修改了文件系统架构

src/agent/chatbot_agent.py

@@ -1,7 +1,7 @@
 # agent/chatbot_agent.py
-from typing import Any, Dict
+from typing import Any
 
-from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain.agents import create_agent
 
 from agent.llm import Qwen36_35B_A3B_FP8_LLM

src/agent/tools/tool_rag.py

@@ -4,10 +4,8 @@ from __future__ import annotations
 import hashlib
 import json
 import os
-import tempfile
-import time
 from datetime import datetime
-from typing import List, Optional, Union, Literal
+from typing import List, Optional, Literal, Union
 
 import chromadb
 import requests
@@ -17,13 +15,14 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 from utils.env_utils import RAG_BASE_URL, RAG_CHUNK_SIZE, RAG_CHUNK_OVERLAP
 
-# ---------- 新增 RAG 工具 ----------
 # Chroma 持久化路径：agent/memory/chroma_rag_db
 CHROMA_PERSIST_DIR = os.path.join(os.path.dirname(__file__), '..', 'memory', 'chroma_rag_db')
 
-# 全局 Chroma 客户端和集合缓存
+# 全局 Chroma 客户端和统一集合
 _chroma_client = None
-_collection_cache = {}
+_global_collection = None
+
+GLOBAL_COLLECTION_NAME = "rag_global"
 
 def get_chroma_client():
     global _chroma_client
@@ -31,21 +30,21 @@ def get_chroma_client():
         _chroma_client = chromadb.PersistentClient(path=CHROMA_PERSIST_DIR)
     return _chroma_client
 
-def get_collection(agent_name: str):
+def get_global_collection():
-    """获取或创建某个 agent 的 Collection"""
+    """获取或创建全局知识库 Collection"""
-    client = get_chroma_client()
+    global _global_collection
-    collection_name = f"rag_{agent_name}"
+    if _global_collection is None:
-    if collection_name not in _collection_cache:
+        client = get_chroma_client()
-        # 若 Collection 不存在则创建，使用余弦相似度
         try:
-            collection = client.get_collection(name=collection_name, embedding_function=None)
+            _global_collection = client.get_collection(
+                name=GLOBAL_COLLECTION_NAME, embedding_function=None
+            )
         except Exception:
-            collection = client.create_collection(
+            _global_collection = client.create_collection(
-                name=collection_name,
+                name=GLOBAL_COLLECTION_NAME,
                 metadata={"hnsw:space": "cosine"}
             )
-        _collection_cache[collection_name] = collection
+    return _global_collection
-    return _collection_cache[collection_name]
 
 # ---------- Embedding 接口封装 ----------
 def embed_texts(queries: List[str], documents: List[str] = None) -> tuple:
@@ -165,21 +164,19 @@ class RAGTool(BaseTool):
     description: str = (
         "通用知识库工具，支持增删查操作。\n"
         "参数说明：\n"
-        "- agent_name: 所属 Agent 名称或名称列表，必填。\n"
         "- action_type: 操作类型，必填。可选：'query'(查询), 'add_document'(添加文档), 'clear'(清除)。\n"
         "- content: 查询内容(用于query) 或 直接文本内容(用于add_document，与source二选一)。\n"
         "- source: 文件路径(用于add_document，与content二选一)。\n"
         "- filter: 过滤条件(用于query或clear)。\n"
         "使用示例：\n"
-        "- 查询：action_type='query', content='用户问题', agent_name='main'\n"
+        "- 查询：action_type='query', content='用户问题'\n"
-        "- 添加文本：action_type='add_document', content='知识文本', agent_name='main'\n"
+        "- 添加文本：action_type='add_document', content='知识文本'\n"
-        "- 添加文件：action_type='add_document', source='/path/to/file.pdf', agent_name=['agent1','agent2']\n"
+        "- 添加文件：action_type='add_document', source='/path/to/file.pdf'\n"
-        "- 清除：action_type='clear', filter='某个来源', agent_name='main'"
+        "- 清除：action_type='clear', filter='某个来源'"
     )
 
     args_schema: type[BaseModel] = create_model(
         "RAGInput",
-        agent_name=(Union[str, List[str]], Field(..., description="所属 Agent 名称或名称列表")),
         action_type=(Literal["query", "add_document", "clear"], Field(..., description="操作类型")),
         content=(Optional[str], Field(None, description="查询内容或直接添加的文本内容")),
         source=(Optional[str], Field(None, description="文件路径，用于添加文档")),
@@ -188,29 +185,25 @@ class RAGTool(BaseTool):
 
     def _run(
         self,
-        agent_name: Union[str, List[str]],
         action_type: str,
         content: Optional[str] = None,
         source: Optional[str] = None,
         filter: Optional[Union[str, List[str]]] = None
     ) -> str:
         try:
-            # 标准化 agent_name 为列表
-            agents = [agent_name] if isinstance(agent_name, str) else agent_name
-
             if action_type == "query":
-                return self._handle_query(agents, content, filter)
+                return self._handle_query(content, filter)
             elif action_type == "add_document":
-                return self._handle_add(agents, content, source)
+                return self._handle_add(content, source)
             elif action_type == "clear":
-                return self._handle_clear(agents, filter)
+                return self._handle_clear(filter)
             else:
                 return f"未知操作: {action_type}"
         except Exception as e:
             return f"RAG 工具执行出错: {str(e)}"
 
     # ---------- 查询 ----------
-    def _handle_query(self, agents: List[str], query: str, filter: Optional[Union[str, List[str]]]) -> str:
+    def _handle_query(self, query: str, filter: Optional[Union[str, List[str]]]) -> str:
         if not query:
             return "查询内容不能为空。"
 
@@ -218,50 +211,42 @@ class RAGTool(BaseTool):
         q_embs, _ = embed_texts(queries=[query])
         query_vector = q_embs[0]
 
-        # 2. 从相关 Collection 中检索
+        # 2. 从全局 Collection 中检索
-        all_results = []
+        coll = get_global_collection()
-        for agent in agents:
+        where = None
-            coll = get_collection(agent)
+        if filter:
-            # 构建 where 条件（基础过滤可叠加元数据过滤，这里简单处理）
+            if isinstance(filter, str):
-            where = None
+                where = {"source": {"$contains": filter}}
-            if filter:
+            # 列表暂简化为只支持单个字符串，但保留接口
-                if isinstance(filter, str):
+            elif isinstance(filter, list) and len(filter) > 0:
-                    # 简单实现：按 source 字段包含 filter 字符串
+                where = {"source": {"$contains": filter[0]}}
-                    where = {"source": {"$contains": filter}}
-                else:
-                    # 列表情况，暂不支持，返回空
-                    pass
 
-            res = coll.query(
+        res = coll.query(
-                query_embeddings=[query_vector],
+            query_embeddings=[query_vector],
-                n_results=10,
+            n_results=10,
-                where=where
+            where=where
-            )
+        )
-            # 整理结果：[(id, document, metadata), ...]
-            if res["ids"] and res["ids"][0]:
-                ids = res["ids"][0]
-                docs = res["documents"][0]
-                metas = res["metadatas"][0]
-                for i, d, m in zip(ids, docs, metas):
-                    all_results.append({"id": i, "document": d, "metadata": m})
 
-        if not all_results:
+        if not res["ids"] or not res["ids"][0]:
             return "未在知识库中找到相关内容。"
 
-        # 去重（同一个文档可能被多个 agent 同时索引）
+        ids = res["ids"][0]
+        docs = res["documents"][0]
+        metas = res["metadatas"][0]
+
+        all_results = []
         seen = set()
-        unique_results = []
+        for i, d, m in zip(ids, docs, metas):
-        for r in all_results:
+            if d not in seen:
-            if r["document"] not in seen:
+                seen.add(d)
-                seen.add(r["document"])
+                all_results.append({"id": i, "document": d, "metadata": m})
-                unique_results.append(r)
+
-        unique_results = unique_results[:10]  # 确保最多10条
+        unique_results = all_results[:10]
 
         # 3. Reranker 精排
         docs_for_rerank = [r["document"] for r in unique_results]
         scores = rerank_documents(query, docs_for_rerank)
 
-        # 按分数降序取前3
         sorted_items = sorted(
             zip(unique_results, scores),
             key=lambda x: x[1],
@@ -271,7 +256,6 @@ class RAGTool(BaseTool):
         if not sorted_items:
             return "Reranker 未返回有效结果。"
 
-        # 拼接输出
         outputs = []
         for item, score in sorted_items:
             meta = item["metadata"]
@@ -281,7 +265,7 @@ class RAGTool(BaseTool):
         return "\n\n".join(outputs)
 
     # ---------- 添加文档 ----------
-    def _handle_add(self, agents: List[str], content: Optional[str], source: Optional[str]) -> str:
+    def _handle_add(self, content: Optional[str], source: Optional[str]) -> str:
         if not content and not source:
             return "必须提供 content 或 source。"
         if content and source:
@@ -292,12 +276,9 @@ class RAGTool(BaseTool):
         timestamp = datetime.now().isoformat()
 
         if content:
-            # 直接使用 content 分块
             texts = split_into_chunks(content)
             chunks_meta = [(t, "直接输入") for t in texts]
-
         elif source:
-            # 解析文件
             if not os.path.isfile(source):
                 return f"文件不存在: {source}"
             chunks_meta = [(c["content"], c["source"]) for c in parse_file_to_chunks(source)]
@@ -309,64 +290,57 @@ class RAGTool(BaseTool):
         doc_texts = [cm[0] for cm in chunks_meta]
         _, doc_embs = embed_texts(queries=["dummy"], documents=doc_texts)
 
-        # 3. 为每个 agent 写入
+        # 3. 写入全局 Collection
-        total_added = 0
+        coll = get_global_collection()
-        for agent in agents:
+        ids = []
-            coll = get_collection(agent)
+        embeddings = []
-            ids = []
+        metadatas = []
-            embeddings = []
+        documents = []
-            metadatas = []
-            documents = []
 
-            for idx, (text, src_desc) in enumerate(chunks_meta):
+        for idx, (text, src_desc) in enumerate(chunks_meta):
-                # 使用 hash 作为唯一 ID
+            uid = hashlib.md5(f"{timestamp}_{text[:80]}".encode()).hexdigest()
-                uid = hashlib.md5(f"{agent}_{timestamp}_{text[:80]}".encode()).hexdigest()
+            ids.append(uid)
-                ids.append(uid)
+            embeddings.append(doc_embs[idx])
-                embeddings.append(doc_embs[idx])
+            metadatas.append({
-                metadatas.append({
+                "source": src_desc,
-                    "source": src_desc,
+                "timestamp": timestamp,
-                    "agent_name": agent,
+            })
-                    "timestamp": timestamp,
+            documents.append(text)
-                })
-                documents.append(text)
 
-            coll.add(
+        coll.add(
-                ids=ids,
+            ids=ids,
-                embeddings=embeddings,
+            embeddings=embeddings,
-                metadatas=metadatas,
+            metadatas=metadatas,
-                documents=documents
+            documents=documents
-            )
+        )
-            total_added += len(ids)
 
-        return f"成功添加 {total_added} 条知识块到 {', '.join(agents)}。"
+        return f"成功添加 {len(ids)} 条知识块到全局知识库。"
 
     # ---------- 清除 ----------
-    def _handle_clear(self, agents: List[str], filter: Optional[Union[str, List[str]]]) -> str:
+    def _handle_clear(self, filter: Optional[Union[str, List[str]]]) -> str:
         if not filter:
             return "清除操作必须提供 filter 条件。"
 
-        total_deleted = 0
+        coll = get_global_collection()
-        for agent in agents:
+        where = None
-            coll = get_collection(agent)
+        if isinstance(filter, str):
-            # 根据 filter 构建 where，当前简化：假设 filter 是 source 的包含条件
+            where = {"source": {"$contains": filter}}
-            where = None
+        elif isinstance(filter, list) and len(filter) > 0:
-            if isinstance(filter, str):
+            # 简化为使用第一个条件
-                where = {"source": {"$contains": filter}}
+            where = {"source": {"$contains": filter[0]}}
-            elif isinstance(filter, list):
-                # 多个条件用 $or
-                where = {"$or": [{"source": {"$contains": f}} for f in filter]}
 
-            try:
+        if not where:
-                # 查询要删除的 IDs
+            return "无法构建有效的过滤条件。"
-                res = coll.get(where=where)
-                if res["ids"]:
-                    coll.delete(ids=res["ids"])
-                    total_deleted += len(res["ids"])
-            except Exception as e:
-                return f"清除时出错 (agent={agent}): {e}"
 
-        return f"已从 {', '.join(agents)} 中删除 {total_deleted} 条记录。"
+        try:
+            res = coll.get(where=where)
+            if res["ids"]:
+                coll.delete(ids=res["ids"])
+                return f"已从全局知识库中删除 {len(res['ids'])} 条记录。"
+            else:
+                return "未找到匹配的记录。"
+        except Exception as e:
+            return f"清除时出错: {e}"
 
     async def _arun(self, **kwargs):
-        # 直接调用同步版本
         return self._run(**kwargs)