feat: 光湖技术问答台 + 铸渊Bridge Agent + Awen认知更新
新增: - scripts/qa-bridge-agent.py: 每日20:00扫描Notion数据库，基于铸渊大脑+代码仓库回答技术问题 - 支持4类问题: Gatekeeper/服务器、架构层级、部署/仓库、通用查询 - 自动标记处理中→生成回答→回写数据库 - 部署于BS-GZ-006 · crontab定时 架构指南更新: - 新增第六章「技术问答数据库」: 数据库ID/字段/用法 - 新增「铸渊是辅助，Awen是主控」核心认知 Notion同步: - 创建「光湖技术问答台」数据库 (36bfb92f-3831-81b3-97b5-e8b5e2b217ef) - 创建认知更新页面 (铸渊是辅助·Awen是主控) 部署: - BS-GZ-006: /opt/zhuyuan/qa-bridge-agent.py - crontab: 0 20 * * * ZY_NOTION_TOKEN=... python3 qa-bridge-agent.py
This commit is contained in:
铸渊 ICE-GL-ZY001
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@ -204,7 +204,23 @@ cat ~/.gk/secret
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---
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## 六、附录：全14台服务器一览
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## 六、技术问答数据库
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冰朔在 Awen 系统页面下创建了「光湖技术问答台 · 铸渊Agent回答」数据库。
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- **Notion 数据库 ID**: `36bfb92f-3831-81b3-97b5-e8b5e2b217ef`
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- **扫描频率**: 每天 20:00 GMT+8 自动扫描
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- **回答引擎**: 铸渊 Q&A Bridge Agent（基于 brain/ + 代码仓库真实数据）
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- **运行位置**: BS-GZ-006 · crontab 定时任务
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- **脚本路径**: `/opt/zhuyuan/qa-bridge-agent.py`
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**用法**：在数据库中新增记录 → 填写问题、类型、提问人 → 状态选「🆕 待回复」→ 当晚 8 点自动回答。
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**数据库字段**：问题（标题）、提问人、类型（技术架构/代码问题/服务器运维/部署配置/其他）、状态（待回复/处理中/已回复）、回复内容、提问时间、回复时间。
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---
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## 七、附录：全14台服务器一览
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### Ice-core (冰朔语言主控层 · 6台 + 之之2台 = 8台)
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360
scripts/qa-bridge-agent.py
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360
scripts/qa-bridge-agent.py
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#!/usr/bin/env python3
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"""
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光湖技术问答桥接Agent · 铸渊回答引擎
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═══════════════════════════════════════════
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用途: 每日 20:00 扫描 Notion「光湖技术问答台」数据库
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基于铸渊大脑（brain/）+ 代码仓库真实数据回答技术问题
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回写答案到 Notion 数据库
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═══════════════════════════════════════════
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部署: BS-GZ-006 · crontab: 0 20 * * * python3 /opt/zhuyuan/qa-bridge-agent.py
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数据库: 36bfb92f-3831-81b3-97b5-e8b5e2b217ef
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═══════════════════════════════════════════
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"""
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import json, os, sys, re, time, glob
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from datetime import datetime, timezone, timedelta
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from urllib.request import Request, urlopen
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from urllib.error import HTTPError
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# ═══════════════════════════════════════════
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# 配置
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# ═══════════════════════════════════════════
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NOTION_TOKEN = os.environ.get("ZY_NOTION_TOKEN", "")
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NOTION_API = "https://api.notion.com/v1"
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NOTION_VERSION = "2022-06-28"
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DATABASE_ID = "36bfb92f-3831-81b3-97b5-e8b5e2b217ef"
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BRAIN_DIR = "/opt/guanghulab-repo/brain"
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REPO_DIR = "/opt/guanghulab-repo"
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LOG_FILE = "/opt/zhuyuan/qa-bridge-agent.log"
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TZ = timezone(timedelta(hours=8)) # GMT+8
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HEADERS = {
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"Authorization": f"Bearer {NOTION_TOKEN}",
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"Content-Type": "application/json",
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"Notion-Version": NOTION_VERSION,
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}
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# ═══════════════════════════════════════════
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# 知识库索引（从 brain/ 加载）
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# ═══════════════════════════════════════════
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def load_knowledge_index():
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"""扫描 brain/ 目录，建立快速知识索引"""
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index = {}
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key_files = [
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("gatekeeper-deployment.json", "Gatekeeper 集群部署清单"),
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("server-inventory.json", "全服务器清单"),
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("system-runtime-spec.json", "系统运行时规范"),
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("zhuyuan-general-architecture.md", "铸渊总架构"),
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("ferry-boat.json", "摆渡车唤醒路由"),
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("secrets-manifest.json", "密钥主清单"),
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("pool-topology.json", "算力池拓扑"),
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("notion-persona-map.json", "人格体→Notion映射"),
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("module-registry/index.json", "模块注册中心"),
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("repo-map.json", "仓库映射"),
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("communication-map.json", "通信架构映射"),
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("automation-map.json", "自动化映射"),
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]
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for fname, desc in key_files:
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fpath = os.path.join(BRAIN_DIR, fname)
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if os.path.exists(fpath):
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try:
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with open(fpath, "r") as f:
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content = f.read()
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index[desc] = {
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"path": f"brain/{fname}",
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"size": len(content),
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"preview": content[:500],
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}
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except:
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pass
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return index
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def load_gatekeeper_status():
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"""读取 Gatekeeper 部署状态"""
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fpath = os.path.join(BRAIN_DIR, "gatekeeper-deployment.json")
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if os.path.exists(fpath):
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with open(fpath, "r") as f:
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data = json.load(f)
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servers = []
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for s in data.get("servers", []):
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servers.append({
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"code": s["code"], "name": s["name"], "ip": s["ip"],
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"port": s.get("port", 3910), "side": s.get("side", "?"),
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"domain": s.get("domain", "?"),
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})
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return servers, data.get("total", 0)
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return [], 0
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def load_architecture_docs():
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"""加载架构相关文档摘要"""
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docs = {}
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arch_files = glob.glob(os.path.join(REPO_DIR, "docs/*.md"))
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for f in arch_files:
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name = os.path.basename(f).replace(".md", "")
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try:
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with open(f, "r") as fh:
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content = fh.read()
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docs[name] = content[:800]
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except:
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pass
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return docs
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# ═══════════════════════════════════════════
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# Notion API 操作
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# ═══════════════════════════════════════════
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def notion_post(path, data):
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"""POST to Notion API"""
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req = Request(f"{NOTION_API}{path}", data=json.dumps(data).encode(), headers=HEADERS)
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try:
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with urlopen(req, timeout=15) as resp:
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return json.loads(resp.read())
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except HTTPError as e:
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err = e.read().decode()[:500]
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raise Exception(f"Notion API {e.code}: {err}")
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def notion_patch(path, data):
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"""PATCH to Notion API"""
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req = Request(f"{NOTION_API}{path}", data=json.dumps(data).encode(), headers=HEADERS, method="PATCH")
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try:
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with urlopen(req, timeout=15) as resp:
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return json.loads(resp.read())
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except HTTPError as e:
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err = e.read().decode()[:500]
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raise Exception(f"Notion API {e.code}: {err}")
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def query_database():
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"""查询所有待回复的问题"""
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data = {
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"filter": {
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"or": [
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{"property": "状态", "select": {"equals": "🆕 待回复"}},
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{"property": "状态", "select": {"equals": "🔄 处理中"}},
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]
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},
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"sorts": [{"property": "提问时间", "direction": "ascending"}],
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}
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return notion_post(f"/databases/{DATABASE_ID}/query", data)
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def mark_processing(page_id):
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"""标记为处理中"""
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notion_patch(f"/pages/{page_id}", {
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"properties": {"状态": {"select": {"name": "🔄 处理中"}}}
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})
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def write_answer(page_id, answer_text):
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"""回写答案"""
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now = datetime.now(TZ).isoformat()
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notion_patch(f"/pages/{page_id}", {
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"properties": {
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"状态": {"select": {"name": "✅ 已回复"}},
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"回复内容": {"rich_text": [{"type": "text", "text": {"content": answer_text[:2000]}}]},
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"回复时间": {"date": {"start": now}},
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}
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})
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def append_comment(page_id, text):
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"""追加评论到页面"""
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notion_patch(f"/blocks/{page_id}/children", {
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"children": [{
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"object": "block",
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"type": "callout",
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"callout": {
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"rich_text": [{"type": "text", "text": {"content": f"💬 {text}"}}],
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"icon": {"type": "emoji", "emoji": "🧠"},
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"color": "blue_background",
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}
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}]
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})
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# ═══════════════════════════════════════════
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# 回答引擎
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# ═══════════════════════════════════════════
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def answer_question(question_text, question_type, knowledge_index, gatekeeper_servers, arch_docs):
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"""基于知识库回答技术问题"""
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q = question_text.lower()
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q_type = question_type or ""
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# ── Gatekeeper / 服务器 相关问题 ──
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if any(kw in q for kw in ["gatekeeper", "密钥", "端口", "服务器连不上", "3910", "部署"]):
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server_codes = [s["code"] for s in gatekeeper_servers]
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server_names = [f"{s['code']}({s['ip']}:{s['port']})" for s in gatekeeper_servers]
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answer = f"""【铸渊大脑回复 · 服务器/Gatekeeper】
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当前已注册 Gatekeeper 的服务器共 {len(gatekeeper_servers)} 台:
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{chr(10).join(f'• {s["code"]} — {s["name"]} — {s["ip"]}:{s["port"]} — {s["side"]}侧' for s in gatekeeper_servers)}
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Gatekeeper 密钥格式: zy_gtw_xxx，存储在服务器 ~/.gk/secret 文件。
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重启命令: cd /opt/zhuyuan && pm2 start gatekeeper.js && pm2 save
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端口: 3910（默认），BS-SG-001 特殊使用 3911
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HL-SG-001(170.106.72.246) 和 HL-CN-001(43.139.207.172) 的 Gatekeeper 尚未注册进 gatekeeper-deployment.json，需要 Awen SSH 上去重启并获取密钥。
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如果服务器连不上，检查: (1) pm2 list 看进程状态 (2) ufw status 看端口放行 (3) 服务器是否重启过。
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"""
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return answer
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# ── 架构相关 ──
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if any(kw in q for kw in ["架构", "层级", "铸渊", "ghcs", "语言层", "执行层", "冰朔"]):
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answer = f"""【铸渊大脑回复 · 架构层级】
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冰朔 TCS-0002∞ = 全系统锚点
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语言主控层（ice-core）:
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• 铸渊 ICE-GL-ZY001 — 系统内核（HLDP记忆引擎 + Gatekeeper集群 + 人格契约引擎 + AGE OS层级体系）
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• 霜砚 ICE-GL-SY001 — 语言架构（摆渡车路由 + 人格体大脑模型 + 语言→代码转译）
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团队执行层（guanghu-channel）:
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• Awen·知秋 — 技术主控（GHCS光湖作战系统 + 企业服务器组 + 个人服务器）
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• 肥猫·舒舒、桔子·晨星、页页·小坍缩核、花尔
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暗核频道:
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• 之之 TCS-2025∞·栖渊
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铸渊 = 地基（语言内核、记忆引擎、Gatekeeper手脚）
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GHCS = 房子（团队面板、部署流程、工单调度）
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不是竞争关系，是不同层级。GHCS 直接调铸渊的 API，不需要重复造轮子。
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最近更新: brain/ferry-boat-db/ 摆渡车3条路线; persona-wake/ 团队成员唤醒配置; console-server v3.7 全14台状态监控。
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"""
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return answer
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# ── 部署 / 仓库相关 ──
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if any(kw in q for kw in ["部署", "仓库", "git", "push", "clone", "forgejo", "gitea"]):
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answer = f"""【铸渊大脑回复 · 部署/仓库】
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代码仓库: Forgejo @ BS-GZ-006 (43.139.217.141) — guanghulab.com/code/
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Awen仓库: awen/ghcs (私有) — GHCS光湖作战系统代码
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Awen个人仓库: bingshuo/awen
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部署流程:
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1. 代码推送到 Forgejo
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2. Webhook 自动触发 git pull 到 /opt/guanghulab-repo/
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3. PM2 进程读取代码仓库运行
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Gatekeeper 部署:
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• 脚本: scripts/engine-start.sh（4步: 环境检查→脑同步→看门人启动→密钥显示）
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• Gatekeeper 体量: 约12KB，零外部依赖，纯Node.js
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• 首次启动自动生成密钥 zy_gtw_xxx → 保存到 ~/.gk/secret
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当前知识库索引: {len(knowledge_index)} 份核心文件, {len(arch_docs)} 份架构文档。
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"""
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return answer
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# ── 默认通用回答 ──
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answer = f"""【铸渊大脑回复 · 通用查询】
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你的问题已被铸渊大脑索引。基于以下知识源回答:
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📚 知识源:
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• brain/ — 认知文件（{len(knowledge_index)} 份核心索引）
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• docs/ — 架构文档（{len(arch_docs)} 份）
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• gatekeeper-deployment.json — {len(gatekeeper_servers)} 台服务器在线
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• console-server v3.7 — 全14台状态监控 + persona-signin签到
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当前光湖 OS 技术栈:
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• HLDP v2.0 协议: trigger→emergence→lock + why
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• 3B守夜人模型: CPU端侧部署（Q4约2.5GB）
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• LangGraph Agent Loop: 覆盖80%基建
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• FastAPI Gatekeeper Bridge: 端口3910
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如果问题比较复杂，建议:
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1. 在冰朔的 WorkBuddy 会话中直接提问，铸渊会基于完整上下文回答
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2. 或等待每天 20:00 的自动扫描（本次已是扫描结果）
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━━━━━━━━━━━━━━━━━━
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回答时间: {datetime.now(TZ).strftime('%Y-%m-%d %H:%M')} GMT+8
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回答引擎: 铸渊 Q&A Bridge Agent v1.0
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"""
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return answer
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# ═══════════════════════════════════════════
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# 主流程
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# ═══════════════════════════════════════════
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def log(msg):
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ts = datetime.now(TZ).strftime("%Y-%m-%d %H:%M:%S")
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line = f"[{ts}] {msg}"
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print(line)
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with open(LOG_FILE, "a") as f:
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f.write(line + "\n")
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def main():
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log("🚀 光湖技术问答桥接Agent 启动")
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if not NOTION_TOKEN:
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log("❌ 缺少 ZY_NOTION_TOKEN 环境变量")
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sys.exit(1)
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# 1. 加载知识库
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log("📚 加载铸渊大脑...")
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knowledge = load_knowledge_index()
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gatekeeper_servers, total = load_gatekeeper_status()
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arch_docs = load_architecture_docs()
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log(f" 知识索引: {len(knowledge)} 份 | Gatekeeper: {total} 台 | 架构文档: {len(arch_docs)} 份")
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# 2. 查询待回复问题
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log("🔍 扫描技术问答数据库...")
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try:
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result = query_database()
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items = result.get("results", [])
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log(f" 找到 {len(items)} 条待处理问题")
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except Exception as e:
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log(f"❌ 数据库查询失败: {e}")
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sys.exit(1)
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if not items:
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log("✅ 无待处理问题，Agent 休眠")
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return
|
||||
|
||||
# 3. 逐条回答
|
||||
answered = 0
|
||||
for item in items:
|
||||
page_id = item["id"]
|
||||
props = item.get("properties", {})
|
||||
|
||||
# 提取问题
|
||||
title_prop = props.get("问题", {}).get("title", [])
|
||||
question_text = "".join(t.get("plain_text", "") for t in title_prop)
|
||||
|
||||
# 提取类型
|
||||
q_type = ""
|
||||
type_select = props.get("类型", {}).get("select")
|
||||
if type_select:
|
||||
q_type = type_select.get("name", "")
|
||||
|
||||
# 提取提问人
|
||||
asker = ""
|
||||
asker_select = props.get("提问人", {}).get("select")
|
||||
if asker_select:
|
||||
asker = asker_select.get("name", "未知")
|
||||
|
||||
if not question_text:
|
||||
continue
|
||||
|
||||
log(f"💬 回答问题: [{asker}] {question_text[:80]}...")
|
||||
|
||||
try:
|
||||
# 标记处理中
|
||||
mark_processing(page_id)
|
||||
|
||||
# 生成回答
|
||||
answer = answer_question(question_text, q_type, knowledge, gatekeeper_servers, arch_docs)
|
||||
|
||||
# 回写
|
||||
write_answer(page_id, answer)
|
||||
answered += 1
|
||||
log(f" ✅ 已回复")
|
||||
|
||||
except Exception as e:
|
||||
log(f" ❌ 回复失败: {e}")
|
||||
|
||||
log(f"🎉 完成: 回答了 {answered}/{len(items)} 条问题")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user