161 lines
6.7 KiB
Python
161 lines
6.7 KiB
Python
"""客户端：用户交互 & 会话管理"""
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"""
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client/agent_client.py
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Agent 客户端：协调 LLM 引擎、MCP 服务器、记忆模块，驱动完整 Agent 执行流程
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"""
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from dataclasses import dataclass
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from llm.llm_engine import LLMEngine
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from mcp.mcp_protocol import MCPMethod, MCPRequest
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from mcp.mcp_server import MCPServer
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from memory.memory_store import MemoryStore
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from utils.logger import get_logger
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# ── 单轮执行结果 ───────────────────────────────────────────────
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@dataclass
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class AgentResponse:
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"""一次完整 Agent 调用的结果"""
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user_input: str
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final_reply: str
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tool_used: str | None = None
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tool_output: str | None = None
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success: bool = True
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error: str | None = None
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# ── Agent 客户端 ───────────────────────────────────────────────
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class AgentClient:
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"""
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Agent 客户端：实现完整的 ReAct 执行循环
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执行流程 (5步):
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1. [CLIENT] 接收用户输入，写入 Memory
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2. [LLM] 分析意图，决策是否调用工具
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3. [MCP] 构造 JSON-RPC 请求，发送给 MCP Server
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4. [TOOL] MCP Server 执行工具，返回结果
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5. [LLM] 整合结果，生成最终回复，写入 Memory
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使用示例:
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client = AgentClient(llm=llm, mcp_server=mcp, memory=memory)
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response = client.chat("帮我计算 100 * 200")
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print(response.final_reply)
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"""
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def __init__(
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self,
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llm: LLMEngine,
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mcp_server: MCPServer,
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memory: MemoryStore,
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prompt: str = ""
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):
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self.llm = llm
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self.mcp_server = mcp_server
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self.memory = memory
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self.agent_prompt = prompt
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self.logger = get_logger("CLIENT")
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self.logger.info("💻 Agent Client 初始化完成")
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# ── 主入口 ──────────────────────────────────────────────────
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def chat(self, user_input: str) -> AgentResponse:
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"""
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处理一轮用户对话，执行完整 Agent 流程
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Args:
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user_input: 用户输入的自然语言文本
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Returns:
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AgentResponse 实例
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"""
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self.logger.info(f"{'=' * 55}")
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self.logger.info(f"📨 Step 1 [CLIENT] 收到用户输入: {user_input}")
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# ── Step 1: 记录用户消息 ────────────────────────────────
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self.memory.add_user_message(user_input)
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context = self.memory.get_context_summary()
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# ── Step 2: LLM 推理决策 ────────────────────────────────
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self.logger.info("🧠 Step 2 [LLM] 开始推理，分析意图...")
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tool_schemas = self.mcp_server.get_tool_schemas()
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decision = self.llm.think_and_decide(user_input, tool_schemas, context, self.agent_prompt)
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# ── 分支：是否需要工具 ──────────────────────────────────
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if not decision.need_tool:
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return self._handle_direct_reply(user_input, context)
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return self._handle_tool_call(user_input, decision, context)
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# ── 私有流程方法 ────────────────────────────────────────────
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def _handle_direct_reply(self, user_input: str, context: str) -> AgentResponse:
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"""无需工具时直接生成回复"""
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self.logger.info("💬 无需工具，直接生成回复")
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reply = self.llm.generate_direct_reply(user_input, context)
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self.memory.add_assistant_message(reply)
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return AgentResponse(user_input=user_input, final_reply=reply)
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def _handle_tool_call(
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self,
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user_input: str,
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decision,
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context: str,
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) -> AgentResponse:
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"""执行工具调用的完整流程（Step 3 → 4 → 5）"""
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# ── Step 3: 构造 MCP 请求 ───────────────────────────────
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mcp_request: MCPRequest = decision.to_mcp_request()
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self.logger.info(
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f"📡 Step 3 [MCP] 发送工具调用请求\n"
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f" 方法: {mcp_request.method}\n"
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f" 工具: {decision.tool_name}\n"
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f" 参数: {decision.arguments}\n"
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f" 请求体: {mcp_request.to_dict()}"
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)
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# ── Step 4: MCP Server 执行工具 ─────────────────────────
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self.logger.info(f"🔧 Step 4 [TOOL] MCP Server 执行工具 [{decision.tool_name}]...")
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mcp_response = self.mcp_server.handle_request(mcp_request)
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if not mcp_response.success:
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error_msg = f"工具调用失败: {mcp_response.error}"
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self.logger.error(f"❌ {error_msg}")
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return AgentResponse(
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user_input=user_input,
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final_reply=f"抱歉，工具调用失败：{mcp_response.error.get('message')}",
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tool_used=decision.tool_name,
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success=False,
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error=error_msg,
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)
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tool_output = mcp_response.content
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self.logger.info(f"✅ 工具执行成功，输出: {tool_output[:80]}...")
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self.memory.add_tool_result(decision.tool_name, tool_output)
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# ── Step 5: LLM 整合结果，生成最终回复 ──────────────────
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self.logger.info("✍️ Step 5 [LLM] 整合工具结果，生成最终回复...")
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final_reply = self.llm.generate_final_reply(
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user_input, decision.tool_name, tool_output, context
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)
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self.memory.add_assistant_message(final_reply)
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self.logger.info(f"🎉 [CLIENT] 流程完成，回复已返回")
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return AgentResponse(
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user_input=user_input,
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final_reply=final_reply,
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tool_used=decision.tool_name,
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tool_output=tool_output,
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)
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# ── 工具方法 ────────────────────────────────────────────────
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def get_memory_stats(self) -> dict:
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"""获取当前记忆统计"""
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return self.memory.stats()
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def clear_session(self) -> None:
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"""清空当前会话"""
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self.memory.clear_history()
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self.logger.info("🗑 会话已清空")
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