feat[chat]: vectorize extraction result for improved chat content #45
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## release/v0.3.3 #45 +/- ##
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+ Coverage 60.15% 60.22% +0.07%
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Files 37 37
Lines 1719 1750 +31
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+ Hits 1034 1054 +20
- Misses 685 696 +11 ☔ View full report in Codecov by Sentry. |
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (4)
backend/tests/processing/test_process_queue.py (3)
185-220: Consider adding edge case assertions.The test effectively verifies the basic functionality of vectorizing a single reference. However, consider adding assertions for:
- Special characters in the filename or sources
- Very long source strings
- Unicode characters
267-267: Remove the placeholder comment.The comment
# Replace with the correct module pathappears to be a leftover development note and should be removed.
268-287: Consider additional empty/null scenarios.While the test correctly verifies empty sources handling, consider adding test cases for:
nullsources- Empty reference name
- Empty references array
nullreferencesbackend/app/processing/process_queue.py (1)
375-375: Specify a more precise type annotation forreferencesparameterThe
referencesparameter is annotated asdict, but it appears to be a list of lists containing dictionaries. Providing a more specific type annotation improves code readability and can help with type checking tools.Suggested change:
def vectorize_extraction_process_step(project_id: int, process_step_id: int, filename: str, references: List[List[Dict[str, Any]]]) -> None: + from typing import List, Dict, Any
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📒 Files selected for processing (2)
- backend/app/processing/process_queue.py (3 hunks)
- backend/tests/processing/test_process_queue.py (2 hunks)
🔇 Additional comments (2)
backend/tests/processing/test_process_queue.py (2)
3-3: LGTM! Import changes are appropriate.The addition of
MagicMockandvectorize_extraction_process_stepimports aligns with the new test requirements.Also applies to: 9-10
221-265: LGTM! Comprehensive test for multiple references.The test effectively verifies:
- Multiple references for the same field
- Multiple fields
- Correct concatenation of sources
- Proper metadata structure
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Actionable comments posted: 4
🧹 Outside diff range and nitpick comments (3)
backend/app/config.py (1)
29-31: Add documentation for the new configuration parameters.Please add docstrings to explain:
- The purpose of
chat_extraction_doc_thresholdand what the 0.5 threshold represents- The significance of
chat_extraction_max_docsand its impact on performance/memory# Extraction References for chat - chat_extraction_doc_threshold: float = 0.5 - chat_extraction_max_docs: int = 50 + # Minimum similarity score (0-1) required for documents to be considered relevant + chat_extraction_doc_threshold: float = 0.5 + # Maximum number of documents to retrieve during chat extraction + chat_extraction_max_docs: int = 50backend/app/processing/process_queue.py (2)
88-96: Improve error message clarity in vectorization error handling.The error handling for vectorization is well-implemented and isolated from the main process flow. However, the error message could be more specific.
Consider this improvement:
- logger.error(f"Failed to vectorize extraction results for chat {traceback.format_exc()}") + logger.error(f"Failed to vectorize extraction results for project {project_id}, process step {process_step_id}: {traceback.format_exc()}")
375-375: Add type hints and docstring for better code documentation.The function signature could be improved with proper type hints and documentation.
Consider this improvement:
-def vectorize_extraction_process_step(project_id: int, process_step_id: int, filename: str, references: dict) -> None: +def vectorize_extraction_process_step( + project_id: int, + process_step_id: int, + filename: str, + references: List[List[Dict[str, Any]]] +) -> None: + """Vectorize extraction results and store them in the database. + + Args: + project_id: The ID of the project + process_step_id: The ID of the process step + filename: The name of the processed file + references: List of extraction references containing sources to vectorize + + Returns: + None + """
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📒 Files selected for processing (3)
- backend/app/api/v1/chat.py (1 hunks)
- backend/app/config.py (1 hunks)
- backend/app/processing/process_queue.py (3 hunks)
🔇 Additional comments (1)
backend/app/processing/process_queue.py (1)
397-409:⚠️ Potential issueFix mismatched content between docs and metadata.
The documents being stored don't contain the actual content from field_references, making the vectorization ineffective.
Apply this fix:
- docs = [f"{filename} {key}" for key in field_references] + docs = list(field_references.values()) metadatas = [ { "project_id": project_id, "process_step_id": process_step_id, "filename": filename, - "reference": reference + "field_name": key } - for reference in field_references.values() + for key in field_references.keys() ]Likely invalid or redundant comment.
* feat[chat]: vectorize extraction result for improved chat content * feat[Chat]: use references from extraction results as well
Summary by CodeRabbit
New Features
Bug Fixes
Configuration
Tests