595 lines
23 KiB
Python
595 lines
23 KiB
Python
import asyncio
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import json
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import traceback
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import uuid
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import yaml
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from pathlib import Path
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from fastapi import APIRouter, Body, Depends, HTTPException
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from fastapi.responses import StreamingResponse
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from langchain_core.messages import AIMessageChunk, HumanMessage, ToolMessage
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from pydantic import BaseModel
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from sqlalchemy.orm import Session
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from src.storage.db.models import User
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from src.storage.conversation import ConversationManager
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from server.routers.auth_router import get_admin_user
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from server.utils.auth_middleware import get_db, get_required_user
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from src import executor
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from src import config as conf
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from src.agents import agent_manager
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from src.agents.common.tools import gen_tool_info, get_buildin_tools
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from src.models import select_model
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from src.plugins.guard import content_guard
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from src.utils.logging_config import logger
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chat = APIRouter(prefix="/chat", tags=["chat"])
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# =============================================================================
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# > === 智能体管理分组 ===
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# =============================================================================
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@chat.get("/default_agent")
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async def get_default_agent(current_user: User = Depends(get_required_user)):
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"""获取默认智能体ID（需要登录）"""
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try:
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default_agent_id = conf.default_agent_id
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# 如果没有设置默认智能体，尝试获取第一个可用的智能体
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if not default_agent_id:
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agents = await agent_manager.get_agents_info()
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if agents:
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default_agent_id = agents[0].get("id", "")
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return {"default_agent_id": default_agent_id}
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except Exception as e:
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logger.error(f"获取默认智能体出错: {e}")
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raise HTTPException(status_code=500, detail=f"获取默认智能体出错: {str(e)}")
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@chat.post("/set_default_agent")
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async def set_default_agent(request_data: dict = Body(...), current_user=Depends(get_admin_user)):
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"""设置默认智能体ID (仅管理员)"""
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try:
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agent_id = request_data.get("agent_id")
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if not agent_id:
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raise HTTPException(status_code=422, detail="缺少必需的 agent_id 字段")
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# 验证智能体是否存在
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agents = await agent_manager.get_agents_info()
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agent_ids = [agent.get("id", "") for agent in agents]
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if agent_id not in agent_ids:
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raise HTTPException(status_code=404, detail=f"智能体 {agent_id} 不存在")
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# 设置默认智能体ID
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conf.default_agent_id = agent_id
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# 保存配置
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conf.save()
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return {"success": True, "default_agent_id": agent_id}
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except HTTPException as he:
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raise he
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except Exception as e:
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logger.error(f"设置默认智能体出错: {e}")
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raise HTTPException(status_code=500, detail=f"设置默认智能体出错: {str(e)}")
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# =============================================================================
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# > === 对话分组 ===
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# =============================================================================
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@chat.post("/call")
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async def call(query: str = Body(...), meta: dict = Body(None), current_user: User = Depends(get_required_user)):
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"""调用模型进行简单问答（需要登录）"""
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meta = meta or {}
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model = select_model(model_provider=meta.get("model_provider"), model_name=meta.get("model_name"))
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async def call_async(query):
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(executor, model.call, query)
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response = await call_async(query)
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logger.debug({"query": query, "response": response.content})
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return {"response": response.content}
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@chat.get("/agent")
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async def get_agent(current_user: User = Depends(get_required_user)):
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"""获取所有可用智能体（需要登录）"""
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agents = await agent_manager.get_agents_info()
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# logger.debug(f"agents: {agents}")
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metadata = {}
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if Path("src/config/static/agents_meta.yaml").exists():
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with open("src/config/static/agents_meta.yaml") as f:
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metadata = yaml.safe_load(f)
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return {"agents": agents, "metadata": metadata}
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@chat.post("/agent/{agent_id}")
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async def chat_agent(
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agent_id: str,
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query: str = Body(...),
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config: dict = Body({}),
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meta: dict = Body({}),
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current_user: User = Depends(get_required_user),
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db: Session = Depends(get_db),
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):
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"""使用特定智能体进行对话（需要登录）"""
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logger.info(f"agent_id: {agent_id}, query: {query}, config: {config}, meta: {meta}")
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meta.update(
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{
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"query": query,
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"agent_id": agent_id,
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"server_model_name": config.get("model", agent_id),
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"thread_id": config.get("thread_id"),
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"user_id": current_user.id,
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}
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)
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# 将meta和thread_id整合到config中
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def make_chunk(content=None, **kwargs):
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return (
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json.dumps(
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{"request_id": meta.get("request_id"), "response": content, **kwargs}, ensure_ascii=False
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).encode("utf-8")
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+ b"\n"
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)
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async def save_messages_from_langgraph_state(
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agent_instance,
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conversation,
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conv_mgr,
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config_dict,
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):
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"""
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从 LangGraph state 中读取完整消息并保存到数据库
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这样可以获得完整的 tool_calls 参数
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"""
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try:
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graph = await agent_instance.get_graph()
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state = await graph.aget_state(config_dict)
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if not state or not state.values:
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logger.warning("No state found in LangGraph")
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return
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messages = state.values.get("messages", [])
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logger.debug(f"Retrieved {len(messages)} messages from LangGraph state")
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# 获取已保存的消息数量，避免重复保存
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existing_messages = conv_mgr.get_messages(conversation.id)
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existing_count = len(existing_messages)
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# 只保存新增的消息
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new_messages = messages[existing_count:]
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for msg in new_messages:
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msg_dict = msg.model_dump() if hasattr(msg, "model_dump") else {}
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msg_type = msg_dict.get("type", "unknown")
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if msg_type == "human":
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# 用户消息（理论上已经保存过了，跳过）
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continue
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elif msg_type == "ai":
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# AI 消息
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content = msg_dict.get("content", "")
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tool_calls_data = msg_dict.get("tool_calls", [])
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# 保存 AI 消息
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ai_msg = conv_mgr.add_message(
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conversation_id=conversation.id,
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role="assistant",
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content=content,
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message_type="text",
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extra_metadata=msg_dict, # 保存原始 model_dump
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)
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# 保存 tool_calls（如果有）
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if tool_calls_data:
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logger.debug(f"Saving {len(tool_calls_data)} tool calls from AI message")
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for tc in tool_calls_data:
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conv_mgr.add_tool_call(
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message_id=ai_msg.id,
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tool_name=tc.get("name", "unknown"),
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tool_input=tc.get("args", {}), # 完整的参数
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status="pending", # 工具还未执行
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)
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logger.debug(f"Saved AI message {ai_msg.id} with {len(tool_calls_data)} tool calls")
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elif msg_type == "tool":
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# 工具执行结果消息
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# 需要找到对应的 tool_call 记录并更新
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tool_call_id = msg_dict.get("tool_call_id")
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content = msg_dict.get("content", "")
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if tool_call_id:
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# 通过 tool_call_id 找到对应的 tool_call 记录
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# 注意：LangGraph 的 tool_call_id 和我们数据库的 id 不同
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# 我们需要通过最近的 AI 消息找到对应的 tool_call
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recent_ai_msgs = conv_mgr.get_messages(conversation.id, limit=10)
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for recent_msg in reversed(recent_ai_msgs):
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if recent_msg.role == "assistant" and recent_msg.tool_calls:
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for tc in recent_msg.tool_calls:
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# 这里简化处理：按顺序更新 pending 状态的 tool_call
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if tc.status == "pending" and not tc.tool_output:
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tc.tool_output = content
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tc.status = "success"
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db.commit()
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logger.debug(f"Updated tool_call {tc.id} with output")
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break
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break
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logger.debug(f"Processed message type={msg_type}")
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logger.info(f"Saved {len(new_messages)} new messages from LangGraph state")
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except Exception as e:
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logger.error(f"Error saving messages from LangGraph state: {e}")
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logger.error(traceback.format_exc())
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async def stream_messages():
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# 代表服务端已经收到了请求
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yield make_chunk(status="init", meta=meta, msg=HumanMessage(content=query).model_dump())
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# Input guard
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if conf.enable_content_guard and content_guard.check(query):
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yield make_chunk(status="error", message="输入内容包含敏感词", meta=meta)
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return
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try:
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agent = agent_manager.get_agent(agent_id)
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except Exception as e:
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logger.error(f"Error getting agent {agent_id}: {e}, {traceback.format_exc()}")
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yield make_chunk(message=f"Error getting agent {agent_id}: {e}", status="error")
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return
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messages = [{"role": "user", "content": query}]
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# 构造运行时配置，如果没有thread_id则生成一个
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user_id = str(current_user.id)
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thread_id = config.get("thread_id")
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input_context = {"user_id": user_id, "thread_id": thread_id}
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# Initialize conversation manager
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conv_manager = ConversationManager(db)
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# Get or create conversation
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conversation = None
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if thread_id:
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conversation = conv_manager.get_conversation_by_thread_id(thread_id)
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if not conversation:
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try:
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# Auto-create conversation for existing thread
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conversation = conv_manager.create_conversation(
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user_id=user_id,
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agent_id=agent_id,
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title=(query[:50] + "..." if len(query) > 50 else query) if query else "新的对话",
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thread_id=thread_id,
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)
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logger.info(f"Auto-created conversation for thread_id {thread_id}")
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except Exception as e:
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logger.error(f"Failed to auto-create conversation: {e}")
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conversation = None
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# Save user message
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if conversation:
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try:
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conv_manager.add_message(
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conversation_id=conversation.id,
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role="user",
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content=query,
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message_type="text",
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extra_metadata={"raw_message": HumanMessage(content=query).model_dump()},
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)
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except Exception as e:
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logger.error(f"Error saving user message: {e}")
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try:
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# Stream messages (only for display, don't save yet)
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async for msg, metadata in agent.stream_messages(messages, input_context=input_context):
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if isinstance(msg, AIMessageChunk):
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# Content guard
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if conf.enable_content_guard and content_guard.check(msg.content):
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logger.warning("Sensitive content detected in stream")
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yield make_chunk(message="检测到敏感内容，已中断输出", status="error")
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return
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yield make_chunk(content=msg.content, msg=msg.model_dump(), metadata=metadata, status="loading")
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elif isinstance(msg, ToolMessage):
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yield make_chunk(msg=msg.model_dump(), metadata=metadata, status="loading")
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else:
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yield make_chunk(msg=msg.model_dump(), metadata=metadata, status="loading")
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yield make_chunk(status="finished", meta=meta)
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# After streaming finished, save all messages from LangGraph state
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if conversation:
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langgraph_config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}
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await save_messages_from_langgraph_state(
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agent_instance=agent,
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conversation=conversation,
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conv_mgr=conv_manager,
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config_dict=langgraph_config,
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)
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except Exception as e:
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logger.error(f"Error streaming messages: {e}, {traceback.format_exc()}")
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yield make_chunk(message=f"Error streaming messages: {e}", status="error")
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return StreamingResponse(stream_messages(), media_type="application/json")
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# =============================================================================
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# > === 模型管理分组 ===
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# =============================================================================
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@chat.get("/models")
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async def get_chat_models(model_provider: str, current_user: User = Depends(get_admin_user)):
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"""获取指定模型提供商的模型列表（需要登录）"""
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model = select_model(model_provider=model_provider)
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return {"models": model.get_models()}
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@chat.post("/models/update")
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async def update_chat_models(model_provider: str, model_names: list[str], current_user=Depends(get_admin_user)):
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"""更新指定模型提供商的模型列表 (仅管理员)"""
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conf.model_names[model_provider]["models"] = model_names
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conf._save_models_to_file()
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return {"models": conf.model_names[model_provider]["models"]}
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@chat.get("/tools")
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async def get_tools(agent_id: str, current_user: User = Depends(get_required_user)):
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"""获取所有可用工具（需要登录）"""
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# 获取Agent实例和配置类
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if not (agent := agent_manager.get_agent(agent_id)):
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raise HTTPException(status_code=404, detail=f"智能体 {agent_id} 不存在")
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if hasattr(agent, "get_tools"):
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tools = agent.get_tools()
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else:
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tools = get_buildin_tools()
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tools_info = gen_tool_info(tools)
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return {"tools": {tool["id"]: tool for tool in tools_info}}
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@chat.post("/agent/{agent_id}/config")
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async def save_agent_config(agent_id: str, config: dict = Body(...), current_user: User = Depends(get_required_user)):
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"""保存智能体配置到YAML文件（需要登录）"""
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try:
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# 获取Agent实例和配置类
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if not (agent := agent_manager.get_agent(agent_id)):
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raise HTTPException(status_code=404, detail=f"智能体 {agent_id} 不存在")
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# 使用配置类的save_to_file方法保存配置
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result = agent.context_schema.save_to_file(config, agent.module_name)
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if result:
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return {"success": True, "message": f"智能体 {agent.name} 配置已保存"}
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else:
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raise HTTPException(status_code=500, detail="保存智能体配置失败")
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except Exception as e:
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logger.error(f"保存智能体配置出错: {e}, {traceback.format_exc()}")
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raise HTTPException(status_code=500, detail=f"保存智能体配置出错: {str(e)}")
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@chat.get("/agent/{agent_id}/history")
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async def get_agent_history(
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agent_id: str, thread_id: str, current_user: User = Depends(get_required_user), db: Session = Depends(get_db)
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):
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"""获取智能体历史消息（需要登录）- NEW STORAGE ONLY"""
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try:
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# 获取Agent实例验证
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if not agent_manager.get_agent(agent_id):
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raise HTTPException(status_code=404, detail=f"智能体 {agent_id} 不存在")
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# Use new storage system ONLY
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conv_manager = ConversationManager(db)
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messages = conv_manager.get_messages_by_thread_id(thread_id)
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# Convert to frontend-compatible format
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history = []
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for msg in messages:
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# Map role to type that frontend expects
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role_type_map = {
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"user": "human",
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"assistant": "ai",
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"tool": "tool",
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"system": "system"
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}
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msg_dict = {
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"type": role_type_map.get(msg.role, msg.role), # human/ai/tool/system
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"content": msg.content,
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"created_at": msg.created_at.isoformat() if msg.created_at else None,
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}
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# Add tool calls if present (for AI messages)
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if msg.tool_calls and len(msg.tool_calls) > 0:
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msg_dict["tool_calls"] = [
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{
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"id": str(tc.id),
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"name": tc.tool_name,
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"function": {"name": tc.tool_name}, # Frontend compatibility
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"args": tc.tool_input or {},
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"tool_call_result": {"content": tc.tool_output} if tc.tool_output else None,
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"status": tc.status,
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}
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for tc in msg.tool_calls
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]
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history.append(msg_dict)
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logger.info(f"Loaded {len(history)} messages from new storage for thread {thread_id}")
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return {"history": history}
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except Exception as e:
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logger.error(f"获取智能体历史消息出错: {e}, {traceback.format_exc()}")
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raise HTTPException(status_code=500, detail=f"获取智能体历史消息出错: {str(e)}")
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@chat.get("/agent/{agent_id}/config")
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async def get_agent_config(agent_id: str, current_user: User = Depends(get_required_user)):
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"""从YAML文件加载智能体配置（需要登录）"""
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try:
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# 检查智能体是否存在
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if not (agent := agent_manager.get_agent(agent_id)):
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raise HTTPException(status_code=404, detail=f"智能体 {agent_id} 不存在")
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config = await agent.get_config()
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logger.debug(f"config: {config}, ContextClass: {agent.context_schema=}")
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return {"success": True, "config": config}
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except Exception as e:
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logger.error(f"加载智能体配置出错: {e}, {traceback.format_exc()}")
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raise HTTPException(status_code=500, detail=f"加载智能体配置出错: {str(e)}")
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# ==================== 线程管理 API ====================
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class ThreadCreate(BaseModel):
|
||
title: str | None = None
|
||
agent_id: str
|
||
metadata: dict | None = None
|
||
|
||
|
||
class ThreadResponse(BaseModel):
|
||
id: str
|
||
user_id: str
|
||
agent_id: str
|
||
title: str | None = None
|
||
create_at: str
|
||
update_at: str
|
||
|
||
|
||
# =============================================================================
|
||
# > === 会话管理分组 ===
|
||
# =============================================================================
|
||
|
||
|
||
@chat.post("/thread", response_model=ThreadResponse)
|
||
async def create_thread(
|
||
thread: ThreadCreate, db: Session = Depends(get_db), current_user: User = Depends(get_required_user)
|
||
):
|
||
"""创建新对话线程 (使用新存储系统)"""
|
||
thread_id = str(uuid.uuid4())
|
||
logger.debug(f"thread.agent_id: {thread.agent_id}")
|
||
|
||
# Create conversation using new storage system
|
||
conv_manager = ConversationManager(db)
|
||
conversation = conv_manager.create_conversation(
|
||
user_id=str(current_user.id),
|
||
agent_id=thread.agent_id,
|
||
title=thread.title or "新的对话",
|
||
thread_id=thread_id,
|
||
metadata=thread.metadata,
|
||
)
|
||
|
||
logger.info(f"Created conversation with thread_id: {thread_id}")
|
||
|
||
return {
|
||
"id": conversation.thread_id,
|
||
"user_id": conversation.user_id,
|
||
"agent_id": conversation.agent_id,
|
||
"title": conversation.title,
|
||
"create_at": conversation.created_at.isoformat(),
|
||
"update_at": conversation.updated_at.isoformat(),
|
||
}
|
||
|
||
|
||
@chat.get("/threads", response_model=list[ThreadResponse])
|
||
async def list_threads(agent_id: str, db: Session = Depends(get_db), current_user: User = Depends(get_required_user)):
|
||
"""获取用户的所有对话线程 (使用新存储系统)"""
|
||
assert agent_id, "agent_id 不能为空"
|
||
|
||
logger.debug(f"agent_id: {agent_id}")
|
||
|
||
# Use new storage system
|
||
conv_manager = ConversationManager(db)
|
||
conversations = conv_manager.list_conversations(
|
||
user_id=str(current_user.id),
|
||
agent_id=agent_id,
|
||
status="active",
|
||
)
|
||
|
||
return [
|
||
{
|
||
"id": conv.thread_id,
|
||
"user_id": conv.user_id,
|
||
"agent_id": conv.agent_id,
|
||
"title": conv.title,
|
||
"create_at": conv.created_at.isoformat(),
|
||
"update_at": conv.updated_at.isoformat(),
|
||
}
|
||
for conv in conversations
|
||
]
|
||
|
||
|
||
@chat.delete("/thread/{thread_id}")
|
||
async def delete_thread(thread_id: str, db: Session = Depends(get_db), current_user: User = Depends(get_required_user)):
|
||
"""删除对话线程 (使用新存储系统)"""
|
||
# Use new storage system
|
||
conv_manager = ConversationManager(db)
|
||
conversation = conv_manager.get_conversation_by_thread_id(thread_id)
|
||
|
||
if not conversation or conversation.user_id != str(current_user.id):
|
||
raise HTTPException(status_code=404, detail="对话线程不存在")
|
||
|
||
# Soft delete
|
||
success = conv_manager.delete_conversation(thread_id, soft_delete=True)
|
||
|
||
if not success:
|
||
raise HTTPException(status_code=500, detail="删除失败")
|
||
|
||
return {"message": "删除成功"}
|
||
|
||
|
||
class ThreadUpdate(BaseModel):
|
||
title: str | None = None
|
||
|
||
|
||
@chat.put("/thread/{thread_id}", response_model=ThreadResponse)
|
||
async def update_thread(
|
||
thread_id: str,
|
||
thread_update: ThreadUpdate,
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_required_user),
|
||
):
|
||
"""更新对话线程信息 (使用新存储系统)"""
|
||
# Use new storage system
|
||
conv_manager = ConversationManager(db)
|
||
conversation = conv_manager.get_conversation_by_thread_id(thread_id)
|
||
|
||
if not conversation or conversation.user_id != str(current_user.id) or conversation.status == "deleted":
|
||
raise HTTPException(status_code=404, detail="对话线程不存在")
|
||
|
||
# Update conversation
|
||
updated_conv = conv_manager.update_conversation(
|
||
thread_id=thread_id,
|
||
title=thread_update.title,
|
||
)
|
||
|
||
if not updated_conv:
|
||
raise HTTPException(status_code=500, detail="更新失败")
|
||
|
||
return {
|
||
"id": updated_conv.thread_id,
|
||
"user_id": updated_conv.user_id,
|
||
"agent_id": updated_conv.agent_id,
|
||
"title": updated_conv.title,
|
||
"create_at": updated_conv.created_at.isoformat(),
|
||
"update_at": updated_conv.updated_at.isoformat(),
|
||
}
|