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import datetime
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from elasticsearch import Elasticsearch
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from Config.Config import ES_CONFIG
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from T2_Txt2Vec import text_to_embedding
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# 初始化ES连接
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es = Elasticsearch(
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hosts=ES_CONFIG["hosts"],
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basic_auth=ES_CONFIG["basic_auth"],
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verify_certs=ES_CONFIG["verify_certs"],
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ssl_show_warn=ES_CONFIG["ssl_show_warn"]
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)
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def create_vector_index(index_name="knowledge_base"):
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"""创建带有向量字段的索引(适配200维腾讯词向量)"""
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mapping = {
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"mappings": {
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"properties": {
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"text": {"type": "text", "analyzer": "ik_max_word"},
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"vector": {
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"type": "dense_vector",
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"dims": 200, # 修改为腾讯词向量实际维度
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"index": True,
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"similarity": "cosine"
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},
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"timestamp": {"type": "date"}
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}
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}
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}
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try:
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if es.indices.exists(index=index_name):
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es.indices.delete(index=index_name)
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es.indices.create(index=index_name, body=mapping)
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print(f"索引 {index_name} 创建成功(200维)")
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except Exception as e:
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print(f"创建索引失败: {str(e)}")
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raise
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def save_to_es(text, index_name="knowledge_base"):
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"""将文本向量化后保存到ES"""
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vector = text_to_embedding(text)
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doc = {
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"text": text,
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"vector": vector,
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"timestamp": datetime.datetime.now().isoformat()
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}
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try:
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res = es.index(index=index_name, document=doc)
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print(f"文档已保存，ID: {res['_id']}")
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return res
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except Exception as e:
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print(f"保存到ES失败: {str(e)}")
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raise
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# 使用示例
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if __name__ == "__main__":
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create_vector_index() # 首次运行前执行
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save_to_es("如何更换支付宝绑定银行卡") |
