ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

cursor-chats/20250923_111225_dense-embedding_%_python_main.py.md

@@ -0,0 +1,95 @@
+# 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:
+```python
+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.
+
+---
+
+
+*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*