210 lines
7.6 KiB
Python
210 lines
7.6 KiB
Python
import logging
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import warnings
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import json
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import requests
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from typing import List, Tuple, Dict
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from elasticsearch import Elasticsearch
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from Config import Config
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from Config.Config import ES_CONFIG, EMBED_MODEL_NAME, EMBED_BASE_URL, EMBED_API_KEY, RERANK_MODEL, RERANK_BASE_URL, RERANK_BINDING_API_KEY
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from langchain_openai import OpenAIEmbeddings
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from pydantic import SecretStr
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# 初始化日志
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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# 抑制HTTPS相关警告
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warnings.filterwarnings('ignore', message='Connecting to .* using TLS with verify_certs=False is insecure')
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warnings.filterwarnings('ignore', message='Unverified HTTPS request is being made to host')
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def text_to_embedding(text: str) -> List[float]:
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"""
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将文本转换为嵌入向量
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"""
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embeddings = OpenAIEmbeddings(
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model=EMBED_MODEL_NAME,
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base_url=EMBED_BASE_URL,
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api_key=SecretStr(EMBED_API_KEY)
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)
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return embeddings.embed_query(text)
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def rerank_results(query: str, results: List[Dict]) -> List[Tuple[Dict, float]]:
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"""
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对搜索结果进行重排
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"""
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if len(results) <= 1:
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return [(doc, 1.0) for doc in results]
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# 准备重排请求数据
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rerank_data = {
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"model": RERANK_MODEL,
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"query": query,
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"documents": [doc['_source']['user_input'] for doc in results],
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"top_n": len(results)
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}
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# 调用SiliconFlow API进行重排
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {RERANK_BINDING_API_KEY}"
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}
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try:
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response = requests.post(RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data))
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response.raise_for_status()
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rerank_result = response.json()
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# 处理重排结果
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reranked_docs_with_scores = []
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if "results" in rerank_result:
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for item in rerank_result["results"]:
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doc_idx = item.get("index")
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score = item.get("relevance_score", 0.0)
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if 0 <= doc_idx < len(results):
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reranked_docs_with_scores.append((results[doc_idx], score))
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return reranked_docs_with_scores
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except Exception as e:
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logger.error(f"重排失败: {str(e)}")
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return [(doc, 1.0) for doc in results]
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def merge_results(keyword_results: List[Tuple[Dict, float]], vector_results: List[Tuple[Dict, float]]) -> List[Tuple[Dict, float, str]]:
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"""
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合并关键字搜索和向量搜索结果
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"""
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# 标记结果来源并合并
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all_results = []
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for doc, score in keyword_results:
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all_results.append((doc, score, "关键字搜索"))
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for doc, score in vector_results:
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all_results.append((doc, score, "向量搜索"))
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# 去重并按分数排序
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unique_results = {}
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for doc, score, source in all_results:
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doc_id = doc['_id']
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if doc_id not in unique_results or score > unique_results[doc_id][1]:
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unique_results[doc_id] = (doc, score, source)
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# 按分数降序排序
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sorted_results = sorted(unique_results.values(), key=lambda x: x[1], reverse=True)
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return sorted_results
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if __name__ == "__main__":
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# 初始化EsSearchUtil
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esClient = Elasticsearch(
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hosts=Config.ES_CONFIG['hosts'],
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basic_auth=Config.ES_CONFIG['basic_auth'],
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verify_certs=False
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)
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# 获取用户输入
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user_query = input("请输入查询语句（例如：高性能的混凝土）: ")
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if not user_query:
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user_query = "高性能的混凝土"
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print(f"未输入查询语句，使用默认值: {user_query}")
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query_tags = [] # 可以根据需要添加标签过滤
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print(f"\n=== 开始执行查询 ===")
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print(f"原始查询文本: {user_query}")
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# 执行搜索
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es_conn = esClient.es_pool.get_connection()
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try:
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# 1. 向量搜索
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print("\n=== 向量搜索阶段 ===")
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print("1. 文本向量化处理中...")
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query_embedding = text_to_embedding(user_query)
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print(f"2. 生成的查询向量维度: {len(query_embedding)}")
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print(f"3. 前3维向量值: {query_embedding[:3]}")
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print("4. 正在执行Elasticsearch向量搜索...")
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vector_results = es_conn.search(
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index=ES_CONFIG['index_name'],
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body={
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"query": {
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"script_score": {
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"query": {
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"bool": {
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"should": [
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{
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"terms": {
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"tags.tags": query_tags
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}
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}
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] if query_tags else {"match_all": {}},
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"minimum_should_match": 1 if query_tags else 0
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}
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},
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"script": {
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"source": "double score = cosineSimilarity(params.query_vector, 'embedding'); return score >= 0 ? score : 0",
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"params": {"query_vector": query_embedding}
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}
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}
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},
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"size": 5
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}
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)
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vector_hits = vector_results['hits']['hits']
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print(f"5. 向量搜索结果数量: {len(vector_hits)}")
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# 向量结果重排
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print("6. 正在进行向量结果重排...")
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reranked_vector_results = rerank_results(user_query, vector_hits)
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print(f"7. 重排后向量结果数量: {len(reranked_vector_results)}")
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# 2. 关键字搜索
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print("\n=== 关键字搜索阶段 ===")
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print("1. 正在执行Elasticsearch关键字搜索...")
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keyword_results = es_conn.search(
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index=ES_CONFIG['index_name'],
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body={
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"query": {
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"bool": {
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"must": [
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{
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"match": {
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"user_input": user_query
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}
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}
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] + ([
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{
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"terms": {
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"tags.tags": query_tags
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}
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}
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] if query_tags else [])
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}
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},
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"size": 5
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}
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)
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keyword_hits = keyword_results['hits']['hits']
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print(f"2. 关键字搜索结果数量: {len(keyword_hits)}")
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# 3. 合并结果
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print("\n=== 合并搜索结果 ===")
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# 为关键字结果添加默认分数1.0
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keyword_results_with_scores = [(doc, doc['_score']) for doc in keyword_hits]
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merged_results = merge_results(keyword_results_with_scores, reranked_vector_results)
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print(f"合并后唯一结果数量: {len(merged_results)}")
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# 4. 打印最终结果
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print("\n=== 最终搜索结果 ===")
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for i, (doc, score, source) in enumerate(merged_results, 1):
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print(f"{i}. 文档ID: {doc['_id']}, 分数: {score:.2f}, 来源: {source}")
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print(f" 内容: {doc['_source']['user_input']}")
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print(" --- ")
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except Exception as e:
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logger.error(f"搜索过程中发生错误: {str(e)}")
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print(f"搜索失败: {str(e)}")
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finally:
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esClient.es_pool.release_connection(es_conn)
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