From f78efc71250e14131c1b48dff2f92fabb2ab771b Mon Sep 17 00:00:00 2001 From: QingGang Date: Tue, 27 Jan 2026 01:41:45 +0800 Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90RAG=E6=B5=8B=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Dockerfile | 2 +- backend/services/data_service.py | 40 +- backend/services/llm_service.py | 23 + docs/RAGtest.md | 157 +++++++ docs/docker.md | 22 +- docs/开发计划.md | 83 ++-- pyproject.toml | 4 + scripts/test_firecrawl.py | 76 ++++ tests/__init__.py | 0 tests/rag_benchmark/__init__.py | 0 tests/rag_benchmark/benchmark_report.png | Bin 0 -> 102872 bytes tests/rag_benchmark/dataset.json | 86 ++++ tests/rag_benchmark/evaluator.py | 74 +++ tests/rag_benchmark/run_benchmark.py | 168 +++++++ tests/rag_benchmark/visual_benchmark.py | 208 +++++++++ uv.lock | 557 +++++++++++++++++++++++ 16 files changed, 1434 insertions(+), 66 deletions(-) create mode 100644 docs/RAGtest.md create mode 100644 scripts/test_firecrawl.py create mode 100644 tests/__init__.py create mode 100644 tests/rag_benchmark/__init__.py create mode 100644 tests/rag_benchmark/benchmark_report.png create mode 100644 tests/rag_benchmark/dataset.json create mode 100644 tests/rag_benchmark/evaluator.py create mode 100644 tests/rag_benchmark/run_benchmark.py create mode 100644 tests/rag_benchmark/visual_benchmark.py diff --git a/Dockerfile b/Dockerfile index 8205cc6..cc7c2f3 100644 --- a/Dockerfile +++ b/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 diff --git a/backend/services/data_service.py b/backend/services/data_service.py index 10fd24b..3533eb9 100644 --- a/backend/services/data_service.py +++ b/backend/services/data_service.py @@ -143,7 +143,7 @@ class DataService: 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): + 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): @@ -151,26 +151,52 @@ class DataService: 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) - vector_score = (1 - self.db.chunks.c.embedding.cosine_distance(query_vector)) - keyword_score = func.ts_rank(self.db.chunks.c.content_tsvector, keyword_query) - final_score = (vector_score * 0.7 + func.coalesce(keyword_score, 0) * 0.3).label("score") + + # 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: - logger.error(f"Hybrid search failed: {e}") - return self._fallback_vector_search(query_vector, task_id, candidates_num) + # 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"Hybrid found {len(results)}"} + return {"results": results, "msg": f"Found {len(results)}"} def _fallback_vector_search(self, vector, task_id, limit): logger.warning("Fallback to pure vector search") diff --git a/backend/services/llm_service.py b/backend/services/llm_service.py index 806d16e..e1e88ba 100644 --- a/backend/services/llm_service.py +++ b/backend/services/llm_service.py @@ -93,5 +93,28 @@ class LLMService: 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() \ No newline at end of file diff --git a/docs/RAGtest.md b/docs/RAGtest.md new file mode 100644 index 0000000..d37428e --- /dev/null +++ b/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。
**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"} +``` \ No newline at end of file diff --git a/docs/docker.md b/docs/docker.md index 89bda3d..0676c54 100644 --- a/docs/docker.md +++ b/docs/docker.md @@ -6,7 +6,7 @@ | **字段** | **当前值 (示例)** | **说明** | **每次要改吗？** | | -------------------- | ---------------------------------- | ------------------------------ | ---------------------- | -| **Version** | **v1.0.6** | **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)** | @@ -19,18 +19,18 @@ ### 1. 构建镜像 (Build) -**修改命令最后的版本号** **v1.0.6** +**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:v1.0.6 . +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** **v1.0.6** +**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:v1.0.6 +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**。 @@ -43,10 +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** **v1.0.6** +**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:v1.0.6 +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 @@ -61,7 +61,7 @@ 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** **v1.0.6** +**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:v1.0.6 + 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:v1.0.6** +**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)**: **v1.0.6** **-> 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）。 diff --git a/docs/开发计划.md b/docs/开发计划.md index c7ace7a..f03220d 100644 --- a/docs/开发计划.md +++ b/docs/开发计划.md @@ -1,60 +1,49 @@ # 下一步开发计划 -## 2025.1.13 +## 2025.1.20 1. 知识库RAG + 1. 测试 测试相关资料参考链接: - - [ ] 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 + - [ ] 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. 新增v3接口，替代v2和v1。 - 预期实现效果： - - [ ] 添加任务 - - [ ] 查询任务 - - [ ] 执行任务 - - [ ] 知识库搜索 - 2. 包装成MCP + 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 - - [ ] 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。 + - [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与替代方案调研 - - 1. firecrawl付费方案 - - 常规订阅链接: - 注意: 此链接下均是按时间订阅的,每月限制额度, 可额外购买, 但是考虑到客户使用的时候可能会固定时间集中使用(**采集新wiki, 更新旧wiki**) - - 企业订阅方案: 需要联系firecrawl订制 - 2. firecrawl开源方案 - - 开源github链接: - - 优劣对比 - - | 对比维度 | 开源版 (Self-hosted) | 云服务版 (Cloud / SaaS) | 核心差异说明 | - | :-------------------------- | :------------------------------------------------------------- | :-------------------------------------------------------------- | :------------------------------------------------------------------ | - | **部署方式** | 🐳 **Docker 自托管**
需自行配置服务器环境 | ☁️ **开箱即用**
注册 API Key 即可调用 | 云版省去了复杂的环境搭建过程。 | - | **成本** | 🆓 **软件免费**
需支付服务器/带宽费用 | 💰 **订阅制**
按 Credits (页数) 计费，有免费额度 | 量大且有闲置服务器时开源版更省钱；量小或追求稳定时云版更划算。 | - | **反爬虫绕过**
(Proxies) | ❌ **弱 / 需自行配置**
默认使用本机 IP，易被 Cloudflare 拦截 | ✅ **强 / 内置智能代理**
自动轮换 IP，擅长绕过 WAF 和人机验证 | **这是最大的区别。** 云版包含商业代理池成本，开源版需你自己买代理。 | - | **维护难度** | 🛠 **高**
需维护 Redis、队列、无头浏览器更新 | ☕ **零**
官方团队维护基础设施 | 开源版遇到浏览器崩溃或内存泄漏需自己修。 | - | **并发与性能** | ⚠️ **受限于硬件**
取决于你的服务器配置 | 🚀 **弹性扩容**
支持高并发，速度通常更快 | 云版对并行抓取做了优化。 | - | **JS 渲染** | ✅ **支持**
需配置 Playwright/Puppeteer | ✅ **支持**
默认优化，加载更稳定 | 两者核心引擎相同，但云版资源分配更合理。 | - | **数据隐私** | 🔒 **高 (本地化)**
数据不经过第三方服务器 | ☁️ **中**
数据需传输至 Firecrawl 服务器处理 | 对数据合规性要求极高的场景（如金融/医疗）首选开源版。 | - | **适用场景** | 极客折腾、内网抓取、低频低难度网站、数据极度敏感 | 商业项目、大规模抓取、高难度网站 (有反爬)、追求稳定性 | | - - 3. 自主研发爬虫 - 1. 反爬机制: 维基百科对IP有访问频率限制, 且有验证码, 需自行处理 - 2. 动态内容: 维基百科有很多动态内容, 如表格, 图片等, 需自行处理, 如使用Selenium等工具模拟浏览器行为 - - **Firecrawl方案和替代评估总结** - 假设客户的产品需求是: 从不同的网站爬取文档制成知识库, 并且需要定期维护, 那么其实只有在爬取新的站点和维护旧的站点的时候会集中使用firecrawl的额度, 主要特点是**使用时间集中**且**使用时段内额度需求量很大**以及**优先要保证爬虫模块的稳定性** - 因此最推荐的方案是: 定时采购额度, 但是考虑到常规的订阅只有按时间计费, 而客户的需求是**定期维护**, 而**按使用额度计费, 即企业协商订阅**的方案是最符合客户需求的. - - | 类别 | 成本 | 困难 | - | --- | --- | --- | - | 闭源版 | 购买定制服务, 如果企业长期话成本可能几千? 按年也就一年左右的量够用了 | 用起来很顺手, 目前的接口返回值基本能满足开发需求 | - | 开源版 | 需要准备IP池之类的反爬机制, 需要为IP代理付费 | 配置和学习相关的运营维护 | - | 自主研发 | 除了研发的时间精力外, 也必需IP池的购买 | 高 | +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 diff --git a/pyproject.toml b/pyproject.toml index 5f8e940..cf0b56a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ 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", @@ -23,5 +24,8 @@ dependencies = [ "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", ] diff --git a/scripts/test_firecrawl.py b/scripts/test_firecrawl.py new file mode 100644 index 0000000..2f2cac7 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/rag_benchmark/__init__.py b/tests/rag_benchmark/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/rag_benchmark/benchmark_report.png b/tests/rag_benchmark/benchmark_report.png new file mode 100644 index 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+ "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"] + } +] \ No newline at end of file diff --git a/tests/rag_benchmark/evaluator.py b/tests/rag_benchmark/evaluator.py new file mode 100644 index 0000000..858b80c --- /dev/null +++ b/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"} \ No newline at end of file diff --git a/tests/rag_benchmark/run_benchmark.py b/tests/rag_benchmark/run_benchmark.py new file mode 100644 index 0000000..7434d3d --- /dev/null +++ b/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() \ No newline at end of file diff --git a/tests/rag_benchmark/visual_benchmark.py b/tests/rag_benchmark/visual_benchmark.py new file mode 100644 index 0000000..6ca1745 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/uv.lock b/uv.lock index f820977..073b5be 100644 --- a/uv.lock +++ b/uv.lock @@ 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