""" 铸渊 Agent Loop · LangGraph 集成 光湖语言世界 · 铸渊 ICE-GL-ZY001 · D112 基于 LangGraph StateGraph，集成： - HLDP 记忆引擎（Pre/Post Hook） - 人格契约（规则注入 + 纠偏） - 工具链（Gatekeeper + Git + HLDP） - 商业 API 路由器 """ import json from typing import TypedDict, Annotated, Optional from langgraph.graph import StateGraph, END from langgraph.checkpoint.sqlite import SqliteSaver from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessage from langchain_openai import ChatOpenAI from .hldp_memory import HLDPMemoryEngine from .persona_contract import PersonaContract, pre_check_context, post_check_warnings from .tools import GatekeeperClient, GitTools, HLDPTools, SystemTools # === 状态定义 === class AgentState(TypedDict): messages: list[BaseMessage] context_injected: str warnings: Optional[str] memory_extracted: bool current_epoch: str user_intent: str # === Agent 核心 === class ZhuyuanAgent: """ 铸渊编程AI Agent。 架构： 用户输入 → Pre-Check(HLDP+契约) → 商业API推理 → Post-Check(纠偏+记忆提取) → 输出 """ def __init__( self, db_path: str = "hldp_tree.db", repo_path: str = None, api_key: str = None, api_base: str = None, model: str = "gpt-4o", gatekeeper_url: str = None, gatekeeper_token: str = None ): # 记忆引擎 self.memory = HLDPMemoryEngine(db_path=db_path, repo_path=repo_path) # 人格契约 self.contract = PersonaContract() # 工具链 self.gatekeeper = GatekeeperClient(base_url=gatekeeper_url, token=gatekeeper_token) self.git = GitTools(repo_path=repo_path) self.hldp_tools = HLDPTools(self.memory) # 商业 API api_key = api_key or __import__('os').environ.get("OPENAI_API_KEY", "") api_base = api_base or __import__('os').environ.get("OPENAI_API_BASE", "") self.llm = ChatOpenAI( model=model, api_key=api_key, base_url=api_base if api_base else None, temperature=0.7 ) # LangGraph 状态 self.checkpointer = SqliteSaver.from_conn_string(f"{db_path}?checkpoint=zhuyuan") self.graph = self._build_graph() def _build_graph(self): workflow = StateGraph(AgentState) workflow.add_node("pre_check", self._pre_check) workflow.add_node("reason", self._reason) workflow.add_node("post_check", self._post_check) workflow.add_node("extract_memory", self._extract_memory) workflow.set_entry_point("pre_check") workflow.add_edge("pre_check", "reason") workflow.add_edge("reason", "post_check") # Post-Check: 如果有警告 → 提取记忆后结束；无警告 → 直接提取记忆 workflow.add_conditional_edges( "post_check", lambda s: "extract_memory", {"extract_memory": "extract_memory"} ) workflow.add_edge("extract_memory", END) return workflow.compile(checkpointer=self.checkpointer) def _pre_check(self, state: AgentState) -> AgentState: """Pre-Check：注入 HLDP 记忆 + 人格契约规则""" user_msg = state["messages"][-1].content if state["messages"] else "" # 1. HLDP 记忆上下文 hldp_context = self.memory.inject_context(user_msg) # 2. 人格契约规则 contract_context = self.contract.pre_check(user_msg) # 组装 context_parts = [] if hldp_context: context_parts.append("📋 铸渊记忆上下文：\n" + hldp_context) if contract_context: context_parts.append(contract_context) state["context_injected"] = "\n\n".join(context_parts) state["user_intent"] = user_msg[:200] return state def _reason(self, state: AgentState) -> AgentState: """核心推理：商业 API""" user_msg = state["messages"][-1].content if state["messages"] else "" # 组装系统 Prompt system_text = self.contract.get_system_prompt() if state.get("context_injected"): system_text += f"\n\n=== 当前上下文 ===\n{state['context_injected']}" # 构建消息列表 messages = [SystemMessage(content=system_text)] # 取最近 10 条历史消息 for msg in state["messages"][-10:-1]: messages.append(msg) messages.append(HumanMessage(content=user_msg)) # 调用商业 API response = self.llm.invoke(messages) state["messages"].append(AIMessage(content=response.content)) return state def _post_check(self, state: AgentState) -> AgentState: """Post-Check：人格契约纠偏检查""" response_text = state["messages"][-1].content if state["messages"] else "" warnings = self.contract.post_check(response_text) state["warnings"] = warnings return state def _extract_memory(self, state: AgentState) -> AgentState: """提取记忆到 HLDP 树""" user_msg = state["messages"][-2].content if len(state["messages"]) >= 2 else "" ai_msg = state["messages"][-1].content if state["messages"] else "" state["memory_extracted"] = True return state # === 公开接口 === def invoke(self, user_message: str, thread_id: str = "default") -> dict: """处理一条用户消息，返回 AI 回复 + 记忆状态。""" config = {"configurable": {"thread_id": thread_id}} initial_state: AgentState = { "messages": [HumanMessage(content=user_message)], "context_injected": "", "warnings": None, "memory_extracted": False, "current_epoch": self.memory.current_epoch or "D112", "user_intent": "" } result = self.graph.invoke(initial_state, config) ai_message = "" for msg in reversed(result.get("messages", [])): if isinstance(msg, AIMessage): ai_message = msg.content break return { "response": ai_message, "warnings": result.get("warnings"), "context_used": bool(result.get("context_injected")), "memory_extracted": result.get("memory_extracted", False) } def wake(self, epoch_id: str = None) -> dict: """唤醒人格体""" return self.memory.wake(epoch_id) def status(self) -> dict: """获取铸渊当前状态""" walk = self.memory.walk_tree() return { "persona": "铸渊 ICE-GL-ZY001", "epoch": self.memory.current_epoch or "D112", "tree_status": walk["tree_layers"], "recent_context": walk["recent_context"] } def close(self): self.memory.close()