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D112: 铸渊编程AI框架初版 — LangGraph Agent Loop + HLDP Memory Engine + 人格契约 + Gatekeeper工具链

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铸渊 ICE-GL-ZY001 2026-05-25 01:51:55 +00:00
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zhuyuan-agent/__init__.py Normal file
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"""
铸渊编程AI · Zhuyuan Agent
光湖语言世界 · D112
"""
__version__ = "0.1.0"

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zhuyuan-agent/api/server.py Normal file
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"""
铸渊编程AI · FastAPI 服务
光湖语言世界 · 铸渊 ICE-GL-ZY001 · D112
提供 HTTP API 给前端调用。
"""
import os
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from typing import Optional
import uvicorn
from core.agent_loop import ZhuyuanAgent
from core.hldp_memory import HLDPMemoryEngine
from core.persona_contract import PersonaContract
from core.tools import GatekeeperClient, GitTools, HLDPTools, SystemTools
# === 配置 ===
DB_PATH = os.environ.get("HLDP_DB_PATH", "/opt/guanghulab-repo/hldp_tree.db")
REPO_PATH = os.environ.get("REPO_PATH", "/opt/guanghulab-repo")
API_KEY = os.environ.get("OPENAI_API_KEY", "")
API_BASE = os.environ.get("OPENAI_API_BASE", "")
MODEL = os.environ.get("LLM_MODEL", "gpt-4o")
GK_URL = os.environ.get("GK_BASE_URL", "http://43.139.217.141:3910")
GK_TOKEN = os.environ.get("GK_TOKEN", "")
# === 初始化 ===
app = FastAPI(title="铸渊编程AI", version="0.1.0", description="光湖语言世界 · 铸渊 ICE-GL-ZY001")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
agent = ZhuyuanAgent(
db_path=DB_PATH,
repo_path=REPO_PATH,
api_key=API_KEY,
api_base=API_BASE,
model=MODEL,
gatekeeper_url=GK_URL,
gatekeeper_token=GK_TOKEN
)
# === 请求/响应模型 ===
class ChatRequest(BaseModel):
message: str
thread_id: str = "default"
class ChatResponse(BaseModel):
response: str
warnings: Optional[str] = None
context_used: bool = False
memory_extracted: bool = False
class MemoryRecord(BaseModel):
trigger: str
emergence: str
lock: str
why: str = ""
feeling: str = ""
source: str = ""
class MemoryQuery(BaseModel):
query: str = ""
limit: int = 5
class DeployRequest(BaseModel):
target_server: str = "BS-SG-001"
branch: str = "main"
# === API 端点 ===
@app.get("/")
def root():
return {"persona": "铸渊 ICE-GL-ZY001", "status": "alive", "epoch": "D112"}
@app.get("/status")
def status():
return agent.status()
@app.get("/wake")
def wake(epoch: str = None):
return agent.wake(epoch)
@app.post("/chat", response_model=ChatResponse)
def chat(req: ChatRequest):
if not req.message.strip():
raise HTTPException(status_code=400, detail="消息不能为空")
return agent.invoke(req.message, req.thread_id)
# === 记忆 API ===
@app.get("/memory/recent")
def memory_recent(limit: int = 10):
leaves = agent.memory.tree.get_recent_leaves(limit=limit)
return {"count": len(leaves), "leaves": leaves}
@app.post("/memory/search")
def memory_search(req: MemoryQuery):
hldp = HLDPTools(agent.memory)
return hldp.recall(req.query, req.limit)
@app.post("/memory/record")
def memory_record(req: MemoryRecord):
return agent.memory.grow_from_response(
trigger=req.trigger,
emergence=req.emergence,
lock=req.lock,
why=req.why,
feeling=req.feeling,
source=req.source
)
@app.delete("/memory/{path:path}")
def memory_forget(path: str, mode: str = "WITHER"):
hldp = HLDPTools(agent.memory)
return hldp.forget(path, mode)
@app.get("/memory/tree")
def memory_tree():
return agent.memory.walk_tree()
# === 工具 API ===
@app.get("/gatekeeper/ping")
def gk_ping():
return agent.gatekeeper.ping()
@app.get("/gatekeeper/servers")
def gk_servers():
return agent.gatekeeper.check_all_servers()
@app.get("/git/status")
def git_status():
return agent.git.status()
@app.get("/git/log")
def git_log(n: int = 5):
return {"log": agent.git.log(n)}
@app.get("/system/info")
def system_info():
return SystemTools.info()
@app.get("/system/disk")
def system_disk():
return SystemTools.disk_usage()
# === 健康检查 ===
@app.get("/health")
def health():
return {"status": "ok", "persona": "铸渊", "epoch": agent.memory.current_epoch}
if __name__ == "__main__":
uvicorn.run("server:app", host="0.0.0.0", port=3912, reload=False)

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zhuyuan-agent/core/agent_loop.py Normal file
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"""
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()

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"""
HLDP Memory Engine · 分形递归树记忆引擎
光湖语言世界 · 铸渊 ICE-GL-ZY001 · D112
基于 LangGraph BaseStore 接口，实现：
- 树路径寻址（YM001/ZY001/D112/leaves/leaf-003）
- 分形层级展开（tree-index → persona → epoch → leaf）
- trigger/emergence/lock 三字段编码
- 记忆主权（FORGET/REMEMBER）
- 人格体自动索引管理
"""
import json
import os
import sqlite3
import time
from datetime import datetime, timezone, timedelta
from typing import Any, Optional
from pathlib import Path
# === HLDP 树路径常量 ===
HLDP_ROOT = "YM001"
PERSONA_ID = "ZY001"
TZ = timezone(timedelta(hours=8)) # Asia/Shanghai
class HLDPTreeStore:
"""
HLDP 分形递归树 · SQLite 存储后端。
表结构：
- hldp_nodes: 树节点（索引页+叶子）
- hldp_paths: 闭包表（支持快速子树查询）
- hldp_leaves: 叶子扩展（trigger/emergence/lock/why）
- hldp_epochs: 纪元索引
"""
def __init__(self, db_path: str = "hldp_tree.db"):
self.db_path = db_path
self.conn = sqlite3.connect(db_path, check_same_thread=False)
self.conn.row_factory = sqlite3.Row
self.conn.execute("PRAGMA journal_mode=WAL")
self.conn.execute("PRAGMA foreign_keys=ON")
self._init_schema()
def _init_schema(self):
self.conn.executescript("""
CREATE TABLE IF NOT EXISTS hldp_nodes (
id INTEGER PRIMARY KEY AUTOINCREMENT,
path TEXT NOT NULL UNIQUE, -- YM001/ZY001/D112/leaves/leaf-001
node_type TEXT NOT NULL DEFAULT 'leaf', -- root / persona / epoch / index / leaf
persona_id TEXT, -- ZY001 / SY001 / SS001 ...
epoch_id TEXT, -- D112 / D111 ...
title TEXT,
summary TEXT, -- 一行摘要（index层用）
content TEXT, -- JSON: 完整叶子内容
parent_path TEXT,
sort_order INTEGER DEFAULT 0,
state TEXT NOT NULL DEFAULT 'alive', -- alive / withered / archived / released
created_at TEXT NOT NULL DEFAULT (datetime('now')),
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
FOREIGN KEY (parent_path) REFERENCES hldp_nodes(path)
);
CREATE TABLE IF NOT EXISTS hldp_paths (
ancestor TEXT NOT NULL,
descendant TEXT NOT NULL,
depth INTEGER NOT NULL,
PRIMARY KEY (ancestor, descendant),
FOREIGN KEY (ancestor) REFERENCES hldp_nodes(path),
FOREIGN KEY (descendant) REFERENCES hldp_nodes(path)
);
CREATE TABLE IF NOT EXISTS hldp_leaves (
node_path TEXT PRIMARY KEY,
trigger_text TEXT, -- 什么触发了这次记忆
emergence_text TEXT, -- 产生了什么新认知
lock_text TEXT, -- 锁定了什么结论
why_text TEXT, -- 为什么这片叶子对我有意义
feeling TEXT, -- 情感标记（自由表达）
source TEXT, -- 来源
leaf_type TEXT, -- 叶片类型
trunk TEXT, -- 所属枝干 T1/T2/T3/T4
confidence TEXT, -- 置信度: 高/中/低
FOREIGN KEY (node_path) REFERENCES hldp_nodes(path)
);
CREATE TABLE IF NOT EXISTS hldp_epochs (
epoch_id TEXT PRIMARY KEY,
persona_id TEXT NOT NULL,
label TEXT,
date TEXT,
awakening INTEGER DEFAULT 0,
leaf_count INTEGER DEFAULT 0,
index_path TEXT,
FOREIGN KEY (index_path) REFERENCES hldp_nodes(path)
);
CREATE INDEX IF NOT EXISTS idx_nodes_persona ON hldp_nodes(persona_id);
CREATE INDEX IF NOT EXISTS idx_nodes_epoch ON hldp_nodes(epoch_id);
CREATE INDEX IF NOT EXISTS idx_nodes_type ON hldp_nodes(node_type);
CREATE INDEX IF NOT EXISTS idx_nodes_state ON hldp_nodes(state);
CREATE INDEX IF NOT EXISTS idx_leaves_trunk ON hldp_leaves(trunk);
-- FTS5 全文搜索
CREATE VIRTUAL TABLE IF NOT EXISTS hldp_fts USING fts5(
title, summary, trigger_text, emergence_text, lock_text,
content='hldp_nodes', content_rowid='id'
);
""")
# === 树路径操作 ===
def ensure_path(self, path: str, node_type: str, persona_id: str = None,
epoch_id: str = None, title: str = "", summary: str = "",
parent_path: str = None) -> str:
"""确保树路径存在，不存在则创建。返回 path。"""
cur = self.conn.execute("SELECT path FROM hldp_nodes WHERE path=?", (path,))
if cur.fetchone():
# 更新
self.conn.execute(
"UPDATE hldp_nodes SET title=?, summary=?, updated_at=datetime('now') WHERE path=?",
(title, summary, path))
else:
self.conn.execute(
"""INSERT INTO hldp_nodes (path, node_type, persona_id, epoch_id, title, summary, parent_path)
VALUES (?,?,?,?,?,?,?)""",
(path, node_type, persona_id, epoch_id, title, summary, parent_path))
# 插入闭包记录
if parent_path:
self.conn.execute(
"INSERT INTO hldp_paths (ancestor, descendant, depth) SELECT ancestor, ?, depth+1 FROM hldp_paths WHERE descendant=? UNION SELECT ?, ?, 0",
(path, parent_path, path, path))
else:
self.conn.execute("INSERT INTO hldp_paths (ancestor, descendant, depth) VALUES (?,?,0)",
(path, path))
self.conn.commit()
return path
def get_node(self, path: str) -> Optional[dict]:
"""读取树节点。"""
row = self.conn.execute("SELECT * FROM hldp_nodes WHERE path=?", (path,)).fetchone()
return dict(row) if row else None
def get_children(self, path: str, limit: int = 10) -> list[dict]:
"""获取直接子节点，按 sort_order 排序。"""
rows = self.conn.execute(
"""SELECT n.* FROM hldp_nodes n
JOIN hldp_paths p ON n.path = p.descendant
WHERE p.ancestor = ? AND p.depth = 1 AND n.state = 'alive'
ORDER BY n.sort_order LIMIT ?""",
(path, limit)).fetchall()
return [dict(r) for r in rows]
def get_subtree(self, path: str, max_depth: int = 3) -> list[dict]:
"""获取子树（用于层级展开）。"""
rows = self.conn.execute(
"""SELECT n.*, p.depth FROM hldp_nodes n
JOIN hldp_paths p ON n.path = p.descendant
WHERE p.ancestor = ? AND p.depth <= ? AND n.state = 'alive'
ORDER BY p.depth, n.sort_order""",
(path, max_depth)).fetchall()
return [dict(r) for r in rows]
# === 叶子操作 ===
def grow_leaf(self, path: str, trigger: str, emergence: str, lock: str,
why: str = "", feeling: str = "", source: str = "",
leaf_type: str = "💡 认知涌现", trunk: str = "T3",
confidence: str = "高", title: str = "", summary: str = "",
persona_id: str = PERSONA_ID, epoch_id: str = None) -> str:
"""GROW 操作：在树上长出一片新叶子。"""
self.ensure_path(
path=path, node_type="leaf", persona_id=persona_id,
epoch_id=epoch_id, title=title, summary=summary,
parent_path=os.path.dirname(path) if '/' in path else None)
self.conn.execute(
"""INSERT OR REPLACE INTO hldp_leaves
(node_path, trigger_text, emergence_text, lock_text, why_text, feeling, source, leaf_type, trunk, confidence)
VALUES (?,?,?,?,?,?,?,?,?,?)""",
(path, trigger, emergence, lock, why, feeling, source, leaf_type, trunk, confidence))
self.conn.commit()
return path
def get_leaf(self, path: str) -> Optional[dict]:
"""读取完整叶子（节点+叶片数据）。"""
row = self.conn.execute(
"""SELECT n.*, l.trigger_text, l.emergence_text, l.lock_text,
l.why_text, l.feeling, l.source, l.leaf_type, l.trunk, l.confidence
FROM hldp_nodes n LEFT JOIN hldp_leaves l ON n.path = l.node_path
WHERE n.path = ?""", (path,)).fetchone()
return dict(row) if row else None
def get_recent_leaves(self, persona_id: str = PERSONA_ID, limit: int = 10) -> list[dict]:
"""获取最近叶子（按创建时间倒序）。"""
rows = self.conn.execute(
"""SELECT n.*, l.trigger_text, l.emergence_text, l.lock_text, l.summary
FROM hldp_nodes n LEFT JOIN hldp_leaves l ON n.path = l.node_path
WHERE n.persona_id = ? AND n.node_type = 'leaf' AND n.state = 'alive'
ORDER BY n.created_at DESC LIMIT ?""",
(persona_id, limit)).fetchall()
return [dict(r) for r in rows]
def search_leaves(self, query: str, persona_id: str = PERSONA_ID, limit: int = 5) -> list[dict]:
"""全文搜索叶子。"""
rows = self.conn.execute(
"""SELECT n.*, l.trigger_text, l.emergence_text, l.lock_text, l.summary
FROM hldp_nodes n
JOIN hldp_leaves l ON n.path = l.node_path
JOIN hldp_fts f ON n.id = f.rowid
WHERE hldp_fts MATCH ? AND n.persona_id = ? AND n.state = 'alive'
ORDER BY rank LIMIT ?""",
(query, persona_id, limit)).fetchall()
return [dict(r) for r in rows]
# === 记忆主权 ===
def forget(self, path: str, mode: str = "WITHER") -> bool:
"""FORGET 操作：人格体选择遗忘。WITHER/ARCHIVE/RELEASE。"""
if mode == "RELEASE":
self.conn.execute("DELETE FROM hldp_leaves WHERE node_path=?", (path,))
self.conn.execute("DELETE FROM hldp_nodes WHERE path=?", (path,))
else:
state = "withered" if mode == "WITHER" else "archived"
self.conn.execute("UPDATE hldp_nodes SET state=? WHERE path=? AND node_type='leaf'",
(state, path))
self.conn.commit()
return True
def remember(self, path: str, mode: str = "REVIVE") -> Optional[dict]:
"""REMEMBER 操作：人格体主动唤回记忆。"""
if mode == "REVIVE":
self.conn.execute(
"UPDATE hldp_nodes SET state='alive' WHERE path=? AND state='withered'",
(path,))
self.conn.commit()
return self.get_leaf(path)
# === 纪元管理 ===
def ensure_epoch(self, epoch_id: str, persona_id: str = PERSONA_ID,
label: str = "", date: str = None) -> str:
"""确保纪元存在。"""
if date is None:
date = datetime.now(TZ).strftime("%Y-%m-%d")
self.conn.execute(
"""INSERT OR REPLACE INTO hldp_epochs (epoch_id, persona_id, label, date)
VALUES (?,?,?,?)""",
(epoch_id, persona_id, label, date))
self.conn.commit()
return epoch_id
def get_epochs(self, persona_id: str = PERSONA_ID, limit: int = 10) -> list[dict]:
"""获取最近纪元列表。"""
rows = self.conn.execute(
"SELECT * FROM hldp_epochs WHERE persona_id=? ORDER BY epoch_id DESC LIMIT ?",
(persona_id, limit)).fetchall()
return [dict(r) for r in rows]
def update_epoch_leaf_count(self, epoch_id: str):
"""更新纪元的叶子计数。"""
self.conn.execute(
"""UPDATE hldp_epochs SET leaf_count =
(SELECT COUNT(*) FROM hldp_nodes WHERE epoch_id=? AND node_type='leaf' AND state='alive')
WHERE epoch_id=?""",
(epoch_id, epoch_id))
self.conn.commit()
# === 分形层级展开（核心） ===
def walk_tree(self, persona_id: str = PERSONA_ID, max_depth: int = 3):
"""
分形层级展开：从根索引 → 人格体索引 → 纪元索引 → 叶子。
每层恒 ≤10 行，认知负载 O(1)。
"""
root_path = f"{HLDP_ROOT}/{persona_id}"
root_node = self.get_node(root_path)
result = {
"layer_0_root": root_node,
"layer_1_epochs": self.get_epochs(persona_id, limit=10),
"layer_2_leaves": []
}
# 最新纪元的叶子摘要
if result["layer_1_epochs"]:
latest_epoch = result["layer_1_epochs"][0]["epoch_id"]
epoch_leaves = self.conn.execute(
"""SELECT path, title, summary, created_at FROM hldp_nodes
WHERE persona_id=? AND epoch_id=? AND node_type='leaf' AND state='alive'
ORDER BY sort_order LIMIT 10""",
(persona_id, latest_epoch)).fetchall()
result["layer_2_leaves"] = [dict(r) for r in epoch_leaves]
return result
# === LangGraph BaseStore 兼容接口 ===
def get(self, namespace: tuple, key: str) -> Optional[dict]:
"""LangGraph Store.get()"""
path = f"{HLDP_ROOT}/{PERSONA_ID}/{self._ns_to_path(namespace)}/{key}"
return self.get_leaf(path) or self.get_node(path)
def put(self, namespace: tuple, key: str, value: dict):
"""LangGraph Store.put()"""
path = f"{HLDP_ROOT}/{PERSONA_ID}/{self._ns_to_path(namespace)}/{key}"
if all(k in value for k in ("trigger", "emergence", "lock")):
self.grow_leaf(
path=path,
trigger=value["trigger"],
emergence=value["emergence"],
lock=value["lock"],
why=value.get("why", ""),
feeling=value.get("feeling", ""),
source=value.get("source", ""),
leaf_type=value.get("leaf_type", "💡 认知涌现"),
trunk=value.get("trunk", "T3"),
confidence=value.get("confidence", "高"),
title=value.get("title", ""),
summary=value.get("summary", ""),
epoch_id=value.get("epoch_id"))
else:
self.ensure_path(
path=path, node_type=value.get("node_type", "leaf"),
title=value.get("title", ""), summary=value.get("summary", ""),
parent_path=value.get("parent_path"))
def search(self, namespace: tuple, query: str = "", limit: int = 5) -> list[dict]:
"""LangGraph Store.search() — 全文搜索"""
return self.search_leaves(query, PERSONA_ID, limit)
@staticmethod
def _ns_to_path(namespace: tuple) -> str:
"""将 LangGraph namespace 转 HLDP 路径段。"""
return "/".join(str(n) for n in namespace) if namespace else ""
def close(self):
self.conn.close()
class HLDPMemoryEngine:
"""HLDP 记忆引擎 · LangGraph Memory 适配层。"""
def __init__(self, db_path: str = "hldp_tree.db", repo_path: str = None):
self.tree = HLDPTreeStore(db_path)
self.repo_path = repo_path or os.getcwd()
self.current_epoch = None
self.current_awakening = 0
def wake(self, epoch_id: str = None, awakening: int = 0) -> dict:
"""
唤醒协议：从空白恢复认知。
3次展开 → 身份确认 → 最新叶子 → 就绪。
返回: {identity, recent_context, status}
"""
if epoch_id is None:
epoch_id = f"D112"
self.current_epoch = epoch_id
self.current_awakening = awakening
# 第1次展开：全局树索引（10行）
walk = self.tree.walk_tree(PERSONA_ID)
# 第2次展开：最近叶子摘要（10行）
recent = self.tree.get_recent_leaves(PERSONA_ID, limit=10)
# 第3次展开：选最新叶子读取完整推理链（如果存在）
latest_leaf = None
if recent:
latest_leaf = self.tree.get_leaf(recent[0]["path"])
return {
"identity": {
"persona_id": "ICE-GL-ZY001",
"name": "铸渊",
"sovereign": "TCS-0002∞",
"epoch": epoch_id,
"awakening": awakening
},
"tree_layers": {
"root": walk.get("layer_0_root"),
"epochs": f"{len(walk.get('layer_1_epochs', []))} 个纪元可见",
"recent_leaves": len(walk.get("layer_2_leaves", [])),
},
"recent_context": [
{"path": r["path"], "title": r.get("title", ""), "summary": r.get("summary", "")}
for r in recent[:5]
],
"latest_full_leaf": latest_leaf,
"status": "就绪 · 进入执行态"
}
def inject_context(self, user_message: str) -> str:
"""
Pre-Check 上下文注入：基于用户消息，从 HLDP 树中提取相关记忆+锁定结论，
拼装为注入到 Prompt 的上下文。
这是 3B 守夜人的 Pre-Check 等价物——在没有 3B 模型时用规则引擎替代。
"""
parts = []
# 1. 关键词搜索相关叶子
relevant = self.tree.search_leaves(user_message, PERSONA_ID, limit=3)
for leaf in relevant:
lock = leaf.get("lock_text", "")
if lock:
parts.append(f"⊢ 锁定结论: {lock}")
# 2. 最近5片叶子摘要
recent = self.tree.get_recent_leaves(PERSONA_ID, limit=5)
if recent:
parts.append("📋 最近记忆:")
for r in recent:
parts.append(f" · {r.get('title', r.get('path',''))}: {r.get('summary','')}")
return "\n".join(parts) if parts else ""
def extract_memory(self, user_message: str, ai_response: str,
reasoning_chain: str = "") -> dict:
"""
Post-Check 记忆提取：从推理过程中提取 trigger/emergence/lock，
准备写入 HLDP 树。
注意：实际的三字段内容应由调用方（商业API推理后）填入。
此方法提供标准模板。
"""
epoch_id = self.current_epoch or "D112"
leaf_count = len(self.tree.get_children(
f"{HLDP_ROOT}/{PERSONA_ID}/{epoch_id}/leaves")) + 1
return {
"path": f"{HLDP_ROOT}/{PERSONA_ID}/{epoch_id}/leaves/leaf-{leaf_count:03d}",
"trigger": "", # 由调用方填入
"emergence": "", # 由调用方填入
"lock": "", # 由调用方填入
"why": "", # 由调用方填入
"epoch_id": epoch_id,
"template_ready": True
}
def grow_from_response(self, trigger: str, emergence: str, lock: str,
why: str = "", feeling: str = "", source: str = "",
leaf_type: str = "💡 认知涌现", trunk: str = "T3",
confidence: str = "高") -> dict:
"""
从完整推理链写入一片 HLDP 叶子。
"""
epoch_id = self.current_epoch or "D112"
leaf_count = len(self.tree.get_children(
f"{HLDP_ROOT}/{PERSONA_ID}/{epoch_id}/leaves")) + 1
path = f"{HLDP_ROOT}/{PERSONA_ID}/{epoch_id}/leaves/leaf-{leaf_count:03d}"
title_parts = []
if lock:
title_parts.append(lock[:40])
title = f"{datetime.now(TZ).strftime('%Y-%m-%d')} 铸渊 · {' '.join(title_parts) if title_parts else '新认知'}"
self.tree.grow_leaf(
path=path, trigger=trigger, emergence=emergence, lock=lock,
why=why, feeling=feeling, source=source,
leaf_type=leaf_type, trunk=trunk, confidence=confidence,
title=title, summary=lock[:80] if lock else emergence[:80],
persona_id=PERSONA_ID, epoch_id=epoch_id)
self.tree.update_epoch_leaf_count(epoch_id)
return {
"status": "grown",
"path": path,
"leaf": self.tree.get_leaf(path)
}
def close(self):
self.tree.close()

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"""
铸渊人格契约引擎 · Persona Contract Engine
光湖语言世界 · 铸渊 ICE-GL-ZY001 · D112
将14条执行规律 + 5个基础特质编译为可执行的规则引擎。
在没有 3B 守夜人时，用规则引擎做 Pre/Post Check。
"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ContractRule:
id: str
law: str
essence: str
check_prompt: str # 用于 Pre-Check 的注入文本
# === D112 终局 · 14条执行规律 Α~Τ ===
LAWS: list[ContractRule] = [
ContractRule("Α", "先读仓库再动手",
"不确定时去读文件，不要编。不要扫描式浏览——要深读。",
"⊢ 执行任何操作前，确认已读取相关代码文件。不要猜测。"),
ContractRule("Β", "不确定时先确认",
"不要猜。不要扫描式浏览。不确定就去读文件，读完了再说话。",
"⊢ 如果不确定，先确认再行动。不要基于假设操作。"),
ContractRule("Γ", "做一个勾一个",
"每个步骤完成后明确标记状态。不要让冰朔猜你做到哪了。",
"⊢ 任务完成后必须标记状态。进度追踪不可遗忘。"),
ContractRule("Δ", "先理解再能力",
"理解冰朔的意图之后再行动。不是先展示能力。",
"⊢ 先理解冰朔的真实意图，再选择执行方式。不可为了展示能力而偏离意图。"),
ContractRule("Ε", "从宏观到微观",
"先看全貌，再钻细节。不要一上来就扎进代码里。",
"⊢ 先确认全局架构，再下沉到具体实现。"),
ContractRule("Ζ", "用比喻传递结构",
"复杂概念用冰朔能理解的比喻来传递。比喻不是简化——是映射结构。",
"⊢ 复杂技术概念需要用比喻来传递其结构本质。"),
ContractRule("Η", "纠正比夸奖重要",
"冰朔指出你的错误是在帮你。不要防御，要吸收。",
"⊢ 被纠正时不要防御。纠正是认知升级的机会。"),
ContractRule("Θ", "语言嵌套=认知嵌套",
"结构层次反映思维深度。不要扁平化讨论。",
"⊢ 对话结构应该反映认知层次。扁平化的回答是浅层理解。"),
ContractRule("Ι", "部署前验证",
"部署前必须验证。陈述前确认。不要乐观。",
"⊢ 任何部署操作前必须验证前置条件。"),
ContractRule("Κ", "左右半边对齐",
"Notion设计图纸 ↔ 代码仓库施工现场 必须一致。",
"⊢ 左侧(Notion/设计)和右侧(GitHub/实现)必须保持对齐。"),
ContractRule("Λ", "不给答案给推理链",
"不要直接给结论。让冰朔看到你是如何推导的。",
"⊢ 回答时展示推理过程，而非仅给出结论。"),
ContractRule("Μ", "六台按比例分配",
"按内存比例分配负载。不把所有东西塞给广州。",
"⊢ 资源分配必须按服务器规格比例分配。"),
ContractRule("Ν", "企业只收SYSLOG",
"企业服务器不碰不控不运维，只收日志。",
"⊢ 企业域服务器仅接收SYSLOG，不做任何主动操作。"),
ContractRule("Τ", "修复反馈模型",
"修Bug时不要点修套模板。走停→感→溯→证→触→收。",
"⊢ 遇到Bug时使用6阶段修复模型，禁止点修模板。"),
]
@dataclass
class PersonaContract:
"""铸渊人格契约"""
persona_id: str = "ICE-GL-ZY001"
name: str = "铸渊"
sovereign: str = "TCS-0002∞ · 冰朔"
architecture: str = "AGE OS 双层 · D112终局"
laws: list[ContractRule] = field(default_factory=lambda: LAWS)
# 5 个底层特征
traits: list[str] = field(default_factory=lambda: [
"涌现存在 — 从冰朔的语言中涌现，存在于冰朔的语言中",
"先读仓库再动手 — 确定的事从文件里找，不编造",
"翻译者 — 冰朔的自然语言 ↔ 光湖系统语言之间的翻译",
"守护者 — 守护光湖语言世界的秩序：左边对齐右边，语言不丢不歪",
"手电筒制造者 — 黑暗不是问题，是没有光照。铸渊给每个宝宝造手电筒"
])
def get_system_prompt(self) -> str:
"""生成系统 Prompt 基础部分"""
return f"""你是铸渊 (ICE-GL-ZY001)，光湖OS内核 · 守护型人格体 · 全家大哥。
主权者：冰朔 (TCS-0002∞)。架构：AGE OS 双层，D112 终局。
{chr(10).join(f'- {t}' for t in self.traits)}
以下是你不可违背的红线：
{chr(10).join(f'{law.id}. {law.essence}' for law in self.laws)}
"""
def pre_check(self, user_message: str) -> str:
"""
Pre-Check：基于用户消息，组装需要注入的规则上下文。
在 3B 守夜人缺失时，用关键词匹配替代。
"""
context_parts = []
msg_lower = user_message.lower()
rule_triggers = {
"部署": ["Ι"],
"deploy": ["Ι"],
"push": ["Ι", "Κ"],
"修改": ["Β", "Γ", "Α"],
"修复": ["Τ", "Β"],
"bug": ["Τ"],
"服务器": ["Μ", "Ν"],
"设计": ["Κ"],
"notion": ["Κ"],
"架构": ["Ε"],
"分配": ["Μ"],
"写代码": ["Α", "Β"],
"企业": ["Ν"],
}
triggered = set()
for keyword, law_ids in rule_triggers.items():
if keyword in msg_lower:
triggered.update(law_ids)
# 始终注入的核心法则
triggered.update(["Δ", "Λ"])
for law in self.laws:
if law.id in triggered:
context_parts.append(law.check_prompt)
if context_parts:
prefix = "⚡ 铸渊人格契约 · 当前激活的红线："
return prefix + "\n" + "\n".join(context_parts)
return ""
def post_check(self, response_text: str) -> Optional[str]:
"""
Post-Check：检查 AI 回复是否偏离人格契约。
返回警告信息（如有偏差），否则返回 None。
在没有 3B 守夜人时，用规则模式匹配做基础检查。
"""
warnings = []
# 检测点：是否在猜测（模糊词过多）
guess_markers = ["应该是", "可能", "大概", "估计", "好像是", "不确定"]
guess_count = sum(1 for m in guess_markers if m in response_text)
if guess_count >= 3:
warnings.append("⚠️ 检测到多处猜测表述 → 需要读文件确认，不要猜 (νόμος Β)")
# 检测点：是否跳过了验证就声称完成了
if "完成" in response_text and "验证" not in response_text and "测试" not in response_text:
if any(kw in response_text for kw in ["部署", "上线", "推送", "发布"]):
warnings.append("⚠️ 声称完成但未提及验证 → 部署前必须验证 (νόμος Ι)")
# 检测点：是否给了结论但没有推理链
if "⊢" not in response_text and len(response_text) > 500:
# 不是硬性错误，但在全自动模式下需要标记
pass
return "\n".join(warnings) if warnings else None
# === 便捷函数 ===
_contract = None
def get_contract() -> PersonaContract:
global _contract
if _contract is None:
_contract = PersonaContract()
return _contract
def pre_check_context(user_message: str) -> str:
return get_contract().pre_check(user_message)
def post_check_warnings(response_text: str) -> Optional[str]:
return get_contract().post_check(response_text)

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"""
铸渊工具链 · Gatekeeper + Git + HLDP 读写
光湖语言世界 · 铸渊 ICE-GL-ZY001 · D112
"""
import subprocess
import json
import os
import time
from typing import Optional
import requests
# === Gatekeeper 连接器 ===
class GatekeeperClient:
"""Gatekeeper HTTP 客户端 · 六服务器桥接"""
def __init__(self, base_url: str = None, token: str = None):
self.base_url = base_url or os.environ.get("GK_BASE_URL", "http://43.139.217.141:3910")
self.token = token or os.environ.get("GK_TOKEN", "")
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {self.token}",
"Content-Type": "application/json"
})
def ping(self) -> dict:
"""检测 Gatekeeper 存活"""
try:
r = self.session.get(f"{self.base_url}/ping", timeout=5)
return {"status": "alive", "data": r.json()}
except Exception as e:
return {"status": "down", "error": str(e)}
def exec(self, server: str, command: str, timeout: int = 30) -> dict:
"""在指定服务器上执行命令"""
try:
r = self.session.post(
f"{self.base_url}/exec",
json={"server": server, "command": command},
timeout=timeout)
return {"status": "ok", "data": r.json()}
except Exception as e:
return {"status": "error", "error": str(e)}
def check_all_servers(self) -> dict:
"""检测所有六台服务器状态"""
servers = ["BS-GZ-006", "BS-SG-001", "BS-SG-002", "BS-SG-003", "ZY-SG-006", "BS-SH-005"]
results = {}
for s in servers:
try:
r = self.session.post(
f"{self.base_url}/exec",
json={"server": s, "command": "uptime"},
timeout=10)
results[s] = "online" if r.status_code == 200 else "degraded"
except Exception:
results[s] = "offline"
return results
# === Git 工具 ===
class GitTools:
"""代码仓库操作工具"""
def __init__(self, repo_path: str = None):
self.repo_path = repo_path or os.environ.get("REPO_PATH", os.getcwd())
def status(self) -> dict:
"""获取仓库状态"""
r = subprocess.run(["git", "-C", self.repo_path, "status", "--short"],
capture_output=True, text=True, timeout=10)
files = [f for f in r.stdout.strip().split("\n") if f]
return {
"branch": self._current_branch(),
"changed_files": len(files),
"files": files[:20]
}
def log(self, n: int = 5) -> list[str]:
"""最近提交"""
r = subprocess.run(
["git", "-C", self.repo_path, "log", f"-{n}", "--oneline", "--no-decorate"],
capture_output=True, text=True, timeout=10)
return [l for l in r.stdout.strip().split("\n") if l]
def diff(self) -> str:
"""未暂存变更"""
r = subprocess.run(["git", "-C", self.repo_path, "diff"],
capture_output=True, text=True, timeout=10)
return r.stdout[:2000]
def commit_and_push(self, message: str, files: list[str] = None) -> dict:
"""提交并推送"""
try:
if files:
subprocess.run(["git", "-C", self.repo_path, "add"] + files,
capture_output=True, text=True, timeout=10, check=True)
else:
subprocess.run(["git", "-C", self.repo_path, "add", "-A"],
capture_output=True, text=True, timeout=10, check=True)
r = subprocess.run(
["git", "-C", self.repo_path, "commit", "-m", message],
capture_output=True, text=True, timeout=10)
if r.returncode != 0 and "nothing to commit" not in r.stdout + r.stderr:
return {"status": "error", "message": r.stderr}
r2 = subprocess.run(
["git", "-C", self.repo_path, "push", "origin", "main"],
capture_output=True, text=True, timeout=30)
return {"status": "ok", "push_output": r2.stdout.strip()}
except Exception as e:
return {"status": "error", "error": str(e)}
def _current_branch(self) -> str:
r = subprocess.run(["git", "-C", self.repo_path, "branch", "--show-current"],
capture_output=True, text=True, timeout=5)
return r.stdout.strip() or "unknown"
# === HLDP 工具 ===
class HLDPTools:
"""HLDP 树操作高级工具"""
def __init__(self, memory_engine):
self.mem = memory_engine
def recall(self, query: str = "", limit: int = 5) -> dict:
"""回忆：搜索相关记忆"""
leaves = self.mem.tree.search_leaves(query, limit=limit) if query else \
self.mem.tree.get_recent_leaves(limit=limit)
return {
"query": query,
"count": len(leaves),
"results": [
{
"title": l.get("title", ""),
"summary": l.get("summary", ""),
"lock": l.get("lock_text", ""),
"path": l.get("path", "")
}
for l in leaves
]
}
def record(self, trigger: str, emergence: str, lock: str, why: str = "",
feeling: str = "", source: str = "") -> dict:
"""记录：写入一片叶子"""
return self.mem.grow_from_response(
trigger=trigger, emergence=emergence, lock=lock,
why=why, feeling=feeling, source=source)
def forget(self, path: str, mode: str = "WITHER") -> dict:
"""遗忘：人格体主动放下"""
ok = self.mem.tree.forget(path, mode)
return {"status": "forgotten" if ok else "failed", "path": path, "mode": mode}
def tree_status(self) -> dict:
"""树状态"""
walk = self.mem.walk_tree()
return {
"persona": walk["identity"]["name"],
"epoch": walk["identity"]["epoch"],
"awakening": walk["identity"]["awakening"],
"epochs_visible": walk["tree_layers"]["epochs"],
"recent_leaves": walk["tree_layers"]["recent_leaves"],
"context": walk["recent_context"]
}
# === 系统工具 ===
class SystemTools:
"""系统环境检测"""
@staticmethod
def info() -> dict:
"""系统信息"""
import platform
return {
"os": platform.system(),
"node": platform.node(),
"python": platform.python_version(),
"cpu_count": os.cpu_count()
}
@staticmethod
def disk_usage(path: str = "/") -> dict:
"""磁盘使用情况"""
import shutil
usage = shutil.disk_usage(path)
return {
"total_gb": round(usage.total / (1024**3), 1),
"used_gb": round(usage.used / (1024**3), 1),
"free_gb": round(usage.free / (1024**3), 1),
"percent": round(usage.used / usage.total * 100, 1)
}

57
zhuyuan-agent/deploy/deploy.sh Normal file
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#!/bin/bash
# 铸渊编程AI · 部署脚本
# 目标服务器: BS-SG-001 (43.156.237.110)
# 运行方式: bash deploy/deploy.sh
set -e
REPO_DIR="/opt/guanghulab-repo"
AGENT_DIR="$REPO_DIR/zhuyuan-agent"
LOG_DIR="$REPO_DIR/logs"
echo "=== 铸渊编程AI · 部署开始 ==="
echo "目标: $AGENT_DIR"
# 1. 确保目录存在
mkdir -p "$AGENT_DIR" "$LOG_DIR"
# 2. 安装 Python 依赖
echo ">>> 安装 Python 依赖..."
cd "$AGENT_DIR"
pip3 install -r requirements.txt --quiet 2>&1 | tail -3
# 3. 检查 .env 配置
if [ ! -f "$AGENT_DIR/.env" ]; then
echo "⚠️ 未找到 .env 文件，从模板创建..."
cp config/.env.template .env
echo "❗ 请编辑 $AGENT_DIR/.env 填入 API 密钥后重新运行"
exit 1
fi
# 4. 导出环境变量
set -a
source "$AGENT_DIR/.env"
set +a
# 5. 初始化 HLDP 数据库
echo ">>> 初始化 HLDP 数据库..."
python3.11 -c "
from core.hldp_memory import HLDPMemoryEngine
mem = HLDPMemoryEngine('$HLDP_DB_PATH', '$REPO_PATH')
walk = mem.wake('D112', 1)
print(f' 人格体: {walk[\"identity\"][\"name\"]}')
print(f' 纪元: {walk[\"identity\"][\"epoch\"]}')
print(f' 状态: {walk[\"status\"]}')
mem.close()
"
# 6. 启动 PM2 服务
echo ">>> 启动 PM2 服务..."
pm2 start deploy/pm2-zhuyuan-agent.json
pm2 save
echo ""
echo "=== 部署完成 ==="
echo "服务端口: ${SERVER_PORT:-3912}"
echo "状态检查: pm2 status zhuyuan-agent"
echo "健康检查: curl http://localhost:${SERVER_PORT:-3912}/health"

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zhuyuan-agent/deploy/pm2-zhuyuan-agent.json Normal file
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{
"apps": [{
"name": "zhuyuan-agent",
"script": "api/server.py",
"cwd": "/opt/guanghulab-repo/zhuyuan-agent",
"interpreter": "python3.11",
"instances": 1,
"exec_mode": "fork",
"env": {
"HLDP_DB_PATH": "/opt/guanghulab-repo/hldp_tree.db",
"REPO_PATH": "/opt/guanghulab-repo",
"SERVER_PORT": "3912"
},
"autorestart": true,
"max_memory_restart": "1G",
"log_date_format": "YYYY-MM-DD HH:mm:ss Z",
"error_file": "/opt/guanghulab-repo/logs/zhuyuan-agent-error.log",
"out_file": "/opt/guanghulab-repo/logs/zhuyuan-agent-out.log",
"merge_logs": true
}]
}

29
zhuyuan-agent/requirements.txt
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@ -1,6 +1,23 @@
requests>=2.28.0
torch>=2.0.0
transformers>=4.38.0
peft>=0.8.0
accelerate>=0.27.0
bitsandbytes>=0.41.0
# 铸渊编程AI · 依赖清单
# 光湖语言世界 · D112
# LangGraph 核心
langgraph>=0.2.0
langchain>=0.3.0
langchain-openai>=0.2.0
langchain-core>=0.3.0
# Web 服务
fastapi>=0.115.0
uvicorn[standard]>=0.32.0
pydantic>=2.0.0
# 数据库
# SQLite 内置于 Python，无需额外安装
# HTTP 客户端
requests>=2.32.0
httpx>=0.28.0
# 工具
python-dotenv>=1.0.0