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HuangHai
2025-08-19 09:23:36 +08:00
parent 66241b57dd
commit 1fd96fbc4e
1 changed files with 100 additions and 107 deletions
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207
dsSchoolBuddy/ElasticSearch/Utils/EsSearchUtil.py
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@@ -1,18 +1,15 @@
import logging
import warnings
import hashlib # 导入哈希库
import hashlib
import time
from Config.Config import ES_CONFIG
from ElasticSearch.Utils.ElasticsearchConnectionPool import ElasticsearchConnectionPool
from elasticsearch import Elasticsearch
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from pydantic import SecretStr
from Config import Config
# 初始化日志
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
@@ -111,130 +108,126 @@ class EsSearchUtil:
# 释放连接回连接池
self.es_pool.release_connection(conn)
def split_text_into_chunks(self,text: str, chunk_size: int = 200, chunk_overlap: int = 0) -> list:
"""
将文本切割成块
参数:
text: 要切割的文本
chunk_size: 每个块的大小
chunk_overlap: 块之间的重叠大小
def split_text_into_chunks(text: str, chunk_size: int = 200, chunk_overlap: int = 0) -> list:
"""
将文本切割成块
返回:
list: 文本块列表
"""
# 创建文档对象
docs = [Document(page_content=text, metadata={"source": "simulated_document"})]
参数:
text: 要切割的文本
chunk_size: 每个块的大小
chunk_overlap: 块之间的重叠大小
# 切割文档
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True
)
all_splits = text_splitter.split_documents(docs)
print(f"切割后的文档块数量：{len(all_splits)}")
返回:
list: 文本块列表
"""
# 创建文档对象
docs = [Document(page_content=text, metadata={"source": "simulated_document"})]
return [split.page_content for split in all_splits]
# 切割文档
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True
)
all_splits = text_splitter.split_documents(docs)
print(f"切割后的文档块数量：{len(all_splits)}")
def insert_long_text_to_es(self,long_text: str, tags: list = None) -> bool:
"""
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
return [split.page_content for split in all_splits]
参数:
long_text: 要插入的长文本
tags: 可选的标签列表
返回:
bool: 插入是否成功
"""
try:
# 1. 创建EsSearchUtil实例以使用连接池
search_util = EsSearchUtil(Config.ES_CONFIG)
def insert_long_text_to_es(long_text: str, tags: list = None) -> bool:
"""
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
# 2. 从连接池获取连接
conn = search_util.es_pool.get_connection()
参数:
long_text: 要插入的长文本
tags: 可选的标签列表
返回:
bool: 插入是否成功
"""
try:
# 1. 创建EsSearchUtil实例以使用连接池
search_util = EsSearchUtil(Config.ES_CONFIG)
# 2. 从连接池获取连接
conn = search_util.es_pool.get_connection()
# 3. 检查索引是否存在，不存在则创建
index_name = Config.ES_CONFIG['index_name']
if not conn.indices.exists(index=index_name):
# 定义mapping结构
mapping = {
"mappings": {
"properties": {
"embedding": {
"type": "dense_vector",
"dims": 1024, # 根据实际embedding维度调整
"index": True,
"similarity": "l2_norm"
},
"user_input": {"type": "text"},
"tags": {
"type": "object",
"properties": {
"tags": {"type": "keyword"},
"full_content": {"type": "text"}
}
},
"timestamp": {"type": "date"}
# 3. 检查索引是否存在，不存在则创建
index_name = Config.ES_CONFIG['index_name']
if not conn.indices.exists(index=index_name):
# 定义mapping结构
mapping = {
"mappings": {
"properties": {
"embedding": {
"type": "dense_vector",
"dims": 1024, # 根据实际embedding维度调整
"index": True,
"similarity": "l2_norm"
},
"user_input": {"type": "text"},
"tags": {
"type": "object",
"properties": {
"tags": {"type": "keyword"},
"full_content": {"type": "text"}
}
},
"timestamp": {"type": "date"}
}
}
}
}
conn.indices.create(index=index_name, body=mapping)
print(f"索引 '{index_name}' 创建成功")
conn.indices.create(index=index_name, body=mapping)
print(f"索引 '{index_name}' 创建成功")
# 4. 切割文本
text_chunks = split_text_into_chunks(long_text)
# 4. 切割文本
text_chunks = self.split_text_into_chunks(long_text)
# 5. 准备标签
if tags is None:
tags = ["general_text"]
# 5. 准备标签
if tags is None:
tags = ["general_text"]
# 6. 获取当前时间
timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
# 6. 获取当前时间
timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
# 7. 创建嵌入模型
embeddings = OpenAIEmbeddings(
model=Config.EMBED_MODEL_NAME,
base_url=Config.EMBED_BASE_URL,
api_key=SecretStr(Config.EMBED_API_KEY)
)
# 7. 创建嵌入模型
embeddings = OpenAIEmbeddings(
model=Config.EMBED_MODEL_NAME,
base_url=Config.EMBED_BASE_URL,
api_key=SecretStr(Config.EMBED_API_KEY)
)
# 8. 为每个文本块生成向量并插入
for i, chunk in enumerate(text_chunks):
# 生成文本块的哈希值作为文档ID
doc_id = hashlib.md5(chunk.encode('utf-8')).hexdigest()
# 8. 为每个文本块生成向量并插入
for i, chunk in enumerate(text_chunks):
# 生成文本块的哈希值作为文档ID
doc_id = hashlib.md5(chunk.encode('utf-8')).hexdigest()
# 检查文档是否已存在
if conn.exists(index=index_name, id=doc_id):
print(f"文档块 {i+1} 已存在，跳过插入: {doc_id}")
continue
# 检查文档是否已存在
if conn.exists(index=index_name, id=doc_id):
print(f"文档块 {i+1} 已存在，跳过插入: {doc_id}")
continue
# 生成文本块的嵌入向量
embedding = embeddings.embed_documents([chunk])[0]
# 生成文本块的嵌入向量
embedding = embeddings.embed_documents([chunk])[0]
# 准备文档数据
doc = {
'tags': {"tags": tags, "full_content": long_text},
'user_input': chunk,
'timestamp': timestamp,
'embedding': embedding
}
# 准备文档数据
doc = {
'tags': {"tags": tags, "full_content": long_text},
'user_input': chunk,
'timestamp': timestamp,
'embedding': embedding
}
# 插入数据到Elasticsearch
conn.index(index=index_name, id=doc_id, document=doc)
print(f"文档块 {i+1} 插入成功: {doc_id}")
return True
except Exception as e:
print(f"插入数据失败: {e}")
return False
finally:
# 确保释放连接回连接池
if 'conn' in locals() and 'search_util' in locals():
search_util.es_pool.release_connection(conn)
# 插入数据到Elasticsearch
conn.index(index=index_name, id=doc_id, document=doc)
print(f"文档块 {i+1} 插入成功: {doc_id}")
return True
except Exception as e:
print(f"插入数据失败: {e}")
return False
finally:
# 确保释放连接回连接池
if 'conn' in locals() and 'search_util' in locals():
search_util.es_pool.release_connection(conn)
# 添加main函数进行测试