mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-28 09:43:19 +08:00
51301dfe6a
- 开发者
- get_OpenAIClient 的 local_wrap 默认值改为 False，避免 API 服务未启动导致其它功能受阻（如Embeddings）
- 修改 ES 知识库服务：
- 检索策略改为 ApproxRetrievalStrategy
- 设置 timeout 为 60， 避免文档过多导致 ConnecitonTimeout Error
- 修改 LocalAIEmbeddings，使用多线程进行 embed_texts，效果不明显，瓶颈可能主要在提供 Embedding 的服务器上
220 lines
8.5 KiB
Python
220 lines
8.5 KiB
Python
from typing import List
|
||
import os
|
||
import shutil
|
||
from langchain.schema import Document
|
||
from langchain_community.vectorstores.elasticsearch import ElasticsearchStore, ApproxRetrievalStrategy
|
||
from server.knowledge_base.kb_service.base import KBService, SupportedVSType
|
||
from server.knowledge_base.utils import KnowledgeFile
|
||
from server.utils import get_Embeddings
|
||
from elasticsearch import Elasticsearch, BadRequestError
|
||
from configs import logger, kbs_config, KB_ROOT_PATH
|
||
|
||
|
||
class ESKBService(KBService):
|
||
|
||
def do_init(self):
|
||
self.kb_path = self.get_kb_path(self.kb_name)
|
||
self.index_name = os.path.split(self.kb_path)[-1]
|
||
self.IP = kbs_config[self.vs_type()]['host']
|
||
self.PORT = kbs_config[self.vs_type()]['port']
|
||
self.user = kbs_config[self.vs_type()].get("user",'')
|
||
self.password = kbs_config[self.vs_type()].get("password",'')
|
||
self.dims_length = kbs_config[self.vs_type()].get("dims_length",None)
|
||
self.embeddings_model = get_Embeddings(self.embed_model)
|
||
try:
|
||
# ES python客户端连接（仅连接）
|
||
if self.user != "" and self.password != "":
|
||
self.es_client_python = Elasticsearch(f"http://{self.IP}:{self.PORT}",
|
||
basic_auth=(self.user,self.password))
|
||
else:
|
||
logger.warning("ES未配置用户名和密码")
|
||
self.es_client_python = Elasticsearch(f"http://{self.IP}:{self.PORT}")
|
||
except ConnectionError:
|
||
logger.error("连接到 Elasticsearch 失败！")
|
||
raise ConnectionError
|
||
except Exception as e:
|
||
logger.error(f"Error 发生 : {e}")
|
||
raise e
|
||
try:
|
||
# 首先尝试通过es_client_python创建
|
||
mappings = {
|
||
"properties": {
|
||
"dense_vector": {
|
||
"type": "dense_vector",
|
||
"dims": self.dims_length,
|
||
"index": True
|
||
}
|
||
}
|
||
}
|
||
self.es_client_python.indices.create(index=self.index_name, mappings=mappings)
|
||
except BadRequestError as e:
|
||
logger.error("创建索引失败,重新")
|
||
logger.error(e)
|
||
|
||
try:
|
||
# langchain ES 连接、创建索引
|
||
params = dict(
|
||
es_url=f"http://{self.IP}:{self.PORT}",
|
||
index_name=self.index_name,
|
||
query_field="context",
|
||
vector_query_field="dense_vector",
|
||
embedding=self.embeddings_model,
|
||
strategy=ApproxRetrievalStrategy(),
|
||
es_params={
|
||
"timeout": 60,
|
||
}
|
||
)
|
||
if self.user != "" and self.password != "":
|
||
params.update(
|
||
es_user=self.user,
|
||
es_password=self.password
|
||
)
|
||
self.db = ElasticsearchStore(**params)
|
||
except ConnectionError:
|
||
logger.error("### 初始化 Elasticsearch 失败！")
|
||
raise ConnectionError
|
||
except Exception as e:
|
||
logger.error(f"Error 发生 : {e}")
|
||
raise e
|
||
try:
|
||
# 尝试通过db_init创建索引
|
||
self.db._create_index_if_not_exists(
|
||
index_name=self.index_name,
|
||
dims_length=self.dims_length
|
||
)
|
||
except Exception as e:
|
||
logger.error("创建索引失败...")
|
||
logger.error(e)
|
||
# raise e
|
||
|
||
@staticmethod
|
||
def get_kb_path(knowledge_base_name: str):
|
||
return os.path.join(KB_ROOT_PATH, knowledge_base_name)
|
||
|
||
@staticmethod
|
||
def get_vs_path(knowledge_base_name: str):
|
||
return os.path.join(ESKBService.get_kb_path(knowledge_base_name), "vector_store")
|
||
|
||
def do_create_kb(self):
|
||
if os.path.exists(self.doc_path):
|
||
if not os.path.exists(os.path.join(self.kb_path, "vector_store")):
|
||
os.makedirs(os.path.join(self.kb_path, "vector_store"))
|
||
else:
|
||
logger.warning("directory `vector_store` already exists.")
|
||
|
||
def vs_type(self) -> str:
|
||
return SupportedVSType.ES
|
||
|
||
def do_search(self, query:str, top_k: int, score_threshold: float):
|
||
# 文本相似性检索
|
||
docs = self.db.similarity_search_with_score(query=query,
|
||
k=top_k)
|
||
return docs
|
||
|
||
def get_doc_by_ids(self, ids: List[str]) -> List[Document]:
|
||
results = []
|
||
for doc_id in ids:
|
||
try:
|
||
response = self.es_client_python.get(index=self.index_name, id=doc_id)
|
||
source = response["_source"]
|
||
# Assuming your document has "text" and "metadata" fields
|
||
text = source.get("context", "")
|
||
metadata = source.get("metadata", {})
|
||
results.append(Document(page_content=text, metadata=metadata))
|
||
except Exception as e:
|
||
logger.error(f"Error retrieving document from Elasticsearch! {e}")
|
||
return results
|
||
|
||
def del_doc_by_ids(self, ids: List[str]) -> bool:
|
||
for doc_id in ids:
|
||
try:
|
||
self.es_client_python.delete(index=self.index_name,
|
||
id=doc_id,
|
||
refresh=True)
|
||
except Exception as e:
|
||
logger.error(f"ES Docs Delete Error! {e}")
|
||
|
||
def do_delete_doc(self, kb_file, **kwargs):
|
||
if self.es_client_python.indices.exists(index=self.index_name):
|
||
# 从向量数据库中删除索引(文档名称是Keyword)
|
||
query = {
|
||
"query": {
|
||
"term": {
|
||
"metadata.source.keyword": kb_file.filepath
|
||
}
|
||
}
|
||
}
|
||
# 注意设置size，默认返回10个。
|
||
search_results = self.es_client_python.search(body=query, size=50)
|
||
delete_list = [hit["_id"] for hit in search_results['hits']['hits']]
|
||
if len(delete_list) == 0:
|
||
return None
|
||
else:
|
||
for doc_id in delete_list:
|
||
try:
|
||
self.es_client_python.delete(index=self.index_name,
|
||
id=doc_id,
|
||
refresh=True)
|
||
except Exception as e:
|
||
logger.error(f"ES Docs Delete Error! {e}")
|
||
|
||
# self.db.delete(ids=delete_list)
|
||
#self.es_client_python.indices.refresh(index=self.index_name)
|
||
|
||
|
||
def do_add_doc(self, docs: List[Document], **kwargs):
|
||
'''向知识库添加文件'''
|
||
|
||
print(f"server.knowledge_base.kb_service.es_kb_service.do_add_doc 输入的docs参数长度为:{len(docs)}")
|
||
print("*"*100)
|
||
|
||
self.db.add_documents(documents=docs)
|
||
# 获取 id 和 source , 格式：[{"id": str, "metadata": dict}, ...]
|
||
print("写入数据成功.")
|
||
print("*"*100)
|
||
|
||
if self.es_client_python.indices.exists(index=self.index_name):
|
||
file_path = docs[0].metadata.get("source")
|
||
query = {
|
||
"query": {
|
||
"term": {
|
||
"metadata.source.keyword": file_path
|
||
},
|
||
"term": {
|
||
"_index": self.index_name
|
||
}
|
||
}
|
||
}
|
||
# 注意设置size，默认返回10个。
|
||
search_results = self.es_client_python.search(body=query, size=50)
|
||
if len(search_results["hits"]["hits"]) == 0:
|
||
raise ValueError("召回元素个数为0")
|
||
info_docs = [{"id":hit["_id"], "metadata": hit["_source"]["metadata"]} for hit in search_results["hits"]["hits"]]
|
||
return info_docs
|
||
|
||
|
||
def do_clear_vs(self):
|
||
"""从知识库删除全部向量"""
|
||
if self.es_client_python.indices.exists(index=self.kb_name):
|
||
self.es_client_python.indices.delete(index=self.kb_name)
|
||
|
||
|
||
def do_drop_kb(self):
|
||
"""删除知识库"""
|
||
# self.kb_file: 知识库路径
|
||
if os.path.exists(self.kb_path):
|
||
shutil.rmtree(self.kb_path)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
esKBService = ESKBService("test")
|
||
#esKBService.clear_vs()
|
||
#esKBService.create_kb()
|
||
esKBService.add_doc(KnowledgeFile(filename="README.md", knowledge_base_name="test"))
|
||
print(esKBService.search_docs("如何启动api服务"))
|
||
|
||
|
||
|
||
|
||
|