LQAgentPlatform/utils_test/RAG_Test/test_hybrid_search_debug.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
混合检索问题诊断脚本
用于排查 hybrid_search 返回0结果的问题
"""

import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))

from pymilvus import connections, Collection, utility
from foundation.ai.models.model_handler import model_handler
from foundation.observability.logger.loggering import review_logger as logger


def check_milvus_connection():
    """检查 Milvus 连接"""
    print("\n" + "="*60)
    print("1. 检查 Milvus 连接")
    print("="*60)
    try:
        from foundation.infrastructure.config.config import config_handler
        host = config_handler.get('milvus', 'MILVUS_HOST', 'localhost')
        port = int(config_handler.get('milvus', 'MILVUS_PORT', '19530'))
        
        connections.connect(
            alias="debug",
            host=host,
            port=port,
            db_name="lq_db"
        )
        print(f"✅ Milvus 连接成功: {host}:{port}")
        return True
    except Exception as e:
        print(f"❌ Milvus 连接失败: {e}")
        return False


def check_collection_exists(collection_name: str):
    """检查 Collection 是否存在"""
    print(f"\n2. 检查 Collection 是否存在: {collection_name}")
    print("-"*60)
    
    exists = utility.has_collection(collection_name, using="debug")
    if exists:
        print(f"✅ Collection '{collection_name}' 存在")
    else:
        print(f"❌ Collection '{collection_name}' 不存在!")
    return exists


def check_collection_schema(collection_name: str):
    """检查 Collection Schema 结构"""
    print(f"\n3. 检查 Collection Schema 结构")
    print("-"*60)
    
    try:
        col = Collection(collection_name, using="debug")
        schema = col.schema
        
        print(f"Collection: {collection_name}")
        print(f"Description: {schema.description}")
        print(f"\n字段列表:")
        
        has_dense = False
        has_sparse = False
        field_names = []
        
        for field in schema.fields:
            field_names.append(field.name)
            print(f"  - {field.name}: {field.dtype.name}", end="")
            if hasattr(field, 'dim') and field.dim:
                print(f" (dim={field.dim})", end="")
            if field.is_primary:
                print(" [PRIMARY]", end="")
            print()
            
            # 检查关键字段
            if field.name == "dense":
                has_dense = True
            if field.name == "sparse":
                has_sparse = True
        
        print(f"\n混合搜索所需字段检查:")
        print(f"  - dense 字段: {'✅ 存在' if has_dense else '❌ 不存在'}")
        print(f"  - sparse 字段: {'✅ 存在' if has_sparse else '❌ 不存在'}")
        
        if not has_dense or not has_sparse:
            print(f"\n⚠️  警告: Collection 缺少混合搜索所需的字段!")
            print(f"   混合搜索需要 'dense' 和 'sparse' 两个字段")
            print(f"   当前字段: {field_names}")
        
        return has_dense and has_sparse
        
    except Exception as e:
        print(f"❌ 获取 Schema 失败: {e}")
        return False


def check_collection_data(collection_name: str):
    """检查 Collection 数据量"""
    print(f"\n4. 检查 Collection 数据量")
    print("-"*60)
    
    try:
        col = Collection(collection_name, using="debug")
        col.load()
        num_entities = col.num_entities
        
        print(f"数据量: {num_entities} 条")
        
        if num_entities == 0:
            print("❌ Collection 为空，没有数据!")
            return False
        else:
            print("✅ Collection 有数据")
            return True
            
    except Exception as e:
        print(f"❌ 获取数据量失败: {e}")
        return False


def check_collection_index(collection_name: str):
    """检查 Collection 索引"""
    print(f"\n5. 检查 Collection 索引")
    print("-"*60)
    
    try:
        col = Collection(collection_name, using="debug")
        indexes = col.indexes
        
        if not indexes:
            print("❌ 没有索引!")
            return False
        
        for idx in indexes:
            print(f"  - 字段: {idx.field_name}")
            print(f"    索引参数: {idx.params}")
        
        print("✅ 索引存在")
        return True
        
    except Exception as e:
        print(f"❌ 获取索引失败: {e}")
        return False


def test_traditional_search(collection_name: str, query_text: str):
    """测试传统向量搜索（不使用混合搜索）"""
    print(f"\n6. 测试传统向量搜索")
    print("-"*60)
    
    try:
        col = Collection(collection_name, using="debug")
        col.load()
        
        # 获取 embedding
        emdmodel = model_handler.get_embedding_model()
        query_vector = emdmodel.embed_query(query_text)
        
        print(f"查询文本: {query_text}")
        print(f"向量维度: {len(query_vector)}")
        
        # 确定向量字段名
        vector_field = None
        for field in col.schema.fields:
            if "FLOAT_VECTOR" in str(field.dtype):
                vector_field = field.name
                break
        
        if not vector_field:
            print("❌ 未找到向量字段")
            return False
        
        print(f"向量字段: {vector_field}")
        
        # 执行搜索
        search_params = {"metric_type": "COSINE", "params": {"nprobe": 10}}
        results = col.search(
            data=[query_vector],
            anns_field=vector_field,
            param=search_params,
            limit=5,
            output_fields=["text"]
        )
        
        print(f"\n搜索结果: {len(results[0])} 条")
        for i, hit in enumerate(results[0]):
            print(f"  {i+1}. ID={hit.id}, 距离={hit.distance:.4f}")
        
        if len(results[0]) > 0:
            print("✅ 传统向量搜索正常")
            return True
        else:
            print("❌ 传统向量搜索也返回0结果")
            return False
            
    except Exception as e:
        print(f"❌ 传统搜索失败: {e}")
        import traceback
        traceback.print_exc()
        return False


def test_langchain_hybrid_search(collection_name: str, query_text: str):
    """测试 LangChain Milvus 混合搜索"""
    print(f"\n7. 测试 LangChain Milvus 混合搜索")
    print("-"*60)
    
    try:
        from langchain_milvus import Milvus, BM25BuiltInFunction
        from foundation.infrastructure.config.config import config_handler
        
        host = config_handler.get('milvus', 'MILVUS_HOST', 'localhost')
        port = int(config_handler.get('milvus', 'MILVUS_PORT', '19530'))
        
        connection_args = {
            "uri": f"http://{host}:{port}",
            "db_name": "lq_db"
        }
        
        emdmodel = model_handler.get_embedding_model()
        
        print(f"尝试连接 Collection: {collection_name}")
        print(f"连接参数: {connection_args}")
        
        # 尝试创建 vectorstore
        vectorstore = Milvus(
            embedding_function=emdmodel,
            collection_name=collection_name,
            connection_args=connection_args,
            consistency_level="Strong",
            builtin_function=BM25BuiltInFunction(),
            vector_field=["dense", "sparse"]
        )
        
        print("✅ Vectorstore 创建成功")
        
        # 执行混合搜索
        print(f"\n执行混合搜索，查询: {query_text}")
        results = vectorstore.similarity_search_with_score(
            query=query_text,
            k=5,
            ranker_type="weighted",
            ranker_params={"weights": [0.7, 0.3]}
        )
        
        print(f"搜索结果: {len(results)} 条")
        for i, (doc, score) in enumerate(results):
            content = doc.page_content[:50] if doc.page_content else "N/A"
            print(f"  {i+1}. score={score:.4f}, content={content}...")
        
        if len(results) > 0:
            print("✅ 混合搜索正常")
            return True
        else:
            print("❌ 混合搜索返回0结果")
            return False
            
    except Exception as e:
        print(f"❌ 混合搜索失败: {e}")
        import traceback
        traceback.print_exc()
        return False


def test_retrieval_manager(collection_name: str, query_text: str):
    """测试 RetrievalManager 的混合搜索"""
    print(f"\n8. 测试 RetrievalManager 混合搜索")
    print("-"*60)
    
    try:
        from foundation.ai.rag.retrieval.retrieval import retrieval_manager
        
        results = retrieval_manager.hybrid_search_recall(
            collection_name=collection_name,
            query_text=query_text,
            top_k=5,
            ranker_type="weighted",
            dense_weight=0.7,
            sparse_weight=0.3
        )
        
        print(f"搜索结果: {len(results)} 条")
        for i, result in enumerate(results):
            content = result.get('text_content', '')[:50]
            print(f"  {i+1}. {content}...")
        
        if len(results) > 0:
            print("✅ RetrievalManager 混合搜索正常")
            return True
        else:
            print("❌ RetrievalManager 混合搜索返回0结果")
            return False
            
    except Exception as e:
        print(f"❌ RetrievalManager 测试失败: {e}")
        import traceback
        traceback.print_exc()
        return False


def main():
    """主诊断函数"""
    print("\n" + "="*60)
    print("混合检索问题诊断")
    print("="*60)
    
    # 配置
    collection_name = "first_bfp_collection_entity"
    query_text = "高空作业"
    
    print(f"\n诊断目标:")
    print(f"  - Collection: {collection_name}")
    print(f"  - 查询文本: {query_text}")
    
    # 执行诊断
    results = {}
    
    # 1. 检查连接
    results['connection'] = check_milvus_connection()
    if not results['connection']:
        print("\n❌ Milvus 连接失败，无法继续诊断")
        return
    
    # 2. 检查 Collection 存在
    results['exists'] = check_collection_exists(collection_name)
    if not results['exists']:
        print(f"\n❌ Collection '{collection_name}' 不存在，无法继续诊断")
        return
    
    # 3. 检查 Schema
    results['schema'] = check_collection_schema(collection_name)
    
    # 4. 检查数据量
    results['data'] = check_collection_data(collection_name)
    
    # 5. 检查索引
    results['index'] = check_collection_index(collection_name)
    
    # 6. 测试传统搜索
    results['traditional'] = test_traditional_search(collection_name, query_text)
    
    # 7. 测试 LangChain 混合搜索
    results['langchain'] = test_langchain_hybrid_search(collection_name, query_text)
    
    # 8. 测试 RetrievalManager
    results['retrieval'] = test_retrieval_manager(collection_name, query_text)
    
    # 总结
    print("\n" + "="*60)
    print("诊断总结")
    print("="*60)
    
    for key, value in results.items():
        status = "✅" if value else "❌"
        print(f"  {status} {key}")
    
    # 给出建议
    print("\n" + "="*60)
    print("问题分析与建议")
    print("="*60)
    
    if not results.get('schema'):
        print("""
⚠️  主要问题: Collection Schema 不支持混合搜索

原因: Collection 缺少 'dense' 和 'sparse' 字段
      混合搜索需要在创建 Collection 时使用 BM25BuiltInFunction

解决方案:
1. 使用 create_hybrid_collection 方法重新创建 Collection
2. 或者修改代码，对不支持混合搜索的 Collection 使用传统向量搜索
""")
    
    if results.get('traditional') and not results.get('langchain'):
        print("""
⚠️  问题: 传统搜索正常，但混合搜索失败

可能原因:
1. Collection 创建时未启用 BM25 功能
2. LangChain Milvus 版本兼容性问题
3. vector_field 配置与实际字段名不匹配

建议:
1. 检查 Collection 创建方式
2. 确认 langchain-milvus 版本
""")
    
    if not results.get('data'):
        print("""
⚠️  问题: Collection 为空

解决方案: 先向 Collection 中导入数据
""")


if __name__ == "__main__":
    main()