from Config.Config import ES_CONFIG
from Util.EsSearchUtil import EsSearchUtil


def queryByEs(query, query_tags,logger):
    # 获取EsSearchUtil实例
    es_search_util = EsSearchUtil(ES_CONFIG)

    # 执行混合搜索
    es_conn = es_search_util.es_pool.get_connection()
    try:
        # 向量搜索
        logger.info(f"\n=== 开始执行查询 ===")
        logger.info(f"原始查询文本: {query}")
        logger.info(f"查询标签: {query_tags}")

        logger.info("\n=== 向量搜索阶段 ===")
        logger.info("1. 文本分词和向量化处理中...")
        query_embedding = es_search_util.text_to_embedding(query)
        logger.info(f"2. 生成的查询向量维度: {len(query_embedding)}")
        logger.info(f"3. 前3维向量值: {query_embedding[:3]}")

        logger.info("4. 正在执行Elasticsearch向量搜索...")
        vector_results = es_conn.search(
            index=ES_CONFIG['index_name'],
            body={
                "query": {
                    "script_score": {
                        "query": {
                            "bool": {
                                "should": [
                                    {
                                        "terms": {
                                            "tags.tags": query_tags
                                        }
                                    }
                                ],
                                "minimum_should_match": 1
                            }
                        },
                        "script": {
                            "source": "double score = cosineSimilarity(params.query_vector, 'embedding'); return score >= 0 ? score : 0",
                            "params": {"query_vector": query_embedding}
                        }
                    }
                },
                "size": 3
            }
        )
        logger.info(f"5. 向量搜索结果数量: {len(vector_results['hits']['hits'])}")

        # 文本精确搜索
        logger.info("\n=== 文本精确搜索阶段 ===")
        logger.info("1. 正在执行Elasticsearch文本精确搜索...")
        text_results = es_conn.search(
            index=ES_CONFIG['index_name'],
            body={
                "query": {
                    "bool": {
                        "must": [
                            {
                                "match": {
                                    "user_input": query
                                }
                            },
                            {
                                "terms": {
                                    "tags.tags": query_tags
                                }
                            }
                        ]
                    }
                },
                "size": 3
            }
        )
        logger.info(f"2. 文本搜索结果数量: {len(text_results['hits']['hits'])}")

        # 合并结果
        logger.info("\n=== 最终搜索结果 ===")
        logger.info(f"向量搜索结果: {len(vector_results['hits']['hits'])}条")
        for i, hit in enumerate(vector_results['hits']['hits'], 1):
            logger.info(f"   {i}. 文档ID: {hit['_id']}, 相似度分数: {hit['_score']:.2f}")
            logger.info(f"      内容: {hit['_source']['user_input']}")

        logger.info("文本精确搜索结果:")
        for i, hit in enumerate(text_results['hits']['hits']):
            logger.info(f"  {i + 1}. 文档ID: {hit['_id']}, 匹配分数: {hit['_score']:.2f}")
            logger.info(f"      内容: {hit['_source']['user_input']}")

        # 去重处理：去除vector_results和text_results中重复的user_input
        vector_sources = [hit['_source'] for hit in vector_results['hits']['hits']]
        text_sources = [hit['_source'] for hit in text_results['hits']['hits']]

        # 构建去重后的结果
        unique_text_sources = []
        text_user_inputs = set()

        # 先处理text_results，保留所有
        for source in text_sources:
            text_user_inputs.add(source['user_input'])
            unique_text_sources.append(source)

        # 处理vector_results，只保留不在text_results中的
        unique_vector_sources = []
        for source in vector_sources:
            if source['user_input'] not in text_user_inputs:
                unique_vector_sources.append(source)

        # 计算优化掉的记录数量和节约的tokens
        removed_count = len(vector_sources) - len(unique_vector_sources)
        saved_tokens = sum(len(source['user_input']) for source in vector_sources
                           if source['user_input'] in text_user_inputs)

        logger.info(f"优化掉 {removed_count} 条重复记录，节约约 {saved_tokens} tokens")

        search_results = {
            "vector_results": unique_vector_sources,
            "text_results": unique_text_sources
        }
        return search_results
    finally:
        es_search_util.es_pool.release_connection(es_conn)