mudapakka
commented
4 months ago 目前调用模型是成功的，也可以看到请求使用情况。就是检索的时候提示404错误 以下是具体信息 2024-07-31 14:27:13,147 httpx 4626 INFO HTTP Request: POST https://api.deepseek.com/embeddings "HTTP/1.1 404 Not Found" 2024-07-31 14:27:13.148 | ERROR | chatchat.server.utils:check_embed_model:333 - failed to access embed model 'bge-large-zh-v1.5': Error code: 404 Traceback (most recent call last):
File "/root/miniconda3/envs/chatchat/lib/python3.8/threading.py", line 890, in _bootstrap self._bootstrap_inner() │ └ <function Thread._bootstrap_inner at 0x7f8e544afdc0> └ <WorkerThread(AnyIO worker thread, started 140248494556928)> File "/root/miniconda3/envs/chatchat/lib/python3.8/threading.py", line 932, in _bootstrap_inner self.run() │ └ <function WorkerThread.run at 0x7f8e36dcfb80> └ <WorkerThread(AnyIO worker thread, started 140248494556928)> File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/anyio/_backends/_asyncio.py", line 859, in run result = context.run(func, *args) │ │ │ └ () │ │ └ functools.partial(<function search_docs at 0x7f8e3c21a5e0>, query='数据并行说说是什么', knowledge_base_name='samples', topk=3, score... │ └ <method 'run' of 'Context' objects> └ <Context object at 0x7f8e345cc8c0>
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/knowledge_base/kb_doc_api.py", line 70, in search_docs docs = kb.search_docs(query, top_k, score_threshold) │ │ │ │ └ 2.0 │ │ │ └ 3 │ │ └ '数据并行说说是什么' │ └ <function KBService.search_docs at 0x7f8e40eccd30> └ samples @ bge-large-zh-v1.5
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/base.py", line 215, in search_docs if not self.check_embed_model( │ └ <function KBService.check_embed_model at 0x7f8e40ecc700> └ samples @ bge-large-zh-v1.5
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/knowledge_base/kb_service/base.py", line 84, in check_embed_model if not _check_embed_model(self.embed_model): │ │ └ 'bge-large-zh-v1.5' │ └ samples @ bge-large-zh-v1.5 └ <function check_embed_model at 0x7f8e40f2b670>
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/utils.py", line 330, in check_embed_model embeddings.embed_query("this is a test") │ └ <function LocalAIEmbeddings.embed_query at 0x7f8e348b0550> └ LocalAIEmbeddings(client=<openai.resources.embeddings.Embeddings object at 0x7f8e34907220>, async_client=<openai.resources.em...
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/localai_embeddings.py", line 367, in embed_query embedding = self._embedding_func(text, engine=self.deployment) │ │ │ │ └ 'text-embedding-ada-002' │ │ │ └ LocalAIEmbeddings(client=<openai.resources.embeddings.Embeddings object at 0x7f8e34907220>, async_client=<openai.resources.em... │ │ └ 'this is a test' │ └ <function LocalAIEmbeddings._embedding_func at 0x7f8e348b0310> └ LocalAIEmbeddings(client=<openai.resources.embeddings.Embeddings object at 0x7f8e34907220>, async_client=<openai.resources.em...
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/localai_embeddings.py", line 288, in _embedding_func embed_with_retry( └ <function embed_with_retry at 0x7f8e348eef70>
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/localai_embeddings.py", line 108, in embed_with_retry
return _embed_with_retry(**kwargs)
│ └ {'input': ['this is a test'], 'model': 'bge-large-zh-v1.5', 'timeout': None, 'extra_headers': None}
└ <function embed_with_retry.
File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/tenacity/init.py", line 336, in wrapped_f
return copy(f, *args, *kw)
│ │ │ └ {'input': ['this is a test'], 'model': 'bge-large-zh-v1.5', 'timeout': None, 'extra_headers': None}
│ │ └ ()
│ └ <function embed_with_retry.
File "/root/Langchain-Chatchat/libs/chatchat-server/chatchat/server/localai_embeddings.py", line 105, in _embed_with_retry response = embeddings.client.create(**kwargs) │ │ │ └ {'input': ['this is a test'], 'model': 'bge-large-zh-v1.5', 'timeout': None, 'extra_headers': None} │ │ └ <function Embeddings.create at 0x7f8e436e4700> │ └ <openai.resources.embeddings.Embeddings object at 0x7f8e34907220> └ LocalAIEmbeddings(client=<openai.resources.embeddings.Embeddings object at 0x7f8e34907220>, async_client=<openai.resources.em...
File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/openai/resources/embeddings.py", line 114, in create return self._post( │ └ <bound method SyncAPIClient.post of <openai.OpenAI object at 0x7f8e3461f700>> └ <openai.resources.embeddings.Embeddings object at 0x7f8e34907220> File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/openai/_base_client.py", line 1266, in post return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) │ │ │ │ │ │ │ └ None │ │ │ │ │ │ └ False │ │ │ │ │ └ FinalRequestOptions(method='post', url='/embeddings', params={}, headers=NOT_GIVEN, max_retries=NOT_GIVEN, timeout=None, file... │ │ │ │ └ <class 'openai.types.create_embedding_response.CreateEmbeddingResponse'> │ │ │ └ <function SyncAPIClient.request at 0x7f8e43935f70> │ │ └ <openai.OpenAI object at 0x7f8e3461f700> │ └ ~ResponseT └ <function cast at 0x7f8e54230040> File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/openai/_base_client.py", line 942, in request return self._request( │ └ <function SyncAPIClient._request at 0x7f8e43938040> └ <openai.OpenAI object at 0x7f8e3461f700> File "/root/miniconda3/envs/chatchat/lib/python3.8/site-packages/openai/_base_client.py", line 1046, in _request raise self._make_status_error_from_response(err.response) from None │ └ <function BaseClient._make_status_error_from_response at 0x7f8e43926c10> └ <openai.OpenAI object at 0x7f8e3461f700>
openai.NotFoundError: Error code: 404 2024-07-31 14:27:13.200 | ERROR | chatchat.server.knowledge_base.kb_service.base:check_embed_model:85 - could not search docs because failed to access embed model.
SuMiaoALi
问题描述 / Problem Description 修改model_settings.yaml中api_base_url为openai的api后，embedding模型也从这个链接获取了。报错404。如何实现调用api调用模型，使用本地的bge-large-zh-v1.5模型？
复现问题的步骤 / Steps to Reproduce
预期的结果 / Expected Result 描述应该出现的结果 / Describe the expected result.
实际结果 / Actual Result 描述实际发生的结果 / Describe the actual result.
环境信息 / Environment Information
附加信息 / Additional Information 添加与问题相关的任何其他信息 / Add any other information related to the issue.