liqiang
b119135836
4.5 KiB
Cursor Chat: ai-agent-book
Metadata
- Project: ai-agent-book
- Path:
/Users/boj - Date: 2025-09-23 11:12:25
- Session ID:
1a4ba658-e14f-47c4-bfea-28fb63187e6c
Conversation
👤 You
dense-embedding % python main.py /Users/boj/miniconda3/lib/python3.11/site-packages/pydantic/_internal/fields.py:151: UserWarning: Field "model_name" has conflict with protected namespace "model".
You may be able to resolve this warning by setting model_config['protected_namespaces'] = ().
warnings.warn(
/Users/boj/miniconda3/lib/python3.11/site-packages/pydantic/_internal/fields.py:151: UserWarning: Field "model_name" has conflict with protected namespace "model".
You may be able to resolve this warning by setting model_config['protected_namespaces'] = ().
warnings.warn(
INFO: Started server process [85763]
INFO: Waiting for application startup.
2025-09-23 11:12:01 - vector_search - [INFO] - main.py:97 - lifespan() - ================================================================================
2025-09-23 11:12:01 - vector_search - [INFO] - main.py:98 - lifespan() - 🚀 Starting Vector Similarity Search Service
2025-09-23 11:12:01 - vector_search - [INFO] - main.py:99 - lifespan() - ================================================================================
2025-09-23 11:12:01 - vector_search - [INFO] - main.py:102 - lifespan() - Initializing BGE-M3 embedding service...
2025-09-23 11:12:01 - vector_search - [INFO] - embedding_service.py:39 - _initialize_model() - 🚀 Initializing BGE-M3 model: BAAI/bge-m3
2025-09-23 11:12:01 - vector_search - [DEBUG] - embedding_service.py:40 - _initialize_model() - - Using FP16: True
2025-09-23 11:12:01 - vector_search - [DEBUG] - embedding_service.py:41 - _initialize_model() - - Max sequence length: 512
2025-09-23 11:12:01 - vector_search - [ERROR] - embedding_service.py:67 - _initialize_model() - Failed to load model: BGEM3FlagModel.init() got an unexpected keyword argument 'query_instruction_for_retrieval'
ERROR: Traceback (most recent call last):
File "/Users/boj/miniconda3/lib/python3.11/site-packages/starlette/routing.py", line 734, in lifespan
async with self.lifespan_context(app) as maybe_state:
File "/Users/boj/miniconda3/lib/python3.11/contextlib.py", line 204, in aenter
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/boj/ai-agent-book/projects/week3/dense-embedding/main.py", line 103, in lifespan
embedding_service = EmbeddingService(
^^^^^^^^^^^^^^^^^
File "/Users/boj/ai-agent-book/projects/week3/dense-embedding/embedding_service.py", line 32, in init
self._initialize_model()
File "/Users/boj/ai-agent-book/projects/week3/dense-embedding/embedding_service.py", line 44, in _initialize_model
self.model = BGEM3FlagModel(
^^^^^^^^^^^^^^^
TypeError: BGEM3FlagModel.init() got an unexpected keyword argument 'query_instruction_for_retrieval'
ERROR: Application startup failed. Exiting.
🤖 Cursor Assistant
I see the issue. The BGEM3FlagModel initialization is receiving an unexpected keyword argument. Let me examine the code and fix it.
🤖 Cursor Assistant
I can see the issue. The BGEM3FlagModel constructor doesn't accept the query_instruction_for_retrieval parameter in the current version. Let me check what parameters it actually accepts and fix the initialization.
🤖 Cursor Assistant
Now I'll fix the issue by removing the unsupported query_instruction_for_retrieval parameter from the BGEM3FlagModel initialization:
🤖 Cursor Assistant
Now let me check for any linting issues in the file:
🤖 Cursor Assistant
I've fixed the issue! The problem was that the BGEM3FlagModel constructor in FlagEmbedding version 1.2.11 doesn't support the query_instruction_for_retrieval and max_length parameters that were being passed to it.
I removed these unsupported parameters from the initialization on lines 44-47, keeping only the model_name and use_fp16 parameters which are supported in this version. The model will now initialize properly with just these two parameters.
The corrected initialization now looks like this:
self.model = BGEM3FlagModel(
self.model_name,
use_fp16=self.use_fp16
)
You should now be able to run python main.py successfully and the BGE-M3 embedding service should start without errors.
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