk_content
@@ -535,12 +607,9 @@ class LangChainAgent:
output_messages = last_event.get("data", {}).get("output", {}).get("messages", [])
for msg in reversed(output_messages):
if isinstance(msg, AIMessage):
response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
total_tokens = response_meta.get("token_usage", {}).get(
"total_tokens",
0
) if response_meta else 0
yield total_tokens
stream_total_tokens = self._extract_tokens_from_message(msg)
logger.info(f"流式 token 统计: total_tokens={stream_total_tokens}")
yield stream_total_tokens
break
except Exception as e:

3
api/app/core/memory/src/search.py
View File

@@ -758,8 +758,7 @@ async def run_hybrid_search(
model_name=embedder_config_dict["model_name"],
provider=embedder_config_dict["provider"],
api_key=embedder_config_dict["api_key"],
base_url=embedder_config_dict["base_url"],
type="llm"
base_url=embedder_config_dict["base_url"]
)
config_load_time = time.time() - config_load_start
logger.info(f"[PERF] Config loading took {config_load_time:.4f}s")

75
api/app/core/models/base.py
View File

@@ -14,6 +14,7 @@ from pydantic import BaseModel, Field
from app.core.error_codes import BizCode
from app.core.exceptions import BusinessException
from app.models.models_model import ModelProvider, ModelType
from app.core.models.volcano_chat import VolcanoChatOpenAI
T = TypeVar("T")
@@ -25,6 +26,9 @@ class RedBearModelConfig(BaseModel):
api_key: str
base_url: Optional[str] = None
is_omni: bool = False # 是否为 Omni 模型
deep_thinking: bool = False # 是否启用深度思考模式
thinking_budget_tokens: Optional[int] = None # 深度思考 token 预算
support_thinking: bool = False # 模型是否支持 enable_thinking 参数（capability 含 thinking）
# 请求超时时间（秒）- 默认120秒以支持复杂的LLM调用，可通过环境变量 LLM_TIMEOUT 配置
timeout: float = Field(default_factory=lambda: float(os.getenv("LLM_TIMEOUT", "120.0")))
# 最大重试次数 - 默认2次以避免过长等待，可通过环境变量 LLM_MAX_RETRIES 配置
@@ -44,7 +48,7 @@ class RedBearModelFactory:
# 打印供应商信息用于调试
from app.core.logging_config import get_business_logger
logger = get_business_logger()
logger.debug(f"获取模型参数 - Provider: {provider}, Model: {config.model_name}, is_omni: {config.is_omni}")
logger.debug(f"获取模型参数 - Provider: {provider}, Model: {config.model_name}, is_omni: {config.is_omni}, deep_thinking: {config.deep_thinking}")
# dashscope 的 omni 模型使用 OpenAI 兼容模式
if provider == ModelProvider.DASHSCOPE and config.is_omni:
@@ -58,7 +62,7 @@ class RedBearModelFactory:
write=60.0,
pool=10.0,
)
return {
params: Dict[str, Any] = {
"model": config.model_name,
"base_url": config.base_url,
"api_key": config.api_key,
@@ -66,6 +70,21 @@ class RedBearModelFactory:
"max_retries": config.max_retries,
**config.extra_params
}
# 流式模式下启用 stream_usage 以获取 token 统计
is_streaming = bool(config.extra_params.get("streaming"))
if is_streaming:
params["stream_usage"] = True
# 只有支持 thinking 的模型才传 enable_thinking
if config.support_thinking:
model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
if is_streaming:
model_kwargs["enable_thinking"] = config.deep_thinking
if config.deep_thinking and config.thinking_budget_tokens:
model_kwargs["thinking_budget"] = config.thinking_budget_tokens
else:
model_kwargs["enable_thinking"] = False
params["model_kwargs"] = model_kwargs
return params
if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.OLLAMA, ModelProvider.VOLCANO]:
# 使用 httpx.Timeout 对象来设置详细的超时配置
@@ -78,7 +97,7 @@ class RedBearModelFactory:
write=60.0, # 写入超时：60秒
pool=10.0, # 连接池超时：10秒
)
return {
params: Dict[str, Any] = {
"model": config.model_name,
"base_url": config.base_url,
"api_key": config.api_key,
@@ -86,16 +105,47 @@ class RedBearModelFactory:
"max_retries": config.max_retries,
**config.extra_params
}
# 流式模式下启用 stream_usage 以获取 token 统计
if config.extra_params.get("streaming"):
params["stream_usage"] = True
# 深度思考模式
is_streaming = bool(config.extra_params.get("streaming"))
if is_streaming:
if provider == ModelProvider.VOLCANO:
# 火山引擎深度思考仅流式调用支持，非流式时不传 thinking 参数
thinking_config: Dict[str, Any] = {
"type": "enabled" if config.deep_thinking else "disabled"
}
if config.deep_thinking and config.thinking_budget_tokens:
thinking_config["budget_tokens"] = config.thinking_budget_tokens
params["extra_body"] = {"thinking": thinking_config}
else:
# 始终显式传递 enable_thinking，不支持该参数的模型（如 DeepSeek-R1）会直接忽略
model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
model_kwargs["enable_thinking"] = config.deep_thinking
if config.deep_thinking and config.thinking_budget_tokens:
model_kwargs["thinking_budget"] = config.thinking_budget_tokens
params["model_kwargs"] = model_kwargs
return params
elif provider == ModelProvider.DASHSCOPE:
# DashScope (通义千问) 使用自己的参数格式
# 注意: DashScopeEmbeddings 不支持 timeout 和 base_url 参数
# 只支持: model, dashscope_api_key, max_retries, client
return {
params = {
"model": config.model_name,
"dashscope_api_key": config.api_key,
"max_retries": config.max_retries,
**config.extra_params
}
# 只有支持 thinking 的模型才传 enable_thinking
if config.support_thinking:
is_streaming = bool(config.extra_params.get("streaming"))
model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
if is_streaming:
model_kwargs["enable_thinking"] = config.deep_thinking
if config.deep_thinking and config.thinking_budget_tokens:
model_kwargs["thinking_budget"] = config.thinking_budget_tokens
else:
model_kwargs["enable_thinking"] = False
params["model_kwargs"] = model_kwargs
return params
elif provider == ModelProvider.BEDROCK:
# Bedrock 使用 AWS 凭证
# api_key 格式: "access_key_id:secret_access_key" 或只是 access_key_id
@@ -134,6 +184,13 @@ class RedBearModelFactory:
elif "region_name" not in params:
params["region_name"] = "us-east-1" # 默认区域
# 深度思考模式：Claude 3.7 Sonnet 等支持思考的模型
# 通过 additional_model_request_fields 传递 thinking 块，关闭时不传（Bedrock 无 disabled 选项）
if config.deep_thinking:
budget = config.thinking_budget_tokens or 10000
params["additional_model_request_fields"] = {
"thinking": {"type": "enabled", "budget_tokens": budget}
}
return params
else:
raise BusinessException(f"不支持的提供商: {provider}", code=BizCode.PROVIDER_NOT_SUPPORTED)
@@ -160,7 +217,9 @@ 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:
return ChatOpenAI
if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.VOLCANO]:
if provider == ModelProvider.VOLCANO:
return VolcanoChatOpenAI
if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
if type == ModelType.LLM:
return OpenAI
elif type == ModelType.CHAT:

17
api/app/core/models/scripts/bedrock_models.yaml
View File

@@ -11,6 +11,7 @@ models:
tags:
- 大语言模型
logo: bedrock
- name: amazon nova
type: llm
provider: bedrock
@@ -27,6 +28,7 @@ models:
- stream-tool-call
- vision
logo: bedrock
- name: anthropic claude
type: llm
provider: bedrock
@@ -35,6 +37,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -44,6 +47,7 @@ models:
- stream-tool-call
- document
logo: bedrock
- name: cohere
type: llm
provider: bedrock
@@ -58,6 +62,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: deepseek
type: llm
provider: bedrock
@@ -66,6 +71,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -74,6 +80,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: meta
type: llm
provider: bedrock
@@ -87,6 +94,7 @@ models:
- agent-thought
- tool-call
logo: bedrock
- name: mistral
type: llm
provider: bedrock
@@ -100,6 +108,7 @@ models:
- agent-thought
- tool-call
logo: bedrock
- name: openai
type: llm
provider: bedrock
@@ -114,6 +123,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: qwen
type: llm
provider: bedrock
@@ -128,6 +138,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: amazon.rerank-v1:0
type: rerank
provider: bedrock
@@ -139,6 +150,7 @@ models:
tags:
- 重排序模型
logo: bedrock
- name: cohere.rerank-v3-5:0
type: rerank
provider: bedrock
@@ -150,6 +162,7 @@ models:
tags:
- 重排序模型
logo: bedrock
- name: amazon.nova-2-multimodal-embeddings-v1:0
type: embedding
provider: bedrock
@@ -163,6 +176,7 @@ models:
- 文本嵌入模型
- vision
logo: bedrock
- name: amazon.titan-embed-text-v1
type: embedding
provider: bedrock
@@ -174,6 +188,7 @@ models:
tags:
- 文本嵌入模型
logo: bedrock
- name: amazon.titan-embed-text-v2:0
type: embedding
provider: bedrock
@@ -185,6 +200,7 @@ models:
tags:
- 文本嵌入模型
logo: bedrock
- name: cohere.embed-english-v3
type: embedding
provider: bedrock
@@ -196,6 +212,7 @@ models:
tags:
- 文本嵌入模型
logo: bedrock
- name: cohere.embed-multilingual-v3
type: embedding
provider: bedrock

225
api/app/core/models/scripts/dashscope_models.yaml
View File

@@ -6,36 +6,42 @@ models:
description: DeepSeek-R1-Distill-Qwen-14B大语言模型，支持智能体思考，32000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-r1-distill-qwen-32b
type: llm
provider: dashscope
description: DeepSeek-R1-Distill-Qwen-32B大语言模型，支持智能体思考，32000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-r1
type: llm
provider: dashscope
description: DeepSeek-R1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3.1
type: llm
provider: dashscope
@@ -48,6 +54,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3.2-exp
type: llm
provider: dashscope
@@ -60,6 +67,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3.2
type: llm
provider: dashscope
@@ -72,6 +80,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3
type: llm
provider: dashscope
@@ -84,6 +93,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: farui-plus
type: llm
provider: dashscope
@@ -98,6 +108,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: glm-4.7
type: llm
provider: dashscope
@@ -112,6 +123,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qvq-max-latest
type: llm
provider: dashscope
@@ -119,7 +131,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -127,6 +140,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qvq-max
type: llm
provider: dashscope
@@ -134,7 +148,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -142,6 +157,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-coder-turbo-0919
type: llm
provider: dashscope
@@ -155,13 +171,15 @@ models:
- 代码模型
- agent-thought
logo: dashscope
- name: qwen-max-latest
type: llm
provider: dashscope
description: qwen-max-latest大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -169,6 +187,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-max-longcontext
type: llm
provider: dashscope
@@ -183,13 +202,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-max
type: llm
provider: dashscope
description: qwen-max大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -197,6 +218,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-mt-plus
type: llm
provider: dashscope
@@ -210,6 +232,7 @@ models:
- 翻译模型
- agent-thought
logo: dashscope
- name: qwen-mt-turbo
type: llm
provider: dashscope
@@ -223,6 +246,7 @@ models:
- 翻译模型
- agent-thought
logo: dashscope
- name: qwen-plus-0112
type: llm
provider: dashscope
@@ -237,6 +261,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0125
type: llm
provider: dashscope
@@ -251,6 +276,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0723
type: llm
provider: dashscope
@@ -265,6 +291,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0806
type: llm
provider: dashscope
@@ -279,6 +306,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0919
type: llm
provider: dashscope
@@ -293,6 +321,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-1125
type: llm
provider: dashscope
@@ -307,6 +336,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-1127
type: llm
provider: dashscope
@@ -321,6 +351,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-1220
type: llm
provider: dashscope
@@ -335,6 +366,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-vl-max
type: chat
provider: dashscope
@@ -342,8 +374,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -352,6 +384,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-0809
type: chat
provider: dashscope
@@ -359,8 +392,8 @@ models:
is_deprecated: true
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -369,6 +402,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-2025-01-02
type: chat
provider: dashscope
@@ -376,8 +410,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -386,6 +420,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-2025-01-25
type: chat
provider: dashscope
@@ -393,8 +428,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -403,6 +438,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-latest
type: chat
provider: dashscope
@@ -410,8 +446,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -420,6 +456,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus
type: chat
provider: dashscope
@@ -427,8 +464,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -437,6 +474,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen2.5-0.5b-instruct
type: llm
provider: dashscope
@@ -451,13 +489,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-14b
type: llm
provider: dashscope
description: qwen3-14b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -465,13 +505,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-235b-a22b-instruct-2507
type: llm
provider: dashscope
description: qwen3-235b-a22b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -479,13 +521,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-235b-a22b-thinking-2507
type: llm
provider: dashscope
description: qwen3-235b-a22b-thinking-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -493,13 +537,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-235b-a22b
type: llm
provider: dashscope
description: qwen3-235b-a22b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -507,13 +553,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-30b-a3b-instruct-2507
type: llm
provider: dashscope
description: qwen3-30b-a3b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -521,13 +569,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-30b-a3b
type: llm
provider: dashscope
description: qwen3-30b-a3b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -535,13 +585,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-32b
type: llm
provider: dashscope
description: qwen3-32b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -549,13 +601,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-4b
type: llm
provider: dashscope
description: qwen3-4b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -563,13 +617,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-8b
type: llm
provider: dashscope
description: qwen3-8b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -577,65 +633,75 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-coder-30b-a3b-instruct
type: llm
provider: dashscope
description: qwen3-coder-30b-a3b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-coder-480b-a35b-instruct
type: llm
provider: dashscope
description: qwen3-coder-480b-a35b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-coder-plus-2025-09-23
type: llm
provider: dashscope
description: qwen3-coder-plus-2025-09-23大语言模型，支持智能体思考，1000000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-coder-plus
type: llm
provider: dashscope
description: qwen3-coder-plus大语言模型，支持智能体思考，1000000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-max-2025-09-23
type: llm
provider: dashscope
description: qwen3-max-2025-09-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -644,13 +710,15 @@ models:
- stream-tool-call
- 联网搜索
logo: dashscope
- name: qwen3-max-2026-01-23
type: llm
provider: dashscope
description: qwen3-max-2026-01-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -659,13 +727,15 @@ models:
- stream-tool-call
- 联网搜索
logo: dashscope
- name: qwen3-max-preview
type: llm
provider: dashscope
description: qwen3-max-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -673,13 +743,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-max
type: llm
provider: dashscope
description: qwen3-max大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -688,13 +760,15 @@ models:
- stream-tool-call
- 联网搜索
logo: dashscope
- name: qwen3-next-80b-a3b-instruct
type: llm
provider: dashscope
description: qwen3-next-80b-a3b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -702,13 +776,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-next-80b-a3b-thinking
type: llm
provider: dashscope
description: qwen3-next-80b-a3b-thinking大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -716,6 +792,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-omni-flash-2025-12-01
type: llm
provider: dashscope
@@ -723,9 +800,10 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- audio
- vision
- video
- audio
- thinking
is_omni: true
tags:
- 大语言模型
@@ -735,6 +813,7 @@ models:
- video
- audio
logo: dashscope
- name: qwen3-vl-235b-a22b-instruct
type: chat
provider: dashscope
@@ -742,8 +821,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -754,6 +834,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-235b-a22b-thinking
type: chat
provider: dashscope
@@ -761,8 +842,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -773,6 +855,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-30b-a3b-instruct
type: chat
provider: dashscope
@@ -780,8 +863,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -792,6 +876,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-30b-a3b-thinking
type: chat
provider: dashscope
@@ -799,8 +884,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -811,6 +897,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-flash
type: chat
provider: dashscope
@@ -818,8 +905,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -830,6 +918,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-plus-2025-09-23
type: chat
provider: dashscope
@@ -837,8 +926,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -847,6 +937,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen3-vl-plus
type: chat
provider: dashscope
@@ -854,8 +945,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -864,45 +956,52 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwq-32b
type: llm
provider: dashscope
description: qwq-32b大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwq-plus-0305
type: llm
provider: dashscope
description: qwq-plus-0305大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwq-plus
type: llm
provider: dashscope
description: qwq-plus大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
- stream-tool-call
logo: dashscope
- name: gte-rerank-v2
type: rerank
provider: dashscope
@@ -914,6 +1013,7 @@ models:
tags:
- 重排序模型
logo: dashscope
- name: gte-rerank
type: rerank
provider: dashscope
@@ -925,6 +1025,7 @@ models:
tags:
- 重排序模型
logo: dashscope
- name: multimodal-embedding-v1
type: embedding
provider: dashscope
@@ -932,13 +1033,14 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- vision
is_omni: false
tags:
- 嵌入模型
- 多模态模型
- vision
logo: dashscope
- name: text-embedding-v1
type: embedding
provider: dashscope
@@ -951,6 +1053,7 @@ models:
- 嵌入模型
- 文本嵌入
logo: dashscope
- name: text-embedding-v2
type: embedding
provider: dashscope
@@ -963,6 +1066,7 @@ models:
- 嵌入模型
- 文本嵌入
logo: dashscope
- name: text-embedding-v3
type: embedding
provider: dashscope
@@ -975,6 +1079,7 @@ models:
- 嵌入模型
- 文本嵌入
logo: dashscope
- name: text-embedding-v4
type: embedding
provider: dashscope
@@ -986,4 +1091,4 @@ models:
tags:
- 嵌入模型
- 文本嵌入
logo: dashscope
logo: dashscope

36
api/app/core/models/scripts/openai_models.yaml
View File

@@ -20,6 +20,7 @@ models:
- audio
- video
logo: openai
- name: gpt-3.5-turbo-0125
type: llm
provider: openai
@@ -34,6 +35,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-3.5-turbo-1106
type: llm
provider: openai
@@ -48,6 +50,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-3.5-turbo-16k
type: llm
provider: openai
@@ -62,6 +65,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-3.5-turbo-instruct
type: llm
provider: openai
@@ -73,6 +77,7 @@ models:
tags:
- 大语言模型
logo: openai
- name: gpt-3.5-turbo
type: llm
provider: openai
@@ -87,6 +92,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-0125-preview
type: llm
provider: openai
@@ -101,6 +107,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-1106-preview
type: llm
provider: openai
@@ -115,6 +122,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-turbo-2024-04-09
type: llm
provider: openai
@@ -131,6 +139,7 @@ models:
- stream-tool-call
- vision
logo: openai
- name: gpt-4-turbo-preview
type: llm
provider: openai
@@ -145,6 +154,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-turbo
type: llm
provider: openai
@@ -161,6 +171,7 @@ models:
- stream-tool-call
- vision
logo: openai
- name: o1-preview
type: llm
provider: openai
@@ -173,6 +184,7 @@ models:
- 大语言模型
- agent-thought
logo: openai
- name: o1
type: llm
provider: openai
@@ -181,6 +193,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -190,6 +203,7 @@ models:
- vision
- structured-output
logo: openai
- name: o3-2025-04-16
type: llm
provider: openai
@@ -198,6 +212,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -207,13 +222,15 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o3-mini-2025-01-31
type: llm
provider: openai
description: o3-mini-2025-01-31大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -222,13 +239,15 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o3-mini
type: llm
provider: openai
description: o3-mini大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -237,6 +256,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o3-pro-2025-06-10
type: llm
provider: openai
@@ -245,6 +265,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -253,6 +274,7 @@ models:
- vision
- structured-output
logo: openai
- name: o3-pro
type: llm
provider: openai
@@ -261,6 +283,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -269,6 +292,7 @@ models:
- vision
- structured-output
logo: openai
- name: o3
type: llm
provider: openai
@@ -277,6 +301,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -286,6 +311,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o4-mini-2025-04-16
type: llm
provider: openai
@@ -294,6 +320,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -303,6 +330,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o4-mini
type: llm
provider: openai
@@ -311,6 +339,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -320,6 +349,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: text-embedding-3-large
type: embedding
provider: openai
@@ -331,6 +361,7 @@ models:
tags:
- 文本向量模型
logo: openai
- name: text-embedding-3-small
type: embedding
provider: openai
@@ -342,6 +373,7 @@ models:
tags:
- 文本向量模型
logo: openai
- name: text-embedding-ada-002
type: embedding
provider: openai

9
api/app/core/models/scripts/volcano_models.yaml
View File

@@ -10,6 +10,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -24,6 +25,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -38,6 +40,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -52,6 +55,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -82,6 +86,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -96,6 +101,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -110,6 +116,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -124,6 +131,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -139,6 +147,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型

38
api/app/core/models/volcano_chat.py Normal file
View File

@@ -0,0 +1,38 @@
"""
火山引擎 ChatOpenAI 扩展
ChatOpenAI 在解析流式 SSE 时只取 delta.content，会丢弃 delta.reasoning_content。
此类仅重写 _convert_chunk_to_generation_chunk，将 reasoning_content 补入 additional_kwargs。
"""
from __future__ import annotations
from typing import Any, Optional
from langchain_core.outputs import ChatGenerationChunk
from langchain_openai import ChatOpenAI
class VolcanoChatOpenAI(ChatOpenAI):
"""火山引擎 Chat 模型，支持深度思考内容（reasoning_content）的流式透传。"""
def _convert_chunk_to_generation_chunk(
self,
chunk: dict,
default_chunk_class: type,
base_generation_info: Optional[dict],
) -> Optional[ChatGenerationChunk]:
gen_chunk = super()._convert_chunk_to_generation_chunk(
chunk, default_chunk_class, base_generation_info
)
if gen_chunk is None:
return None
# 从原始 chunk 中提取 reasoning_content
choices = chunk.get("choices") or chunk.get("chunk", {}).get("choices", [])
if choices:
delta = choices[0].get("delta") or {}
reasoning: Any = delta.get("reasoning_content")
if reasoning:
gen_chunk.message.additional_kwargs["reasoning_content"] = reasoning
return gen_chunk

10
api/app/core/tools/mcp/client.py
View File

@@ -99,7 +99,7 @@ class SimpleMCPClient:
# 建立 SSE 连接
response = await self._session.get(self.server_url)
if response.status not in (200, 202):
if not (200 <= response.status < 300):
error_text = await response.text()
raise MCPConnectionError(f"SSE 连接失败 {response.status}: {error_text}")
@@ -190,9 +190,7 @@ class SimpleMCPClient:
try:
async with self._session.post(self._endpoint_url, json=request) as response:
# MCP SSE 协议：POST 请求返回 200 或 202 均为正常
# 202 Accepted 表示请求已接受，结果通过 SSE 流异步返回
if response.status not in (200, 202):
if not (200 <= response.status < 300):
error_text = await response.text()
raise MCPConnectionError(f"请求失败 {response.status}: {error_text}")
@@ -207,7 +205,7 @@ class SimpleMCPClient:
raise MCPConnectionError("endpoint URL 未初始化")
async with self._session.post(self._endpoint_url, json=notification) as response:
if response.status not in (200, 202):
if not (200 <= response.status < 300):
logger.warning(f"通知发送失败: {response.status}")
async def _initialize_modelscope_session(self):
@@ -225,7 +223,7 @@ class SimpleMCPClient:
try:
async with self._session.post(self.server_url, json=init_request) as response:
if response.status != 200:
if not (200 <= response.status < 300):
error_text = await response.text()
raise MCPConnectionError(f"初始化失败 {response.status}: {error_text}")

3
api/app/core/workflow/nodes/llm/node.py
View File

@@ -135,7 +135,8 @@ class LLMNode(BaseNode):
api_key=model_info.api_key,
base_url=model_info.api_base,
extra_params=extra_params,
is_omni=model_info.is_omni
is_omni=model_info.is_omni,
support_thinking="thinking" in (model_info.capability or []),
),
type=model_info.model_type
)

4
api/app/core/workflow/nodes/parameter_extractor/node.py
View File

@@ -109,6 +109,7 @@ class ParameterExtractorNode(BaseNode):
api_key = api_config.api_key
api_base = api_config.api_base
is_omni = api_config.is_omni
capability = api_config.capability
model_type = config.type
llm = RedBearLLM(
@@ -117,7 +118,8 @@ class ParameterExtractorNode(BaseNode):
provider=provider,
api_key=api_key,
base_url=api_base,
is_omni=is_omni
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
),
type=ModelType(model_type)
)

4
api/app/core/workflow/nodes/question_classifier/node.py
View File

@@ -62,6 +62,7 @@ class QuestionClassifierNode(BaseNode):
api_key = api_config.api_key
base_url = api_config.api_base
is_omni = api_config.is_omni
capability = api_config.capability
model_type = config.type
return RedBearLLM(
@@ -70,7 +71,8 @@ class QuestionClassifierNode(BaseNode):
provider=provider,
api_key=api_key,
base_url=base_url,
is_omni=is_omni
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
),
type=ModelType(model_type)
)

2
api/app/models/models_model.py
View File

@@ -81,7 +81,7 @@ class ModelConfig(BaseModel):
# 模型配置参数
capability = Column(ARRAY(String), default=list, nullable=False, server_default=text("'{}'::varchar[]"),
comment="模型能力列表（如['vision', 'audio', 'video']）")
comment="模型能力列表（如['vision', 'audio', 'video', 'thinking']）")
is_omni = Column(Boolean, default=False, nullable=False, server_default="false", comment="是否为Omni模型（使用特殊API调用）")
config = Column(JSON, comment="模型配置参数")
# - temperature : 控制生成文本的随机性。值越高，输出越随机、越有创造性；值越低，输出越确定、越保守。

91
api/app/repositories/home_page_repository.py
View File

@@ -1,6 +1,6 @@
from datetime import datetime, timedelta
from datetime import datetime, time
from sqlalchemy.orm import Session
from sqlalchemy import func
from sqlalchemy import func, Table, MetaData
from uuid import UUID
from typing import Dict, Optional, Any
@@ -192,10 +192,63 @@ class HomePageRepository:
return workspaces, app_count_dict, user_count_dict
@staticmethod
def get_latest_version_introduction(db: Session) -> tuple[Optional[str], Optional[Dict[str, Any]]]:
"""
从数据库获取最新已发布的版本说明
使用反射方式读取表结构，不依赖 premium 模型类
Args:
db: 数据库会话
Returns:
(版本号，版本说明字典) 的元组
如果数据库中没有已发布的版本，返回 (None, None)
"""
try:
metadata = MetaData()
version_notes = Table('version_notes', metadata, autoload_with=db.bind)
# 获取最新已发布的版本（按发布时间倒序，日期相同时按版本号倒序）
query = db.query(version_notes).filter(
version_notes.c.is_published == True
).order_by(
version_notes.c.release_date.desc(),
version_notes.c.version.desc()
)
note = query.first()
if not note:
return None, None
version_info = {
"introduction": {
"codeName": note.code_name or "",
"releaseDate": int(datetime.combine(note.release_date, time()).timestamp() * 1000) if note.release_date else 0,
"upgradePosition": note.upgrade_position or "",
"coreUpgrades": note.core_upgrades or []
},
"introduction_en": {
"codeName": note.code_name_en or note.code_name or "",
"releaseDate": int(datetime.combine(note.release_date, time()).timestamp() * 1000) if note.release_date else 0,
"upgradePosition": note.upgrade_position_en or note.upgrade_position or "",
"coreUpgrades": note.core_upgrades_en or []
}
}
return note.version, version_info
except Exception as e:
import traceback
traceback.print_exc()
return None, None
@staticmethod
def get_version_introduction(db: Session, version: str) -> Optional[Dict[str, Any]]:
"""
从数据库获取版本说明（优先读取已发布的版本）
从数据库获取指定版本说明（优先读取已发布的版本）
使用反射方式读取表结构，不依赖 premium 模型类
Args:
@@ -207,11 +260,8 @@ class HomePageRepository:
如果数据库中没有该版本，返回 None
"""
try:
from sqlalchemy import Table, MetaData
metadata = MetaData()
version_notes = Table('version_notes', metadata, autoload_with=db.engine)
version_note_items = Table('version_note_items', metadata, autoload_with=db.engine)
note = db.query(version_notes).filter(
version_notes.c.version == version,
@@ -221,31 +271,18 @@ class HomePageRepository:
if not note:
return None
items = db.query(version_note_items).filter(
version_note_items.c.note_id == note.id
).order_by(version_note_items.c.sort_order).all()
core_upgrades = []
for item in items:
title = item.title
content = item.content
if content:
core_upgrades.append(f"{title}<br>{content}")
else:
core_upgrades.append(title)
return {
"introduction": {
"codeName": "",
"releaseDate": note.release_date.isoformat() if note.release_date else "",
"upgradePosition": "",
"coreUpgrades": core_upgrades
"codeName": note.code_name or "",
"releaseDate": int(datetime.combine(note.release_date, time()).timestamp() * 1000) if note.release_date else 0,
"upgradePosition": note.upgrade_position or "",
"coreUpgrades": note.core_upgrades or []
},
"introduction_en": {
"codeName": "",
"releaseDate": note.release_date.isoformat() if note.release_date else "",
"upgradePosition": "",
"coreUpgrades": core_upgrades
"codeName": note.code_name_en or note.code_name or "",
"releaseDate": int(datetime.combine(note.release_date, time()).timestamp() * 1000) if note.release_date else 0,
"upgradePosition": note.upgrade_position_en or note.upgrade_position or "",
"coreUpgrades": note.core_upgrades_en or []
}
}
except Exception:

3
api/app/schemas/app_schema.py
View File

@@ -241,6 +241,8 @@ class ModelParameters(BaseModel):
presence_penalty: float = Field(default=0.0, ge=-2.0, le=2.0, description="存在惩罚")
n: int = Field(default=1, ge=1, le=10, description="生成的回复数量")
stop: Optional[List[str]] = Field(default=None, description="停止序列")
deep_thinking: bool = Field(default=False, description="是否启用深度思考模式（需模型支持，如 DeepSeek-R1、QwQ 等）")
thinking_budget_tokens: Optional[int] = Field(default=None, ge=1024, le=131072, description="深度思考 token 预算（仅部分模型支持）")
class VariableDefinition(BaseModel):
@@ -612,6 +614,7 @@ class AppChatRequest(BaseModel):
user_id: Optional[str] = Field(default=None, description="用户ID（用于会话管理）")
variables: Optional[Dict[str, Any]] = Field(default=None, description="自定义变量参数值")
stream: bool = Field(default=False, description="是否流式返回")
thinking: bool = Field(default=False, description="是否启用深度思考（需Agent配置支持）")
files: List[FileInput] = Field(default_factory=list, description="附件列表（支持多文件）")

1
api/app/schemas/conversation_schema.py
View File

@@ -31,6 +31,7 @@ class ChatRequest(BaseModel):
stream: bool = Field(default=False, description="是否流式返回")
web_search: bool = Field(default=False, description="是否启用网络搜索")
memory: bool = Field(default=True, description="是否启用记忆功能")
thinking: bool = Field(default=False, description="是否启用深度思考（需Agent配置支持）")
files: Optional[List[FileInput]] = Field(default=None, description="附件列表（支持多文件）")

14
api/app/schemas/memory_api_schema.py
View File

@@ -138,21 +138,13 @@ class CreateEndUserRequest(BaseModel):
"""Request schema for creating an end user.
Attributes:
workspace_id: Workspace ID (required)
other_id: External user identifier (required)
other_name: Display name for the end user
memory_config_id: Optional memory config ID. If not provided, uses workspace default.
"""
workspace_id: str = Field(..., description="Workspace ID (required)")
other_id: str = Field(..., description="External user identifier (required)")
other_name: Optional[str] = Field("", description="Display name")
@field_validator("workspace_id")
@classmethod
def validate_workspace_id(cls, v: str) -> str:
"""Validate that workspace_id is not empty."""
if not v or not v.strip():
raise ValueError("workspace_id is required and cannot be empty")
return v.strip()
memory_config_id: Optional[str] = Field(None, description="Memory config ID. Falls back to workspace default if not provided.")
@field_validator("other_id")
@classmethod
@@ -171,11 +163,13 @@ class CreateEndUserResponse(BaseModel):
other_id: External user identifier
other_name: Display name
workspace_id: Workspace the user belongs to
memory_config_id: Connected memory config ID
"""
id: str = Field(..., description="End user UUID")
other_id: str = Field(..., description="External user identifier")
other_name: str = Field("", description="Display name")
workspace_id: str = Field(..., description="Workspace ID")
memory_config_id: Optional[str] = Field(None, description="Connected memory config ID")
class MemoryConfigItem(BaseModel):

26
api/app/services/app_chat_service.py
View File

@@ -117,7 +117,9 @@ class AppChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
model_info = ModelInfo(
@@ -205,7 +207,8 @@ class AppChatService:
"model": api_key_obj.model_name,
"usage": result.get("usage", {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}),
"audio_url": None,
"citations": filtered_citations
"citations": filtered_citations,
"reasoning_content": result.get("reasoning_content")
}
if files:
for f in files:
@@ -258,6 +261,7 @@ class AppChatService:
"conversation_id": conversation_id,
"message_id": str(message_id),
"message": result["content"],
"reasoning_content": result.get("reasoning_content"),
"usage": result.get("usage", {
"prompt_tokens": 0,
"completion_tokens": 0,
@@ -354,7 +358,10 @@ class AppChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
streaming=True
streaming=True,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
model_info = ModelInfo(
@@ -403,6 +410,7 @@ class AppChatService:
# 流式调用 Agent（支持多模态），同时并行启动 TTS
full_content = ""
full_reasoning = ""
total_tokens = 0
text_queue: asyncio.Queue = asyncio.Queue()
@@ -426,6 +434,9 @@ class AppChatService:
):
if isinstance(chunk, int):
total_tokens = chunk
elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
full_reasoning += chunk['content']
yield f"event: reasoning\ndata: {json.dumps({'content': chunk['content']}, ensure_ascii=False)}\n\n"
else:
full_content += chunk
yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
@@ -472,7 +483,8 @@ class AppChatService:
"model": api_key_obj.model_name,
"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens},
"audio_url": None,
"citations": filtered_citations
"citations": filtered_citations,
"reasoning_content": full_reasoning or None
}
if files:
@@ -652,13 +664,13 @@ class AppChatService:
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id
):
if "sub_usage" in event:
# 拦截 sub_usage 事件，累加 token
if "event: sub_usage" in event:
if "data:" in event:
try:
data_line = event.split("data: ", 1)[1].strip()
data = json.loads(data_line)
if "total_tokens" in data:
total_tokens += data["total_tokens"]
total_tokens += data.get("total_tokens", 0)
except:
pass
else:

13
api/app/services/app_service.py
View File

@@ -13,7 +13,7 @@ import uuid
from typing import Annotated, Any, Dict, List, Optional, Tuple
from fastapi import Depends
from sqlalchemy import and_, delete, func, or_, select
from sqlalchemy import and_, delete, func, or_, select, update as sa_update
from sqlalchemy.orm import Session
from app.core.error_codes import BizCode
@@ -757,6 +757,17 @@ class AppService:
# 逻辑删除应用
app.is_active = False
# 更新 app_shares 表中该应用的所有共享记录为失效状态，并更新 updated_at 时间
stmt = sa_update(AppShare).where(
AppShare.source_app_id == app_id,
AppShare.is_active.is_(True)
).values(
is_active=False,
updated_at=datetime.datetime.now()
)
self.db.execute(stmt)
self.db.commit()
logger.info(

4
api/app/services/conversation_service.py
View File

@@ -534,6 +534,7 @@ class ConversationService:
api_key = api_config.api_key
api_base = api_config.api_base
is_omni = api_config.is_omni
capability = api_config.capability
model_type = config.type
llm = RedBearLLM(
@@ -542,7 +543,8 @@ class ConversationService:
provider=provider,
api_key=api_key,
base_url=api_base,
is_omni=is_omni
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
),
type=ModelType(model_type)
)

41
api/app/services/draft_run_service.py
View File

@@ -458,7 +458,7 @@ class AgentRunService:
statement = opening["statement"]
suggested_questions = opening["suggested_questions"]
# 如果有变量，进行替换（仅支持 {{var_name}} 格式）
if variables:
for var_name, var_value in variables.items():
@@ -595,6 +595,9 @@ class AgentRunService:
max_tokens=effective_params.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
deep_thinking=effective_params.get("deep_thinking", False),
thinking_budget_tokens=effective_params.get("thinking_budget_tokens"),
capability=api_key_config.get("capability", []),
)
# 5. 处理会话ID（创建或验证），新会话时写入开场白
@@ -689,7 +692,8 @@ class AgentRunService:
"prompt_tokens": 0,
"completion_tokens": 0,
"total_tokens": 0
})
}),
"reasoning_content": result.get("reasoning_content")
},
files=files,
processed_files=processed_files,
@@ -701,6 +705,7 @@ class AgentRunService:
response = {
"message": result["content"],
"reasoning_content": result.get("reasoning_content"),
"conversation_id": conversation_id,
"usage": result.get("usage", {
"prompt_tokens": 0,
@@ -838,7 +843,10 @@ class AgentRunService:
max_tokens=effective_params.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
streaming=True
streaming=True,
deep_thinking=effective_params.get("deep_thinking", False),
thinking_budget_tokens=effective_params.get("thinking_budget_tokens"),
capability=api_key_config.get("capability", []),
)
# 5. 处理会话ID（创建或验证），新会话时写入开场白
@@ -898,6 +906,7 @@ class AgentRunService:
# 9. 流式调用 Agent（支持多模态），同时并行启动 TTS
full_content = ""
full_reasoning = ""
total_tokens = 0
# 启动流式 TTS（文本边输出边合成）
@@ -916,6 +925,9 @@ class AgentRunService:
):
if isinstance(chunk, int):
total_tokens = chunk
elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
full_reasoning += chunk["content"]
yield self._format_sse_event("reasoning", {"content": chunk["content"]})
else:
full_content += chunk
yield self._format_sse_event("message", {"content": chunk})
@@ -944,7 +956,8 @@ class AgentRunService:
app_id=agent_config.app_id,
user_id=user_id,
meta_data={
"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens}
"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens},
"reasoning_content": full_reasoning or None
},
files=files,
processed_files=processed_files,
@@ -1665,7 +1678,7 @@ class AgentRunService:
"""从 text_queue 取文本按句子切分后喂给 synthesizer"""
import re
buf = ""
sentence_end = re.compile(r'[\u3002\uff01\uff1f\.!?\n]')
sentence_end = re.compile(r'[\u3002\uff01\uff1f.!?\n]')
while True:
chunk = await text_queue.get()
if chunk is None:
@@ -1894,6 +1907,7 @@ class AgentRunService:
"conversation_id": result['conversation_id'],
"parameters_used": model_info["parameters"],
"message": result.get("message"),
"reasoning_content": result.get("reasoning_content"),
"usage": usage,
"elapsed_time": elapsed,
"tokens_per_second": (
@@ -2012,7 +2026,7 @@ class AgentRunService:
# 需要从 ModelApiKey 获取实际的模型名称，或者在 ModelConfig 中添加 model 字段
return None
def _with_parameters(self, agent_config: AgentConfig, parameters: Dict[str, Any]) -> AgentConfig:
def _with_parameters(self, agent_config: AgentConfig, parameters: Dict[str, Any]) -> tuple[AgentConfig, Any]:
"""创建一个带有覆盖参数的 agent_config（浅拷贝，只修改 model_parameters）
Args:
@@ -2110,6 +2124,7 @@ class AgentRunService:
start_time = time.time()
full_content = ""
full_reasoning = ""
returned_conversation_id = model_conversation_id
audio_url = None
audio_status = None
@@ -2168,6 +2183,18 @@ class AgentRunService:
"content": chunk
}))
# 转发深度思考事件（带模型标识）
if event_type == "reasoning" and event_data:
reasoning_chunk = event_data.get("content", "")
full_reasoning += reasoning_chunk
await event_queue.put(self._format_sse_event("model_reasoning", {
"model_index": idx,
"model_config_id": model_config_id,
"label": model_label,
"conversation_id": returned_conversation_id,
"content": event_data.get("content", "")
}))
# 从 end 事件中提取 features 输出字段
if event_type == "end" and event_data:
audio_url = event_data.get("audio_url")
@@ -2199,6 +2226,7 @@ class AgentRunService:
"conversation_id": returned_conversation_id,
"parameters_used": model_info["parameters"],
"message": full_content,
"reasoning_content": full_reasoning or None,
"elapsed_time": elapsed,
"audio_url": audio_url,
"audio_status": audio_status,
@@ -2351,6 +2379,7 @@ class AgentRunService:
"label": r["label"],
"conversation_id": r.get("conversation_id"),
"message": r.get("message"),
"reasoning_content": r.get("reasoning_content"),
"elapsed_time": r.get("elapsed_time", 0),
"audio_url": r.get("audio_url"),
"audio_status": r.get("audio_status"),

1
api/app/services/llm_router.py
View File

@@ -415,6 +415,7 @@ class LLMRouter:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
temperature=0.3,
max_tokens=500
)

12
api/app/services/master_agent_router.py
View File

@@ -393,6 +393,7 @@ class MasterAgentRouter:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
extra_params = extra_params
)
@@ -403,6 +404,17 @@ class MasterAgentRouter:
response = await llm.ainvoke(prompt)
ModelApiKeyService.record_api_key_usage(self.db, api_key_config.id)
# 提取 token 消耗
self._last_routing_tokens = 0
if hasattr(response, 'usage_metadata') and response.usage_metadata:
um = response.usage_metadata
self._last_routing_tokens = um.get("total_tokens", 0) if isinstance(um, dict) else getattr(um, "total_tokens", 0)
elif hasattr(response, 'response_metadata') and response.response_metadata:
token_usage = response.response_metadata.get("token_usage") or response.response_metadata.get("usage", {})
if isinstance(token_usage, dict):
self._last_routing_tokens = token_usage.get("total_tokens", 0)
logger.info(f"Master Agent 路由 token 消耗: {self._last_routing_tokens}")
# 提取响应内容
if hasattr(response, 'content'):
return response.content

319
api/app/services/memory_dashboard_service.py
View File

@@ -353,15 +353,13 @@ async def get_workspace_total_memory_count(
"details": []
}
# 2. 对每个 host_id 调用 search_all 获取 total
# 2. 使用 search_all_batch 批量查询所有宿主的记忆数量
from app.services import memory_storage_service
total_count = 0
details = []
# 如果提供了 end_user_id，只查询该用户
if end_user_id:
search_result = await memory_storage_service.search_all(end_user_id=end_user_id)
batch_result = await memory_storage_service.search_all_batch([end_user_id])
count = batch_result.get(end_user_id, 0)
# 查询用户名称
from app.repositories.end_user_repository import EndUserRepository
repo = EndUserRepository(db)
@@ -369,42 +367,31 @@ async def get_workspace_total_memory_count(
user_name = end_user.other_name if end_user else None
return {
"total_memory_count": search_result.get("total", 0),
"total_memory_count": count,
"host_count": 1,
"details": [{
"end_user_id": end_user_id,
"count": search_result.get("total", 0),
"count": count,
"name": user_name
}]
}
for host in hosts:
try:
end_user_id_str = str(host.id)
search_result = await memory_storage_service.search_all(
end_user_id=end_user_id_str
)
host_total = search_result.get("total", 0)
total_count += host_total
details.append({
"end_user_id": end_user_id_str,
"count": host_total,
"name": host.other_name # 使用 other_name 字段
})
business_logger.debug(f"EndUser {end_user_id_str} ({host.other_name}) 记忆数: {host_total}")
except Exception as e:
business_logger.warning(f"获取 end_user {host.id} 记忆数失败: {str(e)}")
# 失败的 host 记为 0
details.append({
"end_user_id": str(host.id),
"count": 0,
"name": host.other_name # 使用 other_name 字段
})
# 批量查询所有宿主记忆数量（一次 Neo4j 查询）
end_user_ids = [str(host.id) for host in hosts]
batch_result = await memory_storage_service.search_all_batch(end_user_ids)
# 构建 host name 映射
host_name_map = {str(host.id): host.other_name for host in hosts}
total_count = sum(batch_result.values())
details = [
{
"end_user_id": uid,
"count": batch_result.get(uid, 0),
"name": host_name_map.get(uid)
}
for uid in end_user_ids
]
result = {
"total_memory_count": total_count,
@@ -519,6 +506,206 @@ def get_rag_user_kb_total_chunk(
business_logger.error(f"获取用户知识库总chunk数失败: workspace_id={workspace_id} - {str(e)}")
raise
def get_dashboard_yesterday_changes(
db: Session,
workspace_id: uuid.UUID,
storage_type: str,
today_data: dict
) -> dict:
"""
计算各指标相比昨天的变化量。
Args:
db: 数据库会话
workspace_id: 工作空间ID
storage_type: 存储类型 'neo4j' | 'rag'
today_data: 当前数据，包含 total_memory, total_app, total_knowledge, total_api_call
Returns:
{
"total_memory_change": int | None,
"total_app_change": int | None,
"total_knowledge_change": int | None,
"total_api_call_change": int | None
}
"""
from datetime import datetime, timedelta
from sqlalchemy import func
from app.models.api_key_model import ApiKey, ApiKeyLog
from app.models.knowledge_model import Knowledge
from app.models.app_model import App
from app.models.appshare_model import AppShare
business_logger.info(f"计算昨日对比: workspace_id={workspace_id}, storage_type={storage_type}")
now_local = datetime.now()
today_start = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
yesterday_start = today_start - timedelta(days=1)
changes = {
"total_memory_change": None,
"total_app_change": None,
"total_knowledge_change": None,
"total_api_call_change": None,
}
# --- total_api_call_change ---
try:
# 获取该workspace下所有api_key的id
api_key_ids = [
row[0] for row in db.query(ApiKey.id).filter(
ApiKey.workspace_id == workspace_id
).all()
]
if api_key_ids:
# 今日累计
today_api_count = db.query(func.count(ApiKeyLog.id)).filter(
ApiKeyLog.api_key_id.in_(api_key_ids),
ApiKeyLog.created_at >= today_start,
ApiKeyLog.created_at < now_local
).scalar() or 0
# 昨日全天
yesterday_api_count = db.query(func.count(ApiKeyLog.id)).filter(
ApiKeyLog.api_key_id.in_(api_key_ids),
ApiKeyLog.created_at >= yesterday_start,
ApiKeyLog.created_at < today_start
).scalar() or 0
changes["total_api_call_change"] = today_api_count - yesterday_api_count
else:
# 没有api_key，如果今日也是0则无对比意义
changes["total_api_call_change"] = None
except Exception as e:
business_logger.warning(f"计算API调用昨日对比失败: {str(e)}")
# --- total_knowledge_change ---
try:
# 今天有效总量：当前status=1的知识库总数，排除用户知识库(permission_id='Memory')
today_knowledge = db.query(func.count(Knowledge.id)).filter(
Knowledge.workspace_id == workspace_id,
Knowledge.status == 1,
Knowledge.permission_id != "Memory"
).scalar() or 0
# 昨日有效总量：昨天之前创建的、当前仍有效的知识库，排除用户知识库
yesterday_knowledge = db.query(func.count(Knowledge.id)).filter(
Knowledge.workspace_id == workspace_id,
Knowledge.status == 1,
Knowledge.permission_id != "Memory",
Knowledge.created_at < today_start
).scalar() or 0
# 今日软删：今天被软删的知识库(status=2 且 updated_at >= today_start)，排除用户知识库
today_deleted_knowledge = db.query(func.count(Knowledge.id)).filter(
Knowledge.workspace_id == workspace_id,
Knowledge.status == 2,
Knowledge.permission_id != "Memory",
Knowledge.updated_at >= today_start
).scalar() or 0
if yesterday_knowledge == 0 and today_knowledge == 0 and today_deleted_knowledge == 0:
changes["total_knowledge_change"] = None
else:
# change = 今天有效总量 - 今日软删 - 昨日有效总量
changes["total_knowledge_change"] = today_knowledge - today_deleted_knowledge - yesterday_knowledge
except Exception as e:
business_logger.warning(f"计算知识库昨日对比失败: {str(e)}")
# --- total_app_change ---
try:
# === 自有app ===
# 今天有效总量
today_own_apps = db.query(func.count(App.id)).filter(
App.workspace_id == workspace_id,
App.is_active == True
).scalar() or 0
# 昨日有效总量
yesterday_own_apps = db.query(func.count(App.id)).filter(
App.workspace_id == workspace_id,
App.is_active == True,
App.created_at < today_start
).scalar() or 0
# 今日软删
today_deleted_own_apps = db.query(func.count(App.id)).filter(
App.workspace_id == workspace_id,
App.is_active == False,
App.updated_at >= today_start
).scalar() or 0
# === 被分享app ===
# 今天有效总量
today_shared_apps = db.query(func.count(AppShare.id)).filter(
AppShare.target_workspace_id == workspace_id,
AppShare.is_active == True
).scalar() or 0
# 昨日有效总量
yesterday_shared_apps = db.query(func.count(AppShare.id)).filter(
AppShare.target_workspace_id == workspace_id,
AppShare.is_active == True,
AppShare.created_at < today_start
).scalar() or 0
# 今日软删
today_deleted_shared_apps = db.query(func.count(AppShare.id)).filter(
AppShare.target_workspace_id == workspace_id,
AppShare.is_active == False,
AppShare.updated_at >= today_start
).scalar() or 0
today_total_app = today_own_apps + today_shared_apps
yesterday_total_app = yesterday_own_apps + yesterday_shared_apps
total_deleted = today_deleted_own_apps + today_deleted_shared_apps
if yesterday_total_app == 0 and today_total_app == 0 and total_deleted == 0:
changes["total_app_change"] = None
else:
# change = 今天有效总量 - 今日软删 - 昨日有效总量
changes["total_app_change"] = today_total_app - total_deleted - yesterday_total_app
except Exception as e:
business_logger.warning(f"计算应用数量昨日对比失败: {str(e)}")
# --- total_memory_change ---
try:
today_memory = today_data.get("total_memory")
if today_memory is None:
changes["total_memory_change"] = None
elif storage_type == "neo4j":
# 从 memory_increments 取最近一条 created_at < today_start 的记录
last_record = db.query(MemoryIncrement).filter(
MemoryIncrement.workspace_id == workspace_id,
MemoryIncrement.created_at < today_start
).order_by(desc(MemoryIncrement.created_at)).first()
if last_record is None:
changes["total_memory_change"] = None
else:
changes["total_memory_change"] = today_memory - last_record.total_num
elif storage_type == "rag":
# RAG: 查 documents 表中 created_at < today_start 的 chunk_num 之和
from app.models.document_model import Document
from app.models.end_user_model import EndUser as _EndUser
from app.models.app_model import App as _App
end_user_ids = [
str(eid) for (eid,) in db.query(_EndUser.id)
.join(_App, _EndUser.app_id == _App.id)
.filter(_App.workspace_id == workspace_id)
.all()
]
if not end_user_ids:
changes["total_memory_change"] = None
else:
file_names = [f"{uid}.txt" for uid in end_user_ids]
yesterday_chunk = db.query(func.sum(Document.chunk_num)).filter(
Document.file_name.in_(file_names),
Document.created_at < today_start
).scalar()
if yesterday_chunk is None:
changes["total_memory_change"] = None
else:
changes["total_memory_change"] = today_memory - int(yesterday_chunk)
except Exception as e:
business_logger.warning(f"计算记忆总量昨日对比失败: {str(e)}")
business_logger.info(f"昨日对比计算完成: {changes}")
return changes
def get_current_user_total_chunk(
end_user_id: str,
db: Session,
@@ -881,4 +1068,66 @@ async def generate_rag_profile(
"tags_count": len(tags),
"personas_count": len(personas),
"insight_generated": bool(insight_sections.get("memory_insight")),
}
}
def get_dashboard_common_stats(db: Session, workspace_id) -> dict:
"""
获取 dashboard 中 neo4j/rag 分支共享的统计数据：
total_app、total_knowledge、total_api_call
Returns:
dict: {"total_app": int, "total_knowledge": int, "total_api_call": int}
"""
result = {"total_app": 0, "total_knowledge": 0, "total_api_call": 0}
# total_app: 统计当前空间下的所有app数量（包含自有 + 被分享给本工作空间的app）
try:
from app.services import app_service as _app_svc
_, total_app = _app_svc.AppService(db).list_apps(
workspace_id=workspace_id, include_shared=True, pagesize=1
)
result["total_app"] = total_app
except Exception as e:
business_logger.warning(f"获取应用数量失败: {e}")
# total_knowledge: 统计顶层知识库（parent_id = workspace_id）
try:
from sqlalchemy import func as _func
from app.models.knowledge_model import Knowledge as _Knowledge
total_knowledge = db.query(_func.count(_Knowledge.id)).filter(
_Knowledge.workspace_id == workspace_id,
_Knowledge.status == 1,
_Knowledge.parent_id == _Knowledge.workspace_id
).scalar() or 0
result["total_knowledge"] = total_knowledge
except Exception as e:
business_logger.warning(f"获取知识库数量失败: {e}")
# total_api_call: 仅统计当天 api_key_log 调用次数
try:
from datetime import datetime
from sqlalchemy import func as _api_func
from app.models.api_key_model import ApiKey as _ApiKey, ApiKeyLog as _ApiKeyLog
_now = datetime.now()
_today_start = _now.replace(hour=0, minute=0, second=0, microsecond=0)
_api_key_ids = [
row[0] for row in db.query(_ApiKey.id).filter(
_ApiKey.workspace_id == workspace_id
).all()
]
if _api_key_ids:
total_api_calls = db.query(_api_func.count(_ApiKeyLog.id)).filter(
_ApiKeyLog.api_key_id.in_(_api_key_ids),
_ApiKeyLog.created_at >= _today_start,
_ApiKeyLog.created_at < _now
).scalar() or 0
else:
total_api_calls = 0
result["total_api_call"] = total_api_calls
except Exception as e:
business_logger.warning(f"获取API调用统计失败: {e}")
return result

3
api/app/services/memory_perceptual_service.py
View File

@@ -232,7 +232,8 @@ class MemoryPerceptualService:
provider=model_config.provider,
api_key=model_config.api_key,
base_url=model_config.api_base,
is_omni=model_config.is_omni
is_omni=model_config.is_omni,
support_thinking="thinking" in (model_config.capability or []),
)
)
return llm, model_config

31
api/app/services/memory_storage_service.py
View File

@@ -613,37 +613,6 @@ async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]:
return data
async def search_all(end_user_id: Optional[str] = None) -> Dict[str, Any]:
result = await _neo4j_connector.execute_query(
MemoryConfigRepository.SEARCH_FOR_ALL,
end_user_id=end_user_id,
)
# 检查结果是否为空或长度不足
if not result or len(result) < 4:
data = {
"total": 0,
"counts": {
"dialogue": 0,
"chunk": 0,
"statement": 0,
"entity": 0,
},
}
return data
data = {
"total": result[-1]["Count"],
"counts": {
"dialogue": result[0]["Count"],
"chunk": result[1]["Count"],
"statement": result[2]["Count"],
"entity": result[3]["Count"],
},
}
return data
async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, Any]:
"""统一知识库类型分布接口。

10
api/app/services/model_parameter_merger.py
View File

@@ -45,12 +45,20 @@ class ModelParameterMerger:
"frequency_penalty": 0.0,
"presence_penalty": 0.0,
"n": 1,
"stop": None
"stop": None,
"deep_thinking": False,
"thinking_budget_tokens": None
}
# 合并参数：默认值 -> 模型配置 -> Agent 配置
merged = default_params.copy()
# Pydantic 对象转为 dict
if model_config_params and hasattr(model_config_params, 'model_dump'):
model_config_params = model_config_params.model_dump()
if agent_config_params and hasattr(agent_config_params, 'model_dump'):
agent_config_params = agent_config_params.model_dump()
# 应用模型配置参数
if model_config_params:
for key in default_params:

32
api/app/services/model_service.py
View File

@@ -85,15 +85,16 @@ class ModelConfigService:
@staticmethod
async def validate_model_config(
db: Session,
*,
model_name: str,
provider: str,
api_key: str,
api_base: Optional[str] = None,
model_type: str = "llm",
test_message: str = "Hello",
is_omni: bool = False
db: Session,
*,
model_name: str,
provider: str,
api_key: str,
api_base: Optional[str] = None,
model_type: str = "llm",
test_message: str = "Hello",
is_omni: bool = False,
capability: Optional[list] = None
) -> Dict[str, Any]:
"""验证模型配置是否有效
@@ -124,6 +125,7 @@ class ModelConfigService:
api_key=api_key,
base_url=api_base,
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
temperature=0.7,
max_tokens=100
)
@@ -320,7 +322,8 @@ class ModelConfigService:
api_base=api_key_data.api_base,
model_type=model_data.type,
test_message="Hello",
is_omni=model_data.is_omni
is_omni=model_data.is_omni,
capability=model_data.capability
)
if not validation_result["valid"]:
raise BusinessException(
@@ -590,7 +593,8 @@ class ModelApiKeyService:
api_base=data.api_base,
model_type=model_config.type,
test_message="Hello",
is_omni=data.is_omni
is_omni=data.is_omni,
capability=model_config.capability
)
if not validation_result["valid"]:
# 记录验证失败的模型，但不抛出异常
@@ -675,7 +679,8 @@ class ModelApiKeyService:
api_base=api_key_data.api_base,
model_type=model_config.type,
test_message="Hello",
is_omni=api_key_data.is_omni
is_omni=api_key_data.is_omni,
capability=model_config.capability
)
if not validation_result["valid"]:
raise BusinessException(
@@ -707,7 +712,8 @@ class ModelApiKeyService:
api_base=api_key_data.api_base or existing_api_key.api_base,
model_type=model_config.type,
test_message="Hello",
is_omni=model_config.is_omni
is_omni=model_config.is_omni,
capability=model_config.capability
)
if not validation_result["valid"]:
raise BusinessException(

36
api/app/services/multi_agent_orchestrator.py
View File

@@ -287,6 +287,11 @@ class MultiAgentOrchestrator:
sub_conversation_id = None
total_tokens = 0
# 累加 Master Agent 路由决策消耗的 token
total_tokens += task_analysis.get("routing_tokens", 0)
# 累加 Master Agent 整合消耗的 token
total_tokens += getattr(self, '_last_merge_tokens', 0)
if isinstance(results, dict):
sub_conversation_id = results.get("conversation_id") or results.get("result", {}).get("conversation_id")
# 提取 token 信息
@@ -358,12 +363,16 @@ class MultiAgentOrchestrator:
variables=variables
)
# 获取路由决策消耗的 token
routing_tokens = getattr(self.router, '_last_routing_tokens', 0)
logger.info(
"Master Agent 分析完成",
extra={
"selected_agent": routing_decision.get("selected_agent_id"),
"confidence": routing_decision.get("confidence"),
"strategy": routing_decision.get("strategy")
"strategy": routing_decision.get("strategy"),
"routing_tokens": routing_tokens
}
)
@@ -372,7 +381,8 @@ class MultiAgentOrchestrator:
"variables": variables or {},
"sub_agents": self.config.sub_agents,
"initial_context": variables or {},
"routing_decision": routing_decision
"routing_decision": routing_decision,
"routing_tokens": routing_tokens
}
async def _execute_sequential(
@@ -1032,6 +1042,11 @@ class MultiAgentOrchestrator:
# 5. 流式执行子 Agent
sub_conversation_id = None
# Master Agent 路由决策消耗的 token，通过 sub_usage 事件发送给上层
routing_tokens = task_analysis.get("routing_tokens", 0)
if routing_tokens > 0:
yield self._format_sse_event("sub_usage", {"total_tokens": routing_tokens})
async for event in self._execute_sub_agent_stream(
agent_data["config"],
message,
@@ -1054,6 +1069,7 @@ class MultiAgentOrchestrator:
except:
pass
# 直接透传所有事件（包括 sub_usage），累加统一由上层处理
yield event
# 6. 如果有会话 ID，发送一个包含它的事件
@@ -2600,6 +2616,7 @@ class MultiAgentOrchestrator:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
temperature=0.7, # 整合任务使用中等温度
max_tokens=2000
)
@@ -2612,6 +2629,17 @@ class MultiAgentOrchestrator:
ModelApiKeyService.record_api_key_usage(self.db, api_key_config.id)
# 提取整合消耗的 token
merge_tokens = 0
if hasattr(response, 'usage_metadata') and response.usage_metadata:
um = response.usage_metadata
merge_tokens = um.get("total_tokens", 0) if isinstance(um, dict) else getattr(um, "total_tokens", 0)
elif hasattr(response, 'response_metadata') and response.response_metadata:
token_usage = response.response_metadata.get("token_usage") or response.response_metadata.get("usage", {})
if isinstance(token_usage, dict):
merge_tokens = token_usage.get("total_tokens", 0)
self._last_merge_tokens = merge_tokens
# 提取响应内容
if hasattr(response, 'content'):
merged_response = response.content
@@ -2621,7 +2649,8 @@ class MultiAgentOrchestrator:
logger.info(
"Master Agent 整合完成",
extra={
"merged_length": len(merged_response)
"merged_length": len(merged_response),
"merge_tokens": merge_tokens
}
)
@@ -2766,6 +2795,7 @@ class MultiAgentOrchestrator:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
temperature=0.7,
max_tokens=2000,
extra_params={"streaming": True} # 启用流式输出

3
api/app/services/prompt_optimizer_service.py
View File

@@ -185,7 +185,8 @@ class PromptOptimizerService:
provider=api_config.provider,
api_key=api_config.api_key,
base_url=api_config.api_base,
is_omni=api_config.is_omni
is_omni=api_config.is_omni,
support_thinking="thinking" in (api_config.capability or []),
), type=ModelType(model_config.type))
try:
prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt')

11
api/app/services/shared_chat_service.py
View File

@@ -248,7 +248,9 @@ class SharedChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
# 加载历史消息
@@ -450,7 +452,10 @@ class SharedChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
streaming=True
streaming=True,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
# 加载历史消息
@@ -479,6 +484,8 @@ class SharedChatService:
):
if isinstance(chunk, int):
total_tokens = chunk
elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
yield f"event: reasoning\ndata: {json.dumps({'content': chunk['content']}, ensure_ascii=False)}\n\n"
else:
full_content += chunk
# 发送消息块事件

75
api/app/tasks.py
View File

@@ -61,9 +61,9 @@ def _get_or_create_redis_pool() -> redis.ConnectionPool | None:
db=settings.REDIS_DB_CELERY_BACKEND,
password=settings.REDIS_PASSWORD,
decode_responses=True,
max_connections=10,
max_connections=100,
socket_connect_timeout=5,
socket_timeout=5,
socket_timeout=10,
retry_on_timeout=True,
health_check_interval=30,
)
@@ -1100,7 +1100,7 @@ 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)
@celery_app.task(name="app.core.memory.agent.write_message", bind=True, acks_late=False)
def write_message_task(
self,
end_user_id: str,
@@ -1176,6 +1176,7 @@ def write_message_task(
redis_client = get_sync_redis_client()
lock = None
loop = None
if redis_client is not None:
lock = RedisFairLock(
key=f"memory_write:{end_user_id}",
@@ -1196,6 +1197,7 @@ def write_message_task(
}
try:
task_start_time = int(time.time())
loop = set_asyncio_event_loop()
result = loop.run_until_complete(_run())
@@ -1205,7 +1207,7 @@ def write_message_task(
f"- elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}")
try:
_r = get_sync_redis_client()
_r = redis_client
if _r is not None:
from datetime import timezone as _tz
_now_utc = datetime.now(_tz.utc).isoformat()
@@ -1219,6 +1221,7 @@ def write_message_task(
return {
"status": "SUCCESS",
"result": result,
"start_at": task_start_time,
"end_user_id": end_user_id,
"config_id": config_id,
"elapsed_time": elapsed_time,
@@ -1252,7 +1255,8 @@ def write_message_task(
logger.warning(f"[CELERY WRITE] 释放锁失败: {e}")
# Gracefully shutdown the event loop to prevent
# 'RuntimeError: Event loop is closed' from httpx.AsyncClient.__del__
_shutdown_loop_gracefully(loop)
if loop:
_shutdown_loop_gracefully(loop)
# unused task
@@ -1320,7 +1324,7 @@ def write_total_memory_task(workspace_id: str) -> Dict[str, Any]:
from app.models.app_model import App
from app.models.end_user_model import EndUser
from app.repositories.memory_increment_repository import write_memory_increment
from app.services.memory_storage_service import search_all
from app.services.memory_storage_service import search_all_batch
with get_db_context() as db:
try:
@@ -1354,27 +1358,15 @@ def write_total_memory_task(workspace_id: str) -> Dict[str, Any]:
EndUser.workspace_id == workspace_id
).distinct().all()
# 3. 遍历所有end_user，查询每个宿主的记忆总量并累加
total_num = 0
end_user_details = []
# 3. 批量查询所有宿主的记忆总量
end_user_id_list = [str(eid) for (eid,) in end_users]
batch_result = await search_all_batch(end_user_id_list)
for (end_user_id,) in end_users:
try:
# 调用 search_all 接口查询该宿主的总量
result = await search_all(str(end_user_id))
user_total = result.get("total", 0)
total_num += user_total
end_user_details.append({
"end_user_id": str(end_user_id),
"total": user_total
})
except Exception as e:
# 记录单个用户查询失败，但继续处理其他用户
end_user_details.append({
"end_user_id": str(end_user_id),
"total": 0,
"error": str(e)
})
total_num = sum(batch_result.values())
end_user_details = [
{"end_user_id": uid, "total": batch_result.get(uid, 0)}
for uid in end_user_id_list
]
# 4. 写入数据库
memory_increment = write_memory_increment(
@@ -1437,7 +1429,7 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]:
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
from app.services.memory_storage_service import search_all_batch
with get_db_context() as db:
try:
@@ -1495,28 +1487,15 @@ def write_all_workspaces_memory_task(self) -> Dict[str, Any]:
EndUser.workspace_id == workspace_id
).distinct().all()
# 3. 遍历所有end_user，查询每个宿主的记忆总量并累加
total_num = 0
end_user_details = []
# 3. 批量查询所有宿主的记忆总量
end_user_id_list = [str(eid) for (eid,) in end_users]
batch_result = await search_all_batch(end_user_id_list)
for (end_user_id,) in end_users:
try:
# 调用 search_all 接口查询该宿主的总量
result = await search_all(str(end_user_id))
user_total = result.get("total", 0)
total_num += user_total
end_user_details.append({
"end_user_id": str(end_user_id),
"total": user_total
})
except Exception as e:
# 记录单个用户查询失败，但继续处理其他用户
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,
"error": str(e)
})
total_num = sum(batch_result.values())
end_user_details = [
{"end_user_id": uid, "total": batch_result.get(uid, 0)}
for uid in end_user_id_list
]
# 4. 写入数据库
memory_increment = write_memory_increment(

28
api/app/utils/performance_timer.py
View File

@@ -6,13 +6,13 @@
"""
import time
from contextlib import contextmanager
from contextlib import asynccontextmanager, contextmanager
from app.core.logging_config import get_api_logger
# 获取API专用日志器
api_logger = get_api_logger()
# 同步的上下文管理器，使用@contextmanager修饰
@contextmanager
def timer(label: str, user_count: int = 0):
"""上下文管理器：用于测量代码块执行时间
@@ -35,3 +35,27 @@ def timer(label: str, user_count: int = 0):
elapsed = (time.perf_counter() - start) * 1000 # 转换为毫秒
extra_info = f", 用户数: {user_count}" if user_count > 0 else ""
api_logger.info(f"[性能统计] {label}: {elapsed:.2f}ms{extra_info}")
# 异步的上下文管理器，使用@asynccontextmanager装饰
@asynccontextmanager
async def async_timer(label: str, user_count: int = 0):
"""异步上下文管理器：用于测量包含 await 的异步代码块执行时间
Args:
label: 统计标签，用于标识被测量的代码块
user_count: 用户数，可选参数，用于记录处理的用户数量
Usage:
async with async_timer("获取用户列表"):
users = await get_users()
async with async_timer("批量处理", user_count=len(user_ids)):
await process_users(user_ids)
"""
start = time.perf_counter()
try:
yield
finally:
elapsed = (time.perf_counter() - start) * 1000 # 转换为毫秒
extra_info = f", 用户数: {user_count}" if user_count > 0 else ""
api_logger.info(f"[性能统计] {label}: {elapsed:.2f}ms{extra_info}")

146
api/app/utils/redis_lock.py
View File

@@ -1,14 +1,21 @@
import redis
import uuid
import time
import logging
import threading
import time
import uuid
import redis
from redis.exceptions import (
ConnectionError,
TimeoutError,
RedisError,
)
UNLOCK_SCRIPT = """
if redis.call("get", KEYS[1]) == ARGV[1] then
return redis.call("del", KEYS[1])
else
return 0
end
return 0
"""
RENEW_SCRIPT = """
@@ -19,38 +26,44 @@ else
end
"""
CLEANUP_DEAD_HEAD_SCRIPT = """
ACQUIRE_SCRIPT = """
local queue_key = KEYS[1]
local lock_key = KEYS[2]
local first = redis.call("lindex", queue_key, 0)
if not first then
return 0
local client_id = ARGV[1]
local expire = tonumber(ARGV[2])
local time_out = tonumber(ARGV[3])
local now = tonumber(redis.call("time")[1])
if redis.call("zscore", queue_key, client_id) == false then
redis.call("zadd", queue_key, now, client_id)
end
if redis.call("exists", lock_key) == 1 then
return 0
local expired = redis.call("zrangebyscore", queue_key, 0, now - time_out)
for _, v in ipairs(expired) do
redis.call("zrem", queue_key, v)
end
redis.call("lpop", queue_key)
return 1
"""
local first = redis.call("zrange", queue_key, 0, 0)[1]
if first == client_id then
SAFE_RELEASE_QUEUE_SCRIPT = """
local queue_key = KEYS[1]
local value = ARGV[1]
if redis.call("set", lock_key, client_id, "NX", "EX", expire) then
redis.call("zrem", queue_key, client_id)
return 1
end
local first = redis.call("lindex", queue_key, 0)
if first == value then
redis.call("lpop", queue_key)
return 1
if redis.call("get", lock_key) == client_id then
redis.call("expire", lock_key, expire)
return 1
end
end
return 0
"""
def _ensure_str(val):
"""统一将 Redis 返回值转为 str，兼容 decode_responses=True/False"""
if val is None:
return None
if isinstance(val, bytes):
@@ -59,18 +72,27 @@ def _ensure_str(val):
class RedisFairLock:
# ZOMBIE CLEAN BUFFER
CLEANUP_BUFFER = 30
# Redis 操作失败时的最大重试次数
MAX_RETRIES = 3
# 重试间隔基数（秒），实际间隔 = base * 2^attempt（指数退避）
RETRY_BACKOFF_BASE = 0.1
_logger = logging.getLogger(__name__)
def __init__(
self,
key: str,
redis_client: redis.StrictRedis,
expire: int = 30,
retry_interval: float = 0.05,
retry_interval: float = 1,
timeout: float = 600,
auto_renewal: bool = True
):
self.key = key
self.queue_key = f"{key}:queue"
self.value = str(uuid.uuid4())
self.queue_key = f"{key}:zset"
self.value = f"{uuid.uuid4().hex}:{int(time.time())}"
self.expire = expire
self.retry_interval = retry_interval
self.timeout = timeout
@@ -80,28 +102,56 @@ class RedisFairLock:
self._renew_thread = None
self._stop_renew = threading.Event()
def _exec_with_retry(self, func, *args, raise_on_fail=True, **kwargs):
"""
带指数退避重试的 Redis 操作执行器。
对 ConnectionError / TimeoutError 自动重试，其他异常直接抛出。
"""
last_err = None
for attempt in range(self.MAX_RETRIES):
try:
return func(*args, **kwargs)
except (ConnectionError, TimeoutError) as e:
last_err = e
wait = self.RETRY_BACKOFF_BASE * (2 ** attempt)
self._logger.warning(
f"[RedisFairLock] Redis error on attempt {attempt + 1}/{self.MAX_RETRIES} "
f"for key={self.key}: {e}, retrying in {wait:.2f}s"
)
time.sleep(wait)
except RedisError:
raise
if raise_on_fail:
raise last_err
return None
def acquire(self):
start = time.time()
self.redis.rpush(self.queue_key, self.value)
while True:
first = _ensure_str(self.redis.lindex(self.queue_key, 0))
ok = self._exec_with_retry(
self.redis.eval,
ACQUIRE_SCRIPT,
2,
self.queue_key,
self.key,
self.value,
str(self.expire),
str(self.timeout + self.CLEANUP_BUFFER),
)
if first == self.value:
ok = self.redis.set(self.key, self.value, nx=True, ex=self.expire)
if ok:
self._locked = True
if self.auto_renewal:
self._start_renewal()
return True
if first:
self.redis.eval(CLEANUP_DEAD_HEAD_SCRIPT, 2, self.queue_key, self.key)
if ok == 1:
self._locked = True
if self.auto_renewal:
self._start_renewal()
return True
if time.time() - start > self.timeout:
self.redis.lrem(self.queue_key, 0, self.value)
self._exec_with_retry(
self.redis.zrem, self.queue_key, self.value,
raise_on_fail=False,
)
return False
time.sleep(self.retry_interval)
@@ -112,13 +162,17 @@ class RedisFairLock:
if self._stop_renew.is_set():
break
self.redis.eval(
success = self._exec_with_retry(
self.redis.eval,
RENEW_SCRIPT,
1,
self.key,
self.value,
str(self.expire)
str(self.expire),
raise_on_fail=False,
)
if not success:
break
def _start_renewal(self):
self._stop_renew = threading.Event()
@@ -137,9 +191,10 @@ class RedisFairLock:
if self.auto_renewal:
self._stop_renewal()
self.redis.eval(UNLOCK_SCRIPT, 1, self.key, self.value)
self.redis.eval(SAFE_RELEASE_QUEUE_SCRIPT, 1, self.queue_key, self.value)
self._exec_with_retry(
self.redis.eval, UNLOCK_SCRIPT, 1, self.key, self.value,
raise_on_fail=False,
)
self._locked = False
@@ -151,4 +206,3 @@ class RedisFairLock:
def __exit__(self, exc_type, exc_val, exc_tb):
self.release()

2
web/src/api/knowledgeBase.ts
View File

@@ -68,7 +68,7 @@ export const getModelTypeList = async () => {
return response as any[];
};
// 获取模型列表
export const getModelList = async (types: string[], pageInfo: PageRequest) => {
export const getModelList = async (pageInfo: PageRequest, types?: string[]) => {
const response = await request.get(`${apiPrefix}/models`, { ...pageInfo, type: types?.join(','), is_active: true });
return response as any;
};

2
web/src/views/KnowledgeBase/components/CreateModal.tsx
View File

@@ -162,7 +162,7 @@ const CreateModal = forwardRef<CreateModalRef, CreateModalRefProps>(({
// If model data hasn't been fetched yet, fetch it once
if (!models) {
try {
models = await getModelList({ page: 1, pagesize: 100 });
models = await getModelList({ page: 1, pagesize: 100 }, ['llm', 'embedding', 'rerank', 'chat']);
} catch (error) {
console.error('Failed to fetch models:', error);
models = { items: [] };

2
web/src/views/KnowledgeBase/index.tsx
View File

@@ -207,7 +207,7 @@ const KnowledgeBaseManagement: FC = () => {
};
const fetchModelList = async () => {
try {
const response = await getModelList(['llm', 'embedding', 'rerank', 'chat'], { page: 1, pagesize: 100 });
const response = await getModelList({ page: 1, pagesize: 100 }, ['llm', 'embedding', 'rerank', 'chat']);
// 缓存模型列表，建立 id -> name 的映射
if (response?.items && Array.isArray(response.items)) {
const cache: Record<string, string> = {};

186
web/src/views/Workflow/components/Editor/Jinja2Editor.tsx Normal file
View File

@@ -0,0 +1,186 @@
/*
* @Author: ZhaoYing
* @Date: 2026-04-02 15:15:36
* @Last Modified by: ZhaoYing
* @Last Modified time: 2026-04-02 15:15:36
*/
import { type FC, useEffect, useMemo } from 'react';
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 AutocompletePlugin, { type Suggestion } from './plugin/AutocompletePlugin';
import CharacterCountPlugin from './plugin/CharacterCountPlugin';
import InitialValuePlugin from './plugin/InitialValuePlugin';
import CommandPlugin from './plugin/CommandPlugin';
import Jinja2HighlightPlugin from './plugin/Jinja2HighlightPlugin';
import LineNumberPlugin from './plugin/LineNumberPlugin';
import BlurPlugin from './plugin/BlurPlugin';
const jinja2Theme = {
paragraph: 'editor-paragraph',
code: 'jinja2-expression',
text: {
bold: 'editor-text-bold',
italic: 'editor-text-italic',
code: 'jinja2-inline',
},
};
const initialConfig = {
namespace: 'AutocompleteEditor',
theme: jinja2Theme,
nodes: [],
onError: (error: Error) => console.error(error),
};
const STYLE_ID = 'code-editor-styles';
const JINJA2_STYLES = `
.jinja2-expression {
background-color: #f6f8fa !important;
border: 1px solid #d1d9e0 !important;
border-radius: 3px !important;
padding: 2px 4px !important;
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace !important;
font-size: 13px !important;
color: #0969da !important;
}
.jinja2-inline {
background-color: #f6f8fa !important;
padding: 1px 3px !important;
border-radius: 2px !important;
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace !important;
font-size: 13px !important;
color: #0969da !important;
}
.editor-paragraph { margin: 0; }
.editor-with-line-numbers { display: flex; }
.line-numbers {
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace;
font-size: 12px;
line-height: 16px;
padding: 4px 8px;
text-align: right;
user-select: none;
display: flex;
flex-direction: column;
}
.line-numbers > div { min-height: 20px; display: flex; align-items: flex-start; }
.editor-content-wrapper { flex: 1; }
.editor-content-with-numbers {
white-space: pre-wrap;
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace;
}
.editor-content-with-numbers p { margin: 0; min-height: 20px; }
`;
export interface Jinja2EditorProps {
placeholder?: string;
value?: string;
onChange?: (value: string) => void;
options?: Suggestion[];
variant?: 'outlined' | 'borderless';
height?: number;
size?: 'default' | 'small';
className?: string;
}
const Jinja2Editor: FC<Jinja2EditorProps> = ({
placeholder = '请输入内容...',
value = '',
onChange,
options = [],
variant = 'borderless',
size = 'default',
height,
className,
}) => {
useEffect(() => {
if (!document.getElementById(STYLE_ID)) {
const style = document.createElement('style');
style.id = STYLE_ID;
style.textContent = JINJA2_STYLES;
document.head.appendChild(style);
}
}, []);
const minheight = useMemo(
() => `${height ?? (size === 'small' ? 60 : 120)}px`,
[height, size],
);
const fontSize = size === 'small' ? '12px' : '14px';
const lineHeight = useMemo(
() => `${height ? height - 10 : size === 'small' ? 16 : 20}px`,
[height, size],
);
const placeHolderMinheight = `${height ? 16 : size === 'small' ? 16 : 30}px`;
return (
<LexicalComposer initialConfig={initialConfig}>
<div style={{ position: 'relative' }} className={className}>
<RichTextPlugin
contentEditable={
<div
className="editor-with-line-numbers"
style={{
border: variant === 'borderless' ? 'none' : '1px solid #DFE4ED',
borderRadius: '6px',
minHeight: minheight,
}}
>
<div className="line-numbers">
<div>1</div>
</div>
<div className="editor-content-wrapper">
<ContentEditable
className="editor-content-with-numbers"
style={{
minHeight: minheight,
padding: variant === 'borderless' ? '0' : '4px 0',
outline: 'none',
resize: 'none',
fontSize,
lineHeight,
border: 'none',
}}
/>
</div>
</div>
}
placeholder={
<div
style={{
minHeight: placeHolderMinheight,
position: 'absolute',
top: '4px',
left: '16px',
color: '#A8A9AA',
fontSize,
lineHeight: placeHolderMinheight,
pointerEvents: 'none',
}}
>
{placeholder}
</div>
}
ErrorBoundary={LexicalErrorBoundary}
/>
<HistoryPlugin />
<CommandPlugin />
<Jinja2HighlightPlugin />
<LineNumberPlugin />
<AutocompletePlugin options={options} enableJinja2 />
<CharacterCountPlugin setCount={() => {}} onChange={onChange} />
<InitialValuePlugin value={value} options={options} enableLineNumbers />
<BlurPlugin enableJinja2 />
</div>
</LexicalComposer>
);
};
export default Jinja2Editor;

185
web/src/views/Workflow/components/Editor/index.tsx
View File

@@ -4,26 +4,20 @@
* @Last Modified by: ZhaoYing
* @Last Modified time: 2026-03-25 10:58:47
*/
import { type FC, useState, useEffect, useMemo } from 'react';
import { type FC, useState, useMemo } from 'react';
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 { AutoFocusPlugin } from '@lexical/react/LexicalAutoFocusPlugin';
import { LexicalErrorBoundary } from '@lexical/react/LexicalErrorBoundary';
// import { HeadingNode, QuoteNode } from '@lexical/rich-text';
// import { ListItemNode, ListNode } from '@lexical/list';
// import { LinkNode } from '@lexical/link';
// import { CodeNode } from '@lexical/code';
import AutocompletePlugin, { type Suggestion } from './plugin/AutocompletePlugin'
import CharacterCountPlugin from './plugin/CharacterCountPlugin'
import InitialValuePlugin from './plugin/InitialValuePlugin';
import CommandPlugin from './plugin/CommandPlugin';
import Jinja2HighlightPlugin from './plugin/Jinja2HighlightPlugin';
import LineNumberPlugin from './plugin/LineNumberPlugin';
import BlurPlugin from './plugin/BlurPlugin';
import { VariableNode } from './nodes/VariableNode'
import Jinja2Editor from './Jinja2Editor';
// Props interface for Lexical Editor component
export interface LexicalEditorProps {
@@ -50,16 +44,6 @@ const theme = {
},
};
// Theme with Jinja2 syntax highlighting
const jinja2Theme = {
...theme,
code: 'jinja2-expression',
text: {
...theme.text,
code: 'jinja2-inline',
},
};
// Main Lexical Editor component
const Editor: FC<LexicalEditorProps> =({
placeholder = "请输入内容...",
@@ -74,97 +58,27 @@ const Editor: FC<LexicalEditorProps> =({
className
}) => {
const [_count, setCount] = useState(0);
const [enableJinja2, setEnableJinja2] = useState(false)
const [enableLineNumbers, setEnableLineNumbers] = useState(false)
// Setup Jinja2 mode and inject styles when language changes
useEffect(() => {
const needsLineNumbers = language === 'jinja2';
setEnableJinja2(language === 'jinja2');
setEnableLineNumbers(needsLineNumbers);
if (needsLineNumbers) {
const styleId = 'code-editor-styles';
let existingStyle = document.getElementById(styleId);
if (!existingStyle) {
const style = document.createElement('style');
style.id = styleId;
style.textContent = `
.jinja2-expression {
background-color: #f6f8fa !important;
border: 1px solid #d1d9e0 !important;
border-radius: 3px !important;
padding: 2px 4px !important;
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace !important;
font-size: 13px !important;
color: #0969da !important;
}
.jinja2-inline {
background-color: #f6f8fa !important;
padding: 1px 3px !important;
border-radius: 2px !important;
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace !important;
font-size: 13px !important;
color: #0969da !important;
}
.editor-paragraph {
margin: 0;
}
.editor-paragraph:has-text('{') .editor-text,
.editor-paragraph:has-text('[') .editor-text {
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace !important;
}
.editor-with-line-numbers {
display: flex;
}
.line-numbers {
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace;
font-size: 12px;
line-height: 16px;
padding: 4px 8px;
text-align: right;
user-select: none;
display: flex;
flex-direction: column;
}
.line-numbers > div {
min-height: 20px;
display: flex;
align-items: flex-start;
}
.editor-content-wrapper {
flex: 1;
}
.editor-content-with-numbers {
white-space: pre-wrap;
font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace;
}
.editor-content-with-numbers p {
margin: 0;
min-height: 20px;
}
`;
document.head.appendChild(style);
}
}
}, [language])
if (language === 'jinja2') {
return (
<Jinja2Editor
placeholder={placeholder}
value={value}
onChange={onChange}
options={options}
variant={variant}
size={size}
height={height}
className={className}
/>
);
}
// Lexical editor configuration
const initialConfig = {
namespace: 'AutocompleteEditor',
theme: enableJinja2 ? jinja2Theme : theme,
nodes: enableJinja2 ? [
// When Jinja2 is enabled, use plain text instead of VariableNode
] : [
// HeadingNode,
// QuoteNode,
// ListItemNode,
// ListNode,
// LinkNode,
// CodeNode,
VariableNode,
],
theme,
nodes: [VariableNode],
onError: (error: Error) => {
console.error(error);
},
@@ -198,54 +112,26 @@ const Editor: FC<LexicalEditorProps> =({
<div style={{ position: 'relative' }} className={className}>
<RichTextPlugin
contentEditable={
enableLineNumbers ? (
// Editor with line numbers for Jinja2 mode
<div className="editor-with-line-numbers" style={{
border: variant === 'borderless' ? 'none' : '1px solid #DFE4ED',
borderRadius: '6px',
<ContentEditable
style={{
minHeight: minheight,
}}>
<div className="line-numbers">
<div>1</div>
</div>
<div className="editor-content-wrapper">
<ContentEditable
className="editor-content-with-numbers"
style={{
minHeight: minheight,
padding: variant === 'borderless' ? '0' : '4px 0',
outline: 'none',
resize: 'none',
fontSize: fontSize,
lineHeight: lineHeight,
border: 'none',
}}
/>
</div>
</div>
) : (
// Standard editor without line numbers
<ContentEditable
style={{
minHeight: minheight,
padding: height ? '4px 6px' : variant === 'borderless' ? '0' : '6px 8px',
border: variant === 'borderless' ? 'none' : '1px solid #EBEBEB',
borderRadius: '8px',
outline: 'none',
resize: 'none',
fontSize: fontSize,
lineHeight: lineHeight,
}}
/>
)
padding: height ? '4px 6px' : variant === 'borderless' ? '0' : '6px 8px',
border: variant === 'borderless' ? 'none' : '1px solid #EBEBEB',
borderRadius: '8px',
outline: 'none',
resize: 'none',
fontSize: fontSize,
lineHeight: lineHeight,
}}
/>
}
placeholder={
<div
style={{
minHeight: placeHolderMinheight,
position: 'absolute',
top: enableLineNumbers ? '4px' : variant === 'borderless' ? '0' : '6px',
left: enableLineNumbers ? '16px' : (variant === 'borderless' ? '0' : '11px'),
top: variant === 'borderless' ? '0' : '6px',
left: variant === 'borderless' ? '0' : '11px',
color: '#A8A9AA',
fontSize: fontSize,
lineHeight: placeHolderMinheight,
@@ -257,15 +143,12 @@ const Editor: FC<LexicalEditorProps> =({
}
ErrorBoundary={LexicalErrorBoundary}
/>
{/* Editor plugins */}
<HistoryPlugin />
<CommandPlugin />
{language === 'jinja2' && <Jinja2HighlightPlugin />}
{enableLineNumbers && <LineNumberPlugin />}
<AutocompletePlugin options={options} enableJinja2={enableJinja2} />
<AutocompletePlugin options={options} enableJinja2={false} />
<CharacterCountPlugin setCount={(count) => { setCount(count) }} onChange={onChange} />
<InitialValuePlugin value={value} options={options} enableLineNumbers={enableLineNumbers} />
<BlurPlugin enableJinja2={enableJinja2} />
<InitialValuePlugin value={value} options={options} enableLineNumbers={false} />
<BlurPlugin enableJinja2={false} />
</div>
</LexicalComposer>
);

10
web/src/views/Workflow/components/Editor/plugin/CharacterCountPlugin.tsx
View File

@@ -1,4 +1,4 @@
import { useEffect } from 'react';
import { useEffect, useRef } from 'react';
import { $getRoot, $isParagraphNode } from 'lexical';
import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
@@ -6,9 +6,15 @@ import { $isVariableNode } from '../nodes/VariableNode';
const CharacterCountPlugin = ({ setCount, onChange }: { setCount: (count: number) => void; onChange?: (value: string) => void }) => {
const [editor] = useLexicalComposerContext();
const isReadyRef = useRef(false);
useEffect(() => {
return editor.registerUpdateListener(({ editorState }) => {
return editor.registerUpdateListener(({ editorState, tags }) => {
if (tags.has('programmatic')) {
isReadyRef.current = true;
return;
}
if (!isReadyRef.current) return;
editorState.read(() => {
const root = $getRoot();
let serializedContent = '';

52
web/src/views/Workflow/hooks/useWorkflowGraph.ts
View File

@@ -491,32 +491,36 @@ export const useWorkflowGraph = ({
* @param node - Clicked node
*/
const nodeClick = ({ node }: { node: Node }) => {
// Ignore add-node type node clicks
const nodeData = node.getData()
if (nodeData?.type === 'add-node' || nodeData.type === 'break' || nodeData.type === 'cycle-start') {
setSelectedNode(null)
return;
}
blankClick()
const nodes = graphRef.current?.getNodes();
nodes?.forEach(vo => {
const data = vo.getData();
if (data.isSelected) {
vo.setData({
...data,
isSelected: false,
});
setTimeout(() => {
// Ignore add-node type node clicks
const nodeData = node.getData()
if (nodeData?.type === 'add-node' || nodeData.type === 'break' || nodeData.type === 'cycle-start') {
setSelectedNode(null)
return;
}
});
node.setData({
...nodeData,
isSelected: true,
});
clearEdgeSelect()
if (nodeData.type !== 'notes') {
setSelectedNode(node);
}
const nodes = graphRef.current?.getNodes();
nodes?.forEach(vo => {
const data = vo.getData();
if (data.isSelected) {
vo.setData({
...data,
isSelected: false,
});
}
});
node.setData({
...nodeData,
isSelected: true,
});
clearEdgeSelect()
if (nodeData.type !== 'notes') {
setSelectedNode(node);
}
}, 0)
};
/**
* Handle edge click event