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构建并部署 AI Agent 服务 / deploy (push) Has been cancelled
主要修复： 1. 修复 RAG 推理无限循环问题（大小写不匹配 + 缺少已检索结果检查） 2. 修复 intent_classifier.py 的绝对导入错误 3. 删除旧的 start.sh 脚本，添加新的启动脚本 4. 优化路由逻辑和状态管理
146 lines
3.9 KiB
Python
146 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""
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完整的 RAG Pipeline 测试
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测试从查询改写 → 检索 → RRF融合 → 重排序 → 格式化输出的整个流程
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"""
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import asyncio
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from backend.app.rag.pipeline import RAGPipeline, create_rag_pipeline
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from backend.app.rag.tools import create_rag_tool
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async def test_rag_pipeline_direct():
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"""测试 1: 直接使用 RAGPipeline（默认用小模型做查询改写）"""
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print("="*80)
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print("测试 1: 直接使用 RAGPipeline（默认用小模型做查询改写）")
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print("="*80)
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# 创建 pipeline（默认用小模型）
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pipeline = create_rag_pipeline(
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collection_name="rag_documents",
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num_queries=3,
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rerank_top_n=5
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)
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query = "吕布的经历"
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print(f"\n用户查询: {query}")
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print("-" * 80)
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# 执行检索
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docs = await pipeline.aretrieve(query)
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if docs:
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print(f"\n✓ 找到 {len(docs)} 个相关文档")
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print("-" * 80)
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for i, doc in enumerate(docs, 1):
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print(f"\n{i}. 来源: {doc.metadata.get('source', 'unknown')}")
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print(f" 内容:\n{doc.page_content}")
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print("-" * 80)
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# 格式化输出
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print("\n" + "="*80)
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print("格式化后的上下文:")
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print("="*80)
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formatted_context = pipeline.format_context(docs)
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print(formatted_context)
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else:
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print("\n✗ 未找到相关文档")
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print("\n" + "="*80)
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async def test_rag_tool():
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"""测试 2: 使用 RAG Tool（默认用小模型做查询改写）"""
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print("\n"+"="*80)
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print("测试 2: 使用 RAG Tool（默认用小模型做查询改写）")
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print("="*80)
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# 创建 tool（默认用小模型）
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rag_tool = create_rag_tool(
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collection_name="rag_documents",
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num_queries=3,
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rerank_top_n=5
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)
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query = "吕布的经历"
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print(f"\n用户查询: {query}")
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print("-" * 80)
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# 使用 tool (异步调用 ainvoke)
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result = await rag_tool.ainvoke(query)
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print("\nTool 返回结果:")
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print("="*80)
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print(result)
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print("="*80)
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async def test_custom_pipeline():
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"""测试 3: 自定义参数的 RAGPipeline（默认用小模型）"""
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print("\n"+"="*80)
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print("测试 3: 自定义参数的 RAGPipeline（默认用小模型）")
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print("="*80)
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# 自定义参数（默认用小模型）
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pipeline = RAGPipeline(
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collection_name="rag_documents",
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num_queries=2, # 只生成 2 个查询变体
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rerank_top_n=3 # 只返回前 3 个最相关文档
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)
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query = "吕布的经历"
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print(f"\n用户查询: {query}")
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print(f"配置: num_queries=2, rerank_top_n=3")
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print("-" * 80)
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docs = await pipeline.aretrieve(query)
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if docs:
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print(f"\n✓ 找到 {len(docs)} 个相关文档")
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print("-" * 80)
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for i, doc in enumerate(docs, 1):
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print(f"\n{i}. 来源: {doc.metadata.get('source', 'unknown')}")
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preview = doc.page_content[:200].strip()
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if len(doc.page_content) > 200:
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preview += "..."
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print(f" 内容预览: {preview}")
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print("\n" + "="*80)
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print("格式化后的上下文:")
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print("="*80)
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print(pipeline.format_context(docs))
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else:
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print("\n✗ 未找到相关文档")
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print("\n" + "="*80)
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async def main():
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"""主测试函数"""
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print("\n" + "="*80)
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print("完整 RAG Pipeline 测试")
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print("查询: '吕布的经历'")
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print("="*80)
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# 测试 1: 直接使用 pipeline
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await test_rag_pipeline_direct()
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# 测试 2: 使用 tool
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await test_rag_tool()
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# 测试 3: 自定义参数
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await test_custom_pipeline()
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print("\n" + "="*80)
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print("🎉 所有 RAG Pipeline 测试完成！")
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print("="*80)
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if __name__ == "__main__":
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asyncio.run(main())
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