from Config.Config import ES_CONFIG
from elasticsearch import Elasticsearch
from T2_Txt2Vec import text_to_embedding
import datetime
import warnings

# 初始化ES连接
es = Elasticsearch(
    hosts=ES_CONFIG["hosts"],
    basic_auth=ES_CONFIG["basic_auth"],
    verify_certs=ES_CONFIG["verify_certs"],
    ssl_show_warn=ES_CONFIG["ssl_show_warn"]
)

# 修改create_vector_index和save_to_es函数中使用ES_CONFIG["default_index"]
def create_vector_index(index_name="knowledge_base"):
    """创建带有向量字段的索引"""
    mapping = {
        "mappings": {
            "properties": {
                "text": {"type": "text", "analyzer": "ik_max_word"},
                "vector": {
                    "type": "dense_vector",
                    "dims": 768,  # 需与text2vec模型维度一致
                    "index": True,
                    "similarity": "cosine"
                },
                "timestamp": {"type": "date"}
            }
        }
    }
    
    try:
        if es.indices.exists(index=index_name):
            es.indices.delete(index=index_name)
        es.indices.create(index=index_name, body=mapping)
        print(f"索引 {index_name} 创建成功")
    except Exception as e:
        print(f"创建索引失败: {str(e)}")
        raise

def save_to_es(text, index_name="knowledge_base"):
    """将文本向量化后保存到ES"""
    vector = text_to_embedding(text)
    doc = {
        "text": text,
        "vector": vector,
        "timestamp": datetime.datetime.now().isoformat()
    }
    
    try:
        res = es.index(index=index_name, document=doc)
        print(f"文档已保存，ID: {res['_id']}")
        return res
    except Exception as e:
        print(f"保存到ES失败: {str(e)}")
        raise

# 使用示例
if __name__ == "__main__":
    create_vector_index()  # 首次运行前执行
    save_to_es("如何更换支付宝绑定银行卡")