255 lines
9.3 KiB
Python
255 lines
9.3 KiB
Python
import logging
|
||
import warnings
|
||
import hashlib
|
||
import time
|
||
from Config.Config import ES_CONFIG
|
||
from ElasticSearch.Utils.ElasticsearchConnectionPool import ElasticsearchConnectionPool
|
||
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)
|
||
|
||
class EsSearchUtil:
|
||
def __init__(self, es_config):
|
||
"""
|
||
初始化Elasticsearch搜索工具
|
||
:param es_config: Elasticsearch配置字典，包含hosts, username, password, index_name等
|
||
"""
|
||
# 抑制HTTPS相关警告
|
||
warnings.filterwarnings('ignore', message='Connecting to .* using TLS with verify_certs=False is insecure')
|
||
warnings.filterwarnings('ignore', message='Unverified HTTPS request is being made to host')
|
||
|
||
self.es_config = es_config
|
||
|
||
# 初始化连接池
|
||
self.es_pool = ElasticsearchConnectionPool(
|
||
hosts=es_config['hosts'],
|
||
basic_auth=es_config['basic_auth'],
|
||
verify_certs=es_config.get('verify_certs', False),
|
||
max_connections=50
|
||
)
|
||
|
||
self.index_name = es_config['index_name']
|
||
logger.info(f"EsSearchUtil初始化成功，索引名称: {self.index_name}")
|
||
|
||
def rebuild_mapping(self):
|
||
"""
|
||
重建Elasticsearch索引和mapping结构
|
||
|
||
返回:
|
||
bool: 操作是否成功
|
||
"""
|
||
try:
|
||
# 从连接池获取连接
|
||
conn = self.es_pool.get_connection()
|
||
|
||
# 定义mapping结构
|
||
mapping = {
|
||
"mappings": {
|
||
"properties": {
|
||
"embedding": {
|
||
"type": "dense_vector",
|
||
"dims": 1024, # embedding维度为1024
|
||
"index": True,
|
||
"similarity": "l2_norm" # 使用L2距离
|
||
},
|
||
"user_input": {"type": "text"},
|
||
"tags": {
|
||
"type": "object",
|
||
"properties": {
|
||
"tags": {"type": "keyword"},
|
||
"full_content": {"type": "text"}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
# 检查索引是否存在，存在则删除
|
||
if conn.indices.exists(index=self.index_name):
|
||
conn.indices.delete(index=self.index_name)
|
||
logger.info(f"删除已存在的索引 '{self.index_name}'")
|
||
print(f"删除已存在的索引 '{self.index_name}'")
|
||
|
||
# 创建索引和mapping
|
||
conn.indices.create(index=self.index_name, body=mapping)
|
||
logger.info(f"索引 '{self.index_name}' 创建成功，mapping结构已设置")
|
||
print(f"索引 '{self.index_name}' 创建成功，mapping结构已设置。")
|
||
|
||
return True
|
||
except Exception as e:
|
||
logger.error(f"重建mapping失败: {str(e)}")
|
||
print(f"重建mapping失败: {e}")
|
||
return False
|
||
finally:
|
||
# 释放连接回连接池
|
||
self.es_pool.release_connection(conn)
|
||
|
||
def text_search(self, query, size=10):
|
||
# 从连接池获取连接
|
||
conn = self.es_pool.get_connection()
|
||
try:
|
||
# 使用连接执行搜索
|
||
result = conn.search(
|
||
index=self.es_config['index_name'],
|
||
query={"match": {"user_input": query}},
|
||
size=size
|
||
)
|
||
return result
|
||
except Exception as e:
|
||
logger.error(f"文本搜索失败: {str(e)}")
|
||
raise
|
||
finally:
|
||
# 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
|
||
|
||
返回:
|
||
list: 文本块列表
|
||
"""
|
||
# 创建文档对象
|
||
docs = [Document(page_content=text, metadata={"source": "simulated_document"})]
|
||
|
||
# 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)}")
|
||
|
||
return [split.page_content for split in 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
|
||
|
||
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"}
|
||
}
|
||
}
|
||
}
|
||
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 = self.split_text_into_chunks(long_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())
|
||
|
||
# 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()
|
||
|
||
# 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]
|
||
|
||
# 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)
|
||
|
||
|
||
# 添加main函数进行测试
|
||
if __name__ == "__main__":
|
||
try:
|
||
# 创建EsSearchUtil实例
|
||
search_util = EsSearchUtil(ES_CONFIG)
|
||
|
||
# 查询"混凝土"
|
||
query = "混凝土"
|
||
logger.info(f"开始查询关键词: {query}")
|
||
results = search_util.text_search(query, size=5)
|
||
|
||
print(f"查询 '{query}' 完成，共找到 {len(results['hits']['hits'])} 条结果")
|
||
# 在打印结果数量后添加
|
||
for i, hit in enumerate(results['hits']['hits'], 1):
|
||
print(f"结果 {i}:")
|
||
print(f"得分: {hit['_score']}")
|
||
print(f"内容: {hit['_source'].get('user_input', '无内容')}")
|
||
print("-" * 50)
|
||
|
||
except Exception as e:
|
||
logger.error(f"测试失败: {str(e)}")
|
||
print(f"测试失败: {str(e)}")
|