'commit'
This commit is contained in:
@@ -1,140 +1,12 @@
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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_core.documents import Document
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_openai import OpenAIEmbeddings
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from pydantic import SecretStr
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from Config import Config
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from ElasticSearch.Utils.EsSearchUtil import insert_long_text_to_es
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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 split_text_into_chunks(text: str, chunk_size: int = 200, chunk_overlap: int = 0) -> list:
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"""
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将文本切割成块
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参数:
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text: 要切割的文本
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chunk_size: 每个块的大小
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chunk_overlap: 块之间的重叠大小
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返回:
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list: 文本块列表
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"""
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# 创建文档对象
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docs = [Document(page_content=text, metadata={"source": "simulated_document"})]
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# 切割文档
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True
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)
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all_splits = text_splitter.split_documents(docs)
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print(f"切割后的文档块数量：{len(all_splits)}")
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return [split.page_content for split in all_splits]
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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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将长文本切割后向量化并插入到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. 切割文本
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text_chunks = split_text_into_chunks(long_text)
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# 4. 准备标签
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if tags is None:
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tags = ["general_text"]
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# 5. 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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# 6. 创建嵌入模型
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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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# 7. 为每个文本块生成向量并插入
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for i, chunk in enumerate(text_chunks):
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# 生成文本块的哈希值作为文档ID
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doc_id = hashlib.md5(chunk.encode('utf-8')).hexdigest()
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# 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"文档块 {i+1} 已存在，跳过插入: {doc_id}")
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continue
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# 生成文本块的嵌入向量
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embedding = embeddings.embed_documents([chunk])[0]
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# 准备文档数据
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doc = {
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'tags': {"tags": tags, "full_content": long_text},
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'user_input': chunk,
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'timestamp': timestamp,
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'embedding': embedding
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}
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# 插入数据到Elasticsearch
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es.index(index=index_name, id=doc_id, document=doc)
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print(f"文档块 {i+1} 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 = """混凝土是一种广泛使用的建筑材料，由水泥、砂、石子和水混合而成。它具有高强度、耐久性和良好的可塑性，被广泛应用于建筑、桥梁、道路等土木工程领域。
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@@ -1,9 +1,16 @@
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import logging
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import warnings
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import hashlib # 导入哈希库
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import time
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from Config.Config import ES_CONFIG
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from ElasticSearch.Utils.ElasticsearchConnectionPool import ElasticsearchConnectionPool
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from elasticsearch import Elasticsearch
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from langchain_core.documents import Document
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_openai import OpenAIEmbeddings
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from pydantic import SecretStr
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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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@@ -50,6 +57,128 @@ class EsSearchUtil:
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self.es_pool.release_connection(conn)
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def split_text_into_chunks(text: str, chunk_size: int = 200, chunk_overlap: int = 0) -> list:
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"""
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将文本切割成块
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参数:
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text: 要切割的文本
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chunk_size: 每个块的大小
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chunk_overlap: 块之间的重叠大小
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返回:
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list: 文本块列表
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"""
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# 创建文档对象
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docs = [Document(page_content=text, metadata={"source": "simulated_document"})]
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# 切割文档
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True
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)
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all_splits = text_splitter.split_documents(docs)
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print(f"切割后的文档块数量：{len(all_splits)}")
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return [split.page_content for split in all_splits]
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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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将长文本切割后向量化并插入到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. 切割文本
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text_chunks = split_text_into_chunks(long_text)
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# 4. 准备标签
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if tags is None:
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tags = ["general_text"]
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# 5. 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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# 6. 创建嵌入模型
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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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# 7. 为每个文本块生成向量并插入
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for i, chunk in enumerate(text_chunks):
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# 生成文本块的哈希值作为文档ID
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doc_id = hashlib.md5(chunk.encode('utf-8')).hexdigest()
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# 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"文档块 {i+1} 已存在，跳过插入: {doc_id}")
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continue
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# 生成文本块的嵌入向量
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embedding = embeddings.embed_documents([chunk])[0]
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# 准备文档数据
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doc = {
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'tags': {"tags": tags, "full_content": long_text},
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'user_input': chunk,
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'timestamp': timestamp,
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'embedding': embedding
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}
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# 插入数据到Elasticsearch
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es.index(index=index_name, id=doc_id, document=doc)
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print(f"文档块 {i+1} 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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# 添加main函数进行测试
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if __name__ == "__main__":
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try:
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