117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
import hashlib # 导入哈希库
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import time
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import warnings
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from elasticsearch import Elasticsearch
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from langchain_openai import OpenAIEmbeddings # 直接导入嵌入模型
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from pydantic import SecretStr # 用于包装API密钥
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from Config import Config
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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 insert_long_text_to_es(long_text: str, tags: list = None) -> bool:
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"""
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将长文本向量化并插入到Elasticsearch，基于文本内容哈希实现去重
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参数:
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long_text: 要插入的长文本
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tags: 可选的标签列表
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返回:
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bool: 插入是否成功
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"""
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try:
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# 1. 初始化Elasticsearch连接
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es = 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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# 2. 检查索引是否存在，不存在则创建
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index_name = Config.ES_CONFIG['index_name']
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if not es.indices.exists(index=index_name):
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# 定义mapping结构
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mapping = {
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"mappings": {
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"properties": {
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"embedding": {
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"type": "dense_vector",
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"dims": 1024, # 根据实际embedding维度调整
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"index": True,
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"similarity": "l2_norm"
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},
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"user_input": {"type": "text"},
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"tags": {
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"type": "object",
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"properties": {
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"tags": {"type": "keyword"},
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"full_content": {"type": "text"}
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}
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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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es.indices.create(index=index_name, body=mapping)
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print(f"索引 '{index_name}' 创建成功")
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# 3. 生成文本内容的哈希值作为文档ID（实现去重）
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doc_id = hashlib.md5(long_text.encode('utf-8')).hexdigest()
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print(f"文本哈希值: {doc_id}")
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# 4. 检查文档是否已存在
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if es.exists(index=index_name, id=doc_id):
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print(f"文档已存在，跳过插入: {doc_id}")
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return True
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# 5. 准备标签
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if tags is None:
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tags = ["general_text"]
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tags_dict = {"tags": tags, "full_content": long_text}
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# 6. 获取当前时间
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timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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# 7. 直接创建嵌入模型并生成向量
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embeddings = OpenAIEmbeddings(
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model=Config.EMBED_MODEL_NAME,
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base_url=Config.EMBED_BASE_URL,
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api_key=SecretStr(Config.EMBED_API_KEY)
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)
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# 8. 生成文本嵌入向量
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embedding = embeddings.embed_documents([long_text])[0]
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# 9. 准备文档数据
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doc = {
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'tags': tags_dict,
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'user_input': long_text[:500], # 取前500个字符作为摘要
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'timestamp': timestamp,
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'embedding': embedding
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}
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# 10. 插入数据到Elasticsearch（使用哈希值作为ID）
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es.index(index=index_name, id=doc_id, document=doc)
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print(f"长文本数据插入成功: {doc_id}")
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return True
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except Exception as e:
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print(f"插入数据失败: {e}")
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return False
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def main():
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# 示例1：插入单个长文本
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long_text = "这是一个测试长文本，用于演示基于内容哈希的去重机制。"
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tags = ["test", "hash_deduplication"]
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insert_long_text_to_es(long_text, tags)
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if __name__ == "__main__":
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main()
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