feat: add AI学习助手 agent (KG+RAG ideal) and renshenguo feishu bot
- Add AI学习助手 agent creation script with all 39 tools, 3-layer KG+RAG memory - Add renshenguo (人参果) feishu bot integration (app_service + ws_handler) - Register renshenguo WS client in main.py startup - Add RENSHENGUO_APP_ID / RENSHENGUO_APP_SECRET / RENSHENGUO_AGENT_ID config - Reorganize docs from root into docs/ subdirectories - Move startup scripts to scripts/startup/ - Various backend optimizations and tool improvements Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
renjianbo
@@ -1756,9 +1756,9 @@ async def schedule_create_tool(
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if not agent:
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return json.dumps({"error": f"Agent 不存在: {agent_id}"}, ensure_ascii=False)
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# 尝试计算下次执行时间
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# 尝试计算下次执行时间（使用北京时间，与 schedule 表 timezone 默认值一致）
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try:
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next_run = compute_next_run(cron_expression)
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next_run = compute_next_run(cron_expression, tz="Asia/Shanghai")
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except (ValueError, KeyError) as e:
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return json.dumps({"error": f"cron 表达式无效: {e}（标准 5 位格式，如 0 9 * * *）"}, ensure_ascii=False)
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@@ -4388,3 +4388,314 @@ SELF_REVIEW_SCHEMA = {
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},
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},
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}
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# ── knowledge_graph_search ────────────────────────────────────
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async def knowledge_graph_search_tool(
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query: str,
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top_k: int = 5,
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include_graph: bool = True,
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scope_id: str = "",
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) -> str:
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"""知识图谱混合检索：向量语义搜索 + 图谱邻居展开。
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当用户询问学习相关问题时，用此工具搜索已构建的知识图谱，
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同时获取语义相似的知识实体和图谱中关联的邻居知识点。
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Args:
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query: 搜索查询（用户的问题或关键词）
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top_k: 返回的向量匹配实体数（默认 5）
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include_graph: 是否展开图谱邻居（默认 true）
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scope_id: 作用域 ID，默认使用当前 Agent ID
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"""
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import asyncio as _asyncio
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from app.services.knowledge_graph_service import hybrid_search
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try:
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result = await hybrid_search(
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query=query,
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scope_kind="agent",
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scope_id=scope_id or "learning_assistant",
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top_k=top_k,
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include_neighbors=include_graph,
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)
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formatted = result.get("formatted_context", "")
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vector_count = len(result.get("vector_matches", []))
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graph_count = len(result.get("graph_expansion", {}).get("entities", []))
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return json.dumps({
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"query": query,
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"vector_matches_count": vector_count,
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"graph_entities_count": graph_count,
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"context": formatted,
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"raw": {
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"vector_matches": result.get("vector_matches", []),
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"graph_expansion": result.get("graph_expansion", {}),
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},
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"knowledge_graph_search 失败: {e}")
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return json.dumps({"error": f"知识图谱搜索失败: {e}"}, ensure_ascii=False)
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KNOWLEDGE_GRAPH_SEARCH_SCHEMA = {
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"type": "function",
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"function": {
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"name": "knowledge_graph_search",
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"description": (
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"知识图谱混合检索：用向量语义搜索找到相关知识实体，"
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"再展开图谱邻居获取关联知识点。适合学习场景中的知识检索、"
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"概念关联、前置知识查找。返回结构化的知识上下文。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "搜索查询（用户问题或关键词）"},
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"top_k": {"type": "integer", "description": "返回的向量匹配实体数", "default": 5},
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"include_graph": {"type": "boolean", "description": "是否展开图谱邻居", "default": True},
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"scope_id": {"type": "string", "description": "作用域 ID（默认 learning_assistant）"},
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},
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"required": ["query"],
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},
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},
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}
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# ── knowledge_graph_add ────────────────────────────────────────
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async def knowledge_graph_add_tool(
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text: str,
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scope_id: str = "",
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) -> str:
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"""从文本中提取知识点实体和关系，写入知识图谱。
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当用户分享学习内容、知识点总结、对话中有价值的信息时，
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调用此工具自动提取实体和关系并持久化到知识图谱。
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Args:
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text: 要提取知识的文本内容
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scope_id: 作用域 ID，默认使用当前 Agent ID
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"""
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import asyncio as _asyncio
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from app.services.knowledge_graph_service import extract_from_text
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if not text or len(text.strip()) < 20:
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return json.dumps({
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"error": "文本太短（至少需要 20 个字符）",
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"entity_count": 0,
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"relation_count": 0,
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}, ensure_ascii=False)
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try:
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result = await extract_from_text(
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text=text,
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scope_kind="agent",
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scope_id=scope_id or "learning_assistant",
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)
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return json.dumps({
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"entity_count": result.get("entity_count", 0),
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"relation_count": result.get("relation_count", 0),
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"entities": [
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{"name": e["name"], "type": e["entity_type"], "description": e.get("description", "")[:200]}
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for e in result.get("entities", [])
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],
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"relations": result.get("relations", []),
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"hint": f"已从文本中提取 {result.get('entity_count', 0)} 个实体和 {result.get('relation_count', 0)} 个关系，可使用 knowledge_graph_search 检索",
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"knowledge_graph_add 失败: {e}")
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return json.dumps({"error": f"知识图谱添加失败: {e}"}, ensure_ascii=False)
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KNOWLEDGE_GRAPH_ADD_SCHEMA = {
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"type": "function",
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"function": {
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"name": "knowledge_graph_add",
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"description": (
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"从文本中提取知识点实体和关系，写入知识图谱。"
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"自动识别概念、公式、术语、事实等实体类型，"
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"并建立前置/扩展/包含/示例/应用等语义关系。"
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"适合将学习材料、对话内容转化为结构化知识网络。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"text": {"type": "string", "description": "从中提取知识的文本内容（学习材料、知识点总结等）"},
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"scope_id": {"type": "string", "description": "作用域 ID（默认 learning_assistant）"},
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},
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"required": ["text"],
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},
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},
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}
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# ── entity_search ──────────────────────────────────────────────
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async def entity_search_tool(
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keyword: str = "",
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entity_type: str = "",
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scope_id: str = "",
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limit: int = 10,
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) -> str:
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"""关键词搜索知识图谱中的实体。
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Args:
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keyword: 搜索关键词（在名称和描述中查找）
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entity_type: 实体类型筛选（concept/formula/fact/term/task/skill），留空=全部
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scope_id: 作用域 ID
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limit: 返回数量
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"""
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from app.services.knowledge_graph_service import search_entities
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from app.core.database import SessionLocal
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db = None
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try:
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db = SessionLocal()
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entities = search_entities(
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db,
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keyword=keyword,
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entity_type=entity_type or None,
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scope_kind="agent",
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scope_id=scope_id or "learning_assistant",
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limit=limit,
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)
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return json.dumps({
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"keyword": keyword,
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"count": len(entities),
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"entities": [
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{
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"id": e.id,
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"name": e.name,
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"type": e.entity_type,
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"description": e.description,
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"confidence": e.confidence,
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}
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for e in entities
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],
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"entity_search 失败: {e}")
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return json.dumps({"error": f"实体搜索失败: {e}"}, ensure_ascii=False)
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finally:
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if db:
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db.close()
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ENTITY_SEARCH_SCHEMA = {
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"type": "function",
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"function": {
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"name": "entity_search",
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"description": (
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"在知识图谱中按关键词搜索实体。"
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"可按实体类型（概念/公式/术语/事实/任务/技能）筛选。"
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"适合查找特定知识点或浏览知识库。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"keyword": {"type": "string", "description": "搜索关键词（在名称和描述中查找）", "default": ""},
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"entity_type": {
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"type": "string",
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"enum": ["concept", "formula", "fact", "term", "task", "skill"],
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"description": "实体类型筛选，留空=全部",
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},
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"scope_id": {"type": "string", "description": "作用域 ID"},
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"limit": {"type": "integer", "description": "返回数量", "default": 10},
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},
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"required": [],
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},
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},
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}
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# ── learning_path ─────────────────────────────────────────────
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async def learning_path_tool(
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entity_names: str,
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scope_id: str = "",
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) -> str:
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"""基于知识图谱推荐学习路径。
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给定一组目标知识点名称（逗号分隔），分析其前置依赖关系，
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返回建议的学习顺序。
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Args:
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entity_names: 目标知识点名称，逗号分隔（如 "微积分,导数,极限"）
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scope_id: 作用域 ID
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"""
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from app.services.knowledge_graph_service import search_entities, recommend_learning_path
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from app.core.database import SessionLocal
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if not entity_names or not entity_names.strip():
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return json.dumps({"error": "请提供至少一个知识点名称"}, ensure_ascii=False)
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names = [n.strip() for n in entity_names.split(",") if n.strip()]
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db = None
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try:
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db = SessionLocal()
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entity_ids = []
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found_names = []
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for name in names:
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entities = search_entities(
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db, keyword=name,
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scope_kind="agent", scope_id=scope_id or "learning_assistant",
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limit=3,
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)
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for e in entities:
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if e.name == name or name in e.name:
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entity_ids.append(e.id)
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found_names.append(e.name)
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break
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if not entity_ids:
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return json.dumps({
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"message": f"未在知识图谱中找到这些知识点: {', '.join(names)}",
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"hint": "请先用 knowledge_graph_add 添加相关知识，或使用更通用的名称搜索",
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}, ensure_ascii=False)
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path = recommend_learning_path(
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db, entity_ids,
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scope_kind="agent", scope_id=scope_id or "learning_assistant",
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)
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return json.dumps({
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"target_entities": names,
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"found_entities": found_names,
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"suggested_order": path.get("suggested_order", []),
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"summary": path.get("summary", ""),
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"prerequisites": [
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{
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"prerequisite": p["prerequisite"]["name"],
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"target": p["target"]["name"],
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"relation": p["relation"].get("description", ""),
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}
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for p in path.get("prerequisites", [])
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],
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}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"learning_path 失败: {e}")
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return json.dumps({"error": f"学习路径推荐失败: {e}"}, ensure_ascii=False)
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finally:
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if db:
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db.close()
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LEARNING_PATH_SCHEMA = {
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"type": "function",
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"function": {
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"name": "learning_path",
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"description": (
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"基于知识图谱推荐学习路径。给定一组目标知识点，"
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"分析前置依赖关系，给出建议的学习顺序。"
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"适合制定学习计划、了解知识点间的先后关系。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"entity_names": {"type": "string", "description": "目标知识点名称，逗号分隔（如 \"微积分,导数,极限\"）"},
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"scope_id": {"type": "string", "description": "作用域 ID"},
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},
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"required": ["entity_names"],
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},
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},
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}
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