Add FastAPI backend with agent system

backend/app/agents/tools/search.py

@@ -0,0 +1,159 @@
+"""
+Agent 工具集 - 知识库 & 图谱相关
+
+这些工具在 LangChain ToolNode 中被调用。
+由于 LangChain 工具系统是同步的，内部用 run_in_executor 处理 async 逻辑。
+"""
+
+from langchain_core.tools import tool
+from concurrent.futures import ThreadPoolExecutor
+from app.database import async_session
+from app.agents.context import get_current_user
+import asyncio
+
+_executor = ThreadPoolExecutor(max_workers=4)
+
+
+def _run_async(coro, timeout: int = 30):
+    """在同步上下文中运行 async 代码"""
+    try:
+        loop = asyncio.get_running_loop()
+        future = loop.run_in_executor(_executor, lambda: asyncio.run(coro))
+        return future.result(timeout=timeout)
+    except RuntimeError:
+        return asyncio.run(coro)
+
+
+@tool
+def search_knowledge(query: str, top_k: int = 5) -> str:
+    """
+    搜索用户的私人知识库。根据查询返回最相关的文档片段，支持语义检索。
+
+    Args:
+        query: 搜索查询
+        top_k: 返回结果数量，默认5条
+
+    Returns:
+        包含相关文档片段和来源信息的格式化文本
+    """
+    from app.services.knowledge_service import KnowledgeService
+    uid = get_current_user()
+
+    async def _search():
+        async with async_session() as db:
+            service = KnowledgeService(db, user_id=uid)
+            results = await service.retrieve(query, user_id=uid, top_k=top_k)
+            if not results:
+                return "未找到相关知识。知识库可能为空，或尝试用其他关键词搜索。"
+            texts = []
+            for i, r in enumerate(results, 1):
+                prev = f"\n上一段: {r.prev_chunk[:100]}..." if r.prev_chunk else ""
+                next_ = f"\n下一段: {r.next_chunk[:100]}..." if r.next_chunk else ""
+                texts.append(
+                    f"[{i}] 来源: {r.document_title}\n"
+                    f"相关度: {r.score:.2f}\n"
+                    f"{prev}{next_}\n"
+                    f"内容: {r.content[:300]}{'...' if len(r.content) > 300 else ''}"
+                )
+            return "\n\n---\n\n".join(texts)
+
+    try:
+        return _run_async(_search(), timeout=30)
+    except Exception as e:
+        return f"知识检索失败: {str(e)}"
+
+
+@tool
+def get_knowledge_graph_context(entity: str | None = None) -> str:
+    """
+    获取用户知识图谱的上下文信息。
+
+    Args:
+        entity: 可选，指定要查询的实体名称。如果为空则返回整体图谱摘要。
+
+    Returns:
+        知识图谱节点和关系的描述
+    """
+    from app.services.graph_service import GraphService
+    uid = get_current_user()
+
+    async def _get():
+        async with async_session() as db:
+            service = GraphService(db)
+            if entity:
+                return await service.get_entity_context(entity, uid)
+            return await service.get_graph_summary(uid)
+
+    try:
+        return _run_async(_get(), timeout=30)
+    except Exception as e:
+        return f"图谱查询失败: {str(e)}"
+
+
+@tool
+def build_knowledge_graph(document_ids: list[str] | None = None) -> str:
+    """
+    从文档构建/更新知识图谱。
+
+    Args:
+        document_ids: 可选，指定要处理的文档ID列表。如果为空则处理所有文档。
+
+    Returns:
+        构建结果摘要
+    """
+    from app.services.graph_service import GraphService
+    uid = get_current_user()
+
+    async def _build():
+        async with async_session() as db:
+            service = GraphService(db)
+            await service.build_graph(user_id=uid, document_ids=document_ids)
+            return "知识图谱构建完成"
+
+    try:
+        return _run_async(_build(), timeout=120)
+    except Exception as e:
+        return f"图谱构建失败: {str(e)}"
+
+
+@tool
+def hybrid_search(query: str, top_k: int = 5) -> str:
+    """
+    混合搜索，结合向量语义检索和关键词匹配，返回最相关结果。
+
+    Args:
+        query: 搜索查询
+        top_k: 返回结果数量，默认5条
+
+    Returns:
+        混合检索结果
+    """
+    from app.services.knowledge_service import KnowledgeService
+    uid = get_current_user()
+
+    async def _search():
+        async with async_session() as db:
+            service = KnowledgeService(db, user_id=uid)
+            results = await service.hybrid_search(query, user_id=uid, top_k=top_k)
+            if not results:
+                return "未找到相关知识。"
+            texts = []
+            for i, r in enumerate(results, 1):
+                texts.append(
+                    f"[{i}] {r.document_title} (相关度: {r.score:.2f})\n"
+                    f"{r.content[:200]}{'...' if len(r.content) > 200 else ''}"
+                )
+            return "\n\n---\n\n".join(texts)
+
+    try:
+        return _run_async(_search(), timeout=30)
+    except Exception as e:
+        return f"混合搜索失败: {str(e)}"
+
+
+__all__ = [
+    "search_knowledge",
+    "get_knowledge_graph_context",
+    "build_knowledge_graph",
+    "hybrid_search",
+]