Quick Start can not work

[vron8632]

When follow the exmaple to ask questions about the videos! there is a error:

INFO:nano-graphrag:Load KV video_path with 0 data
INFO:nano-graphrag:Load KV video_segments with 0 data
INFO:nano-graphrag:Load KV text_chunks with 0 data
INFO:nano-graphrag:Load KV llm_response_cache with 0 data
INFO:nano-vectordb:Init {'embedding_dim': 1536, 'metric': 'cosine', 'storage_file': './videorag-workdir\vdb_entities.json'} 0 data
INFO:nano-vectordb:Init {'embedding_dim': 1536, 'metric': 'cosine', 'storage_file': './videorag-workdir\vdb_chunks.json'} 0 data
INFO:nano-vectordb:Init {'embedding_dim': 1024, 'metric': 'cosine', 'storage_file': './videorag-workdir\vdb_video_segment_feature.json'} 0 data
INFO:transformers_modules.MiniCPM-V-2_6-int4.configuration_minicpm:vision_config is None, using default vision config
Loading checkpoint shards: 100%|██████████| 2/2 [00:02<00:00, 1.27s/it]
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
INFO:openai._base_client:Retrying request to /embeddings in 0.496831 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.751075 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.396852 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.857678 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.430649 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.993724 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.455960 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.949595 seconds
INFO:openai._base_client:Retrying request to /embeddings in 0.496733 seconds
INFO:openai.base_client:Retrying request to /embeddings in 0.812199 seconds
Traceback (most recent call last):
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\tenacity\asyncio_init.py", line 114, in call
result = await fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag_llm.py", line 148, in openai_embedding
response = await openai_async_client.embeddings.create(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai\resources\embeddings.py", line 243, in create
return await self._post(
^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai_base_client.py", line 1767, in post
return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai_base_client.py", line 1461, in request
return await self._request(
^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai_base_client.py", line 1547, in _request
return await self._retry_request(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai_base_client.py", line 1594, in _retry_request
return await self._request(
^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai_base_client.py", line 1547, in _request
return await self._retry_request(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai_base_client.py", line 1594, in _retry_request
return await self._request(
^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\openai_base_client.py", line 1562, in _request
raise self._make_status_error_from_response(err.response) from None
openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}

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

Traceback (most recent call last):
File "D:\Projects\027_mllm\VideoRAG-long\start02_ask.py", line 34, in
response = videorag.query(query=query, param=param)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag\videorag.py", line 304, in query
return loop.run_until_complete(self.aquery(query, param))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\asyncio\base_events.py", line 654, in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag\videorag.py", line 308, in aquery
response = await videorag_query(
^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag_op.py", line 598, in videorag_query
results = await chunks_vdb.query(query, top_k=query_param.top_k)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag_storage\vdb_nanovectordb.py", line 59, in query
embedding = await self.embedding_func([query])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag_utils.py", line 190, in wait_func
result = await func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag_utils.py", line 173, in call
return await self.func(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\027_mllm\VideoRAG-long\videorag_utils.py", line 173, in call
return await self.func(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\tenacity\asyncio_init_.py", line 189, in async_wrapped
return await copy(fn, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\tenacity\asyncio_init_.py", line 111, in call
do = await self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\tenacity\asyncio_init_.py", line 153, in iter
result = await action(retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\tenacity_utils.py", line 99, in inner
return call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\miniconda3\envs\videorag\Lib\site-packages\tenacity_init_.py", line 421, in exc_check
raise retry_exc from fut.exception()
tenacity.RetryError: RetryError[<Future at 0x1cb2e0c6990 state=finished raised RateLimitError>]

[xlrrrr]

Thank you for your interest in our work!

The error message indicates:
openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}

You may want to check whether your OpenAI API key has sufficient funds or an active plan to access the service normally.

[sherlockma11]

Thank you for your interest in our work!

The error message indicates: openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}

You may want to check whether your OpenAI API key has sufficient funds or an active plan to access the service normally.

Can I use my local model,I can not find how to modify the url.

[vron8632]

Thank you for your interest in our work!

The error message indicates: openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}

You may want to check whether your OpenAI API key has sufficient funds or an active plan to access the service normally.

Thank you for your answer, one more question, how can I use the local model such as Qwen2.5-VL instead of OpenAI API service?