import logging
import os
from logging.handlers import RotatingFileHandler
import jieba
from gensim.models import KeyedVectors

from Config.Config import MODEL_LIMIT, MODEL_PATH
from ElasticSearch.Utils.ElasticsearchConnectionPool import ElasticsearchConnectionPool

# 初始化日志
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# 确保日志目录存在
os.makedirs('Logs', exist_ok=True)
handler = RotatingFileHandler('Logs/start.log', maxBytes=1024 * 1024, backupCount=5)
handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
logger.addHandler(handler)

class EsSearchUtil:
    def __init__(self, es_config):
        """
                初始化Elasticsearch搜索工具
                :param es_config: Elasticsearch配置字典，包含hosts, username, password, index_name等
                """
        self.es_config = es_config

        # 初始化连接池
        self.es_pool = ElasticsearchConnectionPool(
            hosts=es_config['hosts'],
            basic_auth=es_config['basic_auth'],
            verify_certs=es_config.get('verify_certs', False),
            max_connections=50
        )

        # 保留直接连接用于兼容
        from elasticsearch import Elasticsearch
        self.es = Elasticsearch(
            hosts=es_config['hosts'],
            basic_auth=es_config['basic_auth'],
            verify_certs=es_config.get('verify_certs', False)
        )

        # 确保es_conn属性存在以兼容旧代码
        self.es_conn = self.es

        # 确保es_conn属性存在以兼容旧代码
        self.es_conn = self.es


        # 加载预训练模型
        self.model = KeyedVectors.load_word2vec_format(MODEL_PATH, binary=False, limit=MODEL_LIMIT)
        logger.info(f"模型加载成功，词向量维度: {self.model.vector_size}")
        
        # 初始化Elasticsearch连接
        self.es = Elasticsearch(
            hosts=es_config['hosts'],
            basic_auth=es_config['basic_auth'],
            verify_certs=False
        )
        self.index_name = es_config['index_name']
        
    def text_to_embedding(self, text):
        # 使用已加载的模型
        # 对文本分词并计算平均向量
        words = jieba.lcut(text)
        vectors = [self.model[word] for word in words if word in self.model]
        
        if not vectors:
            return [0.0] * self.model.vector_size
            
        # 计算平均向量
        avg_vector = [sum(dim)/len(vectors) for dim in zip(*vectors)]
        return avg_vector

    def vector_search(self, query, size=10):
        query_embedding = self.text_to_embedding(query)
        script_query = {
            "script_score": {
                "query": {"match_all": {}},
                "script": {
                    "source": "double score = cosineSimilarity(params.query_vector, 'embedding'); return score >= 0 ? score : 0",
                    "params": {"query_vector": query_embedding}
                }
            }
        }
        
        return self.es_conn.search(
            index=self.es_config['index_name'],
            query=script_query,
            size=size
        )

    def text_search(self, query, size=10):
        return self.es_conn.search(
            index=self.es_config['index_name'],
            query={"match": {"user_input": query}},
            size=size
        )

    def hybrid_search(self, query, size=10):
        """
        执行混合搜索（向量搜索+文本搜索）
        :param query: 搜索查询文本
        :param size: 返回结果数量
        :return: 包含两种搜索结果的字典
        """
        vector_results = self.vector_search(query, size)
        text_results = self.text_search(query, size)
        
        return {
            'vector_results': vector_results,
            'text_results': text_results
        }
        
    def search(self, query, search_type='hybrid', size=10):
        """
        统一搜索接口
        :param query: 搜索查询文本
        :param search_type: 搜索类型（'vector', 'text' 或 'hybrid'）
        :param size: 返回结果数量
        :return: 搜索结果
        """
        if search_type == 'vector':
            return self.vector_search(query, size)
        elif search_type == 'text':
            return self.text_search(query, size)
        else:
            return self.hybrid_search(query, size)