515 lines
18 KiB
Python
515 lines
18 KiB
Python
"""Handoffs 服务 - 基于 LangGraph 的多 Agent 协作"""
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import json
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import uuid
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from typing import List, Dict, Any, Optional, AsyncGenerator
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from typing_extensions import TypedDict
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from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
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from langchain_openai import ChatOpenAI
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from langgraph.graph import StateGraph, START, END
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from langgraph.types import Command
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from langgraph.checkpoint.memory import MemorySaver
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from langchain_core.tools import tool
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from pydantic import BaseModel, Field
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from app.core.logging_config import get_business_logger
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logger = get_business_logger()
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# ==================== 状态定义 ====================
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class HandoffState(TypedDict):
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"""Handoff 状态"""
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messages: List[BaseMessage]
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active_agent: Optional[str]
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# ==================== 工具输入模型 ====================
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class TransferInput(BaseModel):
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"""转移工具的输入参数"""
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reason: str = Field(description="转移原因")
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# ==================== 默认配置 ====================
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DEFAULT_AGENT_CONFIGS = {
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"sales_agent": {
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"description": "转移到销售 Agent。当用户询问价格、购买或销售相关问题时使用。",
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"system_prompt": """你是一个销售 Agent。帮助用户解答销售相关问题。
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如果用户询问技术问题或需要技术支持，使用 transfer_to_support_agent 工具转移到支持 Agent。""",
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"can_transfer_to": ["support_agent"]
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},
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"support_agent": {
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"description": "转移到支持 Agent。当用户询问技术问题或需要帮助时使用。",
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"system_prompt": """你是一个技术支持 Agent。帮助用户解决技术问题。
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如果用户询问价格或购买相关问题，使用 transfer_to_sales_agent 工具转移到销售 Agent。""",
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"can_transfer_to": ["sales_agent"]
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}
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}
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DEFAULT_LLM_CONFIG = {
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"api_key": "sk-8e9e40cd171749858ce2d3722ea75669",
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"model": "qwen-plus",
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"temperature": 0.7
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}
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# ==================== 工具创建 ====================
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def create_transfer_tool(target_agent: str, description: str):
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"""动态创建转移工具
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Args:
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target_agent: 目标 Agent 名称
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description: 工具描述
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Returns:
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转移工具函数
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"""
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tool_name = f"transfer_to_{target_agent}"
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@tool(tool_name, args_schema=TransferInput)
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def transfer_tool(reason: str) -> Command:
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"""动态生成的转移工具"""
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return Command(
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goto=target_agent,
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update={"active_agent": target_agent},
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)
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transfer_tool.__doc__ = description
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transfer_tool.description = description
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return transfer_tool
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def create_tools_for_agent(agent_name: str, configs: Dict) -> List:
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"""根据 Agent 配置动态创建其可用的转移工具
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Args:
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agent_name: 当前 Agent 名称
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configs: Agent 配置字典
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Returns:
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该 Agent 可用的工具列表
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"""
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config = configs.get(agent_name, {})
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can_transfer_to = config.get("can_transfer_to", [])
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tools = []
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for target_agent in can_transfer_to:
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target_config = configs.get(target_agent, {})
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description = target_config.get("description", f"转移到 {target_agent}")
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tools.append(create_transfer_tool(target_agent, description))
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return tools
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# ==================== Agent 节点创建 ====================
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def create_agent_node(agent_name: str, system_prompt: str, tools: List,
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api_key: str, base_url: str, model: str, temperature: float = 0.7):
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"""创建 Agent 节点（非流式）
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Args:
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agent_name: Agent 名称
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system_prompt: 系统提示词
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tools: 工具列表
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api_key: API Key
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base_url: API Base URL
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model: 模型名称
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temperature: 温度参数
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Returns:
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Agent 节点函数
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"""
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llm = ChatOpenAI(
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model=model,
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temperature=temperature,
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api_key=api_key,
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base_url=base_url
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).bind_tools(tools)
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async def agent_node(state: HandoffState) -> Dict[str, Any]:
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"""Agent 节点执行函数"""
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logger.debug(f"Agent {agent_name} 执行, active_agent: {state.get('active_agent')}")
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messages = state.get("messages", [])
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full_messages = [{"role": "system", "content": system_prompt}] + messages
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response = await llm.ainvoke(full_messages)
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# 检查工具调用
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if hasattr(response, 'tool_calls') and response.tool_calls:
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tool_call = response.tool_calls[0]
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tool_name = tool_call["name"]
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tool_args = tool_call["args"]
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if not tool_args.get("reason"):
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tool_args["reason"] = "用户请求转移"
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for t in tools:
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if t.name == tool_name:
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logger.info(f"Agent {agent_name} 调用工具: {tool_name}")
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result = t.invoke(tool_args)
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if isinstance(result, Command):
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return result
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return {"messages": [response]}
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return agent_node
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def create_streaming_agent_node(agent_name: str, system_prompt: str, tools: List,
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api_key: str, base_url: str, model: str, temperature: float = 0.7):
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"""创建支持流式输出的 Agent 节点
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Args:
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agent_name: Agent 名称
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system_prompt: 系统提示词
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tools: 工具列表
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api_key: API Key
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base_url: API Base URL
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model: 模型名称
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temperature: 温度参数
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Returns:
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Agent 节点函数
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"""
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llm = ChatOpenAI(
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model=model,
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temperature=temperature,
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api_key=api_key,
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base_url=base_url,
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streaming=True
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).bind_tools(tools)
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async def agent_node(state: HandoffState):
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"""Agent 节点执行函数（流式）"""
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logger.debug(f"Agent {agent_name} 流式执行, active_agent: {state.get('active_agent')}")
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messages = state.get("messages", [])
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full_messages = [{"role": "system", "content": system_prompt}] + messages
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full_content = ""
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collected_tool_calls = {}
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async for chunk in llm.astream(full_messages):
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if chunk.content:
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full_content += chunk.content
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# 收集工具调用
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if hasattr(chunk, 'tool_calls') and chunk.tool_calls:
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for tc in chunk.tool_calls:
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tc_id = tc.get("id") or "0"
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if tc_id not in collected_tool_calls:
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collected_tool_calls[tc_id] = {"id": tc_id, "name": "", "args": ""}
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if tc.get("name"):
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collected_tool_calls[tc_id]["name"] = tc["name"]
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if tc.get("args"):
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if isinstance(tc["args"], dict):
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collected_tool_calls[tc_id]["args"] = tc["args"]
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elif isinstance(tc["args"], str):
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if isinstance(collected_tool_calls[tc_id]["args"], str):
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collected_tool_calls[tc_id]["args"] += tc["args"]
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# 处理 tool_call_chunks
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if hasattr(chunk, 'tool_call_chunks') and chunk.tool_call_chunks:
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for tc_chunk in chunk.tool_call_chunks:
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idx = str(tc_chunk.get("index", 0))
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if idx not in collected_tool_calls:
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collected_tool_calls[idx] = {"id": tc_chunk.get("id", idx), "name": "", "args": ""}
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if tc_chunk.get("id"):
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collected_tool_calls[idx]["id"] = tc_chunk["id"]
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if tc_chunk.get("name"):
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collected_tool_calls[idx]["name"] = tc_chunk["name"]
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if tc_chunk.get("args"):
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if isinstance(collected_tool_calls[idx]["args"], str):
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collected_tool_calls[idx]["args"] += tc_chunk["args"]
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# 解析工具调用
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tool_calls_list = list(collected_tool_calls.values())
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for tc in tool_calls_list:
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if isinstance(tc.get("args"), str) and tc["args"]:
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try:
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tc["args"] = json.loads(tc["args"])
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except (json.JSONDecodeError, ValueError):
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tc["args"] = {}
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elif not tc.get("args"):
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tc["args"] = {}
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# 执行工具调用
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if tool_calls_list and tool_calls_list[0].get("name"):
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tool_call = tool_calls_list[0]
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tool_name = tool_call.get("name", "")
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tool_args = tool_call.get("args", {})
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if not tool_args.get("reason"):
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tool_args["reason"] = "用户请求转移"
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for t in tools:
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if t.name == tool_name:
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logger.info(f"Agent {agent_name} 调用工具: {tool_name}")
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result = t.invoke(tool_args)
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if isinstance(result, Command):
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return result
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return {"messages": [AIMessage(content=full_content)]}
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return agent_node
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# ==================== 路由函数 ====================
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def create_route_initial(default_agent: str = "sales_agent"):
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"""创建初始路由函数"""
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def route_initial(state: HandoffState) -> str:
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active = state.get("active_agent")
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if active:
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return active
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return default_agent
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return route_initial
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def route_after_agent(state: HandoffState) -> str:
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"""Agent 执行后的路由"""
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messages = state.get("messages", [])
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if messages:
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last_msg = messages[-1]
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if isinstance(last_msg, AIMessage) and not getattr(last_msg, 'tool_calls', None):
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return END
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return state.get("active_agent", "sales_agent")
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# ==================== Handoffs 服务类 ====================
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class HandoffsService:
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"""Handoffs 服务 - 管理多 Agent 协作"""
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def __init__(
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self,
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agent_configs: Dict[str, Dict] = None,
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llm_config: Dict[str, Any] = None,
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streaming: bool = True
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):
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"""初始化 Handoffs 服务
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Args:
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agent_configs: Agent 配置字典
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llm_config: LLM 配置
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streaming: 是否启用流式输出
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"""
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self.agent_configs = agent_configs or DEFAULT_AGENT_CONFIGS
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self.llm_config = llm_config or DEFAULT_LLM_CONFIG
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self.streaming = streaming
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self._graph = None
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logger.info(f"HandoffsService 初始化, agents: {list(self.agent_configs.keys())}")
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def _build_graph(self):
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"""构建 LangGraph 图"""
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builder = StateGraph(HandoffState)
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agent_names = list(self.agent_configs.keys())
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for agent_name in agent_names:
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config = self.agent_configs[agent_name]
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tools = create_tools_for_agent(agent_name, self.agent_configs)
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if self.streaming:
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agent_node = create_streaming_agent_node(
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agent_name=agent_name,
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system_prompt=config.get("system_prompt", f"你是 {agent_name}"),
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tools=tools,
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api_key=self.llm_config.get("api_key"),
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base_url=self.llm_config.get("base_url"),
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model=self.llm_config.get("model"),
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temperature=self.llm_config.get("temperature", 0.7)
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)
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else:
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agent_node = create_agent_node(
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agent_name=agent_name,
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system_prompt=config.get("system_prompt", f"你是 {agent_name}"),
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tools=tools,
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api_key=self.llm_config.get("api_key"),
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base_url=self.llm_config.get("base_url"),
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model=self.llm_config.get("model"),
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temperature=self.llm_config.get("temperature", 0.7)
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)
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builder.add_node(agent_name, agent_node)
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# 添加边
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default_agent = agent_names[0] if agent_names else "sales_agent"
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builder.add_conditional_edges(START, create_route_initial(default_agent), agent_names)
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for agent_name in agent_names:
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builder.add_conditional_edges(agent_name, route_after_agent, agent_names + [END])
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memory = MemorySaver()
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return builder.compile(checkpointer=memory)
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@property
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def graph(self):
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"""获取图实例（懒加载）"""
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if self._graph is None:
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self._graph = self._build_graph()
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return self._graph
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def reset(self):
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"""重置图实例"""
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self._graph = None
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logger.info("HandoffsService 图已重置")
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async def chat(
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self,
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message: str,
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conversation_id: str = None
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) -> Dict[str, Any]:
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"""非流式聊天
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Args:
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message: 用户消息
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conversation_id: 会话 ID
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Returns:
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聊天结果
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"""
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conversation_id = conversation_id or f"conv-{uuid.uuid4().hex[:8]}"
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config = {"configurable": {"thread_id": str(conversation_id)}}
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logger.info(f"Handoffs chat: conversation_id={conversation_id}, message={message[:50]}...")
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result = await self.graph.ainvoke({
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"messages": [HumanMessage(content=message)]
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}, config=config)
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# 提取响应
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response_content = ""
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for msg in result.get("messages", []):
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if isinstance(msg, AIMessage):
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response_content = msg.content
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break
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return {
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"conversation_id": str(conversation_id),
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"active_agent": result.get("active_agent"),
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"response": response_content,
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"message_count": len(result.get("messages", []))
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}
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async def chat_stream(
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self,
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message: str,
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conversation_id: str = None
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) -> AsyncGenerator[str, None]:
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"""流式聊天
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Args:
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message: 用户消息
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conversation_id: 会话 ID
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Yields:
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SSE 格式的事件
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"""
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conversation_id = conversation_id or f"conv-{uuid.uuid4().hex[:8]}"
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config = {"configurable": {"thread_id": str(conversation_id)}}
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logger.info(f"Handoffs stream chat: conversation_id={conversation_id}, message={message[:50]}...")
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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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current_agent = None
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try:
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async for event in self.graph.astream_events(
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{"messages": [HumanMessage(content=message)]},
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config=config,
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version="v2"
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):
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kind = event["event"]
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# 捕获节点开始（Agent 切换）
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if kind == "on_chain_start":
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node_name = event.get("name", "")
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if node_name in self.agent_configs:
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if current_agent != node_name:
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current_agent = node_name
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yield f"event: agent\ndata: {json.dumps({'agent': node_name}, ensure_ascii=False)}\n\n"
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# 捕获 LLM 流式输出
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elif kind == "on_chat_model_stream":
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content = event["data"]["chunk"].content
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if content:
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yield f"event: message\ndata: {json.dumps({'content': content}, ensure_ascii=False)}\n\n"
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# 捕获工具调用（Handoff）
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elif kind == "on_tool_start":
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tool_name = event.get("name", "")
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if tool_name.startswith("transfer_to_"):
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target_agent = tool_name.replace("transfer_to_", "")
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yield f"event: handoff\ndata: {json.dumps({'from': current_agent, 'to': target_agent}, ensure_ascii=False)}\n\n"
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# 发送结束事件
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yield f"event: end\ndata: {json.dumps({'conversation_id': str(conversation_id), 'final_agent': current_agent}, ensure_ascii=False)}\n\n"
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except Exception as e:
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logger.error(f"Handoffs stream error: {str(e)}")
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yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
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def get_agents(self) -> List[Dict[str, Any]]:
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"""获取可用的 Agent 列表
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Returns:
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Agent 列表
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"""
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agents = []
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for name, config in self.agent_configs.items():
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agents.append({
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"name": name,
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"description": config.get("description", ""),
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"can_transfer_to": config.get("can_transfer_to", [])
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})
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return agents
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# ==================== 全局实例 ====================
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_default_service: Optional[HandoffsService] = None
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def get_handoffs_service(
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agent_configs: Dict[str, Dict] = None,
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llm_config: Dict[str, Any] = None,
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streaming: bool = True
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) -> HandoffsService:
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"""获取 Handoffs 服务实例
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Args:
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agent_configs: Agent 配置（可选）
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llm_config: LLM 配置（可选）
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streaming: 是否流式
|
||
|
||
Returns:
|
||
HandoffsService 实例
|
||
"""
|
||
global _default_service
|
||
|
||
# 如果有自定义配置，创建新实例
|
||
if agent_configs or llm_config:
|
||
return HandoffsService(agent_configs, llm_config, streaming)
|
||
|
||
# 否则使用默认实例
|
||
if _default_service is None:
|
||
_default_service = HandoffsService(streaming=streaming)
|
||
|
||
return _default_service
|
||
|
||
|
||
def reset_default_service():
|
||
"""重置默认服务实例"""
|
||
global _default_service
|
||
if _default_service:
|
||
_default_service.reset()
|
||
_default_service = None
|