Initial commit
This commit is contained in:
Ke Sun
759
app/services/shared_chat_service.py
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759
app/services/shared_chat_service.py
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@@ -0,0 +1,759 @@
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"""基于分享链接的聊天服务"""
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import uuid
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import time
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import asyncio
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from typing import Optional, Dict, Any, AsyncGenerator
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from sqlalchemy.orm import Session
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from app.models import ReleaseShare, AppRelease, Conversation
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from app.services.conversation_service import ConversationService
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from app.services.draft_run_service import create_web_search_tool
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from app.services.release_share_service import ReleaseShareService
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from app.core.exceptions import BusinessException, ResourceNotFoundException
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from app.core.error_codes import BizCode
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from app.core.logging_config import get_business_logger
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from app.services.multi_agent_service import MultiAgentService
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from app.models import MultiAgentConfig
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from app.repositories import knowledge_repository
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import json
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logger = get_business_logger()
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class SharedChatService:
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"""基于分享链接的聊天服务"""
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def __init__(self, db: Session):
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self.db = db
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self.conversation_service = ConversationService(db)
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self.share_service = ReleaseShareService(db)
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def _get_release_by_share_token(
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self,
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share_token: str,
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password: Optional[str] = None
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) -> tuple[ReleaseShare, AppRelease]:
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"""通过 share_token 获取发布版本"""
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# 获取分享配置
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share = self.share_service.repo.get_by_share_token(share_token)
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if not share:
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raise ResourceNotFoundException("分享链接", share_token)
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# 验证分享是否启用
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if not share.is_enabled:
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raise BusinessException("该分享链接已被禁用", BizCode.SHARE_DISABLED)
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# 验证密码
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if share.require_password:
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if not password:
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raise BusinessException("需要提供访问密码", BizCode.PASSWORD_REQUIRED)
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if not self.share_service.verify_password(share_token, password):
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raise BusinessException("访问密码错误", BizCode.INVALID_PASSWORD)
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# 获取发布版本
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release = self.db.get(AppRelease, share.release_id)
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if not release:
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raise ResourceNotFoundException("发布版本", str(share.release_id))
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# 更新访问统计
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try:
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self.share_service.repo.increment_view_count(share.id)
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except Exception as e:
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logger.warning(f"更新访问统计失败: {str(e)}")
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return share, release
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def create_or_get_conversation(
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self,
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share_token: str,
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conversation_id: Optional[uuid.UUID] = None,
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user_id: Optional[str] = None,
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password: Optional[str] = None
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) -> Conversation:
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"""创建或获取会话"""
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share, release = self._get_release_by_share_token(share_token, password)
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# 如果提供了 conversation_id，尝试获取现有会话
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if conversation_id:
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try:
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conversation = self.conversation_service.get_conversation(
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conversation_id=conversation_id,
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workspace_id=release.app.workspace_id
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)
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# 验证会话是否属于该应用
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if conversation.app_id != release.app_id:
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raise BusinessException("会话不属于该应用", BizCode.INVALID_CONVERSATION)
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return conversation
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except ResourceNotFoundException:
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logger.warning(
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f"会话不存在，将创建新会话",
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extra={"conversation_id": str(conversation_id)}
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)
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# 创建新会话（使用发布版本的配置）
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conversation = self.conversation_service.create_conversation(
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app_id=release.app_id,
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workspace_id=release.app.workspace_id,
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user_id=user_id,
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is_draft=False, # 分享链接使用发布版本
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config_snapshot=release.config
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)
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logger.info(
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f"为分享链接创建新会话",
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extra={
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"conversation_id": str(conversation.id),
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"share_token": share_token,
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"release_id": str(release.id)
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}
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)
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return conversation
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async def chat(
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self,
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share_token: str,
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message: str,
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conversation_id: Optional[uuid.UUID] = None,
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user_id: Optional[str] = None,
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variables: Optional[Dict[str, Any]] = None,
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password: Optional[str] = None,
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web_search: bool = False,
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memory: bool = True
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) -> Dict[str, Any]:
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"""聊天（非流式）"""
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from app.core.agent.langchain_agent import LangChainAgent
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from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool
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from app.services.model_parameter_merger import ModelParameterMerger
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from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole
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from sqlalchemy import select
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from app.models import ModelApiKey
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start_time = time.time()
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if variables is None:
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variables = {}
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# 获取发布版本和配置
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share, release = self._get_release_by_share_token(share_token, password)
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# 获取 Agent 配置
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config = release.config or {}
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# 获取模型配置ID
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model_config_id = release.default_model_config_id
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if not model_config_id:
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raise BusinessException("发布版本未配置模型", BizCode.AGENT_CONFIG_MISSING)
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# 获取模型配置
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from app.models import ModelConfig
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model_config = self.db.get(ModelConfig, model_config_id)
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if not model_config:
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raise ResourceNotFoundException("模型配置", str(model_config_id))
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# 获取 API Key
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stmt = (
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select(ModelApiKey)
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.where(
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ModelApiKey.model_config_id == model_config_id,
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ModelApiKey.is_active == True
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)
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.order_by(ModelApiKey.priority.desc())
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.limit(1)
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)
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api_key_obj = self.db.scalars(stmt).first()
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if not api_key_obj:
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raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING)
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# 获取或创建会话
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conversation = self.create_or_get_conversation(
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share_token=share_token,
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conversation_id=conversation_id,
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user_id=user_id,
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password=password
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)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.get("system_prompt", "你是一个专业的AI助手")
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 添加知识库检索工具
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knowledge_retrieval = config.get("knowledge_retrieval")
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if knowledge_retrieval:
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knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
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kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
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if kb_ids:
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kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids,user_id)
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tools.append(kb_tool)
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# 添加长期记忆工具
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if memory==True:
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memory_config = config.get("memory", {})
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if memory_config.get("enabled") and user_id:
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memory_tool = create_long_term_memory_tool(memory_config, user_id)
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tools.append(memory_tool)
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web_tools=config.get("tools")
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web_search_choice = web_tools.get("web_search", {})
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web_search_enable = web_search_choice.get("enabled",False)
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if web_search==True:
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if web_search_enable==True:
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search_tool = create_web_search_tool({})
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tools.append(search_tool)
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logger.debug(
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"已添加网络搜索工具",
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extra={
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"tool_count": len(tools)
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}
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)
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# 获取模型参数
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model_parameters = config.get("model_parameters", {})
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# 创建 LangChain Agent
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agent = LangChainAgent(
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model_name=api_key_obj.model_name,
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api_key=api_key_obj.api_key,
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provider=api_key_obj.provider,
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api_base=api_key_obj.api_base,
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temperature=model_parameters.get("temperature", 0.7),
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max_tokens=model_parameters.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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)
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# 加载历史消息
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history = []
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memory_config={"enabled":True,'max_history':10}
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if memory_config.get("enabled"):
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messages = self.conversation_service.get_messages(
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conversation_id=conversation.id,
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limit=memory_config.get("max_history", 10)
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)
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history = [
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{"role": msg.role, "content": msg.content}
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for msg in messages
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]
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# 调用 Agent
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result = await agent.chat(
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message=message,
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history=history,
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context=None,
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end_user_id=user_id
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)
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# 保存消息
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self.conversation_service.save_conversation_messages(
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conversation_id=conversation.id,
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user_message=message,
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assistant_message=result["content"]
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)
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# self.conversation_service.add_message(
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# conversation_id=conversation.id,
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# role="user",
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# content=message
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# )
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# self.conversation_service.add_message(
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# conversation_id=conversation.id,
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# role="assistant",
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# content=result["content"],
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# meta_data={
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# "model": api_key_obj.model_name,
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# "usage": result.get("usage", {})
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# }
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# )
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elapsed_time = time.time() - start_time
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return {
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"conversation_id": conversation.id,
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"message": result["content"],
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"usage": result.get("usage", {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}),
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"elapsed_time": elapsed_time
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}
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async def chat_stream(
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self,
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share_token: str,
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message: str,
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conversation_id: Optional[uuid.UUID] = None,
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user_id: Optional[str] = None,
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variables: Optional[Dict[str, Any]] = None,
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password: Optional[str] = None,
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web_search: bool = False,
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memory: bool = True
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) -> AsyncGenerator[str, None]:
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"""聊天（流式）"""
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from app.core.agent.langchain_agent import LangChainAgent
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from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool
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from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole
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from sqlalchemy import select
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from app.models import ModelApiKey
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import json
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start_time = time.time()
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if variables is None:
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variables = {}
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memory_config = {"enabled": memory, "memory_content": "17", "max_history": 10}
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try:
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# 获取发布版本和配置
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share, release = self._get_release_by_share_token(share_token, password)
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# 获取 Agent 配置
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config = release.config or {}
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agent_config_data = config.get("agent_config", {})
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# 获取模型配置ID
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model_config_id = release.default_model_config_id
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if not model_config_id:
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raise BusinessException("发布版本未配置模型", BizCode.AGENT_CONFIG_MISSING)
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# 获取模型配置
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from app.models import ModelConfig
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model_config = self.db.get(ModelConfig, model_config_id)
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if not model_config:
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raise ResourceNotFoundException("模型配置", str(model_config_id))
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# 获取 API Key
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stmt = (
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select(ModelApiKey)
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.where(
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ModelApiKey.model_config_id == model_config_id,
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ModelApiKey.is_active == True
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)
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.order_by(ModelApiKey.priority.desc())
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.limit(1)
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)
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api_key_obj = self.db.scalars(stmt).first()
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if not api_key_obj:
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raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING)
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# 获取或创建会话
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conversation = self.create_or_get_conversation(
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share_token=share_token,
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conversation_id=conversation_id,
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user_id=user_id,
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password=password
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)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.get("system_prompt", "你是一个专业的AI助手")
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 添加知识库检索工具
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knowledge_retrieval = config.get("knowledge_retrieval")
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if knowledge_retrieval:
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knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
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kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
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if kb_ids:
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kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids,user_id)
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tools.append(kb_tool)
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# 添加长期记忆工具
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if memory:
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memory_config = config.get("memory", {})
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if memory_config.get("enabled") and user_id:
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memory_tool = create_long_term_memory_tool(memory_config, user_id)
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tools.append(memory_tool)
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web_tools = config.get("tools")
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web_search_choice = web_tools.get("web_search", {})
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web_search_enable = web_search_choice.get("enabled", False)
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if web_search == True:
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if web_search_enable == True:
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search_tool = create_web_search_tool({})
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tools.append(search_tool)
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logger.debug(
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"已添加网络搜索工具",
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extra={
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"tool_count": len(tools)
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}
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)
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# 获取模型参数
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model_parameters = config.get("model_parameters", {})
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# 创建 LangChain Agent
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agent = LangChainAgent(
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model_name=api_key_obj.model_name,
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api_key=api_key_obj.api_key,
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provider=api_key_obj.provider,
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api_base=api_key_obj.api_base,
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temperature=model_parameters.get("temperature", 0.7),
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max_tokens=model_parameters.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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streaming=True
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)
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# 加载历史消息
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history = []
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memory_config = {"enabled": True, 'max_history': 10}
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if memory_config.get("enabled"):
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messages = self.conversation_service.get_messages(
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conversation_id=conversation.id,
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limit=memory_config.get("max_history", 10)
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)
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history = [
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{"role": msg.role, "content": msg.content}
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for msg in messages
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]
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# 发送开始事件
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yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation.id)}, ensure_ascii=False)}\n\n"
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# 流式调用 Agent
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full_content = ""
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async for chunk in agent.chat_stream(
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message=message,
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history=history,
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context=None,
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end_user_id=user_id
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):
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full_content += chunk
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# 发送消息块事件
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yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
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elapsed_time = time.time() - start_time
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# 保存消息
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self.conversation_service.add_message(
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conversation_id=conversation.id,
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role="user",
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content=message
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)
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self.conversation_service.add_message(
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conversation_id=conversation.id,
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role="assistant",
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content=full_content,
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meta_data={
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"model": api_key_obj.model_name,
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"usage": {}
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||||
}
|
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)
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# 发送结束事件
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||||
end_data = {"elapsed_time": elapsed_time, "message_length": len(full_content)}
|
||||
yield f"event: end\ndata: {json.dumps(end_data, ensure_ascii=False)}\n\n"
|
||||
|
||||
logger.info(
|
||||
f"流式聊天完成",
|
||||
extra={
|
||||
"conversation_id": str(conversation.id),
|
||||
"elapsed_time": elapsed_time,
|
||||
"message_length": len(full_content)
|
||||
}
|
||||
)
|
||||
|
||||
except (GeneratorExit, asyncio.CancelledError):
|
||||
# 生成器被关闭或任务被取消，正常退出
|
||||
logger.debug("流式聊天被中断")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"流式聊天失败: {str(e)}", exc_info=True)
|
||||
# 发送错误事件
|
||||
yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
||||
|
||||
def get_conversation_messages(
|
||||
self,
|
||||
share_token: str,
|
||||
conversation_id: uuid.UUID,
|
||||
password: Optional[str] = None
|
||||
) -> Conversation:
|
||||
"""获取会话消息"""
|
||||
share, release = self._get_release_by_share_token(share_token, password)
|
||||
|
||||
# 获取会话
|
||||
conversation = self.conversation_service.get_conversation(
|
||||
conversation_id=conversation_id,
|
||||
workspace_id=release.app.workspace_id
|
||||
)
|
||||
|
||||
# 验证会话是否属于该应用
|
||||
if conversation.app_id != release.app_id:
|
||||
raise BusinessException("会话不属于该应用", BizCode.INVALID_CONVERSATION)
|
||||
|
||||
return conversation
|
||||
|
||||
def list_conversations(
|
||||
self,
|
||||
share_token: str,
|
||||
user_id: Optional[str] = None,
|
||||
password: Optional[str] = None,
|
||||
page: int = 1,
|
||||
pagesize: int = 20
|
||||
) -> tuple[list[Conversation], int]:
|
||||
"""列出会话"""
|
||||
share, release = self._get_release_by_share_token(share_token, password)
|
||||
|
||||
conversations, total = self.conversation_service.list_conversations(
|
||||
app_id=release.app_id,
|
||||
workspace_id=release.app.workspace_id,
|
||||
user_id=user_id,
|
||||
is_draft=False, # 只显示发布版本的会话
|
||||
page=page,
|
||||
pagesize=pagesize
|
||||
)
|
||||
|
||||
return conversations, total
|
||||
|
||||
async def multi_agent_chat(
|
||||
self,
|
||||
share_token: str,
|
||||
message: str,
|
||||
conversation_id: Optional[uuid.UUID] = None,
|
||||
user_id: Optional[str] = None,
|
||||
variables: Optional[Dict[str, Any]] = None,
|
||||
password: Optional[str] = None,
|
||||
web_search: bool = False,
|
||||
memory: bool = True
|
||||
) -> Dict[str, Any]:
|
||||
"""多 Agent 聊天（非流式）"""
|
||||
from app.services.multi_agent_service import MultiAgentService
|
||||
from app.models import MultiAgentConfig
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
if variables is None:
|
||||
variables = {}
|
||||
|
||||
# 获取发布版本和配置
|
||||
share, release = self._get_release_by_share_token(share_token, password)
|
||||
|
||||
# 获取或创建会话
|
||||
conversation = self.create_or_get_conversation(
|
||||
share_token=share_token,
|
||||
conversation_id=conversation_id,
|
||||
user_id=user_id,
|
||||
password=password
|
||||
)
|
||||
|
||||
# 获取多 Agent 配置
|
||||
multi_agent_config = self.db.query(MultiAgentConfig).filter(
|
||||
MultiAgentConfig.app_id == release.app_id,
|
||||
MultiAgentConfig.is_active == True
|
||||
).first()
|
||||
|
||||
if not multi_agent_config:
|
||||
raise BusinessException("多 Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING)
|
||||
|
||||
# 构建多 Agent 运行请求
|
||||
from app.schemas.multi_agent_schema import MultiAgentRunRequest
|
||||
|
||||
multi_agent_request = MultiAgentRunRequest(
|
||||
message=message,
|
||||
conversation_id=conversation.id,
|
||||
user_id=user_id,
|
||||
variables=variables,
|
||||
use_llm_routing=True,
|
||||
web_search=web_search,
|
||||
memory=memory
|
||||
)
|
||||
|
||||
# 使用多 Agent 服务执行
|
||||
multi_agent_service = MultiAgentService(self.db)
|
||||
result = await multi_agent_service.run(
|
||||
app_id=release.app_id,
|
||||
request=multi_agent_request
|
||||
)
|
||||
|
||||
elapsed_time = time.time() - start_time
|
||||
|
||||
# 保存消息
|
||||
self.conversation_service.add_message(
|
||||
conversation_id=conversation.id,
|
||||
role="user",
|
||||
content=message
|
||||
)
|
||||
|
||||
self.conversation_service.add_message(
|
||||
conversation_id=conversation.id,
|
||||
role="assistant",
|
||||
content=result.get("message", ""),
|
||||
meta_data={
|
||||
"mode": result.get("mode"),
|
||||
"elapsed_time": result.get("elapsed_time"),
|
||||
"sub_results": result.get("sub_results")
|
||||
}
|
||||
)
|
||||
|
||||
return {
|
||||
"conversation_id": conversation.id,
|
||||
"message": result.get("message", ""),
|
||||
"usage": {
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"total_tokens": 0
|
||||
},
|
||||
"elapsed_time": elapsed_time
|
||||
}
|
||||
|
||||
async def multi_agent_chat_stream(
|
||||
self,
|
||||
share_token: str,
|
||||
message: str,
|
||||
conversation_id: Optional[uuid.UUID] = None,
|
||||
user_id: Optional[str] = None,
|
||||
variables: Optional[Dict[str, Any]] = None,
|
||||
password: Optional[str] = None,
|
||||
web_search: bool = False,
|
||||
memory: bool = True
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""多 Agent 聊天（流式）"""
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
if variables is None:
|
||||
variables = {}
|
||||
|
||||
try:
|
||||
# 获取发布版本和配置
|
||||
share, release = self._get_release_by_share_token(share_token, password)
|
||||
|
||||
# 获取或创建会话
|
||||
conversation = self.create_or_get_conversation(
|
||||
share_token=share_token,
|
||||
conversation_id=conversation_id,
|
||||
user_id=user_id,
|
||||
password=password
|
||||
)
|
||||
|
||||
# 获取多 Agent 配置
|
||||
multi_agent_config = self.db.query(MultiAgentConfig).filter(
|
||||
MultiAgentConfig.app_id == release.app_id,
|
||||
MultiAgentConfig.is_active == True
|
||||
).first()
|
||||
|
||||
if not multi_agent_config:
|
||||
raise BusinessException("多 Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING)
|
||||
|
||||
# 获取 storage_type 和 user_rag_memory_id
|
||||
workspace_id = release.app.workspace_id
|
||||
storage_type = 'neo4j' # 默认值
|
||||
user_rag_memory_id = ''
|
||||
|
||||
try:
|
||||
# 获取工作空间的存储类型（不需要用户权限检查，因为是公开分享）
|
||||
from app.models import Workspace
|
||||
workspace = self.db.get(Workspace, workspace_id)
|
||||
if workspace and workspace.storage_type:
|
||||
storage_type = workspace.storage_type
|
||||
|
||||
# 获取 USER_RAG_MERORY 知识库 ID
|
||||
knowledge = knowledge_repository.get_knowledge_by_name(
|
||||
db=self.db,
|
||||
name="USER_RAG_MERORY",
|
||||
workspace_id=workspace_id
|
||||
)
|
||||
if knowledge:
|
||||
user_rag_memory_id = str(knowledge.id)
|
||||
except Exception as e:
|
||||
logger.warning(f"获取 storage_type 或 user_rag_memory_id 失败，使用默认值: {str(e)}")
|
||||
|
||||
# 发送开始事件
|
||||
yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation.id)}, ensure_ascii=False)}\n\n"
|
||||
|
||||
# 构建多 Agent 运行请求
|
||||
from app.schemas.multi_agent_schema import MultiAgentRunRequest
|
||||
|
||||
multi_agent_request = MultiAgentRunRequest(
|
||||
message=message,
|
||||
conversation_id=conversation.id,
|
||||
user_id=user_id,
|
||||
variables=variables,
|
||||
use_llm_routing=True,
|
||||
web_search=web_search,
|
||||
memory=memory
|
||||
)
|
||||
|
||||
# 使用多 Agent 服务流式执行
|
||||
multi_agent_service = MultiAgentService(self.db)
|
||||
full_content = ""
|
||||
|
||||
async for event in multi_agent_service.run_stream(
|
||||
app_id=release.app_id,
|
||||
request=multi_agent_request,
|
||||
storage_type=storage_type,
|
||||
user_rag_memory_id=user_rag_memory_id
|
||||
):
|
||||
# 直接转发事件
|
||||
yield event
|
||||
|
||||
# 尝试提取内容（用于保存）
|
||||
if "data:" in event:
|
||||
try:
|
||||
data_line = event.split("data: ", 1)[1].strip()
|
||||
data = json.loads(data_line)
|
||||
if "content" in data:
|
||||
full_content += data["content"]
|
||||
except:
|
||||
pass
|
||||
|
||||
elapsed_time = time.time() - start_time
|
||||
|
||||
# 保存消息
|
||||
self.conversation_service.add_message(
|
||||
conversation_id=conversation.id,
|
||||
role="user",
|
||||
content=message
|
||||
)
|
||||
|
||||
self.conversation_service.add_message(
|
||||
conversation_id=conversation.id,
|
||||
role="assistant",
|
||||
content=full_content,
|
||||
meta_data={
|
||||
"elapsed_time": elapsed_time
|
||||
}
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"多 Agent 流式聊天完成",
|
||||
extra={
|
||||
"conversation_id": str(conversation.id),
|
||||
"elapsed_time": elapsed_time,
|
||||
"message_length": len(full_content)
|
||||
}
|
||||
)
|
||||
|
||||
except (GeneratorExit, asyncio.CancelledError):
|
||||
# 生成器被关闭或任务被取消，正常退出
|
||||
logger.debug("多 Agent 流式聊天被中断")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"多 Agent 流式聊天失败: {str(e)}", exc_info=True)
|
||||
# 发送错误事件
|
||||
yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
||||
Reference in New Issue
Block a user