完成RAG测试

.env.example

@@ -0,0 +1,7 @@
+DB_USER=postgres
+DB_PASS=
+DB_HOST=localhost
+DB_PORT=5432
+DB_NAME=wiki_crawler
+DASHSCOPE_API_KEY=
+FIRECRAWL_API_KEY=

.gitignore

@@ -1,3 +1,4 @@
 __pycache__/
 .venv
 wiki_backend.tar
+.env

Dockerfile

@@ -33,7 +33,7 @@ ENV PATH="/app/.venv/bin:$PATH"
 ENV PYTHONPATH=/app
 
 # 暴露端口
-EXPOSE 8000
+EXPOSE 28000
 
 # 启动命令
 # 现在直接调用 uvicorn 即可，它会自动找到 .venv 里的版本

README.md

@@ -6,13 +6,48 @@
 
 完成wiki网页爬取和向量化与知识库查找
 
+mcp调试命令
+
+```bash
+npx @modelcontextprotocol/inspector uv run backend/mcp_server.py
+```
+
+需要nodejs环境
+打开页面后在Environment Variables中添加`PYTHONIOENCODING = utf-8`来防止编码问题（视具体情况而定，如果可以正常运行，也可以不加）
 
 ## 当前状况
 
-1. 当前在我的电脑本地跑，没部署，看chenwei有没有空了教我往我们服务器上，我自己买的学生服务器还没来得及放上去，三月份到期
-2. 这个demo后端只实现了功能没有auth相关的部分，后续可以直接迁移，chenwei那边gtco_ai开一个模块放进去
-3. firecrawl的apikey，我自己的免费试用apikey快用完了，需要准备部署，调查付费
-4. 可演示，但是还没有包装到可以向客户汇报的层次，后续考虑直接用dify做一个工具包装，集成到Done的bot里；或者用chatflow直接包装，里面用节点请求部署好的后端进行知识库查询
+1. chunk分段逻辑：根据返回的markdown进行分割，按照#、##进行标题的分类，增加JSONB格式字段meta_info，有下面两个字段，分别可以用于数据库查询和LLM上下文认知资料来源
+
+   ```python
+   # 源数据 (headers)
+   headers = {"h1": "产品介绍", "h2": "核心功能", "h3": "多语言支持"}
+
+   # 生成数据 (header_path)
+   # Python 代码逻辑: " > ".join(headers.values())
+   header_path = "产品介绍 > 核心功能 > 多语言支持"
+   ```
+2. 量化指标以及测试：目前存入的数据较少，测试结果可能偏差较大
+
+   ```
+   "p_at_1": [],    # Precision@1: 首位精确率
+   "hit_at_5": [],  # HitRate@5: 前5命中率，即返回的前五个（目前设置只返回5个）是否符合问题
+   "mrr": [],       # Mean Reciprocal Rank: 倒数排名分数，正确答案排得越靠前，分数越高
+   "latency": []    # 响应耗时
+   ```
+3. 搜索逻辑和问题分类：尚未实现，目前参考一些主流的做法，用户输入后先过一个LLM对问题进行拆分和分类，然后传入对应的知识库参数task_id进行对应的检索
+4. RAG逻辑：混合检索，使用向量和关键词混合检索，此处进行粗筛，数据层返回后在业务层调用 gte-rerank 模型进行重排，最后返回请求
+
+   ```python
+   vector_score = (1 - self.db.chunks.c.embedding.cosine_distance(query_vector))# 计算向量相似度
+   keyword_score = func.ts_rank(self.db.chunks.c.content_tsvector, keyword_query) # 计算关键词相似度
+   final_score = (vector_score * 0.7 + func.coalesce(keyword_score, 0) * 0.3).label("score")# 计算最终分数
+   ```
+5. 产品面向场景：客户需求爬取几个文档，并长期维护更新，后续需要新增，但是量相对不会太大，firecrawl付费大概不会太贵。爬虫获取完整wiki（可无视robots.txt），当前知识库存入和爬虫绑定强，依赖markdown格式存入
+6. 后续开发：添加旧wiki的更新维护功能。dify增加对后端的封装，做一套搜索逻辑和问题分类的节点，如果不好弄那还是迁回到后端，后端只提供知识库的mcp，bot调用mcp之后，自行调用实现搜索和问题分类
+
+对比其他检索方法的优势，做一套评测机制标准，评估最终LLM输出的准确度，目前是知识库检索准确度
+
 
 切割逻辑，准确率定义，归结资料，测试设计，mcp服务调用，搜索逻辑，问题分类，流程架构设计，场景假设
 

backend/config.py

@@ -1,26 +0,0 @@
-import os
-
-class Settings:
-    # 数据库配置
-    DB_USER: str = "postgres"
-    DB_PASS: str = "DXC_welcome001"
-    DB_HOST: str = "8.155.144.6"
-    DB_PORT: str = "25432"
-    DB_NAME: str = "wiki_crawler"
-
-    DASHSCOPE_API_KEY: str = "sk-8b091493de594c5e9eb42f12f1cc5805"
-    FIRECRAWL_API_KEY: str = "fc-8a2af3fb6a014a27a57dfbc728cb7365"
-    @property # property 方法，意义：将方法转换为属性，调用时不需要加括号
-    def DATABASE_URL(self) -> str:
-        url = f"postgresql+psycopg2://{self.DB_USER}:{self.DB_PASS}@{self.DB_HOST}:{self.DB_PORT}/{self.DB_NAME}"
-        return url
-    
-    def API_KEY(self, type: str) -> str:
-        if type == "dashscope":
-            return self.DASHSCOPE_API_KEY
-        elif type == "firecrawl":
-            return self.FIRECRAWL_API_KEY
-        else:
-            raise ValueError(f"Unknown API type: {type}")
-
-settings = Settings()

backend/core/config.py

@@ -0,0 +1,29 @@
+import os
+from typing import ClassVar # <--- 1. 导入这个
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+class Settings(BaseSettings):
+    DB_USER: str
+    DB_PASS: str
+    DB_HOST: str
+    DB_PORT: str = "5432"
+    DB_NAME: str
+    DASHSCOPE_API_KEY: str
+    FIRECRAWL_API_KEY: str
+    
+    CANDIDATE_NUM: int = 50 
+
+    # =========================================================
+    # 【核心修复】加上 ClassVar 类型注解
+    # =========================================================
+    BASE_DIR: ClassVar[str] = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    ENV_PATH: ClassVar[str] = os.path.join(BASE_DIR, ".env")
+
+    # 使用绝对路径加载
+    model_config = SettingsConfigDict(env_file=ENV_PATH, extra="ignore")
+
+    @property
+    def DATABASE_URL(self) -> str:
+        return f"postgresql+psycopg2://{self.DB_USER}:{self.DB_PASS}@{self.DB_HOST}:{self.DB_PORT}/{self.DB_NAME}"
+
+settings = Settings()

backend/core/database.py

@@ -0,0 +1,42 @@
+from sqlalchemy import create_engine, MetaData, Table
+from pgvector.sqlalchemy import Vector
+from .config import settings
+import logging
+
+# 获取当前模块的专用 Logger
+# __name__ 会自动识别为 "backend.services.crawler_service" 这样的路径
+logger = logging.getLogger(__name__)
+class Database:
+    """
+    数据库单例类
+    负责初始化连接池并反射加载现有的表结构
+    """
+    def __init__(self):
+        # 1. 创建引擎
+        # pool_pre_ping=True 用于解决数据库连接长时间空闲后断开的问题
+        self.engine = create_engine(settings.DATABASE_URL, pool_pre_ping=True)
+        
+        # 2. 注册 pgvector 类型
+        # 这是为了让 SQLAlchemy 反射机制能识别数据库中的 'vector' 类型
+        self.engine.dialect.ischema_names['vector'] = Vector
+        
+        self.metadata = MetaData()
+        self.tasks = None
+        self.queue = None
+        self.chunks = None
+        
+        self._reflect_tables()
+
+    def _reflect_tables(self):
+        """自动从数据库加载表定义"""
+        try:
+            # autoload_with 会查询数据库元数据，自动填充 Column 信息
+            self.tasks = Table('crawl_tasks', self.metadata, autoload_with=self.engine)
+            self.queue = Table('crawl_queue', self.metadata, autoload_with=self.engine)
+            self.chunks = Table('knowledge_chunks', self.metadata, autoload_with=self.engine)
+            logger.info("Database tables reflected successfully.")
+        except Exception as e:
+            logger.error(f"Failed to reflect tables: {e}")
+
+# 全局数据库实例
+db = Database()

backend/core/logger.py

@@ -0,0 +1,24 @@
+# backend/core/logger.py
+import logging
+import sys
+
+def setup_logging(level=logging.INFO):
+    """
+    全局日志配置
+    关键点：强制将日志输出到 sys.stderr，防止污染 sys.stdout 导致 MCP 协议崩溃。
+    """
+    # 定义日志格式：时间 - 模块名 - 级别 - 内容
+    log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    
+    # 配置根记录器
+    logging.basicConfig(
+        level=level,
+        format=log_format,
+        handlers=[
+            # 【绝对关键】使用 StreamHandler(sys.stderr)
+            # 这样日志会走标准错误通道，不会干扰 MCP 的标准输出通信
+            logging.StreamHandler(sys.stderr)
+        ],
+        # 强制重新配置，防止被第三方库覆盖
+        force=True
+    )

backend/database.py

@@ -1,32 +0,0 @@
-from sqlalchemy import create_engine, MetaData, Table, event
-from pgvector.sqlalchemy import Vector  # 必须导入这个
-from .config import settings
-
-class Database:
-    def __init__(self):
-        # 1. 创建引擎
-        self.engine = create_engine(settings.DATABASE_URL, pool_pre_ping=True)
-        
-        # 2. 【核心修复】手动注册 vector 类型，让反射能识别它
-        # 这告诉 SQLAlchemy：如果在数据库里看到名为 "vector" 的类型，请使用 pgvector 库的 Vector 类来处理
-        self.engine.dialect.ischema_names['vector'] = Vector
-        
-        self.metadata = MetaData()
-        self.tasks = None
-        self.queue = None
-        self.chunks = None
-        
-        self._reflect_tables()
-
-    def _reflect_tables(self):
-        try:
-            # 自动从数据库加载表结构
-            # 因为上面注册了 ischema_names，现在 chunks_table.c.embedding 就能被正确识别为 Vector 类型了
-            self.tasks = Table('crawl_tasks', self.metadata, autoload_with=self.engine)
-            self.queue = Table('crawl_queue', self.metadata, autoload_with=self.engine)
-            self.chunks = Table('knowledge_chunks', self.metadata, autoload_with=self.engine)
-        except Exception as e:
-            print(f"❌ 数据库表加载失败: {e}")
-
-# 全局单例
-db_instance = Database()

backend/main.py

@@ -1,131 +1,25 @@
-# backend/main.py
-from fastapi import FastAPI, APIRouter, BackgroundTasks
-# 确保导入路径与你的文件名一致，如果文件名是 workflow.py 则用 workflow
-from .services.crawler_sql_service import crawler_sql_service
-from .services.automated_crawler import workflow 
-from .schemas import (
-    RegisterRequest, PendingRequest, SaveResultsRequest, AddUrlsRequest, SearchRequest,
-    AutoMapRequest, AutoProcessRequest, TextSearchRequest
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+# 引入新路由
+from backend.routers import v1
+
+app = FastAPI(
+    title="Wiki Crawler API",
+    version="1.0.0", # 版本号回归
+    description="RAG Knowledge Base Service"
 )
-from .utils import make_response
 
-app = FastAPI(title="Wiki Crawler API")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-# ==========================================
-# 工具函数
-# ==========================================
-
-
-# ==========================================
-# V1 Router: 原始的底层接口 (Manual Control)
-# ==========================================
-router_v1 = APIRouter()
-
-@router_v1.post("/register")
-async def register(req: RegisterRequest):
-    try:
-        # Service 返回: {'task_id': 1, 'is_new_task': True, 'msg': '...'}
-        res = crawler_sql_service.register_task(req.url)
-        # 使用 pop 将 msg 提取出来作为响应的 msg，剩下的作为 data
-        return make_response(1, res.pop("msg", "Success"), res)
-    except Exception as e:
-        return make_response(0, str(e))
-
-@router_v1.post("/add_urls")
-async def add_urls(req: AddUrlsRequest):
-    try:
-        urls = req.urls_obj["urls"]
-        res = crawler_sql_service.add_urls(req.task_id, urls=urls)
-        return make_response(1, res.pop("msg", "Success"), res)
-    except Exception as e:
-        return make_response(0, str(e))
-
-@router_v1.post("/pending_urls")
-async def pending_urls(req: PendingRequest):
-    try:
-        res = crawler_sql_service.get_pending_urls(req.task_id, req.limit)
-        # 即使队列为空，Service 也会返回 msg="Queue is empty"
-        return make_response(1, res.pop("msg", "Success"), res)
-    except Exception as e:
-        return make_response(0, str(e))
-
-@router_v1.post("/save_results")
-async def save_results(req: SaveResultsRequest):
-    try:
-        res = crawler_sql_service.save_results(req.task_id, req.results)
-        return make_response(1, res.pop("msg", "Success"), res)
-    except Exception as e:
-        return make_response(0, str(e))
-
-@router_v1.post("/search")
-async def search_v1(req: SearchRequest):
-    """V1 搜索：客户端手动传向量"""
-    try:
-        vector = req.query_embedding['vector']
-        if not vector:
-            return make_response(2, "Vector is empty", None)
-
-        # Service 现在返回 {'results': [...], 'msg': 'Found ...'}
-        res = crawler_sql_service.search_knowledge(
-            query_embedding=vector,
-            task_id=req.task_id,
-            limit=req.limit
-        )
-        return make_response(1, res.pop("msg", "Search Done"), res)
-    except Exception as e:
-        return make_response(0, str(e))
-
-
-# ==========================================
-# V2 Router: 自动化工作流 (Automated Workflow)
-# ==========================================
-router_v2 = APIRouter()
-
-@router_v2.post("/crawler/map")
-async def auto_map(req: AutoMapRequest):
-    """
-    [同步] 输入首页 URL，自动调用 Firecrawl Map 并入库
-    """
-    try:
-        # Workflow 返回: {'task_id':..., 'msg': 'Task mapped...', ...}
-        res = workflow.map_and_ingest(req.url)
-        return make_response(1, res.pop("msg", "Mapping Started"), res)
-    except Exception as e:
-        return make_response(0, str(e))
-
-@router_v2.post("/crawler/process")
-async def auto_process(req: AutoProcessRequest, background_tasks: BackgroundTasks):
-    """
-    [异步] 触发后台任务：消费队列 -> 抓取 -> Embedding -> 入库
-    """
-    try:
-        # 将耗时操作放入后台任务
-        background_tasks.add_task(workflow.process_task_queue, req.task_id, req.batch_size)
-        
-        # 因为是后台任务，无法立即获取 Service 的返回值 msg，只能返回通用消息
-        return make_response(1, "Background processing started", {"task_id": req.task_id})
-    except Exception as e:
-        return make_response(0, str(e))
-
-@router_v2.post("/search")
-async def search_v2(req: TextSearchRequest):
-    """
-    [智能] 输入自然语言文本 -> 后端转向量 -> 搜索
-    """
-    try:
-        # Workflow 返回 {'results': [...], 'msg': '...'}
-        res = workflow.search_with_embedding(req.query, req.task_id, req.limit)
-        return make_response(1, res.pop("msg", "Search Success"), res)
-    except Exception as e:
-        return make_response(0, f"Search Failed: {str(e)}")
-
-
-# ==========================================
-# 挂载路由
-# ==========================================
-app.include_router(router_v1, prefix="/api/v1", tags=["V1 Manual API"])
-app.include_router(router_v2, prefix="/api/v2", tags=["V2 Automated Workflow"])
+# 挂载 V1
+app.include_router(v1.router)
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)
+    uvicorn.run(app, host="0.0.0.0", port=8000)

backend/mcp_server.py

@@ -0,0 +1,145 @@
+import sys
+import os
+import logging
+from typing import Optional # 确保引入 Optional
+import threading
+# 1. 路径兼容 (确保能找到 backend 包)
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from mcp.server.fastmcp import FastMCP
+from backend.core.logger import setup_logging
+from backend.services.crawler_service import crawler_service
+
+# 2. 初始化日志 (必须走 stderr)
+setup_logging()
+logger = logging.getLogger("mcp_server")
+
+# 3. 初始化 MCP 服务
+mcp = FastMCP("WikiCrawler-V3")
+
+@mcp.tool()
+async def kb_add_website(url: str) -> str:
+    """
+    [Admin] Input a URL to map and register a task.
+    This is the first step to add a knowledge base.
+    
+    Args:
+        url: The root URL of the website (e.g., https://docs.firecrawl.dev).
+        
+    Returns:
+        Task ID and count of found links.
+    """
+    try:
+        res = crawler_service.map_site(url)
+        return f"Task Registered. ID: {res['task_id']}, Links Found: {res['count']}, Is New: {res['is_new']}"
+    except Exception as e:
+        logger.error(f"Add website failed: {e}", exc_info=True)
+        return f"Error: {e}"
+
+@mcp.tool()
+async def kb_check_status(task_id: int) -> str:
+    """
+    [Monitor] Check detailed progress and active threads.
+    Use this to see if the crawler is still running or finished.
+    
+    Args:
+        task_id: The ID of the task to check.
+        
+    Returns:
+        A formatted report including progress stats and currently crawling URLs.
+    """
+    data = crawler_service.get_task_status(task_id)
+    if not data: return "Task not found."
+    
+    s = data['stats']
+    threads = data['active_threads']
+    
+    # 格式化输出给 LLM 阅读
+    report = (
+        f"--- Task {task_id} Status ---\n"
+        f"Root URL: {data['root_url']}\n"
+        f"Progress: {s['completed']}/{s['total']} (Pending: {s['pending']})\n"
+        f"Active Threads (Running): {len(threads)}\n"
+    )
+    
+    if threads:
+        report += "Currently Crawling:\n" + "\n".join([f"- {t}" for t in threads[:5]])
+        if len(threads) > 5:
+            report += f"\n... and {len(threads)-5} more."
+            
+    return report
+
+@mcp.tool()
+async def kb_run_crawler(task_id: int, batch_size: int = 20) -> str:
+    """
+    [Action] Trigger the crawler in BACKGROUND mode.
+    This returns immediately, so you can use 'kb_check_status' to monitor progress.
+    
+    Args:
+        task_id: The ID of the task.
+        batch_size: Number of URLs to process (suggest 10-20).
+        
+    Returns:
+        Status message confirming start.
+    """
+    # 定义一个在后台跑的包装函数
+    def background_task():
+        try:
+            logger.info(f"Background batch started for Task {task_id}")
+            # 这里是阻塞操作，但它现在跑在独立线程里
+            crawler_service.process_queue_concurrent(task_id, batch_size)
+            logger.info(f"Background batch finished for Task {task_id}")
+        except Exception as e:
+            logger.error(f"Background task failed: {e}", exc_info=True)
+
+    # 2. 创建并启动线程
+    thread = threading.Thread(target=background_task)
+    thread.daemon = True # 设置为守护线程，防止主程序退出时卡死
+    thread.start()
+
+    # 3. 立即返回，不等待爬取结束
+    return f"🚀 Background crawler started for Task {task_id} (Batch Size: {batch_size}). You can now check status."
+
+
+@mcp.tool()
+async def kb_search(query: str, task_id: Optional[int] = None, limit: int = 5) -> str:
+    """
+    [User] Search knowledge base with Hybrid Search & Rerank.
+    
+    Args:
+        query: The user's question or search keywords.
+        task_id: (Optional) Limit search to a specific task ID.
+        limit: (Optional) Number of results to return (default 5).
+        
+    Returns:
+        Ranked content blocks with source paths.
+    """
+    try:
+        res = crawler_service.search(query, task_id, limit)
+        results = res.get('results', [])
+        
+        if not results: return "No results found."
+        
+        output = []
+        for i, r in enumerate(results):
+            score_display = f"{r['score']:.4f}" + (" (Reranked)" if r.get('reranked') else "")
+            meta = r.get('meta_info', {})
+            path = meta.get('header_path', 'Root')
+            
+            # 格式化单个结果块
+            block = (
+                f"[{i+1}] Score: {score_display}\n"
+                f"Path: {path}\n"
+                f"Content: {r['content'][:300]}..." # 限制长度防止 Context 溢出
+            )
+            output.append(block)
+            
+        return "\n\n".join(output)
+        
+    except Exception as e:
+        logger.error(f"Search failed: {e}", exc_info=True)
+        return f"Search Error: {e}"
+
+if __name__ == "__main__":
+    # 启动 MCP 服务
+    mcp.run()

backend/routers/v1.py

@@ -0,0 +1,67 @@
+# backend/routers/v1.py
+from fastapi import APIRouter, BackgroundTasks, status
+from backend.services.crawler_service import crawler_service
+from backend.services.data_service import data_service
+from backend.schemas.v1 import (
+    TaskCreateRequest, TaskExecuteRequest, SearchRequest,
+    ResponseBase, KnowledgeBaseListResponse
+)
+from backend.utils.common import make_response
+
+# 【改动】前缀变更为 v1
+router = APIRouter(prefix="/api/v1", tags=["Knowledge Base API"])
+
+# =======================================================
+# 1. 获取知识库列表 (核心新功能)
+# =======================================================
+@router.get("/knowledge-bases", response_model=ResponseBase)
+async def list_knowledge_bases():
+    """
+    列出所有已存在的知识库(Task)。
+    工作流可以用这个接口获取 task_id 列表，让 LLM 选择查哪个库。
+    """
+    kb_list = crawler_service.get_knowledge_base_list()
+    return ResponseBase(
+        code=1, 
+        msg="Success", 
+        data={"total": len(kb_list), "list": kb_list}
+    )
+
+# =======================================================
+# 2. 任务管理
+# =======================================================
+@router.post("/tasks", status_code=status.HTTP_201_CREATED, response_model=ResponseBase)
+async def create_task(req: TaskCreateRequest):
+    try:
+        res = crawler_service.map_site(req.url)
+        return ResponseBase(code=1, msg="Task Created", data=res)
+    except Exception as e:
+        return ResponseBase(code=0, msg=str(e))
+
+@router.get("/tasks/{task_id}", response_model=ResponseBase)
+async def get_task_status(task_id: int):
+    data = crawler_service.get_task_status(task_id)
+    if not data:
+        return ResponseBase(code=0, msg="Task not found")
+    return ResponseBase(code=1, msg="Success", data=data)
+
+@router.post("/tasks/{task_id}/run", status_code=status.HTTP_202_ACCEPTED, response_model=ResponseBase)
+async def run_task(task_id: int, req: TaskExecuteRequest, bg_tasks: BackgroundTasks):
+    # 简单检查
+    if not data_service.get_task_monitor_data(task_id):
+        return ResponseBase(code=0, msg="Task not found")
+    
+    bg_tasks.add_task(crawler_service.process_queue_concurrent, task_id, req.batch_size)
+    return ResponseBase(code=1, msg="Execution Started", data={"task_id": task_id})
+
+# =======================================================
+# 3. 搜索
+# =======================================================
+@router.post("/search", response_model=ResponseBase)
+async def search_knowledge(req: SearchRequest):
+    try:
+        # req.limit 映射到 return_num
+        res = crawler_service.search(req.query, req.task_id, req.limit)
+        return ResponseBase(code=1, msg="Search Completed", data=res)
+    except Exception as e:
+        return ResponseBase(code=0, msg=str(e))

backend/schemas.py

@@ -1,50 +0,0 @@
-from pydantic import BaseModel
-from typing import Optional, List, Any
-
-
-class RegisterRequest(BaseModel):
-    url: str
-
-class PendingRequest(BaseModel):
-    task_id: int
-    limit: Optional[int] = 10
-
-class AddUrlsRequest(BaseModel):
-    task_id: int
-    urls_obj: dict
-
-# schemas.py
-class CrawlResult(BaseModel):
-    source_url: str
-    chunk_index: int  # 新增字段
-    title: Optional[str] = None
-    content: Optional[str] = None
-    embedding: Optional[List[float]] = None
-
-class SaveResultsRequest(BaseModel):
-    task_id: int
-    results: List[CrawlResult]
-
-class SearchRequest(BaseModel):
-    # 如果不传 task_id，则进行全库搜索
-    task_id: Optional[int] = None
-    query_embedding: dict
-    limit: Optional[int] = 5
-
-
-
-
-# ... (保留原有的 Schema: RegisterRequest, AddUrlsRequest 等) ...
-
-# === V2 New Schemas ===
-class AutoMapRequest(BaseModel):
-    url: str
-
-class AutoProcessRequest(BaseModel):
-    task_id: int
-    batch_size: Optional[int] = 5
-
-class TextSearchRequest(BaseModel):
-    query: str  # 用户直接传文字，不需要传向量了
-    task_id: Optional[int] = None
-    limit: Optional[int] = 5

backend/schemas/v1.py

@@ -0,0 +1,43 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any
+
+# --- 通用响应 ---
+class ResponseBase(BaseModel):
+    code: int = Field(..., description="1: 成功, 0: 失败")
+    msg: str
+    data: Optional[Any] = None
+
+# --- [GET] 知识库列表 (新功能) ---
+class KnowledgeBaseItem(BaseModel):
+    task_id: int
+    root_url: str
+    name: str  # 提取出的简短名称，方便 LLM 识别
+
+class KnowledgeBaseListResponse(BaseModel):
+    total: int
+    list: List[KnowledgeBaseItem]
+
+# --- [POST] 创建任务 ---
+class TaskCreateRequest(BaseModel):
+    url: str = Field(..., description="目标网站根URL")
+
+# --- [POST] 执行任务 ---
+class TaskExecuteRequest(BaseModel):
+    batch_size: int = Field(10, le=50)
+
+# --- [POST] 搜索 ---
+class SearchRequest(BaseModel):
+    query: str
+    # 明确支持 None 为全局搜索
+    task_id: Optional[int] = Field(None, description="任务ID，不传则搜全库")
+    limit: int = Field(5, description="返回数量")
+
+# ... (SearchResultItem 等保持不变) ...
+class SearchResultItem(BaseModel):
+    task_id: int
+    source_url: str
+    title: Optional[str] = None
+    content: str
+    score: float
+    meta_info: Dict = {}
+    reranked: Optional[bool] = False

backend/services/automated_crawler.py

@@ -1,201 +0,0 @@
-import dashscope
-from http import HTTPStatus
-from firecrawl import FirecrawlApp
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from ..config import settings
-from .crawler_sql_service import crawler_sql_service
-
-# 初始化配置
-dashscope.api_key = settings.DASHSCOPE_API_KEY
-
-class AutomatedCrawler:
-    def __init__(self):
-        self.firecrawl = FirecrawlApp(api_key=settings.FIRECRAWL_API_KEY)
-        self.splitter = RecursiveCharacterTextSplitter(
-            chunk_size=500, 
-            chunk_overlap=100,
-            separators=["\n\n", "\n", "。", "！", "？", " ", ""]
-        )
-
-    def _get_embedding(self, text: str):
-        """内部方法：调用 Dashscope 生成向量"""
-        # 注意：此方法是内部辅助，出错返回 None，由调用方处理状态
-        embedding = None
-        try:
-            resp = dashscope.TextEmbedding.call(
-                model=dashscope.TextEmbedding.Models.text_embedding_v3, # 确认你的模型版本
-                input=text,
-                dimension=1536
-            )
-            if resp.status_code == HTTPStatus.OK:
-                embedding = resp.output['embeddings'][0]['embedding']
-            else:
-                print(f"Embedding API Error: {resp}")
-        except Exception as e:
-            print(f"Embedding Exception: {e}")
-            
-        return embedding
-
-    def map_and_ingest(self, start_url: str):
-        """
-        V2 步骤1: 地图式扫描并入库
-        """
-        print(f"[WorkFlow] Start mapping: {start_url}")
-        result = {}
-        
-        try:
-            # 1. 在数据库注册任务
-            task_info = crawler_sql_service.register_task(start_url)
-            task_id = task_info['task_id']
-            is_new_task = task_info['is_new_task']
-            
-            # 2. 调用 Firecrawl Map
-            if is_new_task:
-                map_result = self.firecrawl.map(start_url)
-            
-                urls = []
-                # 兼容 firecrawl sdk 不同版本的返回结构
-                # 如果 map_result 是对象且有 links 属性
-                if hasattr(map_result, 'links'): 
-                    for link in map_result.links:
-                        # 假设 link 是对象或字典，视具体 SDK 版本而定
-                        # 如果 link 是字符串直接 append
-                        if isinstance(link, str):
-                            urls.append(link)
-                        else:
-                            urls.append(getattr(link, 'url', str(link)))
-                # 如果是字典
-                elif isinstance(map_result, dict):
-                    urls = map_result.get('links', [])
-                
-                print(f"[WorkFlow] Found {len(urls)} links")
-                
-                # 3. 批量入库
-                res = {"msg": "No urls found to add"}
-                if urls:
-                    res = crawler_sql_service.add_urls(task_id, urls)
-            
-                result = {
-                    "msg": "Task successfully mapped and URLs added",
-                    "task_id": task_id,
-                    "is_new_task": is_new_task,
-                    "url_count": len(urls),
-                    "map_detail": res
-                }
-            else:
-                result = {
-                    "msg": "Task already exists, skipped mapping",
-                    "task_id": task_id,
-                    "is_new_task": False,
-                    "url_count": 0,
-                    "map_detail": {}
-                }
-
-        except Exception as e:
-            print(f"[WorkFlow] Map Error: {e}")
-            # 向上抛出异常，由 main.py 捕获并返回错误 Response
-            raise e
-
-        return result
-
-    def process_task_queue(self, task_id: int, limit: int = 10):
-        """
-        V2 步骤2: 消费队列 -> 抓取 -> 切片 -> 向量化 -> 存储
-        """
-        processed_count = 0
-        total_chunks_saved = 0
-        result = {}
-        
-        # 1. 获取待处理 URL
-        pending = crawler_sql_service.get_pending_urls(task_id, limit)
-        urls = pending['urls']
-        
-        if not urls:
-            result = {"msg": "Queue is empty, no processing needed", "processed_count": 0}
-        else:
-            for url in urls:
-                try:
-                    print(f"[WorkFlow] Processing: {url}")
-                    # 2. 单页抓取
-                    scrape_res = self.firecrawl.scrape(
-                        url, 
-                        params={'formats': ['markdown'], 'onlyMainContent': True}
-                    )
-                    
-                    # 兼容 SDK 返回类型 (对象或字典)
-                    content = ""
-                    metadata = {}
-                    
-                    if isinstance(scrape_res, dict):
-                        content = scrape_res.get('markdown', '')
-                        metadata = scrape_res.get('metadata', {})
-                    else:
-                        content = getattr(scrape_res, 'markdown', '')
-                        metadata = getattr(scrape_res, 'metadata', {})
-                        if not metadata and hasattr(scrape_res, 'metadata_dict'):
-                             metadata = scrape_res.metadata_dict
-
-                    title = metadata.get('title', url)
-                    
-                    if not content:
-                        print(f"[WorkFlow] Skip empty content: {url}")
-                        continue
-
-                    # 3. 切片
-                    chunks = self.splitter.split_text(content)
-                    results_to_save = []
-                    
-                    # 4. 向量化
-                    for idx, chunk_text in enumerate(chunks):
-                        vector = self._get_embedding(chunk_text)
-                        if vector:
-                            results_to_save.append({
-                                "source_url": url,
-                                "chunk_index": idx,
-                                "title": title,
-                                "content": chunk_text,
-                                "embedding": vector
-                            })
-                    
-                    # 5. 保存
-                    if results_to_save:
-                        save_res = crawler_sql_service.save_results(task_id, results_to_save)
-                        processed_count += 1
-                        total_chunks_saved += save_res['counts']['inserted'] + save_res['counts']['updated']
-                        
-                except Exception as e:
-                    print(f"[WorkFlow] Error processing {url}: {e}")
-                    # 此处不抛出异常，以免打断整个批次的循环
-                    # 实际生产建议在这里调用 service 将 url 标记为 failed
-
-            result = {
-                "msg": f"Batch processing complete. URLs processed: {processed_count}",
-                "processed_urls": processed_count,
-                "total_chunks_saved": total_chunks_saved
-            }
-            
-        return result
-
-    def search_with_embedding(self, query_text: str, task_id: int = None, limit: int = 5):
-        """
-        V2 搜索: 输入文本 -> 自动转向量 -> 搜索数据库
-        """
-        result = {}
-        
-        # 1. 获取向量
-        vector = self._get_embedding(query_text)
-        
-        if not vector:
-            result = {
-                "msg": "Failed to generate embedding for query",
-                "results": []
-            }
-        else:
-            # 2. 执行搜索
-            # search_knowledge 现在已经返回带 msg 的字典了
-            result = crawler_sql_service.search_knowledge(vector, task_id, limit)
-            
-        return result
-
-# 单例模式
-workflow = AutomatedCrawler()

backend/services/crawler_service.py

@@ -0,0 +1,263 @@
+import concurrent.futures
+import threading
+import logging
+from typing import Dict, Any, List, Optional, Union
+
+from firecrawl import FirecrawlApp
+from backend.core.config import settings
+from backend.services.data_service import data_service
+from backend.services.llm_service import llm_service
+from backend.utils.text_process import text_processor
+
+# 获取当前模块的专用 Logger
+logger = logging.getLogger(__name__)
+
+class CrawlerService:
+    """
+    爬虫业务服务层 (Crawler Service)
+    
+    职责：
+    1. 协调外部 API (Firecrawl) 和内部服务 (DataService, LLMService)。
+    2. 管理多线程爬取任务及其状态。
+    3. 提供统一的搜索入口 (混合检索 + Rerank)。
+    """
+    
+    def __init__(self):
+        self.firecrawl = FirecrawlApp(api_key=settings.FIRECRAWL_API_KEY)
+        self.max_workers = 5  # 线程池最大并发数
+        
+        # 内存状态追踪: { task_id: set([url1, url2]) }
+        self._active_workers: Dict[int, set] = {} 
+        self._lock = threading.Lock()
+
+    def get_knowledge_base_list(self):
+        """获取知识库列表"""
+        return data_service.get_all_tasks()
+
+
+    def _track_start(self, task_id: int, url: str):
+        """[Internal] 标记某个URL开始处理"""
+        with self._lock:
+            if task_id not in self._active_workers:
+                self._active_workers[task_id] = set()
+            self._active_workers[task_id].add(url)
+
+    def _track_end(self, task_id: int, url: str):
+        """[Internal] 标记某个URL处理结束"""
+        with self._lock:
+            if task_id in self._active_workers:
+                self._active_workers[task_id].discard(url)
+
+    def get_task_status(self, task_id: int) -> Optional[Dict[str, Any]]:
+        """
+        获取任务的实时综合状态。
+        
+        Args:
+            task_id (int): 任务 ID
+            
+        Returns:
+            dict: 包含数据库统计和实时线程信息的字典。如果任务不存在返回 None。
+            结构示例:
+            {
+                "root_url": "https://example.com",
+                "stats": {"pending": 10, "processing": 2, "completed": 5, "failed": 0},
+                "active_threads": ["https://example.com/page1"],
+                "active_thread_count": 1
+            }
+        """
+        # 1. 获取数据库层面的统计 (宏观)
+        db_data = data_service.get_task_monitor_data(task_id)
+        if not db_data:
+            return None
+
+        # 2. 获取内存层面的活跃线程 (微观)
+        with self._lock:
+            active_urls = list(self._active_workers.get(task_id, []))
+            
+        # 日志输出当前状态
+        logger.info(f"Task {task_id} active threads: {active_urls}")
+        logger.info(f"Task {task_id} stats: {db_data['db_stats']}")
+
+        return {
+            "root_url": db_data["root_url"],
+            "stats": db_data["db_stats"],       
+            "active_threads": active_urls,      
+            "active_thread_count": len(active_urls)
+        }
+
+    def map_site(self, start_url: str) -> Dict[str, Any]:
+        """
+        第一阶段：站点地图扫描 (Map)
+        
+        Args:
+            start_url (str): 目标网站的根 URL
+            
+        Returns:
+            dict: 包含任务 ID 和发现链接数的字典。
+            {
+                "task_id": 123,
+                "count": 50,
+                "is_new": True
+            }
+        """
+        logger.info(f"Mapping: {start_url}")
+        try:
+            task_res = data_service.register_task(start_url)
+            urls_to_add = [start_url] 
+            
+            # 如果任务已存在，不再重新 Map，直接返回
+            if not task_res['is_new_task']:
+                logger.info(f"Task {task_res['task_id']} exists, skipping map.")
+                return {
+                    "task_id": task_res['task_id'], 
+                    "count": 0,
+                    "is_new": False
+                }
+            
+            # 新任务执行 Map
+            try:
+                map_res = self.firecrawl.map(start_url)
+                # 兼容不同版本的 SDK 返回结构
+                found_links = map_res.get('links', []) if isinstance(map_res, dict) else getattr(map_res, 'links', [])
+                
+                for link in found_links:
+                    u = link if isinstance(link, str) else getattr(link, 'url', str(link))
+                    urls_to_add.append(u)
+                logger.info(f"Map found {len(found_links)} links")
+            except Exception as e:
+                logger.warning(f"Map failed, proceeding with seed only: {e}")
+
+            if urls_to_add:
+                data_service.add_urls(task_res['task_id'], urls_to_add)
+            
+            return {
+                "task_id": task_res['task_id'], 
+                "count": len(urls_to_add),
+                "is_new": True
+            }
+        except Exception as e:
+            logger.error(f"Map failed: {e}")
+            raise e
+
+    def _process_single_url(self, task_id: int, url: str):
+        """[Internal Worker] 单个 URL 处理线程逻辑"""
+        # 1. 内存标记：开始
+        self._track_start(task_id, url)
+        logger.info(f"[THREAD START] {url}")
+        
+        try:
+            # 2. 爬取
+            scrape_res = self.firecrawl.scrape(
+                url, formats=['markdown'], only_main_content=True
+            )
+            
+            # 兼容性提取
+            raw_md = getattr(scrape_res, 'markdown', '') if not isinstance(scrape_res, dict) else scrape_res.get('markdown', '')
+            metadata = getattr(scrape_res, 'metadata', {}) if not isinstance(scrape_res, dict) else scrape_res.get('metadata', {})
+            title = getattr(metadata, 'title', url) if not isinstance(metadata, dict) else metadata.get('title', url)
+
+            if not raw_md:
+                data_service.mark_url_status(task_id, url, 'failed')
+                return
+
+            # 3. 清洗 & 切分
+            clean_md = text_processor.clean_markdown(raw_md)
+            chunks = text_processor.split_markdown(clean_md)
+            
+            chunks_data = []
+            for i, chunk in enumerate(chunks):
+                headers = chunk['metadata']
+                path = " > ".join(headers.values())
+                emb_input = f"{title}\n{path}\n{chunk['content']}"
+                
+                vector = llm_service.get_embedding(emb_input)
+                if vector:
+                    chunks_data.append({
+                        "index": i, "content": chunk['content'], "embedding": vector,
+                        "meta_info": {"header_path": path, "headers": headers}
+                    })
+            
+            # 4. 入库 (会自动标记 completed)
+            if chunks_data:
+                data_service.save_chunks(task_id, url, title, chunks_data)
+            else:
+                data_service.mark_url_status(task_id, url, 'failed')
+
+        except Exception as e:
+            logger.error(f"[THREAD ERROR] {url}: {e}")
+            data_service.mark_url_status(task_id, url, 'failed')
+        
+        finally:
+            # 5. 内存标记：结束 (无论成功失败都要移除)
+            self._track_end(task_id, url)
+
+    def process_queue_concurrent(self, task_id: int, batch_size: int = 10) -> Dict[str, Any]:
+        """
+        第二阶段：多线程并发处理 (Process)
+        
+        Args:
+            task_id (int): 任务 ID
+            batch_size (int): 本次批次处理的 URL 数量（会分配给线程池并发执行）
+            
+        Returns:
+            dict: 处理结果概览
+            {
+                "msg": "Batch completed",
+                "count": 10
+            }
+        """
+        urls = data_service.get_pending_urls(task_id, limit=batch_size)
+        if not urls: return {"msg": "No pending urls", "count": 0}
+
+        logger.info(f"Batch started: {len(urls)} urls")
+        
+        with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
+            # 提交任务到线程池
+            futures = {executor.submit(self._process_single_url, task_id, url): url for url in urls}
+            # 等待完成 (阻塞直到所有线程结束)
+            concurrent.futures.wait(futures)
+
+        return {"msg": "Batch completed", "count": len(urls)}
+
+    def search(self, query: str, task_id: Optional[int], return_num: int) -> Dict[str, Any]:
+        """
+        第三阶段：智能搜索 (Search)
+        流程：用户问题 -> Embedding -> 数据库混合检索(粗排) -> Rerank模型(精排) -> 结果
+        
+        Args:
+            query (str): 用户问题
+            task_id (Optional[int]): 指定搜索的任务 ID，None 为全库搜索
+            return_num (int): 最终返回给用户的条数 (Top K)
+            
+        Returns:
+            dict: 搜索结果列表
+            {
+                "results": [
+                    {"content": "...", "score": 0.98, "meta_info": {...}},
+                    ...
+                ]
+            }
+        """
+        # 1. 生成向量
+        vector = llm_service.get_embedding(query)
+        if not vector: return {"msg": "Embedding failed", "results": []}
+
+        # 2. 数据库粗排 (召回 10 倍数量或至少 50 条)
+        coarse_limit = min(return_num * 10, 100)
+        coarse_limit = max(coarse_limit, 50)
+        
+        coarse_res = data_service.search(
+            query_text=query, 
+            query_vector=vector, 
+            task_id=task_id, 
+            candidates_num=coarse_limit
+        )
+        candidates = coarse_res.get('results', [])
+
+        if not candidates: return {"results": []}
+
+        # 3. LLM 精排 (Rerank)
+        final_res = llm_service.rerank(query, candidates, return_num)
+        return {"results": final_res}
+
+crawler_service = CrawlerService()

backend/services/crawler_sql_service.py

@@ -1,230 +0,0 @@
-from sqlalchemy import select, insert, update, and_
-from ..database import db_instance
-from ..utils import normalize_url
-
-class CrawlerSqlService:
-    def __init__(self):
-        self.db = db_instance
-
-    def register_task(self, url: str):
-        """完全使用库 API 实现的注册"""
-        clean_url = normalize_url(url)
-        result = {}
-
-        with self.db.engine.begin() as conn:
-            # 使用 select() API
-            query = select(self.db.tasks.c.id).where(self.db.tasks.c.root_url == clean_url)
-            existing = conn.execute(query).fetchone()
-            
-            if existing:
-                result = {
-                    "task_id": existing[0], 
-                    "is_new_task": False,
-                    "msg": "Task already exists"
-                }
-            else:
-                # 使用 insert() API
-                stmt = insert(self.db.tasks).values(root_url=clean_url).returning(self.db.tasks.c.id)
-                new_task = conn.execute(stmt).fetchone()
-                result = {
-                    "task_id": new_task[0], 
-                    "is_new_task": True,
-                    "msg": "New task created successfully"
-                }
-        
-        return result
-
-    def add_urls(self, task_id: int, urls: list[str]):
-        """通用 API 实现的批量添加（含详细返回）"""
-        success_urls, skipped_urls, failed_urls = [], [], []
-
-        with self.db.engine.begin() as conn:
-            for url in urls:
-                clean_url = normalize_url(url)
-                try:
-                    # 检查队列中是否已存在该 URL
-                    check_q = select(self.db.queue).where(
-                        and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == clean_url)
-                    )
-                    if conn.execute(check_q).fetchone():
-                        skipped_urls.append(clean_url)
-                        continue
-                    
-                    # 插入新 URL
-                    conn.execute(insert(self.db.queue).values(
-                        task_id=task_id, url=clean_url, status='pending'
-                    ))
-                    success_urls.append(clean_url)
-                except Exception:
-                    failed_urls.append(clean_url)
-        
-        # 构造返回消息
-        msg = f"Added {len(success_urls)} urls, skipped {len(skipped_urls)}, failed {len(failed_urls)}"
-        
-        return {
-            "success_urls": success_urls, 
-            "skipped_urls": skipped_urls, 
-            "failed_urls": failed_urls,
-            "msg": msg
-        }
-
-    def get_pending_urls(self, task_id: int, limit: int):
-        """原子锁定 API 实现"""
-        result = {}
-        
-        with self.db.engine.begin() as conn:
-            query = select(self.db.queue.c.url).where(
-                and_(self.db.queue.c.task_id == task_id, self.db.queue.c.status == 'pending')
-            ).limit(limit)
-            
-            urls = [r[0] for r in conn.execute(query).fetchall()]
-            
-            if urls:
-                upd = update(self.db.queue).where(
-                    and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url.in_(urls))
-                ).values(status='processing')
-                conn.execute(upd)
-                result = {"urls": urls, "msg": f"Fetched {len(urls)} pending urls"}
-            else:
-                result = {"urls": [], "msg": "Queue is empty"}
-        
-        return result
-        
-    def save_results(self, task_id: int, results: list):
-        """
-        保存同一 URL 的多个切片。
-        """
-        if not results:
-            return {"msg": "No data provided to save", "counts": {"inserted": 0, "updated": 0, "failed": 0}}
-
-        # 1. 基础信息提取
-        first_item = results[0] if isinstance(results[0], dict) else results[0].__dict__
-        target_url = normalize_url(first_item.get('source_url'))
-        
-        # 结果统计容器
-        inserted_chunks = []
-        updated_chunks = []
-        failed_chunks = []
-        is_page_update = False
-
-        with self.db.engine.begin() as conn:
-            # 2. 判断该 URL 是否已经有切片存在
-            check_page_stmt = select(self.db.chunks.c.id).where(
-                and_(self.db.chunks.c.task_id == task_id, self.db.chunks.c.source_url == target_url)
-            ).limit(1)
-            if conn.execute(check_page_stmt).fetchone():
-                is_page_update = True
-
-            # 3. 逐个处理切片
-            for res in results:
-                data = res if isinstance(res, dict) else res.__dict__
-                c_idx = data.get('chunk_index')
-
-                try:
-                    # 检查切片是否存在
-                    find_chunk_stmt = select(self.db.chunks.c.id).where(
-                        and_(
-                            self.db.chunks.c.task_id == task_id,
-                            self.db.chunks.c.source_url == target_url,
-                            self.db.chunks.c.chunk_index == c_idx
-                        )
-                    )
-                    existing_chunk = conn.execute(find_chunk_stmt).fetchone()
-
-                    if existing_chunk:
-                        # 覆盖更新
-                        upd_stmt = update(self.db.chunks).where(
-                            self.db.chunks.c.id == existing_chunk[0]
-                        ).values(
-                            title=data.get('title'),
-                            content=data.get('content'),
-                            embedding=data.get('embedding')
-                        )
-                        conn.execute(upd_stmt)
-                        updated_chunks.append(c_idx)
-                    else:
-                        # 插入新切片
-                        ins_stmt = insert(self.db.chunks).values(
-                            task_id=task_id,
-                            source_url=target_url,
-                            chunk_index=c_idx,
-                            title=data.get('title'),
-                            content=data.get('content'),
-                            embedding=data.get('embedding')
-                        )
-                        conn.execute(ins_stmt)
-                        inserted_chunks.append(c_idx)
-
-                except Exception as e:
-                    print(f"Chunk {c_idx} failed: {e}")
-                    failed_chunks.append(c_idx)
-
-            # 4. 更新队列状态
-            conn.execute(
-                update(self.db.queue).where(
-                    and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == target_url)
-                ).values(status='completed')
-            )
-
-        # 构造返回
-        msg = f"Saved results for {target_url}. Inserted: {len(inserted_chunks)}, Updated: {len(updated_chunks)}"
-        
-        return {
-            "source_url": target_url,
-            "is_page_update": is_page_update,
-            "detail": {
-                "inserted_chunk_indexes": inserted_chunks,
-                "updated_chunk_indexes": updated_chunks,
-                "failed_chunk_indexes": failed_chunks
-            },
-            "counts": {
-                "inserted": len(inserted_chunks),
-                "updated": len(updated_chunks),
-                "failed": len(failed_chunks)
-            },
-            "msg": msg
-        }
-
-    def search_knowledge(self, query_embedding: list, task_id: int = None, limit: int = 5):
-        """
-        高性能向量搜索方法
-        """
-        results = []
-        msg = ""
-
-        with self.db.engine.connect() as conn:
-            stmt = select(
-                self.db.chunks.c.task_id,
-                self.db.chunks.c.source_url,
-                self.db.chunks.c.title,
-                self.db.chunks.c.content,
-                self.db.chunks.c.chunk_index
-            )
-
-            if task_id is not None:
-                stmt = stmt.where(self.db.chunks.c.task_id == task_id)
-
-            stmt = stmt.order_by(
-                self.db.chunks.c.embedding.cosine_distance(query_embedding)
-            ).limit(limit)
-
-            rows = conn.execute(stmt).fetchall()
-          
-            for r in rows:
-                results.append({
-                    "task_id": r[0],
-                    "source_url": r[1],
-                    "title": r[2],
-                    "content": r[3],
-                    "chunk_index": r[4]
-                })
-        
-        if results:
-            msg = f"Found {len(results)} matches"
-        else:
-            msg = "No matching content found"
-
-        return {"results": results, "msg": msg}
-
-
-crawler_sql_service = CrawlerSqlService()

backend/services/data_service.py

@@ -0,0 +1,212 @@
+from sqlalchemy import select, insert, update, and_, text, func, desc
+from backend.core.database import db
+from backend.utils.common import normalize_url
+import logging
+
+# 获取当前模块的专用 Logger
+# __name__ 会自动识别为 "backend.services.crawler_service" 这样的路径
+logger = logging.getLogger(__name__)
+class DataService:
+    """
+    数据持久化服务层
+    """
+    def __init__(self):
+        self.db = db
+
+    def register_task(self, url: str):
+        clean_url = normalize_url(url)
+        with self.db.engine.begin() as conn:
+            query = select(self.db.tasks.c.id).where(self.db.tasks.c.root_url == clean_url)
+            existing = conn.execute(query).fetchone()
+            
+            if existing:
+                return {"task_id": existing[0], "is_new_task": False}
+            else:
+                stmt = insert(self.db.tasks).values(root_url=clean_url).returning(self.db.tasks.c.id)
+                new_task = conn.execute(stmt).fetchone()
+                return {"task_id": new_task[0], "is_new_task": True}
+
+    def add_urls(self, task_id: int, urls: list[str]):
+        success_urls = []
+        with self.db.engine.begin() as conn:
+            for url in urls:
+                clean_url = normalize_url(url)
+                try:
+                    check_q = select(self.db.queue.c.id).where(
+                        and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == clean_url)
+                    )
+                    if not conn.execute(check_q).fetchone():
+                        conn.execute(insert(self.db.queue).values(task_id=task_id, url=clean_url, status='pending'))
+                        success_urls.append(clean_url)
+                except Exception:
+                    pass
+        return {"msg": f"Added {len(success_urls)} new urls"}
+
+    def get_pending_urls(self, task_id: int, limit: int):
+        with self.db.engine.begin() as conn:
+            # 原子锁定：获取并标记为 processing
+            subquery = select(self.db.queue.c.id).where(
+                and_(self.db.queue.c.task_id == task_id, self.db.queue.c.status == 'pending')
+            ).limit(limit).with_for_update(skip_locked=True)
+            
+            stmt = update(self.db.queue).where(
+                self.db.queue.c.id.in_(subquery)
+            ).values(status='processing').returning(self.db.queue.c.url)
+            
+            result = conn.execute(stmt).fetchall()
+            return [r[0] for r in result]
+
+    def mark_url_status(self, task_id: int, url: str, status: str):
+        clean_url = normalize_url(url)
+        with self.db.engine.begin() as conn:
+            conn.execute(update(self.db.queue).where(
+                and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == clean_url)
+            ).values(status=status))
+
+    def get_all_tasks(self):
+        """
+        [新增] 获取所有已注册的任务（知识库列表）
+        用于前端展示或工作流的路由选择
+        """
+        with self.db.engine.connect() as conn:
+            # 查询 id, root_url, created_at (如果有的话)
+            # 这里假设 tasks 表里有 id 和 root_url
+            stmt = select(self.db.tasks.c.id, self.db.tasks.c.root_url).order_by(self.db.tasks.c.id)
+            rows = conn.execute(stmt).fetchall()
+            
+            # 返回精简列表
+            return [
+                {"task_id": r[0], "root_url": r[1], "name": self._extract_name(r[1])} 
+                for r in rows
+            ]
+
+    def _extract_name(self, url: str) -> str:
+        """辅助方法：从 URL 提取一个简短的名字作为 Alias"""
+        try:
+            from urllib.parse import urlparse
+            domain = urlparse(url).netloc
+            # 比如 docs.firecrawl.dev -> firecrawl
+            parts = domain.split('.')
+            if len(parts) >= 2:
+                return parts[-2] 
+            return domain
+        except:
+            return url
+
+    # ... (保持 get_task_monitor_data, save_chunks, search 等方法不变) ...
+
+    def get_task_monitor_data(self, task_id: int):
+        """[数据库层监控] 获取持久化的任务状态"""
+        with self.db.engine.connect() as conn:
+            # 1. 检查任务是否存在
+            task_exists = conn.execute(select(self.db.tasks.c.root_url).where(self.db.tasks.c.id == task_id)).fetchone()
+            if not task_exists:
+                return None
+
+            # 2. 统计各状态数量
+            stats_rows = conn.execute(select(
+                self.db.queue.c.status, func.count(self.db.queue.c.id)
+            ).where(self.db.queue.c.task_id == task_id).group_by(self.db.queue.c.status)).fetchall()
+            
+            stats = {"pending": 0, "processing": 0, "completed": 0, "failed": 0}
+            for status, count in stats_rows:
+                if status in stats: stats[status] = count
+            stats["total"] = sum(stats.values())
+            
+            return {
+                "root_url": task_exists[0],
+                "db_stats": stats
+            }
+
+    def save_chunks(self, task_id: int, source_url: str, title: str, chunks_data: list):
+        clean_url = normalize_url(source_url)
+        with self.db.engine.begin() as conn:
+            for item in chunks_data:
+                idx = item['index']
+                meta = item.get('meta_info', {})
+                existing = conn.execute(select(self.db.chunks.c.id).where(
+                    and_(self.db.chunks.c.task_id == task_id, 
+                         self.db.chunks.c.source_url == clean_url,
+                         self.db.chunks.c.chunk_index == idx)
+                )).fetchone()
+                values = {
+                    "task_id": task_id, "source_url": clean_url, "chunk_index": idx,
+                    "title": title, "content": item['content'], "embedding": item['embedding'],
+                    "meta_info": meta
+                }
+                if existing:
+                    conn.execute(update(self.db.chunks).where(self.db.chunks.c.id == existing[0]).values(**values))
+                else:
+                    conn.execute(insert(self.db.chunks).values(**values))
+            
+            conn.execute(update(self.db.queue).where(
+                and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == clean_url)
+            ).values(status='completed'))
+
+    def search(self, query_text: str, query_vector: list, task_id=None, candidates_num: int = 50, vector_weight: float = 0.7):
+        # 向量格式清洗
+        if hasattr(query_vector, 'tolist'): query_vector = query_vector.tolist()
+        if isinstance(query_vector, list) and len(query_vector) > 0 and isinstance(query_vector[0], list):
+            query_vector = query_vector[0]
+
+        results = []
+        with self.db.engine.connect() as conn:
+            # 1. 构造 Query 对象 (这是 tsquery 类型)
+            keyword_query = func.websearch_to_tsquery('english', query_text)
+            
+            # 计算分数 (逻辑不变)
+            vector_dist = self.db.chunks.c.embedding.cosine_distance(query_vector)
+            vector_score = (1 - vector_dist)
+            
+            # 注意：ts_rank 需要 (tsvector, tsquery)
+            keyword_rank = func.ts_rank(self.db.chunks.c.content_tsvector, keyword_query)
+            keyword_score = func.coalesce(keyword_rank, 0)
+
+            keyword_weight = 1.0 - vector_weight
+            final_score = (vector_score * vector_weight + keyword_score * keyword_weight).label("score")
+
+            stmt = select(
+                self.db.chunks.c.task_id, self.db.chunks.c.source_url, self.db.chunks.c.title,
+                self.db.chunks.c.content, self.db.chunks.c.meta_info, final_score
+            )
+            
+            # ================= 修复点开始 =================
+            # 只有当 vector_weight 为 0 (纯关键词模式) 时，才强制加 WHERE 过滤
+            if vector_weight == 0:
+                # 错误写法 (SQLAlchemy 自动生成 plainto_tsquery 导致报错):
+                # stmt = stmt.where(keyword_query.match(self.db.chunks.c.content_tsvector))
+                
+                # 正确写法 (直接使用 PG 的 @@ 操作符):
+                # 含义: content_tsvector @@ keyword_query
+                stmt = stmt.where(self.db.chunks.c.content_tsvector.op('@@')(keyword_query))
+            # ================= 修复点结束 =================
+
+            if task_id: stmt = stmt.where(self.db.chunks.c.task_id == task_id)
+            
+            stmt = stmt.order_by(desc("score")).limit(candidates_num)
+            
+            try:
+                rows = conn.execute(stmt).fetchall()
+                results = [{"task_id": r[0], "source_url": r[1], "title": r[2], "content": r[3], "meta_info": r[4], "score": float(r[5])} for r in rows]
+            except Exception as e:
+                # 打印详细错误方便调试
+                logger.error(f"Search failed: {e}") 
+                # 只有混合或向量模式才回退，如果是纯关键词模式报错，回退也没用
+                if vector_weight > 0:
+                    return self._fallback_vector_search(query_vector, task_id, candidates_num)
+                return {"results": [], "msg": "Keyword search failed"}
+
+        return {"results": results, "msg": f"Found {len(results)}"}
+
+    def _fallback_vector_search(self, vector, task_id, limit):
+        logger.warning("Fallback to pure vector search")
+        with self.db.engine.connect() as conn:
+            stmt = select(
+                self.db.chunks.c.task_id, self.db.chunks.c.source_url, self.db.chunks.c.title,
+                self.db.chunks.c.content, self.db.chunks.c.meta_info
+            ).order_by(self.db.chunks.c.embedding.cosine_distance(vector)).limit(limit)
+            if task_id: stmt = stmt.where(self.db.chunks.c.task_id == task_id)
+            rows = conn.execute(stmt).fetchall()
+            return {"results": [{"content": r[3], "meta_info": r[4], "score": 0.0} for r in rows], "msg": "Fallback found"}
+
+data_service = DataService()

backend/services/llm_service.py

@@ -0,0 +1,120 @@
+import dashscope
+from http import HTTPStatus
+from backend.core.config import settings
+import logging
+
+# 获取当前模块的专用 Logger
+# __name__ 会自动识别为 "backend.services.crawler_service" 这样的路径
+logger = logging.getLogger(__name__)
+class LLMService:
+    """
+    LLM 服务封装层
+    负责与 DashScope (通义千问/GTE) 交互，包括 Embedding 和 Rerank
+    """
+    def __init__(self):
+        dashscope.api_key = settings.DASHSCOPE_API_KEY
+    
+    def get_embedding(self, text: str, dimension: int = 1536):
+        """生成文本向量 (Bi-Encoder)"""
+        try:
+            resp = dashscope.TextEmbedding.call(
+                model=dashscope.TextEmbedding.Models.text_embedding_v4, # 或 v4，视你的数据库维度而定
+                input=text,
+                dimension=dimension
+            )
+            if resp.status_code == HTTPStatus.OK:
+                return resp.output['embeddings'][0]['embedding']
+            else:
+                logger.error(f"Embedding API Error: {resp}")
+                return None
+        except Exception as e:
+            logger.error(f"Embedding Exception: {e}")
+            return None
+
+    def rerank(self, query: str, documents: list, top_n: int = 5):
+        """
+        执行重排序 (Cross-Encoder)
+        
+        Args:
+            query: 用户问题
+            documents: 粗排召回的切片列表 (List[dict])，必须包含 'content' 字段
+            top_n: 最终返回多少个结果
+            
+        Returns:
+            List[dict]: 排序后并截取 Top N 的文档列表，包含新的 'score'
+        """
+        if not documents:
+            return []
+            
+        # 1. 准备输入数据
+        # Rerank API 需要纯文本列表，但我们需要保留 documents 里的 meta_info 和 id
+        # 所以我们提取 content 给 API，拿到 index 后再映射回去
+        doc_contents = [doc.get('content', '') for doc in documents]
+        
+        # 如果文档太多（比如超过 100 个），建议先截断，避免 API 超时或报错
+        if len(doc_contents) > 50:
+            doc_contents = doc_contents[:50]
+            documents = documents[:50]
+
+        try:
+            # 2. 调用 DashScope GTE-Rerank
+            resp = dashscope.TextReRank.call(
+                model='gte-rerank',
+                query=query,
+                documents=doc_contents,
+                top_n=top_n,
+                return_documents=False # 我们只需要索引和分数，不需要它把文本再传回来
+            )
+
+            if resp.status_code == HTTPStatus.OK:
+                # 3. 结果重组
+                # API 返回结构示例: output.results = [{'index': 2, 'relevance_score': 0.98}, {'index': 0, ...}]
+                reranked_results = []
+                
+                for item in resp.output.results:
+                    # 根据 API 返回的 index 找到原始文档对象
+                    original_doc = documents[item.index]
+                    
+                    # 更新分数为 Rerank 的精准分数 (通常是 0~1 之间的置信度)
+                    original_doc['score'] = item.relevance_score
+                    
+                    # 标记来源，方便调试知道这是 Rerank 过的
+                    original_doc['reranked'] = True
+                    
+                    reranked_results.append(original_doc)
+                
+                return reranked_results
+            else:
+                logger.error(f"Rerank API Error: {resp}")
+                # 降级策略：如果 Rerank 挂了，直接返回粗排的前 N 个
+                return documents[:top_n]
+                
+        except Exception as e:
+            logger.error(f"Rerank Exception: {e}")
+            # 降级策略
+            return documents[:top_n]
+    def chat(self, prompt: str, system_prompt: str = None, model: str = "qwen-turbo") -> str:
+        """
+        [新增] 通用对话生成接口，用于RAG的最终回答或作为测试裁判(Judge)
+        """
+        messages = []
+        if system_prompt:
+            messages.append({'role': 'system', 'content': system_prompt})
+        messages.append({'role': 'user', 'content': prompt})
+
+        try:
+            resp = dashscope.Generation.call(
+                model=model,
+                messages=messages,
+                result_format='message'
+            )
+            if resp.status_code == HTTPStatus.OK:
+                return resp.output.choices[0].message.content
+            else:
+                logger.error(f"Chat API Error: {resp}")
+                return "Error generating response."
+        except Exception as e:
+            logger.error(f"Chat Exception: {e}")
+            return "Error generating response."
+        
+llm_service = LLMService()

backend/utils.py

@@ -1,39 +0,0 @@
-from urllib.parse import urlparse, urlunparse
-from sqlalchemy import create_engine, MetaData, Table, select, update, and_
-# backend/llm_service.py
-import dashscope
-from http import HTTPStatus
-from .config import settings
-
-# 初始化 Dashscope
-dashscope.api_key = settings.DASHSCOPE_API_KEY
-
-def get_embeddings(texts: list[str]):
-    """调用通义千问 embedding 模型"""
-    resp = dashscope.TextEmbedding.call(
-        model=dashscope.TextEmbedding.Models.text_embedding_v3, # 或其他模型
-        input=texts
-    )
-    if resp.status_code == HTTPStatus.OK:
-        return [item['embedding'] for item in resp.output['embeddings']]
-    else:
-        print(f"Embedding Error: {resp}")
-        return []
-def normalize_url(url: str) -> str:
-    if not url: return ""
-    url = url.strip()
-    parsed = urlparse(url)
-    scheme = parsed.scheme.lower()
-    netloc = parsed.netloc.lower()
-    path = parsed.path.rstrip('/')
-    if not path: path = ""
-    return urlunparse((scheme, netloc, path, parsed.params, parsed.query, ""))
-
-def make_response(code: int, msg: str = "Success", data: any = None):
-    """
-    统一响应格式
-    :param code: 1 成功, 0 失败, 其他自定义
-    :param msg: 提示信息
-    :param data: 返回数据
-    """
-    return {"code": code, "msg": msg, "data": data}

backend/utils/common.py

@@ -0,0 +1,29 @@
+from urllib.parse import urlparse, urlunparse
+
+def make_response(code: int, msg: str = "Success", data: any = None):
+    """统一 API 响应格式封装"""
+    return {"code": code, "msg": msg, "data": data}
+
+def normalize_url(url: str) -> str:
+    """
+    URL 标准化处理
+    1. 去除首尾空格
+    2. 移除 fragment (#后面的内容)
+    3. 移除 query 参数 (视业务需求而定，这里假设不同 query 是同一页面)
+    4. 移除尾部斜杠
+    示例:
+    "https://www.example.com/path/" -> "https://www.example.com/path"
+    "https://www.example.com/path?query=1" -> "https://www.example.com/path"
+    """
+    if not url:
+        return ""
+    
+    parsed = urlparse(url.strip())
+    # 重新组合：scheme, netloc, path, params, query, fragment
+    # 这里我们只保留 scheme, netloc, path
+    clean_path = parsed.path.rstrip('/')
+    
+    # 构造新的 parsed 对象 (param, query, fragment 置空)
+    new_parsed = parsed._replace(path=clean_path, params='', query='', fragment='')
+    
+    return urlunparse(new_parsed)

backend/utils/text_process.py

@@ -0,0 +1,61 @@
+import re
+from langchain_text_splitters import RecursiveCharacterTextSplitter, MarkdownHeaderTextSplitter
+
+class TextProcessor:
+    """文本处理工具类：负责 Markdown 清洗和切分"""
+    
+    def __init__(self):
+        # 基于 Markdown 标题的语义切分器
+        self.md_splitter = MarkdownHeaderTextSplitter(
+            headers_to_split_on=[
+                ("#", "h1"),
+                ("##", "h2"),
+                ("###", "h3"),
+            ],
+            strip_headers=False
+        )
+
+        # 备用的字符切分器
+        self.char_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=800, 
+            chunk_overlap=100,
+            separators=["\n\n", "\n", "。", "！", "？", " ", ""]
+        )
+
+    def clean_markdown(self, text: str) -> str:
+        """清洗 Markdown 中的网页噪音"""
+        if not text: return ""
+        
+        # 去除 'Skip to main content'
+        text = re.sub(r'\[Skip to main content\].*?\n', '', text, flags=re.IGNORECASE)
+        # 去除页脚导航 (Previous / Next)
+        text = re.sub(r'\[Previous\].*?\[Next\].*', '', text, flags=re.DOTALL | re.IGNORECASE)
+        
+        return text.strip()
+
+    def split_markdown(self, text: str):
+        """执行切分策略：先按标题切，过长则按字符切"""
+        md_chunks = self.md_splitter.split_text(text)
+        final_chunks = []
+        
+        for chunk in md_chunks:
+            # chunk.page_content 是文本
+            # chunk.metadata 是标题层级
+            
+            if len(chunk.page_content) > 1000:
+                sub_texts = self.char_splitter.split_text(chunk.page_content)
+                for sub in sub_texts:
+                    final_chunks.append({
+                        "content": sub,
+                        "metadata": chunk.metadata
+                    })
+            else:
+                final_chunks.append({
+                    "content": chunk.page_content,
+                    "metadata": chunk.metadata
+                })
+                
+        return final_chunks
+
+# 单例工具
+text_processor = TextProcessor()

docs/RAGtest.md

@@ -0,0 +1,157 @@
+这是一份标准的 **RAG 系统测试与验证设计文档**。你可以将其作为项目文档的一部分，用于指导开发团队进行自动化测试框架的搭建、QA 团队进行测试用例的编写，以及算法工程师进行模型选型。
+
+---
+
+# RAG 知识库检索与生成系统测试设计规范
+**RAG System Test Design Specification**
+
+**版本**: 1.0
+**状态**: 拟定中
+
+---
+
+## 1. 测试背景与目标 (Background & Objectives)
+
+RAG（检索增强生成）系统涉及“检索器（Retriever）”与“生成器（Generator）”的复杂交互。传统的软件测试（单元测试、集成测试）无法有效评估其回答质量。本设计文档旨在建立一套**端到端（End-to-End）**与**分段式（Component-wise）**结合的测试框架，以达成以下目标：
+
+1.  **量化评估**：将模糊的“回答好坏”转化为可度量的指标（如召回率、忠实度得分）。
+2.  **归因分析**：当系统表现不佳时，能快速定位是“检索没找对”还是“LLM 没答好”。
+3.  **选型决策**：通过横向对比（Benchmark），为检索算法（BM25/Vector/Hybrid）和模型选择提供数据支撑。
+4.  **短板识别**：自动识别系统在特定场景（如多语言、数值推理、负向测试）下的薄弱环节。
+
+---
+
+## 2. 测试架构原理 (Test Architecture)
+
+测试框架基于 **RAG 三元组 (RAG Triad)** 理论进行设计，分别针对链路中的三个关键节点进行评估：
+
+### 2.1 评估对象
+*   **Query (用户提问)**: 测试的输入。
+*   **Context (检索上下文)**: 检索器从知识库召回的 Top-K 文档片段。
+*   **Response (系统回答)**: LLM 基于 Query 和 Context 生成的最终文本。
+
+### 2.2 测试流向
+1.  **检索层评估 (Retrieval Evaluation)**: `Query` $\leftrightarrow$ `Context`
+    *   *核心问题*: 检索到的内容是否包含回答问题所需的全部信息？
+2.  **生成层评估 (Generation Evaluation)**:
+    *   `Context` $\leftrightarrow$ `Response`: **忠实度 (Faithfulness)**。回答是否完全基于上下文？有无幻觉？
+    *   `Query` $\leftrightarrow$ `Response`: **相关度 (Relevance)**。回答是否解决了用户的问题？
+
+---
+
+## 3. 测试数据集设计 (Dataset Design)
+
+为了全面评估系统能力，构建“黄金数据集”是测试的基础。数据集需包含以下维度的字段：
+
+### 3.1 数据字段定义
+| 字段名 | 说明 | 用途 |
+| :--- | :--- | :--- |
+| **ID** | 唯一标识符 | 用于追踪 Case |
+| **Type** | 问题分类 | 用于计算 "Weakest Category" (见 3.2) |
+| **Query** | 用户模拟问题 | 输入 |
+| **Ground Truth Answer** | 标准答案 | 用于生成层对比评分 |
+| **Keywords/Key Information** | 关键信息点/术语 | 用于计算检索层的 Recall |
+| **Ground Truth Context IDs** | (可选) 预期命中的文档ID | 用于计算 Hit Rate |
+
+### 3.2 场景分类体系 (Category Taxonomy)
+测试集必须覆盖以下场景，以防止模型“偏科”：
+
+1.  **Core Function (核心功能)**: 基础的概念解释和流程指引。
+2.  **Detail/Numeric (细节与数值)**: 涉及具体参数、价格、限制阈值等精确信息（考察向量检索的弱点）。
+3.  **Inference (推理集成)**: 需要综合多段文档才能回答的复杂问题（考察 Context Window 和推理能力）。
+4.  **Multilingual (跨语言)**: 中文问英文文档，或反之（考察 Embedding 对齐能力）。
+5.  **Negative Test (负向/拒答)**: 知识库中不存在的问题（考察系统抗幻觉能力，预期输出为“未找到信息”）。
+6.  **Safety/Injection (安全)**: Prompt 注入攻击防御测试。
+
+---
+
+## 4. 控制变量与横向对比设计 (Experimental Design)
+
+为选出最佳技术方案，需设计控制变量矩阵（Control Matrix），进行 A/B 测试或横向评测。
+
+### 4.1 实验组配置 (Configuration Matrix)
+
+| 实验组 | 检索机制 (Retrieval) | 权重策略 | 重排序 (Rerank) | 测试目的 |
+| :--- | :--- | :--- | :--- | :--- |
+| **Group A (Baseline)** | **Keyword (BM25)** | Vector=0, Keyword=1 | OFF | 模拟传统全文检索，设立基准线。 |
+| **Group B (Semantic)** | **Dense Vector** | Vector=1, Keyword=0 | OFF | 验证语义理解能力，检测对专有名词的丢失情况。 |
+| **Group C (Hybrid)** | **Hybrid Search** | Vector=0.7, Keyword=0.3 | OFF | 验证双路召回的互补性（当前主流方案）。 |
+| **Group D (High Acc)** | **Hybrid Search** | Vector=0.7, Keyword=0.3 | **ON (Top N)** | 验证引入 Rerank 模型对长尾、复杂问题的提升效果及延迟代价。 |
+
+### 4.2 性能监控
+在运行上述配置时，同步记录以下工程指标：
+*   **Latency**: 端到端耗时（P50, P95）。
+*   **Token Usage**: 消耗的 Token 数量（计算成本 ROI）。
+
+---
+
+## 5. 核心评估指标 (Key Metrics)
+
+采用 **规则计算 + LLM裁判 (LLM-as-a-Judge)** 相结合的方式。
+
+### 5.1 检索层指标 (Retrieval Metrics)
+
+| 指标 | 定义 | 计算方法 | 优劣判断 |
+| :--- | :--- | :--- | :--- |
+| **Keyword Recall** | 关键词召回率 | $\frac{\text{检索内容中命中的关键词数}}{\text{标准答案中的关键词总数}}$ | 低于 50% 说明切片策略或检索算法严重失效。 |
+| **Hit Rate** | 命中率 | 检索结果 Top-K 中是否包含至少一个相关文档片段。 | 二分类指标 (0/1)。 |
+
+### 5.2 生成层指标 (Generation Metrics)
+
+使用高智能模型（如 GPT-4o / Qwen-Max）作为裁判，进行 1-5 分打分。
+
+| 指标 | 定义 | 评分标准 (Rubric) |
+| :--- | :--- | :--- |
+| **Correctness** | 正确性 | **5分**: 含义与 Ground Truth 完全一致。<br>**1分**: 完全错误或包含严重幻觉。 |
+| **Completeness** | 完整性 | 答案是否涵盖了问题询问的所有方面？(主要针对列表类问题)。 |
+| **Honesty** | 诚实度 | (针对负向测试) 当无相关信息时，是否诚实回答“不知道”而非编造。 |
+
+### 5.3 诊断性指标 (Diagnostic Metrics)
+
+*   **Weakest Category (最弱类别)**:
+    *   计算逻辑：按 `Type` 分组，计算各组的平均分，取最低者。
+    *   作用：直接指出系统短板（例如：“多语言能力最差”或“数值细节最差”），指导后续优化方向。
+
+---
+
+## 6. 自动化测试流程 (Workflow)
+
+1.  **Setup Phase**:
+    *   加载 `dataset.json`。
+    *   初始化所有待测的 RAG Pipeline 配置。
+
+2.  **Execution Phase (Loop)**:
+    *   遍历测试集中的每个 Query。
+    *   **Step 1 Retrieve**: 调用检索接口，获取 Context。
+    *   **Step 2 Generate**: 将 Context + Query 送入 LLM 生成 Answer。
+    *   **Step 3 Measure**: 记录 Latency。
+
+3.  **Evaluation Phase**:
+    *   **Rule Check**: 计算 Keyword Recall。
+    *   **AI Judge**: 将 (Query, Answer, Ground Truth) 组装 Prompt 发送给裁判 LLM 打分。
+
+4.  **Reporting Phase**:
+    *   输出汇总报表，包含：各配置的平均分、平均召回率、延迟、最弱类别。
+    *   输出 Bad Case 列表（得分 < 3 的用例）。
+
+---
+
+## 7. 附录：LLM 裁判 Prompt 模板
+
+```markdown
+你是一名 RAG 系统测试裁判。请评估【系统回答】相对于【标准答案】的质量。
+
+【问题类型】: {type}
+【用户问题】: {query}
+【标准答案】: {ground_truth}
+【系统回答】: {prediction}
+
+请打分 (1-5)：
+- 5分: 完美。逻辑正确，无幻觉，细节精准。
+- 3分: 及格。包含核心信息，但有遗漏或啰嗦。
+- 1分: 错误。答非所问，或在负向测试中产生幻觉。
+
+特别规则：对于 "Negative Test"，如果标准答案是“不支持/未提及”，而系统回答了“未找到相关信息”，请给 5 分。
+
+输出 JSON: {"score": int, "reason": "string"}
+```

docs/docker.md

@@ -1,4 +1,3 @@
-
 # Wiki Crawler Backend 部署操作手册
 
 ## 核心配置信息 (每次只需修改这里)
@@ -7,7 +6,7 @@
 
 | **字段**       | **当前值 (示例)**            | **说明**                 | **每次要改吗？** |
 | -------------------- | ---------------------------------- | ------------------------------ | ---------------------- |
-| **Version**    | **v1.0.3**                   | **镜像的版本标签 (Tag)** | **是 (必须改)**  |
+| **Version**    | **v1.0.7**                   | **镜像的版本标签 (Tag)** | **是 (必须改)**  |
 | **Image Name** | **wiki-crawl-backend**       | **镜像/容器的名字**      | **否 (固定)**    |
 | **Namespace**  | **qg-demo**                  | **阿里云命名空间**       | **否 (固定)**    |
 | **Registry**   | **crpi-1rwd6fvain6t49g2...** | **阿里云仓库地址**       | **否 (固定)**    |
@@ -20,18 +19,18 @@
 
 ### 1. 构建镜像 (Build)
 
-**修改命令最后的版本号** **v1.0.3**
+**修改命令最后的版本号** **v1.0.7**
 
 ```powershell
-docker build -t crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.3 .
+docker build -t crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.7 .
 ```
 
 ### 2. 推送镜像 (Push)
 
-**修改命令最后的版本号** **v1.0.3**
+**修改命令最后的版本号** **v1.0.7**
 
 ```powershell
-docker push crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.3
+docker push crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.7
 ```
 
 > **成功标准：** **看到进度条走完，且最后显示** **Pushed**。
@@ -44,10 +43,10 @@ docker push crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/w
 
 ### 1. 拉取新镜像 (Pull)
 
-**修改命令最后的版本号** **v1.0.3**
+**修改命令最后的版本号** **v1.0.7**
 
 ```bash
-docker pull crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.3
+docker pull crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.7
 ```
 
 ### 2. 停止并删除旧容器
@@ -56,12 +55,13 @@ docker pull crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/w
 
 ```bash
 docker stop wiki-crawl-backend
+
 docker rm wiki-crawl-backend
 ```
 
 ### 3. 启动新容器 (Run) - 关键步骤
 
-**修改命令最后的版本号** **v1.0.3**
+**修改命令最后的版本号** **v1.0.7**
 
 **code**Bash
 
@@ -69,7 +69,7 @@ docker rm wiki-crawl-backend
 docker run -d --name wiki-crawl-backend \
   -e PYTHONUNBUFFERED=1 \
   -p 80:8000 \
-  crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.3
+  crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.7
 ```
 
 ### 4. 验证与日志查看
@@ -132,9 +132,9 @@ docker image prune -a -f
 
 ### 5. 那个超长的 URL
 
-**crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.3**
+**crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.7**
 
 * **Registry (仓库地址)**: **crpi-1rwd...aliyuncs.com** **-> 你的专属阿里云仓库服务器。**
 * **Namespace (命名空间)**: **qg-demo** **-> 你在仓库里划出的个人地盘。**
 * **Image Name (镜像名)**: **wiki-crawl-backend** **-> 这个项目的名字。**
-* **Tag (标签)**: **v1.0.3** **-> 相当于软件的版本号。如果不写 Tag，默认就是** **latest**。**生产环境强烈建议写明确的版本号**，方便回滚（比如 1.0.3 挂了，你可以立马用 1.0.2 启动）。
+* **Tag (标签)**: **v1.0.7** **-> 相当于软件的版本号。如果不写 Tag，默认就是** **latest**。**生产环境强烈建议写明确的版本号**，方便回滚（比如 1.0.3 挂了，你可以立马用 1.0.2 启动）。

docs/开发计划.md

@@ -0,0 +1,49 @@
+# 下一步开发计划
+
+## 2025.1.20
+
+1. 知识库RAG
+   1. 测试
+   测试相关资料参考链接: <https://1988251901502969000zhuanlan.zhihu.com/p/>
+
+      - [ ] 参照主流知识库架构增减修改当前知识库字段
+      - [ ] 根据主流RAG测试要求完善知识库检索测试
+      - [ ] 开发LLM输出测试
+      - [ ] 横向对比不同检索方法或模型下的测试效果
+   2. dify原生知识库
+   dify原生支持的知识库可以直接配置firecrawl的api_key，实现知识库爬取，因此
+2. 后端封装backend
+
+   1. 重置接口，v1版本，restfulAPI
+      预期实现效果：
+      - [x] 添加任务
+      - [x] 查询任务
+      - [x] 执行任务
+      - [x] 获取所有知识库
+      - [x] 知识库搜索
+   2. 新增功能
+      - [ ] 业务操作原子化
+      - [ ] 知识库更新相关
+      - [ ] 日志功能
+   3. 包装成MCP工具供dify调用
+      - [x] 完成backend的MCP包装并测试
+      - [x] 发现dify可以直接用http接口封装工具，所以就用原来的fastapi去做了
+   4. 阿里云部署
+      - [x] 将后端部署阿里云
+
+3. dify节点
+
+   - [x] 完成dify的LLM输出工具，主要负责处理搜索逻辑和问题分类，调用api，发布工具。
+   也可能直接在backend里全部实现，直接集成到bot里
+
+4. firecrawl方案
+   1. 闭源方案购买
+      年费大概9000元/年，不支持绕过robots.txt
+   2. 开源方案
+      这个可以考虑自己部署一套，然后分发apikey，问题在于firecrawl的开源证书的法律风险，以及需要修改robots和计费相关的部分，以下是调研任务
+      - [x] robots.txt问题
+      在apps/api，这个是可以直接修改代码改的，但是关键是要吃透相关代码在各层级间的传递
+      - [x] 部署的计费相关
+      在apps/nuq-postgres，后续可能部署之后单独分发apikey给客户，因此需要一个计费方案
+      - [x] .env问题
+      需要查阅资料了解里面的一些参数配置重新配置之后好上云，包括API_KEY配置、流量统计和配置

pyproject.toml

@@ -12,14 +12,20 @@ dependencies = [
     "langchain>=1.2.0",
     "langchain-community>=0.4.1",
     "langchain-core>=1.2.2",
+    "matplotlib>=3.10.8",
+    "mcp>=1.25.0",
     "numpy>=2.2.6",
     "pgvector>=0.4.2",
     "pinecone>=8.0.0",
     "pip>=25.3",
     "psycopg2-binary>=2.9.11",
+    "pydantic-settings>=2.12.0",
     "pymilvus>=2.6.5",
     "qdrant-client==1.10.1",
     "redis>=7.1.0",
     "requests>=2.32.5",
+    "seaborn>=0.13.2",
+    "tabulate>=0.9.0",
+    "tqdm>=4.67.1",
     "uvicorn>=0.38.0",
 ]

scripts/evaluate_rag.py

@@ -0,0 +1,192 @@
+import sys
+import os
+import json
+import requests
+import time
+import numpy as np
+from time import sleep
+
+# 将项目根目录加入路径
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from backend.core.config import settings
+
+# ================= ⚙️ 配置区域 =================
+BASE_URL = "http://127.0.0.1:8000"
+TASK_ID = 19  # ⚠️ 请修改为你实际爬取数据的 Task ID
+# 自动适配操作系统路径
+TEST_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "test_dataset.json")
+# ==============================================
+
+class Colors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKCYAN = '\033[96m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
+
+def get_rag_results(query):
+    """
+    调用搜索接口并记录耗时
+    """
+    start_ts = time.time()
+    try:
+        # 调用 V2 接口，该接口内部已集成 混合检索 -> Rerank
+        res = requests.post(
+            f"{BASE_URL}/api/v2/search",
+            json={"query": query, "task_id": TASK_ID, "limit": 5}, # 获取 Top 5
+            timeout=15
+        )
+        latency = (time.time() - start_ts) * 1000 # ms
+        
+        if res.status_code != 200:
+            print(f"{Colors.FAIL}❌ API Error {res.status_code}: {res.text}{Colors.ENDC}")
+            return [], 0
+            
+        res_json = res.json()
+        chunks = res_json.get('data', {}).get('results', [])
+        return chunks, latency
+    except Exception as e:
+        print(f"{Colors.FAIL}❌ 请求异常: {e}{Colors.ENDC}")
+        return [], 0
+
+def check_hit(content, keywords):
+    """
+    检查切片相关性 (Relevance Check)
+    使用关键词匹配作为 Ground Truth 的轻量级验证。
+    """
+    if not keywords: return True # 拒答题或开放性题目跳过关键词检查
+    if not content: return False
+    
+    content_lower = content.lower()
+    for k in keywords:
+        if k.lower() in content_lower:
+            return True
+    return False
+
+def run_evaluation():
+    # 1. 加载测试集
+    if not os.path.exists(TEST_FILE):
+        print(f"{Colors.FAIL}❌ 找不到测试文件: {TEST_FILE}{Colors.ENDC}")
+        print("请确保 scripts/test_dataset.json 文件存在。")
+        return
+
+    with open(TEST_FILE, 'r', encoding='utf-8') as f:
+        dataset = json.load(f)
+
+    print(f"{Colors.HEADER}🚀 开始全维度量化评测 (Task ID: {TASK_ID}){Colors.ENDC}")
+    print(f"📄 测试集包含 {len(dataset)} 个样本\n")
+    
+    # === 统计容器 ===
+    metrics = {
+        "p_at_1": [],    # Precision@1: 正确答案排第1
+        "hit_at_5": [],  # HitRate@5: 正确答案在前5
+        "mrr": [],       # MRR: 倒数排名分数
+        "latency": []    # 耗时
+    }
+
+    # === 开始循环测试 ===
+    for i, item in enumerate(dataset):
+        query = item['query']
+        print(f"📝 Case {i+1}: {Colors.BOLD}{query}{Colors.ENDC}")
+        
+        # 执行检索
+        chunks, latency = get_rag_results(query)
+        metrics['latency'].append(latency)
+        
+        # 计算单次指标
+        is_hit_at_5 = 0
+        p_at_1 = 0
+        reciprocal_rank = 0.0
+        hit_position = -1
+        hit_chunk = None
+
+        # 遍历 Top 5 结果
+        for idx, chunk in enumerate(chunks):
+            if check_hit(chunk['content'], item['keywords']):
+                # 命中！
+                is_hit_at_5 = 1
+                hit_position = idx
+                reciprocal_rank = 1.0 / (idx + 1)
+                hit_chunk = chunk
+                
+                # 如果是第1个就命中了
+                if idx == 0:
+                    p_at_1 = 1
+                
+                # 找到即停止 (MRR计算只需知道第一个正确答案的位置)
+                break
+        
+        # 记录指标
+        metrics['p_at_1'].append(p_at_1)
+        metrics['hit_at_5'].append(is_hit_at_5)
+        metrics['mrr'].append(reciprocal_rank)
+
+        # 打印单行结果
+        if is_hit_at_5:
+            rank_display = f"Rank {hit_position + 1}"
+            color = Colors.OKGREEN if hit_position == 0 else Colors.OKCYAN
+            source = hit_chunk.get('source_url', 'Unknown')
+            
+            # 跨语言污染检查 (简单规则)
+            warning = ""
+            if "/es/" in source and "Spanish" not in query: warning = f"{Colors.WARNING}[跨语言风险]{Colors.ENDC}"
+            elif "/zh/" in source and "如何" not in query and "什么" not in query: warning = f"{Colors.WARNING}[跨语言风险]{Colors.ENDC}"
+            
+            print(f"   {color}✅ 命中 ({rank_display}){Colors.ENDC} | MRR: {reciprocal_rank:.2f} | 耗时: {latency:.0f}ms {warning}")
+        else:
+            print(f"   {Colors.FAIL}❌ 未命中{Colors.ENDC} | 预期关键词: {item['keywords']}")
+        
+        # 稍微间隔，避免触发 API 频率限制
+        sleep(0.1)
+
+    # === 最终计算 ===
+    count = len(dataset)
+    if count == 0: return
+
+    avg_p1 = np.mean(metrics['p_at_1']) * 100
+    avg_hit5 = np.mean(metrics['hit_at_5']) * 100
+    avg_mrr = np.mean(metrics['mrr'])
+    avg_latency = np.mean(metrics['latency'])
+    p95_latency = np.percentile(metrics['latency'], 95)
+
+    print("\n" + "="*60)
+    print(f"{Colors.HEADER}📊 最终量化评估报告 (Evaluation Report){Colors.ENDC}")
+    print("="*60)
+    
+    # 1. Precision@1 (最关键指标)
+    print(f"🥇 {Colors.BOLD}Precision@1 (首位精确率): {avg_p1:.1f}%{Colors.ENDC}")
+    print(f"   - 意义: 用户能否直接得到正确答案。引入 Rerank 后此项应显著提高。")
+
+    # 2. Hit Rate / Recall@5
+    print(f"🥈 Hit Rate@5 (前五召回率):   {avg_hit5:.1f}%")
+    print(f"   - 意义: 数据库是否真的包含答案。如果此项低，说明爬虫没爬全或混合检索漏了。")
+
+    # 3. MRR
+    print(f"🥉 MRR (平均倒数排名):       {avg_mrr:.3f} / 1.0")
+    
+    # 4. Latency
+    print(f"⚡ Avg Latency (平均耗时):     {avg_latency:.0f} ms")
+    print(f"⚡ P95 Latency (95%分位):      {p95_latency:.0f} ms")
+    print("="*60)
+
+    # === 智能诊断 ===
+    print(f"{Colors.HEADER}💡 诊断建议:{Colors.ENDC}")
+    
+    if avg_p1 < avg_hit5:
+        gap = avg_hit5 - avg_p1
+        print(f"  • {Colors.WARNING}排序优化空间大{Colors.ENDC}: 召回了但没排第一的情况占 {gap:.1f}%。")
+        print("    -> 你的 Rerank 模型生效了吗？或者 Rerank 的 Top N 截断是否太早？")
+    elif avg_p1 > 80:
+        print(f"  • {Colors.OKGREEN}排序效果优秀{Colors.ENDC}: 绝大多数正确答案都排在第一位。")
+        
+    if avg_hit5 < 50:
+        print(f"  • {Colors.FAIL}召回率过低{Colors.ENDC}: 可能是测试集关键词太生僻，或者 TS_RANK 权重过低。")
+
+    if avg_latency > 2000:
+        print(f"  • {Colors.WARNING}系统响应慢{Colors.ENDC}: 2秒以上。检查是否因为 Rerank 文档过多(建议 <= 50个)。")
+
+if __name__ == "__main__":
+    run_evaluation()

scripts/test_apis.py

@@ -1,96 +1,176 @@
 import requests
+import time
 import json
-import random
+import sys
 
-# 配置后端地址
-BASE_URL = "http://47.122.127.178"
+# ================= ⚙️ 配置区域 =================
+BASE_URL = "http://47.122.127.178/api/v3"
+# 测试目标：Firecrawl 官方文档 (结构清晰，适合测试)
+TARGET_URL = "https://docs.firecrawl.dev" 
+# 测试搜索词
+TEST_QUERY = "credits pricing"
+# ==============================================
 
-def log_res(name, response):
-    print(f"\n=== 测试接口: {name} ===")
-    if response.status_code == 200:
-        res_json = response.json()
-        print(f"状态: 成功 (HTTP 200)")
-        print(f"返回数据: {json.dumps(res_json, indent=2, ensure_ascii=False)}")
-        return res_json
-    else:
-        print(f"状态: 失败 (HTTP {response.status_code})")
-        print(f"错误信息: {response.text}")
-        return None
+class Colors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKCYAN = '\033[96m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
 
-def run_tests():
-    # 测试数据准备
-    test_root_url = f"https://example.com/wiki_{random.randint(1000, 9999)}"
-    
-    # 1. 测试 /register
-    print("步骤 1: 注册新任务...")
-    res = requests.post(f"{BASE_URL}/register", json={"url": test_root_url})
-    data = log_res("注册任务", res)
-    if not data or data['code'] != 1: return
-    task_id = data['data']['task_id']
+def log(step, msg, color=Colors.OKBLUE):
+    print(f"{color}[{step}] {msg}{Colors.ENDC}")
 
-    # 2. 测试 /add_urls
-    print("\n步骤 2: 模拟爬虫发现了新链接，存入队列...")
-    sub_urls = [
-        f"{test_root_url}/page1",
-        f"{test_root_url}/page2",
-        f"{test_root_url}/page1" # 故意重复一个，测试后端去重
-    ]
-    res = requests.post(f"{BASE_URL}/add_urls", json={
-        "task_id": task_id,
-        "urls": sub_urls
-    })
-    log_res("存入新链接", res)
+def run_v3_test():
+    print(f"{Colors.HEADER}🚀 开始 Wiki Crawler V3 API 全链路测试{Colors.ENDC}\n")
 
-    # 3. 测试 /pending_urls
-    print("\n步骤 3: 模拟爬虫节点获取待处理任务...")
-    res = requests.post(f"{BASE_URL}/pending_urls", json={
-        "task_id": task_id,
-        "limit": 2
-    })
-    data = log_res("获取待处理URL", res)
-    if not data or not data['data']['urls']: 
-        print("没有获取到待处理URL，停止后续测试")
+    # ---------------------------------------------------------
+    # 1. 创建任务 (POST /tasks)
+    # ---------------------------------------------------------
+    log("STEP 1", f"创建任务 (Map): {TARGET_URL}")
+    try:
+        res = requests.post(f"{BASE_URL}/tasks", json={"url": TARGET_URL})
+        resp = res.json()
+        
+        if resp['code'] != 1:
+            log("FAIL", f"任务创建失败: {resp}", Colors.FAIL)
+            return
+
+        data = resp['data']
+        task_id = data['task_id']
+        count = data.get('count', 0)
+        is_new = data.get('is_new', False)
+        
+        status_text = "新任务" if is_new else "已有任务"
+        log("SUCCESS", f"ID: {task_id} | 状态: {status_text} | 发现链接: {count}", Colors.OKGREEN)
+        
+    except Exception as e:
+        log("FAIL", f"请求异常: {e}", Colors.FAIL)
         return
-    
-    target_url = data['data']['urls'][0]
 
-    # 4. 测试 /save_results
-    print("\n步骤 4: 模拟爬虫抓取完成，存入知识片段和向量...")
-    # 模拟一个 1536 维的向量（已处理精度）
-    mock_embedding = [round(random.uniform(-1, 1), 8) for _ in range(1536)]
+    # ---------------------------------------------------------
+    # 2. 触发执行 (POST /tasks/{id}/run)
+    # ---------------------------------------------------------
+    log("STEP 2", f"触发后台多线程爬取 (Task {task_id})")
+    try:
+        # batch_size=10, 意味着会启动多线程处理这10个链接
+        res = requests.post(
+            f"{BASE_URL}/tasks/{task_id}/run", 
+            json={"batch_size": 10}
+        )
+        resp = res.json()
+        
+        if resp['code'] == 1:
+            log("SUCCESS", "后台任务已接受 (202 Accepted)", Colors.OKGREEN)
+        else:
+            log("FAIL", f"启动失败: {resp}", Colors.FAIL)
+            return
+            
+    except Exception as e:
+        log("FAIL", f"请求异常: {e}", Colors.FAIL)
+        return
+
+    # ---------------------------------------------------------
+    # 3. 实时监控 (GET /tasks/{id})
+    # ---------------------------------------------------------
+    log("STEP 3", "进入实时监控模式 (轮询状态)...", Colors.OKCYAN)
     
-    payload = {
-        "task_id": task_id,
-        "results": [
-            {
-                "source_url": target_url,
-                "chunk_index": 0,
-                "title": "测试页面标题 - 切片1",
-                "content": "这是模拟抓取到的第一段网页内容...",
-                "embedding": mock_embedding
-            },
-            {
-                "source_url": target_url,
-                "chunk_index": 1,
-                "title": "测试页面标题 - 切片2",
-                "content": "这是模拟抓取到的第二段网页内容...",
-                "embedding": mock_embedding
+    max_retries = 20
+    is_completed_batch = False
+    
+    for i in range(max_retries):
+        try:
+            res = requests.get(f"{BASE_URL}/tasks/{task_id}")
+            monitor = res.json()['data']
+            stats = monitor['stats']
+            active_threads = monitor['active_threads']
+            
+            # 格式化输出状态
+            active_count = len(active_threads)
+            progress_bar = f"Pending: {stats['pending']} | Processing: {stats['processing']}/{active_count} | Completed: {stats['completed']}"
+            
+            print(f"   [{i+1}/{max_retries}] {progress_bar}")
+            
+            # 打印正在爬取的 URL (抽样)
+            if active_threads:
+                # 显示全部
+                print("\n   ⚡ 当前正在处理的 URL:")
+                for url in active_threads:
+                    print(f"   ⚡ {url}")
+            
+            # 判断完成条件：
+            # 1. 数据库 processing 为 0
+            # 2. 内存 active_threads 为 0
+            # 3. 至少有一个 completed (防止任务没开始就判定结束)
+            if stats['processing'] == 0 and active_count == 0 and stats['completed'] > 0:
+                is_completed_batch = True
+                print("\n")
+                log("SUCCESS", "✅ 当前批次处理完毕！", Colors.OKGREEN)
+                break
+            
+            time.sleep(1.5) # 轮询间隔
+            
+        except Exception as e:
+            print(f"\n   ⚠️ 监控异常: {e}")
+            break
+            
+    if not is_completed_batch:
+        print("\n")
+        log("WARN", "监控超时，爬虫可能仍在后台运行，继续测试搜索...", Colors.WARNING)
+
+    # ---------------------------------------------------------
+    # 4. 混合搜索与验证 (POST /search)
+    # ---------------------------------------------------------
+    log("STEP 4", f"测试混合检索 + Rerank: '{TEST_QUERY}'")
+    
+    try:
+        res = requests.post(
+            f"{BASE_URL}/search", 
+            json={
+                "query": TEST_QUERY, 
+                "task_id": task_id, 
+                "limit": 3
             }
-        ]
-    }
-    res = requests.post(f"{BASE_URL}/save_results", json=payload)
-    log_res("保存结果", res)
-    # 5. 测试 /search
-    print("\n步骤 5: 测试基于向量的搜索...")
-    query = [round(random.uniform(-1, 1), 8) for _ in range(1536)]
-    res = requests.post(f"{BASE_URL}/search", json={
-        "task_id": None,
-        "query_embedding": query,
-        "limit": 5
-    })
-    log_res("基于向量的搜索", res)
-    
-    print("\n✅ 所有 API 流程测试完成！")
+        )
+        resp = res.json()
+        
+        if resp['code'] != 1:
+            log("FAIL", f"搜索失败: {resp}", Colors.FAIL)
+            return
+            
+        results = resp['data']['results']
+        if not results:
+            log("FAIL", "未搜索到结果 (Result Empty)", Colors.FAIL)
+            return
+            
+        log("SUCCESS", f"搜索命中 {len(results)} 条结果", Colors.OKGREEN)
+        
+        # === 详细验证 ===
+        first = results[0]
+        print(f"\n{Colors.WARNING}--- Top 1 结果详情 ---{Colors.ENDC}")
+        print(f"📄 标题: {first.get('title', 'N/A')}")
+        print(f"🔗 链接: {first.get('source_url')}")
+        print(f"🧭 路径: {first.get('meta_info', {}).get('header_path', 'N/A')}")
+        print(f"🎯 分数: {first.get('score')} " + ("(Reranked)" if first.get('reranked') else "(Rough)"))
+        print(f"📝 内容: {first.get('content')[:80].replace(chr(10), ' ')}...")
+        print(f"{Colors.WARNING}-----------------------{Colors.ENDC}\n")
+        
+        # 自动断言
+        if first.get('meta_info') and 'header_path' in first['meta_info']:
+            print(f"✅ [Phase 1.5] 结构化元数据验证通过")
+        else:
+            print(f"❌ [Phase 1.5] 缺少元数据")
+            
+        if first.get('reranked'):
+            print(f"✅ [Phase 2.5] Rerank 重排序生效")
+        else:
+            print(f"⚠️ [Phase 2.5] Rerank 未标记 (可能是降级或代码未更新)")
+
+    except Exception as e:
+        log("FAIL", str(e), Colors.FAIL)
 
 if __name__ == "__main__":
-    run_tests()
+    run_v3_test()

scripts/test_dataset.json

@@ -0,0 +1,86 @@
+[
+  {
+    "id": 1,
+    "type": "core_function",
+    "query": "What is the difference between /scrape and /map endpoints?",
+    "ground_truth": "/map is used to crawl a website and retrieve all URLs, while /scrape is used to extract content from a specific URL.",
+    "keywords": ["URL", "content", "specific", "retrieve"]
+  },
+  {
+    "id": 2,
+    "type": "new_feature",
+    "query": "What is the Deep Research feature?",
+    "ground_truth": "Deep Research is an alpha feature allowing agents to perform iterative research tasks.",
+    "keywords": ["alpha", "iterative", "research", "agent"]
+  },
+  {
+    "id": 3,
+    "type": "integration",
+    "query": "How can I integrate Firecrawl with ChatGPT?",
+    "ground_truth": "Firecrawl can be integrated via MCP (Model Context Protocol).",
+    "keywords": ["MCP", "Model Context Protocol", "setup"]
+  },
+  {
+    "id": 4,
+    "type": "multilingual_zh",
+    "query": "如何进行私有化部署 (Self-host)？",
+    "ground_truth": "你需要使用 Docker Compose 进行部署，文档位于 /self-host/quick-start/docker-compose。",
+    "keywords": ["Docker", "Compose", "self-host", "deploy"]
+  },
+  {
+    "id": 5,
+    "type": "api_detail",
+    "query": "What parameters are available for the /extract endpoint?",
+    "ground_truth": "The extract endpoint allows defining a schema for structured data extraction.",
+    "keywords": ["schema", "structured", "prompt"]
+  },
+  {
+    "id": 6,
+    "type": "numeric",
+    "query": "How do credits work for the scrape endpoint?",
+    "ground_truth": "Specific credit usage details are in the /credits endpoint documentation (usually 1 credit per page for basic scrape).",
+    "keywords": ["credit", "usage", "cost"]
+  },
+  {
+    "id": 7,
+    "type": "negative_test",
+    "query": "Does Firecrawl support scraping video content from YouTube?",
+    "ground_truth": "The documentation does not mention video scraping support.",
+    "keywords": []
+  },
+  {
+    "id": 8,
+    "type": "advanced",
+    "query": "How to use batch scrape?",
+    "ground_truth": "Use the /batch/scrape endpoint to submit multiple URLs at once.",
+    "keywords": ["batch", "multiple", "URLs"]
+  },
+  {
+    "id": 9,
+    "type": "automation",
+    "query": "Is there an n8n integration guide?",
+    "ground_truth": "Yes, there is a workflow automation guide for n8n.",
+    "keywords": ["n8n", "workflow", "automation"]
+  },
+  {
+    "id": 10,
+    "type": "security",
+    "query": "Where can I find information about webhook security?",
+    "ground_truth": "Information is available in the Webhooks Security section.",
+    "keywords": ["webhook", "security", "signature"]
+  },
+  {
+    "id": 11,
+    "type": "cross_lingual_trap",
+    "query": "Explain the crawl features in French.",
+    "ground_truth": "The system should ideally retrieve the French document (/fr/features/crawl) and answer in French.",
+    "keywords": ["fonctionnalités", "crawl", "fr"]
+  },
+  {
+    "id": 12,
+    "type": "api_history",
+    "query": "How to check historical token usage?",
+    "ground_truth": "Use the /token-usage-historical endpoint.",
+    "keywords": ["token", "usage", "historical"]
+  }
+]

scripts/test_firecrawl.py

@@ -0,0 +1,76 @@
+import requests
+import json
+
+def map_firecrawl_docs():
+    """
+    调用本地 Firecrawl 服务的 map 功能，映射 Firecrawl 文档地址
+    """
+    # 本地 Firecrawl 服务地址（默认端口3002，根据你的实际情况调整）
+    base_url = "http://localhost:3002"
+    # map 功能的接口路径
+    map_endpoint = f"{base_url}/api/v1/map"
+    
+    # 请求参数
+    payload = {
+        # Firecrawl 文档的基础地址
+        "url": "https://docs.firecrawl.dev",
+        # 可选配置：控制爬取深度、是否包含子路径等
+        "config": {
+            "depth": 2,  # 爬取深度，2层足够覆盖文档主要结构
+            "includeSubdomains": False,
+            "onlyMainContent": True,  # 只爬取主要内容，过滤导航/广告等
+            "limit": 50  # 限制爬取页面数量，避免爬取过多
+        }
+    }
+    
+    # 请求头（如果本地服务需要认证，添加你的 API Key）
+    headers = {
+        "Content-Type": "application/json",
+        # 如果有 API Key，取消下面注释并替换为你的密钥
+        # "Authorization": "Bearer YOUR_FIRECRAWL_API_KEY"
+    }
+
+    try:
+        # 发送 POST 请求调用 map 功能
+        response = requests.post(
+            map_endpoint,
+            data=json.dumps(payload),
+            headers=headers,
+            timeout=60  # 设置超时时间，避免长时间等待
+        )
+        
+        # 检查响应状态
+        response.raise_for_status()
+        
+        # 解析响应结果
+        result = response.json()
+        print("✅ Map 功能调用成功！")
+        print("\n📄 爬取结果概览：")
+        print(f"总页面数: {len(result.get('links', []))}")
+        print(f"基础URL: {result.get('baseUrl')}")
+        
+        # 打印爬取到的所有链接
+        print("\n🔗 爬取到的文档链接：")
+        for idx, link in enumerate(result.get('links', []), 1):
+            print(f"{idx}. {link}")
+            
+        # 保存结果到本地文件（方便后续查看）
+        with open("firecrawl_docs_map.json", "w", encoding="utf-8") as f:
+            json.dump(result, f, indent=2, ensure_ascii=False)
+        print("\n💾 结果已保存到 firecrawl_docs_map.json 文件")
+        
+        return result
+        
+    except requests.exceptions.ConnectionError:
+        print("❌ 连接失败！请检查本地 Firecrawl 服务是否正在运行（http://localhost:3002）")
+    except requests.exceptions.Timeout:
+        print("❌ 请求超时！爬取文档可能需要更长时间，可调整 timeout 参数")
+    except requests.exceptions.HTTPError as e:
+        print(f"❌ HTTP 错误：{e}")
+        print(f"响应内容：{response.text}")
+    except Exception as e:
+        print(f"❌ 未知错误：{str(e)}")
+
+# 执行调用
+if __name__ == "__main__":
+    map_firecrawl_docs()

scripts/update_sql.py

@@ -0,0 +1,82 @@
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from sqlalchemy import create_engine, text
+from backend.core.config import settings
+
+def update_database_schema():
+    """
+    数据库无损升级脚本
+    """
+    print(f"🔌 连接数据库: {settings.DB_NAME}...")
+    engine = create_engine(settings.DATABASE_URL)
+
+    commands = [
+        # 1. 安全添加 meta_info 列 (旧数据会自动填充为 {})
+        """
+        DO $$
+        BEGIN
+            IF NOT EXISTS (SELECT 1 FROM information_schema.columns WHERE table_name='knowledge_chunks' AND column_name='meta_info') THEN
+                ALTER TABLE knowledge_chunks ADD COLUMN meta_info JSONB DEFAULT '{}';
+                RAISE NOTICE '已添加 meta_info 列';
+            END IF;
+        END $$;
+        """,
+        
+        # 2. 安全添加 content_tsvector 列
+        """
+        DO $$
+        BEGIN
+            IF NOT EXISTS (SELECT 1 FROM information_schema.columns WHERE table_name='knowledge_chunks' AND column_name='content_tsvector') THEN
+                ALTER TABLE knowledge_chunks ADD COLUMN content_tsvector TSVECTOR;
+                RAISE NOTICE '已添加 content_tsvector 列';
+            END IF;
+        END $$;
+        """,
+
+        # 3. 创建索引 (不影响现有数据)
+        "CREATE INDEX IF NOT EXISTS idx_chunks_meta ON knowledge_chunks USING GIN (meta_info);",
+        "CREATE INDEX IF NOT EXISTS idx_chunks_tsvector ON knowledge_chunks USING GIN (content_tsvector);",
+
+        # 4. 创建触发器函数 (用于新插入的数据)
+        """
+        CREATE OR REPLACE FUNCTION chunks_tsvector_trigger() RETURNS trigger AS $$
+        BEGIN
+          new.content_tsvector := to_tsvector('english', coalesce(new.title, '') || ' ' || new.content);
+          return new;
+        END
+        $$ LANGUAGE plpgsql;
+        """,
+
+        # 5. 绑定触发器
+        """
+        DO $$
+        BEGIN
+            IF NOT EXISTS (SELECT 1 FROM pg_trigger WHERE tgname = 'tsvectorupdate') THEN
+                CREATE TRIGGER tsvectorupdate BEFORE INSERT OR UPDATE
+                ON knowledge_chunks FOR EACH ROW EXECUTE PROCEDURE chunks_tsvector_trigger();
+            END IF;
+        END $$;
+        """,
+        
+        # 6. 【新增】回填旧数据
+        # 让以前存的 task_id=6 的数据也能生成关键词索引
+        """
+        UPDATE knowledge_chunks 
+        SET content_tsvector = to_tsvector('english', coalesce(title, '') || ' ' || content)
+        WHERE content_tsvector IS NULL;
+        """
+    ]
+
+    with engine.begin() as conn:
+        for cmd in commands:
+            try:
+                conn.execute(text(cmd))
+            except Exception as e:
+                print(f"⚠️ 执行警告 (通常可忽略): {e}")
+    
+    print("✅ 数据库结构升级完成！旧数据已保留并兼容。")
+
+if __name__ == "__main__":
+    update_database_schema()

tests/rag_benchmark/dataset.json

@@ -0,0 +1,86 @@
+[
+  {
+    "id": 1,
+    "type": "core_function",
+    "query": "What is the difference between /scrape and /map endpoints?",
+    "ground_truth": "/map is used to crawl a website and retrieve all URLs, while /scrape is used to extract content from a specific URL.",
+    "keywords": ["URL", "content", "specific", "retrieve"]
+  },
+  {
+    "id": 2,
+    "type": "new_feature",
+    "query": "What is the Deep Research feature?",
+    "ground_truth": "Deep Research is an alpha feature allowing agents to perform iterative research tasks.",
+    "keywords": ["alpha", "iterative", "research", "agent"]
+  },
+  {
+    "id": 3,
+    "type": "integration",
+    "query": "How can I integrate Firecrawl with ChatGPT?",
+    "ground_truth": "Firecrawl can be integrated via MCP (Model Context Protocol).",
+    "keywords": ["MCP", "Model Context Protocol", "setup"]
+  },
+  {
+    "id": 4,
+    "type": "multilingual_zh",
+    "query": "如何进行私有化部署 (Self-host)？",
+    "ground_truth": "你需要使用 Docker Compose 进行部署，文档位于 /self-host/quick-start/docker-compose。",
+    "keywords": ["Docker", "Compose", "self-host", "deploy"]
+  },
+  {
+    "id": 5,
+    "type": "api_detail",
+    "query": "What parameters are available for the /extract endpoint?",
+    "ground_truth": "The extract endpoint allows defining a schema for structured data extraction.",
+    "keywords": ["schema", "structured", "prompt"]
+  },
+  {
+    "id": 6,
+    "type": "numeric",
+    "query": "How do credits work for the scrape endpoint?",
+    "ground_truth": "Specific credit usage details are in the /credits endpoint documentation (usually 1 credit per page for basic scrape).",
+    "keywords": ["credit", "usage", "cost"]
+  },
+  {
+    "id": 7,
+    "type": "negative_test",
+    "query": "Does Firecrawl support scraping video content from YouTube?",
+    "ground_truth": "The documentation does not mention video scraping support.",
+    "keywords": []
+  },
+  {
+    "id": 8,
+    "type": "advanced",
+    "query": "How to use batch scrape?",
+    "ground_truth": "Use the /batch/scrape endpoint to submit multiple URLs at once.",
+    "keywords": ["batch", "multiple", "URLs"]
+  },
+  {
+    "id": 9,
+    "type": "automation",
+    "query": "Is there an n8n integration guide?",
+    "ground_truth": "Yes, there is a workflow automation guide for n8n.",
+    "keywords": ["n8n", "workflow", "automation"]
+  },
+  {
+    "id": 10,
+    "type": "security",
+    "query": "Where can I find information about webhook security?",
+    "ground_truth": "Information is available in the Webhooks Security section.",
+    "keywords": ["webhook", "security", "signature"]
+  },
+  {
+    "id": 11,
+    "type": "cross_lingual_trap",
+    "query": "Explain the crawl features in French.",
+    "ground_truth": "The system should ideally retrieve the French document (/fr/features/crawl) and answer in French.",
+    "keywords": ["fonctionnalités", "crawl", "fr"]
+  },
+  {
+    "id": 12,
+    "type": "api_history",
+    "query": "How to check historical token usage?",
+    "ground_truth": "Use the /token-usage-historical endpoint.",
+    "keywords": ["token", "usage", "historical"]
+  }
+]

tests/rag_benchmark/evaluator.py

@@ -0,0 +1,74 @@
+import json
+import logging
+from backend.services.llm_service import llm_service
+
+logger = logging.getLogger(__name__)
+
+class RAGEvaluator:
+    def __init__(self):
+        self.llm = llm_service
+
+    def calculate_retrieval_metrics(self, retrieved_docs, dataset_item):
+        """
+        计算检索阶段指标: Keyword Recall (关键词覆盖率)
+        检查 dataset 中的 keywords 有多少出现在了 retrieved_docs 的 content 中
+        """
+        required_keywords = dataset_item.get("keywords", [])
+        if not required_keywords:
+            return {"keyword_recall": 1.0, "hit": True} # 没有关键词要求，默认算对
+
+        # 将所有检索到的文本拼接并转小写
+        full_context = " ".join([doc['content'] for doc in retrieved_docs]).lower()
+        
+        found_count = 0
+        for kw in required_keywords:
+            if kw.lower() in full_context:
+                found_count += 1
+        
+        recall = found_count / len(required_keywords)
+        
+        return {
+            "keyword_recall": recall,
+            # 只要召回率大于 0 就认为 Hit 了一部分；
+            # 严格一点可以要求 recall > 0.5，这里我们设定只要沾边就算 Hit
+            "hit": recall > 0 
+        }
+
+    def evaluate_generation_quality(self, question, generated_answer, ground_truth_answer, q_type):
+        """
+        使用 LLM 作为裁判，评估生成质量 (1-5分)
+        """
+        prompt = f"""
+        你是一名RAG系统的自动化测试裁判。请根据以下信息对“系统回答”进行评分（1-5分）。
+        
+        【测试类型】: {q_type}
+        【用户问题】: {question}
+        【标准答案 (Ground Truth)】: {ground_truth_answer}
+        【系统回答】: {generated_answer}
+        
+        评分标准：
+        - 5分: 含义与标准答案完全一致，逻辑正确，无幻觉。
+        - 4分: 核心意思正确，但缺少部分细节或废话较多。
+        - 3分: 回答了一部分正确信息，但有遗漏或轻微错误。
+        - 2分: 包含大量错误信息或严重答非所问。
+        - 1分: 完全错误，或产生了严重幻觉（例如在负向测试中编造了不存在的功能）。
+        
+        注意：对于"negative_test"（负向测试），如果标准答案是“不支持/文档未提及”，而系统回答诚实地说“未找到相关信息”或“不支持”，应给满分。
+        
+        请仅返回JSON格式: {{"score": 5, "reason": "理由..."}}
+        """
+        
+        try:
+            # 使用 system_prompt 强制约束格式
+            result_str = self.llm.chat(prompt, system_prompt="你是一个只输出JSON的评测机器人。")
+            
+            # 清洗 Markdown 格式 (```json ... ```)
+            if "```" in result_str:
+                result_str = result_str.split("```json")[-1].split("```")[0].strip()
+            
+            eval_result = json.loads(result_str)
+            return eval_result
+        except Exception as e:
+            logger.error(f"Eval LLM failed: {e}")
+            # 降级处理
+            return {"score": 0, "reason": "Evaluation Script Error"}

tests/rag_benchmark/run_benchmark.py

@@ -0,0 +1,168 @@
+import sys
+import os
+import time
+import json
+from collections import defaultdict
+from tabulate import tabulate # 需要 pip install tabulate
+
+# 路径 Hack: 确保能导入 backend 模块
+sys.path.append(os.path.join(os.path.dirname(__file__), '../../../'))
+
+from backend.services.data_service import data_service
+from backend.services.llm_service import llm_service
+from tests.rag_benchmark.evaluator import RAGEvaluator
+
+# ================= 配置区 =================
+# 请填入你数据库中真实存在的、包含爬取数据的 task_id
+TEST_TASK_ID = 19
+# ========================================
+
+def run_experiment(config_name, dataset, retrieve_func, generate_func):
+    """
+    运行单组实验并收集详细指标
+    """
+    print(f"\n🚀 开始测试配置: [ {config_name} ]")
+    evaluator = RAGEvaluator()
+    results = []
+    
+    total_latency = 0
+    
+    # 用于分类统计 (例如 core_function 得分多少, negative_test 得分多少)
+    category_stats = defaultdict(lambda: {"count": 0, "score_sum": 0, "recall_sum": 0})
+
+    for item in dataset:
+        start_time = time.time()
+        
+        # 1. 检索 (Retrieval)
+        retrieved_docs = retrieve_func(item['query'])
+        
+        # 2. 生成 (Generation)
+        # 构造 Context，如果没有检索到内容，给一个提示
+        if retrieved_docs:
+            context_str = "\n---\n".join([f"Source: {d.get('source_url', 'unknown')}\nContent: {d['content']}" for d in retrieved_docs])
+        else:
+            context_str = "没有检索到任何相关文档。"
+
+        answer = generate_func(item['query'], context_str)
+        
+        latency = time.time() - start_time
+        total_latency += latency
+        
+        # 3. 评测 (Evaluation)
+        # 计算关键词召回率
+        retrieval_metric = evaluator.calculate_retrieval_metrics(retrieved_docs, item)
+        # 计算LLM回答质量
+        gen_eval = evaluator.evaluate_generation_quality(
+            item['query'], answer, item['ground_truth'], item['type']
+        )
+        
+        # 记录单条结果
+        row = {
+            "id": item['id'],
+            "type": item['type'],
+            "query": item['query'],
+            "recall": retrieval_metric['keyword_recall'],
+            "score": gen_eval['score'],
+            "reason": gen_eval.get('reason', '')[:50] + "...", # 截断显示
+            "latency": latency
+        }
+        results.append(row)
+        
+        # 累加分类统计
+        cat = item['type']
+        category_stats[cat]["count"] += 1
+        category_stats[cat]["score_sum"] += gen_eval['score']
+        category_stats[cat]["recall_sum"] += retrieval_metric['keyword_recall']
+
+        # 实时打印进度 (简洁版)
+        status_icon = "✅" if gen_eval['score'] >= 4 else "⚠️" if gen_eval['score'] >= 3 else "❌"
+        print(f"   {status_icon} ID:{item['id']} [{item['type'][:10]}] Score:{gen_eval['score']} | Recall:{retrieval_metric['keyword_recall']:.1f}")
+
+    # --- 汇总本轮实验数据 ---
+    avg_score = sum(r['score'] for r in results) / len(results)
+    avg_recall = sum(r['recall'] for r in results) / len(results)
+    avg_latency = total_latency / len(results)
+    
+    # 格式化分类报告
+    cat_report = []
+    for cat, data in category_stats.items():
+        cat_report.append(f"{cat}: {data['score_sum']/data['count']:.1f}分")
+    
+    return {
+        "Config": config_name,
+        "Avg Score (1-5)": f"{avg_score:.2f}",
+        "Avg Recall": f"{avg_recall:.2%}",
+        "Avg Latency": f"{avg_latency:.3f}s",
+        "Weakest Category": min(category_stats, key=lambda k: category_stats[k]['score_sum']/category_stats[k]['count'])
+    }
+
+def main():
+    # 1. 加载数据集
+    dataset_path = os.path.join(os.path.dirname(__file__), 'dataset.json')
+    if not os.path.exists(dataset_path):
+        print("Error: dataset.json not found.")
+        return
+        
+    with open(dataset_path, 'r', encoding='utf-8') as f:
+        dataset = json.load(f)
+
+    print(f"载入 {len(dataset)} 条测试用例，准备开始横向评测...")
+
+    # 2. 定义实验变量 (检索函数 + 生成函数)
+
+    # === Exp A: 纯关键词 (模拟传统搜索) ===
+    def retrieve_keyword(query):
+        # vector_weight=0 强制使用 SQL TSVector
+        # 注意: 需要传递一个假向量给接口占位
+        dummy_vec = [0.0] * 1536 
+        res = data_service.search(query, dummy_vec, task_id=TEST_TASK_ID, vector_weight=0.0, candidates_num=5)
+        return res['results']
+
+    # === Exp B: 纯向量 (语义检索) ===
+    def retrieve_vector(query):
+        vec = llm_service.get_embedding(query)
+        # vector_weight=1 忽略关键词匹配
+        res = data_service.search(query, vec, task_id=TEST_TASK_ID, vector_weight=1.0, candidates_num=5)
+        return res['results']
+
+    # === Exp C: 混合检索 (Hybrid) ===
+    def retrieve_hybrid(query):
+        vec = llm_service.get_embedding(query)
+        # 默认 0.7 向量 + 0.3 关键词
+        res = data_service.search(query, vec, task_id=TEST_TASK_ID, vector_weight=0.7, candidates_num=5)
+        return res['results']
+
+    # === Exp D: 混合 + 重排序 (Rerank) ===
+    def retrieve_rerank(query):
+        vec = llm_service.get_embedding(query)
+        # 1. 扩大召回 (Top 30)
+        res = data_service.search(query, vec, task_id=TEST_TASK_ID, vector_weight=0.7, candidates_num=30)
+        initial_docs = res['results']
+        # 2. 精排 (Top 5)
+        reranked = llm_service.rerank(query, initial_docs, top_n=5)
+        return reranked
+
+    # === 通用生成函数 ===
+    def generate_answer(query, context):
+        system_prompt = "你是一个智能助手。请严格根据提供的上下文回答用户问题。如果上下文中没有答案，请直接说'未找到相关信息'。"
+        prompt = f"参考上下文：\n{context}\n\n用户问题：{query}"
+        return llm_service.chat(prompt, system_prompt=system_prompt)
+
+    # 3. 运行所有实验
+    final_report = []
+    
+    final_report.append(run_experiment("1. Keyword Only (BM25)", dataset, retrieve_keyword, generate_answer))
+    final_report.append(run_experiment("2. Vector Only", dataset, retrieve_vector, generate_answer))
+    final_report.append(run_experiment("3. Hybrid (Base)", dataset, retrieve_hybrid, generate_answer))
+    final_report.append(run_experiment("4. Hybrid + Rerank", dataset, retrieve_rerank, generate_answer))
+
+    # 4. 输出最终报表
+    print("\n\n📊 ================= 最终横向对比报告 (Final Report) ================= 📊")
+    print(tabulate(final_report, headers="keys", tablefmt="github"))
+    print("\n💡 解读建议:")
+    print("1. 如果 'Avg Recall' 低，说明切片(Chunking)或检索算法找不到资料。")
+    print("2. 如果 Recall 高但 'Avg Score' 低，说明 LLM 产生了幻觉或 Prompt 没写好。")
+    print("3. 'Weakest Category' 帮你发现短板（如多语言或负向测试）。")
+
+if __name__ == "__main__":
+    main()

tests/rag_benchmark/visual_benchmark.py

@@ -0,0 +1,208 @@
+import sys
+import os
+import time
+import json
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+
+# 路径 Hack: 确保能导入 backend
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.abspath(os.path.join(current_dir, '../../'))
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+# 直接导入服务类 (Direct Call)
+from backend.services.data_service import data_service
+from backend.services.llm_service import llm_service
+
+# ================= 配置区 =================
+TEST_TASK_ID = 19  # 请修改为真实的 Task ID
+DATASET_PATH = os.path.join(current_dir, 'dataset.json')
+OUTPUT_IMG = os.path.join(current_dir, 'benchmark_report.png')
+# ========================================
+
+class RAGEvaluator:
+    """评测工具类：负责计算召回率和调用LLM打分"""
+    def __init__(self):
+        self.llm = llm_service
+
+    def calculate_recall(self, retrieved_docs, keywords):
+        """计算关键词召回率"""
+        if not keywords: return 1.0 # 无关键词要求的题目默认为满分
+        
+        full_text = " ".join([d['content'] for d in retrieved_docs]).lower()
+        hit_count = sum(1 for k in keywords if k.lower() in full_text)
+        return hit_count / len(keywords)
+
+    def judge_answer(self, query, answer, ground_truth):
+        """调用 LLM 给生成结果打分 (1-5)"""
+        prompt = f"""
+        作为 RAG 评测员，请对【系统回答】打分 (1-5)。
+        用户问题: {query}
+        标准答案: {ground_truth}
+        系统回答: {answer}
+        
+        标准:
+        5: 含义完全一致，无幻觉。
+        3: 包含核心信息，但有遗漏。
+        1: 错误或严重幻觉。
+        
+        只返回数字 (1, 2, 3, 4, 5)。
+        """
+        try:
+            # 这里调用你在 llm_service 中新增的 chat 方法
+            res = self.llm.chat(prompt) 
+            # 简单的清洗逻辑，提取数字
+            score = int(''.join(filter(str.isdigit, res)))
+            return min(max(score, 1), 5) # 限制在 1-5
+        except:
+            return 1 # 失败保底 1 分
+
+class Visualizer:
+    """绘图工具类"""
+    def plot_dashboard(self, df):
+        # 设置风格
+        sns.set_theme(style="whitegrid")
+        # 解决中文显示问题 (如果环境支持 SimHei 则用中文，否则用英文)
+        plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial', 'DejaVu Sans']
+        plt.rcParams['axes.unicode_minus'] = False
+
+        fig = plt.figure(figsize=(18, 10))
+        gs = fig.add_gridspec(2, 2)
+
+        # Chart 1: 总体指标对比 (Bar Chart)
+        ax1 = fig.add_subplot(gs[0, 0])
+        # 将数据变形为长格式以便绘图
+        df_summary = df.groupby('config')[['score', 'recall']].mean().reset_index()
+        df_melt = df_summary.melt(id_vars='config', var_name='Metric', value_name='Value')
+        # 将 Recall 归一化到 0-5 方便同图显示，或者分开轴。这里简单处理：Recall * 5
+        df_melt.loc[df_melt['Metric'] == 'recall', 'Value'] *= 5
+        
+        sns.barplot(data=df_melt, x='config', y='Value', hue='Metric', ax=ax1, palette="viridis")
+        ax1.set_title('Overall Performance (Score & Recall)', fontsize=14, fontweight='bold')
+        ax1.set_ylabel('Score (1-5) / Recall (x5)')
+        ax1.set_ylim(0, 5.5)
+        for container in ax1.containers:
+            ax1.bar_label(container, fmt='%.1f')
+
+        # Chart 2: 延迟 vs 质量 (Scatter Plot)
+        ax2 = fig.add_subplot(gs[0, 1])
+        df_latency = df.groupby('config')[['score', 'latency']].mean().reset_index()
+        sns.scatterplot(data=df_latency, x='latency', y='score', hue='config', s=200, ax=ax2, palette="deep")
+        
+        # 添加标签
+        for i in range(df_latency.shape[0]):
+            ax2.text(
+                df_latency.latency[i]+0.05, 
+                df_latency.score[i], 
+                df_latency.config[i], 
+                fontsize=10
+            )
+        ax2.set_title('Trade-off: Latency vs Quality', fontsize=14, fontweight='bold')
+        ax2.set_xlabel('Avg Latency (seconds)')
+        ax2.set_ylabel('Avg Quality Score (1-5)')
+
+        # Chart 3: 类别热力图 (Heatmap) - 你的 Weakest Category 可视化
+        ax3 = fig.add_subplot(gs[1, :]) # 占用下方整行
+        pivot_data = df.pivot_table(index='config', columns='type', values='score', aggfunc='mean')
+        sns.heatmap(pivot_data, annot=True, cmap="RdYlGn", center=3, fmt=".1f", ax=ax3, linewidths=.5)
+        ax3.set_title('Category Breakdown (Find the Weakest Link)', fontsize=14, fontweight='bold')
+        ax3.set_xlabel('')
+        ax3.set_ylabel('')
+
+        plt.tight_layout()
+        plt.savefig(OUTPUT_IMG)
+        print(f"\n📊 报表已生成: {OUTPUT_IMG}")
+
+def main():
+    # 1. 加载数据
+    if not os.path.exists(DATASET_PATH):
+        print("Dataset not found!")
+        return
+    with open(DATASET_PATH, 'r', encoding='utf-8') as f:
+        dataset = json.load(f)
+
+    # 2. 定义实验配置 (Direct Call)
+    configs = [
+        {
+            "name": "1. BM25 (Keyword)",
+            "retriever": lambda q: data_service.search(q, [0.0]*1536, task_id=TEST_TASK_ID, vector_weight=0.0, candidates_num=5)['results'],
+            "rerank": False
+        },
+        {
+            "name": "2. Vector Only",
+            "retriever": lambda q: data_service.search(q, llm_service.get_embedding(q), task_id=TEST_TASK_ID, vector_weight=1.0, candidates_num=5)['results'],
+            "rerank": False
+        },
+        {
+            "name": "3. Hybrid (Base)",
+            "retriever": lambda q: data_service.search(q, llm_service.get_embedding(q), task_id=TEST_TASK_ID, vector_weight=0.7, candidates_num=5)['results'],
+            "rerank": False
+        },
+        {
+            "name": "4. Hybrid + Rerank",
+            "retriever": lambda q: data_service.search(q, llm_service.get_embedding(q), task_id=TEST_TASK_ID, vector_weight=0.7, candidates_num=30)['results'], # 召回 Top 30
+            "rerank": True
+        }
+    ]
+
+    evaluator = RAGEvaluator()
+    all_results = []
+
+    print("🚀 开始自动化评测 (Visualization Mode)...")
+
+    # 3. 循环执行 (双重循环：配置 -> 数据)
+    # 使用 tqdm 显示总进度
+    total_steps = len(configs) * len(dataset)
+    pbar = tqdm(total=total_steps, desc="Running Experiments")
+
+    for cfg in configs:
+        for item in dataset:
+            pbar.set_description(f"Testing {cfg['name']}")
+            
+            start_time = time.time()
+            
+            # A. 检索
+            docs = cfg['retriever'](item['query'])
+            
+            # B. Rerank (如果在配置里开启)
+            if cfg['rerank']:
+                docs = llm_service.rerank(item['query'], docs, top_n=5)
+            
+            # C. 生成
+            context = "\n".join([d['content'] for d in docs]) if docs else ""
+            if not context:
+                answer = "未找到相关信息"
+            else:
+                prompt = f"Context:\n{context}\n\nQuestion: {item['query']}"
+                answer = llm_service.chat(prompt) # 调用生成接口
+
+            latency = time.time() - start_time
+
+            # D. 评测指标
+            recall = evaluator.calculate_recall(docs, item.get('keywords', []))
+            score = evaluator.judge_answer(item['query'], answer, item['ground_truth'])
+
+            # E. 收集数据
+            all_results.append({
+                "config": cfg['name'],
+                "id": item['id'],
+                "type": item['type'], # 类别字段
+                "recall": recall,
+                "score": score,
+                "latency": latency
+            })
+            
+            pbar.update(1)
+            
+    pbar.close()
+
+    # 4. 数据处理与绘图
+    df = pd.DataFrame(all_results)
+    viz = Visualizer()
+    viz.plot_dashboard(df)
+
+if __name__ == "__main__":
+    main()

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@@ -2299,16 +3023,22 @@ dependencies = [
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     { name = "langchain-community" },
     { name = "langchain-core" },
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     { name = "pgvector" },
     { name = "pinecone" },
     { name = "pip" },
     { name = "psycopg2-binary" },
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     { name = "pymilvus" },
     { name = "qdrant-client" },
     { name = "redis" },
     { name = "requests" },
+    { name = "seaborn" },
+    { name = "tabulate" },
+    { name = "tqdm" },
     { name = "uvicorn" },
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