[BUG] 最新版本查询faiss库内数据很慢2万条数据需要10秒

[c935289832]

上传以及检索时间跟0.2版本的差距有点大

[JiadiLee]

我也发现了这个问题，我这边采用的是xinference(GLM-4/bge-large-zh) 进行大模型推理和embedding，11M的文件数据，chunk 都是250 + 50进行分割，在4090服务器上进行测试，0.2.10版本知识库可以快速回答，0.3.0版本需要20s以上的时间！ 将chunk设置为500 + 50的分割方式，在0.3.0版本上仍然需要10s以上的时间，速度下降的很明显

[liunux4odoo]

这个需要排查一下，新的 retriever 里加入了 bm25 检索，带来了多少额外的开销。

[BenjaminFan525]

遇到了同样的问题。其实数据库构建时间长一点无所谓，但是影响检索时间有些难以接受

[ELvis168]

确实慢了不少

[trackcan]

bm25检索确实慢了不少，导致没法使用0.3版本，望大佬尽早排查修复下

[tianxiajianghu]

我判断是 bm25的问题，因为写的很简单，它是将所有的文档从数据库加载到内存，在进行搜索，文件越多，传输开销越大，所有算法没问题，而是加载数据的方法有问题，我改用es数据库，读取数据很慢

[yangpeng-space]

比0.2版本慢很多+1

[trackcan]

大佬，这个bug修复提上日程了吗？ 迟迟不敢升级到0.3版本

[powerbuildre]

同样的问题，本地知识库反应太慢了

[andyzhou1982]

bm25的锅，修改/chatchat-server/chatchat/server/file_rag/retrievers/ensemble.py中的from_vectorstore方法，暂时将bm25_retriever初始化的那段代码屏蔽掉，只用faiss_retriever可以明显提速

[aiChatGPT35User123]

bm25的锅，/chatchat-server/chatchat/server/file_rag/retrievers/ensemble.py中的from_vectorstore方法修改，暂时将bm25_retriever初始化的那段代码发光掉，只用faiss_retriever可以明显提速

并没有很大的提升

[c935289832]

bm25的锅，/chatchat-server/chatchat/server/file_rag/retrievers/ensemble.py中的from_vectorstore方法修改，暂时将bm25_retriever初始化的那段代码发光掉，只用faiss_retriever可以明显提速

并没有很大的提升

我刚试了 能达到0.2版本的效率

[andyzhou1982]

bm25的锅，/chatchat-server/chatchat/server/file_rag/retrievers/ensemble.py中的from_vectorstore方法修改，暂时将bm25_retriever初始化的那段代码发光掉，只用faiss_retriever可以明显提速

并没有很大的提升

LLM回答本来也要耗费一定的时间，只要知识库问答在10秒以内就应该是正常水平的

[JiadiLee]

我在最新代码里面将BM25检索的部分注释掉，在Web界面RAG对话-知识库检索，阈值不管调到多少都检索不到答案可能是什么原因呢？有警告"/home/ubuntu/anaconda3/envs/langchain/lib/python3.10/site-packages/langchain_core/vectorstores.py:342: UserWarning: No relevant docs were retrieved using the relevance score threshold 0.98"

我替换成0.2的方法能快速检索，就是感觉召回率还是有点儿低。

[aiChatGPT35User123]

bm25

我刚刚尝试了能够达到原0.2版本的效率

可能是因为我的数据量少，然后0.2版本的时候，知识库问答是在1s之内，现在慢了600ms左右

[andyzhou1982]

我在最新代码里面将BM25检索的部分注释掉，在Web界面RAG对话-知识库检索，阈值不管调到多少都检索不到答案可能是什么原因呢？有警告"/home/ubuntu/anaconda3/envs/langchain/lib/python3.10/site-packages/langchain_core/vectorstores.py:342: UserWarning: No relevant docs were retrieved using the relevance score threshold 0.98"

我替换成0.2的方法能快速检索，就是感觉召回率还是有点儿低。

注释掉这段代码对召回的结果肯定有一定的影响，这段代码的原意应该是综合了faiss_retriever和bm25_retriever，各占50%的比重，不过bm25_retriever的初始化有点简单粗暴，数据量稍微大一点性能就急剧下降，具体里面的东西我也没有深入去研究，注释掉bm25_retriever只是一个临时的解决方法，最终的解决方案还是等官方出新版本吧

[c935289832]

我在最新代码里面将BM25检索的部分注释掉，在Web界面RAG对话-知识库检索，阈值不管调到多少都检索不到答案可能是什么原因呢？有警告"/home/ubuntu/anaconda3/envs/langchain/lib/python3.10/site-packages/langchain_core/vectorstores.py:342: UserWarning: No relevant docs were retrieved using the relevance score threshold 0.98"

我替换成0.2的方法能快速检索，就是感觉召回率还是有点儿低。

这是提示的没有这个阈值的数据 你稍微拉低点 0.5左右试试呢

[JiadiLee]

这是提示的没有这个阈值的数据 你稍微拉低点 0.5左右试试呢

感谢解答，确实是阈值设置的高了，这个返回的score跟0.2的similarity_search_with_score_by_vector 是不一样的，我理解错了。

[firrice]

耗时是因为BM25_retriever在每一轮回答中都对知识库中的所有chunk做了一遍预处理，具体可参见: https://github.com/chatchat-space/Langchain-Chatchat/issues/4565#issuecomment-2268152564 。这里我提供一个简单的、经过验证的补丁方案，即服务启动前完成预处理操作并将结果固化为txt，之后在对话时只需读取该txt直接获得预处理结果即可，代码分为两块：

提前预处理

import os
import json
os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat"
import jieba
from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples"
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
with kb.load_vector_store().acquire() as vs:
    documents = list(vs.docstore._dict.values())
    texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
    texts_processed = [preprocess_func(t) for t in tqdm(texts)]  
    with open('samples.txt', 'w', encoding='utf-8') as file:  
        json.dump(texts_processed , file) 

读取

refer to: /python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

with open('**/libs/chatchat-server/chatchat/data/knowledge_base/samples/samples.txt', 'r', encoding='utf-8') as file:  
          texts_processed = json.load(file)
        #texts_processed = [preprocess_func(t) for t in texts]

[tianxiajianghu]

耗时是因为BM25_retriever在每一轮回答中都对知识库中的所有chunk做了一遍预处理，具体可参见: #4565 (comment) 。这里我提供一个简单的、经过验证的补丁方案，即服务启动前完成预处理操作并将结果固化为txt，之后在对话时只需读取该txt直接获得预处理结果即可，代码分为两块：

提前预处理

import os
import json
os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat"
import jieba
from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples"
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
with kb.load_vector_store().acquire() as vs:
    documents = list(vs.docstore._dict.values())
    texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
    texts_processed = [preprocess_func(t) for t in tqdm(texts)]  
    with open('samples.txt', 'w', encoding='utf-8') as file:  
        json.dump(texts_processed , file) 

读取

refer to: /python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

with open('**/libs/chatchat-server/chatchat/data/knowledge_base/samples/samples.txt', 'r', encoding='utf-8') as file:  
          texts_processed = json.load(file)
        #texts_processed = [preprocess_func(t) for t in texts]

感觉应该有效果，现在测试的耗时是多少，是不是已经和注释掉bm25这一段代码时间差不多了

[firrice]

耗时是因为BM25_retriever在每一轮回答中都对知识库中的所有chunk做了一遍预处理，具体可参见: #4565 (comment) 。这里我提供一个简单的、经过验证的补丁方案，即服务启动前完成预处理操作并将结果固化为txt，之后在对话时只需读取该txt直接获得预处理结果即可，代码分为两块：

提前预处理

import os
import json
os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat"
import jieba
from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples"
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
with kb.load_vector_store().acquire() as vs:
    documents = list(vs.docstore._dict.values())
    texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
    texts_processed = [preprocess_func(t) for t in tqdm(texts)]  
    with open('samples.txt', 'w', encoding='utf-8') as file:  
        json.dump(texts_processed , file) 

读取

refer to: /python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

with open('**/libs/chatchat-server/chatchat/data/knowledge_base/samples/samples.txt', 'r', encoding='utf-8') as file:  
          texts_processed = json.load(file)
        #texts_processed = [preprocess_func(t) for t in texts]

感觉应该有效果，现在测试的耗时是多少，是不是已经和注释掉bm25这一段代码时间差不多了

yeap

[ChrisKimZHT]

现在 50W 数据量级别的知识库几乎不可用，得花 5 分钟来完成检索，删掉 bm25 后能够秒出结果。

[zhujiajun92]

有解决方案吗？我参考上述方案尝试修改BM25_retriever权重为0，没有效果提升，目前有几十G的文档数据，检索速度非常慢。

[plancktree]

耗时是因为BM25_retriever在每一轮回答中都对知识库中的所有chunk做了一遍预处理，具体可参见: #4565 (comment) 。这里我提供一个简单的、经过验证的补丁方案，即服务启动前完成预处理操作并将结果固化为txt，之后在对话时只需读取该txt直接获得预处理结果即可，代码分为两块：

提前预处理

import os
import json
os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat"
import jieba
from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples"
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
with kb.load_vector_store().acquire() as vs:
    documents = list(vs.docstore._dict.values())
    texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
    texts_processed = [preprocess_func(t) for t in tqdm(texts)]  
    with open('samples.txt', 'w', encoding='utf-8') as file:  
        json.dump(texts_processed , file) 

读取

refer to: /python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

with open('**/libs/chatchat-server/chatchat/data/knowledge_base/samples/samples.txt', 'r', encoding='utf-8') as file:  
          texts_processed = json.load(file)
        #texts_processed = [preprocess_func(t) for t in texts]

具体应该在哪个函数改呢

[plancktree]

bm25的锅，/chatchat-server/chatchat/server/file_rag/retrievers/ensemble.py中的from_vectorstore方法修改，暂时将bm25_retriever初始化的那段代码发光掉，只用faiss_retriever可以明显提速

并没有很大的提升

我刚试了 能达到0.2版本的效率

我这边注释掉后召回非常差，比0.2.0版本差很多

[firrice]

耗时是因为BM25_retriever在每一轮回答中都对知识库中的所有chunk做了一遍预处理，具体可参见: #4565 (comment) 。这里我提供一个简单的、经过验证的补丁方案，即服务启动前完成预处理操作并将结果固化为txt，之后在对话时只需读取该txt直接获得预处理结果即可，代码分为两块：

提前预处理

import os
import json
os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat"
import jieba
from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples"
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
with kb.load_vector_store().acquire() as vs:
    documents = list(vs.docstore._dict.values())
    texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
    texts_processed = [preprocess_func(t) for t in tqdm(texts)]  
    with open('samples.txt', 'w', encoding='utf-8') as file:  
        json.dump(texts_processed , file) 

读取

refer to: /python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

with open('**/libs/chatchat-server/chatchat/data/knowledge_base/samples/samples.txt', 'r', encoding='utf-8') as file:  
          texts_processed = json.load(file)
        #texts_processed = [preprocess_func(t) for t in texts]

具体应该在哪个函数改呢

/python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

[plancktree]

耗时是因为BM25_retriever在每一轮回答中都对知识库中的所有chunk做了一遍预处理，具体可参见: #4565 (comment) 。这里我提供一个简单的、经过验证的补丁方案，即服务启动前完成预处理操作并将结果固化为txt，之后在对话时只需读取该txt直接获得预处理结果即可，代码分为两块：

提前预处理

import os
import json
os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat"
import jieba
from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples"
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
with kb.load_vector_store().acquire() as vs:
    documents = list(vs.docstore._dict.values())
    texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
    texts_processed = [preprocess_func(t) for t in tqdm(texts)]  
    with open('samples.txt', 'w', encoding='utf-8') as file:  
        json.dump(texts_processed , file) 

读取

refer to: /python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

with open('**/libs/chatchat-server/chatchat/data/knowledge_base/samples/samples.txt', 'r', encoding='utf-8') as file:  
          texts_processed = json.load(file)
        #texts_processed = [preprocess_func(t) for t in texts]

具体应该在哪个函数改呢

/python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

“import os import json os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat" import jieba from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples" kb = KBServiceFactory.get_service_by_name(knowledge_base_name) with kb.load_vector_store().acquire() as vs: documents = list(vs.docstore._dict.values()) texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents)) texts_processed = [preprocess_func(t) for t in tqdm(texts)]
with open('samples.txt', 'w', encoding='utf-8') as file:
json.dump(texts_processed , file) ”这个也写在/python3.10/site-packages/langchain_community/retrievers/bm25.py里面吗

[firrice]

耗时是因为BM25_retriever在每一轮回答中都对知识库中的所有chunk做了一遍预处理，具体可参见: #4565 (comment) 。这里我提供一个简单的、经过验证的补丁方案，即服务启动前完成预处理操作并将结果固化为txt，之后在对话时只需读取该txt直接获得预处理结果即可，代码分为两块：

提前预处理

import os
import json
os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat"
import jieba
from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory
from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples"
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
with kb.load_vector_store().acquire() as vs:
    documents = list(vs.docstore._dict.values())
    texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
    texts_processed = [preprocess_func(t) for t in tqdm(texts)]  
    with open('samples.txt', 'w', encoding='utf-8') as file:  
        json.dump(texts_processed , file) 

读取

refer to: /python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

with open('**/libs/chatchat-server/chatchat/data/knowledge_base/samples/samples.txt', 'r', encoding='utf-8') as file:  
          texts_processed = json.load(file)
        #texts_processed = [preprocess_func(t) for t in texts]

具体应该在哪个函数改呢

/python3.10/site-packages/langchain_community/retrievers/bm25.py: from_texts()

“import os import json os.environ['CHATCHAT_ROOT'] = "***/libs/chatchat-server/chatchat" import jieba from tqdm import tqdm

from chatchat.server.knowledge_base.kb_service.base import KBServiceFactory from chatchat.server.db.session import session_scope

preprocess_func=jieba.lcut_for_search

knowledge_base_name = "samples" kb = KBServiceFactory.get_service_by_name(knowledge_base_name) with kb.load_vector_store().acquire() as vs: documents = list(vs.docstore._dict.values()) texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents)) texts_processed = [preprocess_func(t) for t in tqdm(texts)] with open('samples.txt', 'w', encoding='utf-8') as file: json.dump(texts_processed , file) ”这个也写在/python3.10/site-packages/langchain_community/retrievers/bm25.py里面吗

1、"提前预处理"这步操作是单独放一个脚本里，跑完之后会将预处理的结果存到一个txt中；2、然后在我提到的那个函数中的对应位置进行"读取"操作，读取那个txt，同时注释掉那个位置上原来的代码，即 texts_processed = [preprocess_func(t) for t in texts]

[czq001]

@firrice 我执行预处理脚本的时候报错了 `2024-09-18 08:29:04.815 | WARNING | chatchat.server.utils:get_default_embedding:199 - default embedding model bge-m3 is not found in available embeddings, using bge-m3-1 instead 2024-09-18 08:29:04.892 | WARNING | chatchat.server.utils:get_default_embedding:199 - default embedding model bge-m3 is not found in available embeddings, using bge-m3-1 instead 2024-09-18 08:29:04.916 | WARNING | chatchat.server.utils:get_default_embedding:199 - default embedding model bge-m3 is not found in available embeddings, using bge-m3-1 instead Traceback (most recent call last): File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 146, in init self._dbapi_connection = engine.raw_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 3300, in raw_connection return self.pool.connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 449, in connect return _ConnectionFairy._checkout(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 1263, in _checkout fairy = _ConnectionRecord.checkout(pool) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 712, in checkout rec = pool._do_get() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 180, in _do_get self._dec_overflow() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 177, in _do_get return self._create_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 390, in _create_connection return _ConnectionRecord(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 674, in init self.__connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 901, in __connect pool.logger.debug("Error on connect(): %s", e) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 896, in __connect self.dbapi_connection = connection = pool._invoke_creator(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/create.py", line 643, in connect return dialect.connect(*cargs, **cparams) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 620, in connect return self.loaded_dbapi.connect(*cargs, **cparams) sqlite3.OperationalError: unable to open database file

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "preload.py", line 14, in kb = KBServiceFactory.get_service_by_name(knowledge_base_name) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/chatchat/server/knowledge_base/kb_service/base.py", line 435, in get_service_by_name _, vs_type, embed_model = load_kb_from_db(kb_name) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/chatchat/server/db/session.py", line 28, in wrapper result = f(session, *args, **kwargs) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/chatchat/server/db/repository/knowledge_base_repository.py", line 53, in load_kb_from_db session.query(KnowledgeBaseModel) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2728, in first return self.limit(1)._iter().first() # type: ignore File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2827, in _iter result: Union[ScalarResult[_T], Result[_T]] = self.session.execute( File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 2351, in execute return self._execute_internal( File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 2226, in _execute_internal conn = self._connection_for_bind(bind) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 2095, in _connection_for_bind return trans._connection_for_bind(engine, execution_options) File "", line 2, in _connection_for_bind File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/state_changes.py", line 139, in _go ret_value = fn(self, *arg, **kw) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 1189, in _connection_for_bind conn = bind.connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 3276, in connect return self._connection_cls(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 148, in init Connection._handle_dbapi_exception_noconnection( File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 2440, in _handle_dbapi_exception_noconnection raise sqlalchemy_exception.with_traceback(exc_info[2]) from e File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 146, in init self._dbapi_connection = engine.raw_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 3300, in raw_connection return self.pool.connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 449, in connect return _ConnectionFairy._checkout(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 1263, in _checkout fairy = _ConnectionRecord.checkout(pool) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 712, in checkout rec = pool._do_get() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 180, in _do_get self._dec_overflow() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 177, in _do_get return self._create_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 390, in _create_connection return _ConnectionRecord(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 674, in init self.__connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 901, in __connect pool.logger.debug("Error on connect(): %s", e) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 896, in __connect self.dbapi_connection = connection = pool._invoke_creator(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/create.py", line 643, in connect return dialect.connect(*cargs, **cparams) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 620, in connect return self.loaded_dbapi.connect(*cargs, **cparams) sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) unable to open database file (Background on this error at: https://sqlalche.me/e/20/e3q8)`

[firrice]

@firrice 我执行预处理脚本的时候报错了 `2024-09-18 08:29:04.815 | WARNING | chatchat.server.utils:get_default_embedding:199 - default embedding model bge-m3 is not found in available embeddings, using bge-m3-1 instead 2024-09-18 08:29:04.892 | WARNING | chatchat.server.utils:get_default_embedding:199 - default embedding model bge-m3 is not found in available embeddings, using bge-m3-1 instead 2024-09-18 08:29:04.916 | WARNING | chatchat.server.utils:get_default_embedding:199 - default embedding model bge-m3 is not found in available embeddings, using bge-m3-1 instead Traceback (most recent call last): File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 146, in init self._dbapi_connection = engine.raw_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 3300, in raw_connection return self.pool.connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 449, in connect return _ConnectionFairy._checkout(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 1263, in _checkout fairy = _ConnectionRecord.checkout(pool) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 712, in checkout rec = pool._do_get() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 180, in _do_get self._dec_overflow() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 177, in _do_get return self._create_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 390, in _create_connection return _ConnectionRecord(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 674, in init self.__connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 901, in __connect pool.logger.debug("Error on connect(): %s", e) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 896, in __connect self.dbapi_connection = connection = pool._invoke_creator(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/create.py", line 643, in connect return dialect.connect(*cargs, **cparams) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 620, in connect return self.loaded_dbapi.connect(*cargs, **cparams) sqlite3.OperationalError: unable to open database file

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "preload.py", line 14, in kb = KBServiceFactory.get_service_by_name(knowledge_base_name) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/chatchat/server/knowledge_base/kb_service/base.py", line 435, in get_service_by_name _, vs_type, embed_model = load_kb_from_db(kb_name) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/chatchat/server/db/session.py", line 28, in wrapper result = f(session, *args, **kwargs) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/chatchat/server/db/repository/knowledge_base_repository.py", line 53, in load_kb_from_db session.query(KnowledgeBaseModel) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2728, in first return self.limit(1)._iter().first() # type: ignore File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2827, in _iter result: Union[ScalarResult[_T], Result[_T]] = self.session.execute( File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 2351, in execute return self._execute_internal( File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 2226, in _execute_internal conn = self._connection_for_bind(bind) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 2095, in _connection_for_bind return trans._connection_for_bind(engine, execution_options) File "", line 2, in _connection_for_bind File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/state_changes.py", line 139, in _go ret_value = fn(self, *arg, **kw) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 1189, in _connection_for_bind conn = bind.connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 3276, in connect return self._connection_cls(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 148, in init Connection._handle_dbapi_exception_noconnection( File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 2440, in _handle_dbapi_exception_noconnection raise sqlalchemy_exception.with_traceback(exc_info[2]) from e File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 146, in init self._dbapi_connection = engine.raw_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 3300, in raw_connection return self.pool.connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 449, in connect return _ConnectionFairy._checkout(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 1263, in _checkout fairy = _ConnectionRecord.checkout(pool) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 712, in checkout rec = pool._do_get() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 180, in _do_get self._dec_overflow() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/impl.py", line 177, in _do_get return self._create_connection() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 390, in _create_connection return _ConnectionRecord(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 674, in init self.__connect() File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 901, in __connect pool.logger.debug("Error on connect(): %s", e) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 146, in exit raise exc_value.with_traceback(exc_tb) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 896, in __connect self.dbapi_connection = connection = pool._invoke_creator(self) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/create.py", line 643, in connect return dialect.connect(*cargs, **cparams) File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 620, in connect return self.loaded_dbapi.connect(*cargs, **cparams) sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) unable to open database file (Background on this error at: https://sqlalche.me/e/20/e3q8)`%60)

因为你没有连上sqlite数据库，大概率是路径问题(这个预处理脚本对应的是v0.3.0的文件目录架构)。你看一下具体知识库对应的info.db是否是这个路径: "***/libs/chatchat-server/chatchat/data/knowledge_base/info.db"，还有注意os.environ[]那一行不要略掉