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