完成RAG测试
This commit is contained in:
@@ -33,7 +33,7 @@ ENV PATH="/app/.venv/bin:$PATH"
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ENV PYTHONPATH=/app
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# 暴露端口
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EXPOSE 8000
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EXPOSE 28000
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# 启动命令
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# 现在直接调用 uvicorn 即可，它会自动找到 .venv 里的版本
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@@ -143,7 +143,7 @@ class DataService:
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and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == clean_url)
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).values(status='completed'))
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def search(self, query_text: str, query_vector: list, task_id=None, candidates_num: int = 50):
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def search(self, query_text: str, query_vector: list, task_id=None, candidates_num: int = 50, vector_weight: float = 0.7):
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# 向量格式清洗
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if hasattr(query_vector, 'tolist'): query_vector = query_vector.tolist()
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if isinstance(query_vector, list) and len(query_vector) > 0 and isinstance(query_vector[0], list):
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@@ -151,26 +151,52 @@ class DataService:
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results = []
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with self.db.engine.connect() as conn:
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# 1. 构造 Query 对象 (这是 tsquery 类型)
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keyword_query = func.websearch_to_tsquery('english', query_text)
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vector_score = (1 - self.db.chunks.c.embedding.cosine_distance(query_vector))
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keyword_score = func.ts_rank(self.db.chunks.c.content_tsvector, keyword_query)
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final_score = (vector_score * 0.7 + func.coalesce(keyword_score, 0) * 0.3).label("score")
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# 计算分数 (逻辑不变)
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vector_dist = self.db.chunks.c.embedding.cosine_distance(query_vector)
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vector_score = (1 - vector_dist)
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# 注意：ts_rank 需要 (tsvector, tsquery)
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keyword_rank = func.ts_rank(self.db.chunks.c.content_tsvector, keyword_query)
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keyword_score = func.coalesce(keyword_rank, 0)
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keyword_weight = 1.0 - vector_weight
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final_score = (vector_score * vector_weight + keyword_score * keyword_weight).label("score")
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stmt = select(
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self.db.chunks.c.task_id, self.db.chunks.c.source_url, self.db.chunks.c.title,
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self.db.chunks.c.content, self.db.chunks.c.meta_info, final_score
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)
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# ================= 修复点开始 =================
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# 只有当 vector_weight 为 0 (纯关键词模式) 时，才强制加 WHERE 过滤
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if vector_weight == 0:
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# 错误写法 (SQLAlchemy 自动生成 plainto_tsquery 导致报错):
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# stmt = stmt.where(keyword_query.match(self.db.chunks.c.content_tsvector))
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# 正确写法 (直接使用 PG 的 @@ 操作符):
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# 含义: content_tsvector @@ keyword_query
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stmt = stmt.where(self.db.chunks.c.content_tsvector.op('@@')(keyword_query))
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# ================= 修复点结束 =================
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if task_id: stmt = stmt.where(self.db.chunks.c.task_id == task_id)
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stmt = stmt.order_by(desc("score")).limit(candidates_num)
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try:
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rows = conn.execute(stmt).fetchall()
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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]
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except Exception as e:
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logger.error(f"Hybrid search failed: {e}")
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# 打印详细错误方便调试
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logger.error(f"Search failed: {e}")
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# 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
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if vector_weight > 0:
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return self._fallback_vector_search(query_vector, task_id, candidates_num)
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return {"results": [], "msg": "Keyword search failed"}
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return {"results": results, "msg": f"Hybrid found {len(results)}"}
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return {"results": results, "msg": f"Found {len(results)}"}
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def _fallback_vector_search(self, vector, task_id, limit):
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logger.warning("Fallback to pure vector search")
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@@ -93,5 +93,28 @@ class LLMService:
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logger.error(f"Rerank Exception: {e}")
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# 降级策略
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return documents[:top_n]
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def chat(self, prompt: str, system_prompt: str = None, model: str = "qwen-turbo") -> str:
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"""
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[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)
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"""
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messages = []
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if system_prompt:
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messages.append({'role': 'system', 'content': system_prompt})
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messages.append({'role': 'user', 'content': prompt})
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try:
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resp = dashscope.Generation.call(
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model=model,
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messages=messages,
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result_format='message'
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)
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if resp.status_code == HTTPStatus.OK:
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return resp.output.choices[0].message.content
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else:
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logger.error(f"Chat API Error: {resp}")
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return "Error generating response."
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except Exception as e:
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logger.error(f"Chat Exception: {e}")
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return "Error generating response."
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llm_service = LLMService()
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157
docs/RAGtest.md
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157
docs/RAGtest.md
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@@ -0,0 +1,157 @@
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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。
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---
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# RAG 知识库检索与生成系统测试设计规范
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**RAG System Test Design Specification**
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**版本**: 1.0
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**状态**: 拟定中
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---
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## 1. 测试背景与目标 (Background & Objectives)
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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：
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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）。
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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”。
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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。
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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。
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---
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## 2. 测试架构原理 (Test Architecture)
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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：
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### 2.1 评估对象
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* **Query (用户提问)**: 测试的输入。
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* **Context (检索上下文)**: 检索器从知识库召回的 Top-K 文档片段。
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* **Response (系统回答)**: LLM 基于 Query 和 Context 生成的最终文本。
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### 2.2 测试流向
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1. **检索层评估 (Retrieval Evaluation)**: `Query` $\leftrightarrow$ `Context`
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* *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？
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2. **生成层评估 (Generation Evaluation)**:
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* `Context` $\leftrightarrow$ `Response`: **忠实度 (Faithfulness)**。回答是否完全基于上下文？有无幻觉？
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* `Query` $\leftrightarrow$ `Response`: **相关度 (Relevance)**。回答是否解决了用户的问题？
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---
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## 3. 测试数据集设计 (Dataset Design)
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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：
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### 3.1 数据字段定义
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| 字段名 | 说明 | 用途 |
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| :--- | :--- | :--- |
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| **ID** | 唯一标识符 | 用于追踪 Case |
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| **Type** | 问题分类 | 用于计算 "Weakest Category" (见 3.2) |
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| **Query** | 用户模拟问题 | 输入 |
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| **Ground Truth Answer** | 标准答案 | 用于生成层对比评分 |
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| **Keywords/Key Information** | 关键信息点/术语 | 用于计算检索层的 Recall |
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| **Ground Truth Context IDs** | (可选) 预期命中的文档ID | 用于计算 Hit Rate |
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### 3.2 场景分类体系 (Category Taxonomy)
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测试集必须覆盖以下场景，以防止模型“偏科”：
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1. **Core Function (核心功能)**: 基础的概念解释和流程指引。
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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）。
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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）。
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4. **Multilingual (跨语言)**: 中文问英文文档，或反之（考察 Embedding 对齐能力）。
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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”）。
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6. **Safety/Injection (安全)**: Prompt 注入攻击防御测试。
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---
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## 4. 控制变量与横向对比设计 (Experimental Design)
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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。
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### 4.1 实验组配置 (Configuration Matrix)
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| 实验组 | 检索机制 (Retrieval) | 权重策略 | 重排序 (Rerank) | 测试目的 |
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| :--- | :--- | :--- | :--- | :--- |
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| **Group A (Baseline)** | **Keyword (BM25)** | Vector=0, Keyword=1 | OFF | 模拟传统全文检索，设立基准线。 |
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| **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。 |
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| **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）。 |
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| **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。 |
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### 4.2 性能监控
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在运行上述配置时，同步记录以下工程指标：
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* **Latency**: 端到端耗时（P50, P95）。
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* **Token Usage**: 消耗的 Token 数量（计算成本 ROI）。
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---
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## 5. 核心评估指标 (Key Metrics)
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采用 **规则计算 + LLM裁判 (LLM-as-a-Judge)** 相结合的方式。
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### 5.1 检索层指标 (Retrieval Metrics)
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| 指标 | 定义 | 计算方法 | 优劣判断 |
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| :--- | :--- | :--- | :--- |
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| **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。 |
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| **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)。 |
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### 5.2 生成层指标 (Generation Metrics)
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使用高智能模型（如 GPT-4o / Qwen-Max）作为裁判，进行 1-5 分打分。
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| 指标 | 定义 | 评分标准 (Rubric) |
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| :--- | :--- | :--- |
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| **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。 |
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| **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)。 |
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| **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。 |
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### 5.3 诊断性指标 (Diagnostic Metrics)
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* **Weakest Category (最弱类别)**:
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* 计算逻辑：按 `Type` 分组，计算各组的平均分，取最低者。
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* 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。
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---
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## 6. 自动化测试流程 (Workflow)
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1. **Setup Phase**:
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* 加载 `dataset.json`。
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* 初始化所有待测的 RAG Pipeline 配置。
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2. **Execution Phase (Loop)**:
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* 遍历测试集中的每个 Query。
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* **Step 1 Retrieve**: 调用检索接口，获取 Context。
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* **Step 2 Generate**: 将 Context + Query 送入 LLM 生成 Answer。
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* **Step 3 Measure**: 记录 Latency。
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3. **Evaluation Phase**:
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* **Rule Check**: 计算 Keyword Recall。
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* **AI Judge**: 将 (Query, Answer, Ground Truth) 组装 Prompt 发送给裁判 LLM 打分。
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4. **Reporting Phase**:
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* 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。
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* 输出 Bad Case 列表（得分 < 3 的用例）。
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---
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## 7. 附录：LLM 裁判 Prompt 模板
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```markdown
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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。
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【问题类型】: {type}
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【用户问题】: {query}
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【标准答案】: {ground_truth}
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【系统回答】: {prediction}
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请打分 (1-5)：
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- 5分: 完美。逻辑正确，无幻觉，细节精准。
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- 3分: 及格。包含核心信息，但有遗漏或啰嗦。
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- 1分: 错误。答非所问，或在负向测试中产生幻觉。
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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。
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输出 JSON: {"score": int, "reason": "string"}
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```
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@@ -6,7 +6,7 @@
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| **字段** | **当前值 (示例)** | **说明** | **每次要改吗？** |
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| -------------------- | ---------------------------------- | ------------------------------ | ---------------------- |
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| **Version** | **v1.0.6** | **镜像的版本标签 (Tag)** | **是 (必须改)** |
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| **Version** | **v1.0.7** | **镜像的版本标签 (Tag)** | **是 (必须改)** |
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| **Image Name** | **wiki-crawl-backend** | **镜像/容器的名字** | **否 (固定)** |
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| **Namespace** | **qg-demo** | **阿里云命名空间** | **否 (固定)** |
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| **Registry** | **crpi-1rwd6fvain6t49g2...** | **阿里云仓库地址** | **否 (固定)** |
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@@ -19,18 +19,18 @@
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### 1. 构建镜像 (Build)
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**修改命令最后的版本号** **v1.0.6**
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**修改命令最后的版本号** **v1.0.7**
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```powershell
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docker build -t crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.6 .
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docker build -t crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.7 .
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```
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### 2. 推送镜像 (Push)
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**修改命令最后的版本号** **v1.0.6**
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**修改命令最后的版本号** **v1.0.7**
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```powershell
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docker push crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.6
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docker push crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/wiki-crawl-backend:v1.0.7
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```
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> **成功标准：** **看到进度条走完，且最后显示** **Pushed**。
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@@ -43,10 +43,10 @@ docker push crpi-1rwd6fvain6t49g2.cn-hangzhou.personal.cr.aliyuncs.com/qg-demo/w
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### 1. 拉取新镜像 (Pull)
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**修改命令最后的版本号** **v1.0.6**
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**修改命令最后的版本号** **v1.0.7**
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```bash
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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）。
|
||||
|
||||
73
docs/开发计划.md
73
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: <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. 新增v3接口，替代v2和v1。
|
||||
1. 重置接口，v1版本，restfulAPI
|
||||
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
|
||||
- [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: <https://www.firecrawl.dev/pricing>
|
||||
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: <https://github.com/mendableai/firecrawl>
|
||||
- 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**<br>需自行配置服务器环境 | ☁️ **开箱即用**<br>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**<br>需支付服务器/带宽费用 | 💰 **订阅制**<br>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**<br>(Proxies) | ❌ **弱 / 需自行配置**<br>默认使用本机 IP，易被 Cloudflare 拦截 | ✅ **强 / 内置智能代理**<br>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**<br>需维护 Redis、队列、无头浏览器更新 | ☕ **零**<br>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**<br>取决于你的服务器配置 | 🚀 **弹性扩容**<br>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**<br>需配置 Playwright/Puppeteer | ✅ **支持**<br>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)**<br>数据不经过第三方服务器 | ☁️ **中**<br>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
|
||||
|
||||
@@ -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",
|
||||
]
|
||||
|
||||
76
scripts/test_firecrawl.py
Normal file
76
scripts/test_firecrawl.py
Normal file
@@ -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()
|
||||
0
tests/__init__.py
Normal file
0
tests/__init__.py
Normal file
0
tests/rag_benchmark/__init__.py
Normal file
0
tests/rag_benchmark/__init__.py
Normal file
BIN
tests/rag_benchmark/benchmark_report.png
Normal file
BIN
tests/rag_benchmark/benchmark_report.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 100 KiB |
86
tests/rag_benchmark/dataset.json
Normal file
86
tests/rag_benchmark/dataset.json
Normal file
@@ -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"]
|
||||
}
|
||||
]
|
||||
74
tests/rag_benchmark/evaluator.py
Normal file
74
tests/rag_benchmark/evaluator.py
Normal file
@@ -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"}
|
||||
168
tests/rag_benchmark/run_benchmark.py
Normal file
168
tests/rag_benchmark/run_benchmark.py
Normal file
@@ -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()
|
||||
208
tests/rag_benchmark/visual_benchmark.py
Normal file
208
tests/rag_benchmark/visual_benchmark.py
Normal file
@@ -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()
|
||||
557
uv.lock
generated
557
uv.lock
generated
@@ -416,6 +416,163 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.3.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
resolution-markers = [
|
||||
"python_full_version < '3.11'",
|
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]
|
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dependencies = [
|
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{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
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]
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wheels = [
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{ name = "uvicorn", specifier = ">=0.38.0" },
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]
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[[package]]
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||||
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{ name = "pandas" },
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]
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wheels = [
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]
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|
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[[package]]
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name = "setuptools"
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@@ -2641,6 +3177,15 @@ wheels = [
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[[package]]
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252, upload-time = "2022-10-06T17:21:44.262Z" },
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]
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|
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[[package]]
|
||||
name = "tenacity"
|
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version = "9.1.2"
|
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@@ -2650,6 +3195,18 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/e5/30/643397144bfbfec6f6ef821f36f33e57d35946c44a2352d3c9f0ae847619/tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138", size = 28248, upload-time = "2025-04-02T08:25:07.678Z" },
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]
|
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|
||||
[[package]]
|
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name = "tqdm"
|
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{ name = "colorama", marker = "sys_platform == 'win32'" },
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wheels = [
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]
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|
||||
[[package]]
|
||||
name = "typing-extensions"
|
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version = "4.15.0"
|
||||
|
||||
Reference in New Issue
Block a user