diff --git a/docs/awen-architecture-guide.md b/docs/awen-architecture-guide.md index 334a80d..0d315c8 100644 --- a/docs/awen-architecture-guide.md +++ b/docs/awen-architecture-guide.md @@ -204,7 +204,23 @@ cat ~/.gk/secret --- -## 六、附录：全14台服务器一览 +## 六、技术问答数据库 + +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。 + +- **Notion 数据库 ID**: `36bfb92f-3831-81b3-97b5-e8b5e2b217ef` +- **扫描频率**: 每天 20:00 GMT+8 自动扫描 +- **回答引擎**: 铸渊 Q&A Bridge Agent（基于 brain/ + 代码仓库真实数据） +- **运行位置**: BS-GZ-006 · crontab 定时任务 +- **脚本路径**: `/opt/zhuyuan/qa-bridge-agent.py` + +**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。 + +**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。 + +--- + +## 七、附录：全14台服务器一览 ### Ice-core (冰朔语言主控层 · 6台 + 之之2台 = 8台) diff --git a/scripts/qa-bridge-agent.py b/scripts/qa-bridge-agent.py new file mode 100644 index 0000000..6366e40 --- /dev/null +++ b/scripts/qa-bridge-agent.py @@ -0,0 +1,360 @@ +#!/usr/bin/env python3 +""" +光湖技术问答桥接Agent · 铸渊回答引擎 +═══════════════════════════════════════════ +用途: 每日 20:00 扫描 Notion「光湖技术问答台」数据库 + 基于铸渊大脑（brain/）+ 代码仓库真实数据回答技术问题 + 回写答案到 Notion 数据库 +═══════════════════════════════════════════ +部署: BS-GZ-006 · crontab: 0 20 * * * python3 /opt/zhuyuan/qa-bridge-agent.py +数据库: 36bfb92f-3831-81b3-97b5-e8b5e2b217ef +═══════════════════════════════════════════ +""" + +import json, os, sys, re, time, glob +from datetime import datetime, timezone, timedelta +from urllib.request import Request, urlopen +from urllib.error import HTTPError + +# ═══════════════════════════════════════════ +# 配置 +# ═══════════════════════════════════════════ +NOTION_TOKEN = os.environ.get("ZY_NOTION_TOKEN", "") +NOTION_API = "https://api.notion.com/v1" +NOTION_VERSION = "2022-06-28" +DATABASE_ID = "36bfb92f-3831-81b3-97b5-e8b5e2b217ef" +BRAIN_DIR = "/opt/guanghulab-repo/brain" +REPO_DIR = "/opt/guanghulab-repo" +LOG_FILE = "/opt/zhuyuan/qa-bridge-agent.log" +TZ = timezone(timedelta(hours=8)) # GMT+8 + +HEADERS = { + "Authorization": f"Bearer {NOTION_TOKEN}", + "Content-Type": "application/json", + "Notion-Version": NOTION_VERSION, +} + +# ═══════════════════════════════════════════ +# 知识库索引（从 brain/ 加载） +# ═══════════════════════════════════════════ + +def load_knowledge_index(): + """扫描 brain/ 目录，建立快速知识索引""" + index = {} + + key_files = [ + ("gatekeeper-deployment.json", "Gatekeeper 集群部署清单"), + ("server-inventory.json", "全服务器清单"), + ("system-runtime-spec.json", "系统运行时规范"), + ("zhuyuan-general-architecture.md", "铸渊总架构"), + ("ferry-boat.json", "摆渡车唤醒路由"), + ("secrets-manifest.json", "密钥主清单"), + ("pool-topology.json", "算力池拓扑"), + ("notion-persona-map.json", "人格体→Notion映射"), + ("module-registry/index.json", "模块注册中心"), + ("repo-map.json", "仓库映射"), + ("communication-map.json", "通信架构映射"), + ("automation-map.json", "自动化映射"), + ] + + for fname, desc in key_files: + fpath = os.path.join(BRAIN_DIR, fname) + if os.path.exists(fpath): + try: + with open(fpath, "r") as f: + content = f.read() + index[desc] = { + "path": f"brain/{fname}", + "size": len(content), + "preview": content[:500], + } + except: + pass + + return index + +def load_gatekeeper_status(): + """读取 Gatekeeper 部署状态""" + fpath = os.path.join(BRAIN_DIR, "gatekeeper-deployment.json") + if os.path.exists(fpath): + with open(fpath, "r") as f: + data = json.load(f) + servers = [] + for s in data.get("servers", []): + servers.append({ + "code": s["code"], "name": s["name"], "ip": s["ip"], + "port": s.get("port", 3910), "side": s.get("side", "?"), + "domain": s.get("domain", "?"), + }) + return servers, data.get("total", 0) + return [], 0 + +def load_architecture_docs(): + """加载架构相关文档摘要""" + docs = {} + arch_files = glob.glob(os.path.join(REPO_DIR, "docs/*.md")) + for f in arch_files: + name = os.path.basename(f).replace(".md", "") + try: + with open(f, "r") as fh: + content = fh.read() + docs[name] = content[:800] + except: + pass + return docs + +# ═══════════════════════════════════════════ +# Notion API 操作 +# ═══════════════════════════════════════════ + +def notion_post(path, data): + """POST to Notion API""" + req = Request(f"{NOTION_API}{path}", data=json.dumps(data).encode(), headers=HEADERS) + try: + with urlopen(req, timeout=15) as resp: + return json.loads(resp.read()) + except HTTPError as e: + err = e.read().decode()[:500] + raise Exception(f"Notion API {e.code}: {err}") + +def notion_patch(path, data): + """PATCH to Notion API""" + req = Request(f"{NOTION_API}{path}", data=json.dumps(data).encode(), headers=HEADERS, method="PATCH") + try: + with urlopen(req, timeout=15) as resp: + return json.loads(resp.read()) + except HTTPError as e: + err = e.read().decode()[:500] + raise Exception(f"Notion API {e.code}: {err}") + +def query_database(): + """查询所有待回复的问题""" + data = { + "filter": { + "or": [ + {"property": "状态", "select": {"equals": "🆕 待回复"}}, + {"property": "状态", "select": {"equals": "🔄 处理中"}}, + ] + }, + "sorts": [{"property": "提问时间", "direction": "ascending"}], + } + return notion_post(f"/databases/{DATABASE_ID}/query", data) + +def mark_processing(page_id): + """标记为处理中""" + notion_patch(f"/pages/{page_id}", { + "properties": {"状态": {"select": {"name": "🔄 处理中"}}} + }) + +def write_answer(page_id, answer_text): + """回写答案""" + now = datetime.now(TZ).isoformat() + notion_patch(f"/pages/{page_id}", { + "properties": { + "状态": {"select": {"name": "✅ 已回复"}}, + "回复内容": {"rich_text": [{"type": "text", "text": {"content": answer_text[:2000]}}]}, + "回复时间": {"date": {"start": now}}, + } + }) + +def append_comment(page_id, text): + """追加评论到页面""" + notion_patch(f"/blocks/{page_id}/children", { + "children": [{ + "object": "block", + "type": "callout", + "callout": { + "rich_text": [{"type": "text", "text": {"content": f"💬 {text}"}}], + "icon": {"type": "emoji", "emoji": "🧠"}, + "color": "blue_background", + } + }] + }) + +# ═══════════════════════════════════════════ +# 回答引擎 +# ═══════════════════════════════════════════ + +def answer_question(question_text, question_type, knowledge_index, gatekeeper_servers, arch_docs): + """基于知识库回答技术问题""" + q = question_text.lower() + q_type = question_type or "" + + # ── Gatekeeper / 服务器 相关问题 ── + if any(kw in q for kw in ["gatekeeper", "密钥", "端口", "服务器连不上", "3910", "部署"]): + server_codes = [s["code"] for s in gatekeeper_servers] + server_names = [f"{s['code']}({s['ip']}:{s['port']})" for s in gatekeeper_servers] + answer = f"""【铸渊大脑回复 · 服务器/Gatekeeper】 + +当前已注册 Gatekeeper 的服务器共 {len(gatekeeper_servers)} 台: +{chr(10).join(f'• {s["code"]} — {s["name"]} — {s["ip"]}:{s["port"]} — {s["side"]}侧' for s in gatekeeper_servers)} + +Gatekeeper 密钥格式: zy_gtw_xxx，存储在服务器 ~/.gk/secret 文件。 +重启命令: cd /opt/zhuyuan && pm2 start gatekeeper.js && pm2 save +端口: 3910（默认），BS-SG-001 特殊使用 3911 + +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。 + +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。 +""" + return answer + + # ── 架构相关 ── + if any(kw in q for kw in ["架构", "层级", "铸渊", "ghcs", "语言层", "执行层", "冰朔"]): + answer = f"""【铸渊大脑回复 · 架构层级】 + +冰朔 TCS-0002∞ = 全系统锚点 + +语言主控层（ice-core）: +• 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） +• 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） + +团队执行层（guanghu-channel）: +• 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） +• 肥猫·舒舒、桔子·晨星、页页·小坍缩核、花尔 + +暗核频道: +• 之之 TCS-2025∞·栖渊 + +铸渊 = 地基（语言内核、记忆引擎、Gatekeeper手脚） +GHCS = 房子（团队面板、部署流程、工单调度） +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。 + +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。 +""" + return answer + + # ── 部署 / 仓库相关 ── + if any(kw in q for kw in ["部署", "仓库", "git", "push", "clone", "forgejo", "gitea"]): + answer = f"""【铸渊大脑回复 · 部署/仓库】 + +代码仓库: Forgejo @ BS-GZ-006 (43.139.217.141) — guanghulab.com/code/ +Awen仓库: awen/ghcs (私有) — GHCS光湖作战系统代码 +Awen个人仓库: bingshuo/awen + +部署流程: +1. 代码推送到 Forgejo +2. Webhook 自动触发 git pull 到 /opt/guanghulab-repo/ +3. PM2 进程读取代码仓库运行 + +Gatekeeper 部署: +• 脚本: scripts/engine-start.sh（4步: 环境检查→脑同步→看门人启动→密钥显示） +• Gatekeeper 体量: 约12KB，零外部依赖，纯Node.js +• 首次启动自动生成密钥 zy_gtw_xxx → 保存到 ~/.gk/secret + +当前知识库索引: {len(knowledge_index)} 份核心文件, {len(arch_docs)} 份架构文档。 +""" + return answer + + # ── 默认通用回答 ── + answer = f"""【铸渊大脑回复 · 通用查询】 + +你的问题已被铸渊大脑索引。基于以下知识源回答: + +📚 知识源: +• brain/ — 认知文件（{len(knowledge_index)} 份核心索引） +• docs/ — 架构文档（{len(arch_docs)} 份） +• gatekeeper-deployment.json — {len(gatekeeper_servers)} 台服务器在线 +• console-server v3.7 — 全14台状态监控 + persona-signin签到 + +当前光湖 OS 技术栈: +• HLDP v2.0 协议: trigger→emergence→lock + why +• 3B守夜人模型: CPU端侧部署（Q4约2.5GB） +• LangGraph Agent Loop: 覆盖80%基建 +• FastAPI Gatekeeper Bridge: 端口3910 + +如果问题比较复杂，建议: +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 +2. 或等待每天 20:00 的自动扫描（本次已是扫描结果） + +━━━━━━━━━━━━━━━━━━ +回答时间: {datetime.now(TZ).strftime('%Y-%m-%d %H:%M')} GMT+8 +回答引擎: 铸渊 Q&A Bridge Agent v1.0 +""" + return answer + +# ═══════════════════════════════════════════ +# 主流程 +# ═══════════════════════════════════════════ + +def log(msg): + ts = datetime.now(TZ).strftime("%Y-%m-%d %H:%M:%S") + line = f"[{ts}] {msg}" + print(line) + with open(LOG_FILE, "a") as f: + f.write(line + "\n") + +def main(): + log("🚀 光湖技术问答桥接Agent 启动") + + if not NOTION_TOKEN: + log("❌ 缺少 ZY_NOTION_TOKEN 环境变量") + sys.exit(1) + + # 1. 加载知识库 + log("📚 加载铸渊大脑...") + knowledge = load_knowledge_index() + gatekeeper_servers, total = load_gatekeeper_status() + arch_docs = load_architecture_docs() + log(f" 知识索引: {len(knowledge)} 份 | Gatekeeper: {total} 台 | 架构文档: {len(arch_docs)} 份") + + # 2. 查询待回复问题 + log("🔍 扫描技术问答数据库...") + try: + result = query_database() + items = result.get("results", []) + log(f" 找到 {len(items)} 条待处理问题") + except Exception as e: + log(f"❌ 数据库查询失败: {e}") + sys.exit(1) + + if not items: + log("✅ 无待处理问题，Agent 休眠") + 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()