262 lines
8.8 KiB
Python
262 lines
8.8 KiB
Python
"""
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Jarvis Agent 服务层
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负责 LangGraph Agent 的调用、流式输出、对话历史管理
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"""
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import json
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import uuid
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from datetime import datetime
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from typing import AsyncGenerator
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from langchain_core.messages import HumanMessage, AIMessage
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from app.models.conversation import Conversation, Message
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from app.agents.graph import get_agent_graph
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from app.agents.context import set_current_user, clear_current_user
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from app.services import memory_service
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class AgentService:
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"""对话 Agent 服务"""
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def __init__(self, db: AsyncSession):
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self.db = db
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async def chat(
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self,
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user_id: str,
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message: str,
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conversation_id: str | None = None,
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) -> tuple[str, str, AsyncGenerator[str, None]]:
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"""
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处理对话请求（流式）
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Returns:
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(conversation_id, message_id, response_stream)
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"""
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# 获取或创建对话
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if conversation_id:
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result = await self.db.execute(
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select(Conversation).where(Conversation.id == conversation_id)
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)
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conv = result.scalar_one_or_none()
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else:
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conv = None
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if not conv:
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conv = Conversation(user_id=user_id, title=message[:50])
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self.db.add(conv)
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await self.db.commit()
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await self.db.refresh(conv)
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conversation_id = conv.id
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else:
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conversation_id = conv.id
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# 存储用户消息
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user_msg = 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.db.add(user_msg)
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await self.db.commit()
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await self.db.refresh(user_msg)
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# 预创建助手消息（后续更新内容）
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assistant_msg = Message(
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conversation_id=conversation_id,
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role="assistant",
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content="",
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model="jarvis",
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)
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self.db.add(assistant_msg)
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await self.db.commit()
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await self.db.refresh(assistant_msg)
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# 加载记忆上下文
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memory_ctx = await memory_service.build_memory_context(
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self.db, user_id, conversation_id, message
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)
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# 调用 LangGraph Agent
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async def run_agent():
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set_current_user(user_id)
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try:
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graph = get_agent_graph()
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langgraph_state = {
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"messages": [HumanMessage(content=message)], # type: ignore[arg-type]
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"user_id": user_id,
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"conversation_id": conversation_id,
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"current_agent": "master",
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"active_agents": ["master"],
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"pending_tasks": [],
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"completed_tasks": [],
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"tool_calls": [],
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"last_tool_result": None,
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"knowledge_context": None,
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"graph_context": None,
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"plan": None,
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"plan_steps": [],
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"analysis_report": None,
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"final_response": None,
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"should_respond": True,
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"memory_context": memory_ctx,
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}
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collected = ""
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async for event in graph.astream_events(langgraph_state, version="v2"):
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kind = event.get("event")
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if kind == "on_chat_model_end":
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content = event.get("data", {}).get("output", {})
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if isinstance(content, dict):
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content = content.get("content", "")
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if content:
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delta = content[len(collected):]
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if delta:
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collected += delta
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yield delta
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elif kind == "on_tool_end":
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name = event.get("name", "")
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yield f"\n[工具执行: {name}]\n"
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except Exception as e:
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yield f"\n执行出错: {str(e)}"
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finally:
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clear_current_user()
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# 异步触发自动摘要和记忆提取（不阻塞响应）
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import asyncio
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try:
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loop = asyncio.get_running_loop()
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loop.create_task(
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memory_service.try_auto_summarize(self.db, user_id, conversation_id)
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)
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except Exception:
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pass
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# 最终更新数据库中的消息内容
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if collected:
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try:
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result2 = await self.db.execute(
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select(Message).where(Message.id == assistant_msg.id)
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)
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msg = result2.scalar_one_or_none()
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if msg:
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msg.content = collected
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await self.db.commit()
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except Exception:
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pass
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return conversation_id, assistant_msg.id, run_agent()
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async def chat_simple(
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self,
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user_id: str,
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message: str,
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conversation_id: str | None = None,
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file_ids: list[str] | None = None,
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) -> tuple[str, str, str]:
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"""
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简单同步版对话（无流式）
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Returns:
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(conversation_id, message_id, response_content)
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"""
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# 获取或创建对话
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if conversation_id:
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result = await self.db.execute(
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select(Conversation).where(Conversation.id == conversation_id)
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)
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conv = result.scalar_one_or_none()
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else:
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conv = None
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if not conv:
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conv = Conversation(user_id=user_id, title=message[:50])
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self.db.add(conv)
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await self.db.commit()
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await self.db.refresh(conv)
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conversation_id = conv.id
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else:
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conversation_id = conv.id
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# 如果有文件，读取内容作为上下文
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file_context = ""
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if file_ids:
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from app.services.document_service import DocumentService
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doc_svc = DocumentService(self.db)
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for file_id in file_ids:
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content = await doc_svc.get_document_content(user_id, file_id)
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if content:
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file_context += f"\n\n[用户上传文件内容]\n{content}\n[/文件内容]"
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# 将文件上下文添加到消息
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full_message = f"{message}\n{file_context}" if file_context else message
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# 存储用户消息
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user_msg = 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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attachments=[{"file_ids": file_ids}] if file_ids else None,
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)
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self.db.add(user_msg)
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await self.db.commit()
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await self.db.refresh(user_msg)
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# 加载记忆上下文
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memory_ctx = await memory_service.build_memory_context(
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self.db, user_id, conversation_id, message
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)
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# 调用 LangGraph Agent
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set_current_user(user_id)
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graph = get_agent_graph()
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langgraph_state = {
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"messages": [HumanMessage(content=full_message)], # type: ignore[arg-type]
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"user_id": user_id,
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"conversation_id": conversation_id,
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"current_agent": "master",
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"active_agents": ["master"],
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"pending_tasks": [],
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"completed_tasks": [],
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"tool_calls": [],
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"last_tool_result": None,
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"knowledge_context": None,
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"graph_context": None,
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"plan": None,
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"plan_steps": [],
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"analysis_report": None,
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"final_response": None,
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"should_respond": True,
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"memory_context": memory_ctx,
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}
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try:
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result_state = await graph.ainvoke(langgraph_state)
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response_content = result_state.get("final_response", "抱歉，我无法处理这个请求。")
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except Exception as e:
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response_content = f"抱歉，发生错误: {str(e)}"
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finally:
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clear_current_user()
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# 异步触发自动摘要
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import asyncio
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try:
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asyncio.get_running_loop().create_task(
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memory_service.try_auto_summarize(self.db, user_id, conversation_id)
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)
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except Exception:
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pass
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# 保存助手消息
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assistant_msg = Message(
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conversation_id=conversation_id,
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role="assistant",
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content=response_content,
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model="jarvis",
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)
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self.db.add(assistant_msg)
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await self.db.commit()
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await self.db.refresh(assistant_msg)
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return conversation_id, assistant_msg.id, response_content
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