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Enhance logging for scoring and reranking processes Running
Doanh Van Vu- Added detailed logging in `reranker_service.py` to report the ranking of candidates, including mentor ID, rating, and scores. - Improved logging in `scoring.py` to provide insights into the final ranking of mentors, including their scores and applied weights, enhancing traceability and debugging capabilities.3c1cd35
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Refactor mentor availability handling across services
Doanh Van Vu- Removed the `available_slots` and `has_availability` fields from the MentorUpsertRequest and RecommendationRequest models, streamlining the data structure. - Updated the PineconeService and RecommendationService to eliminate references to availability metrics in logging and processing. - Simplified scoring logic in the rerank_mentors function by removing the availability score calculation, enhancing performance and clarity. - Adjusted the mentor text building process to exclude available slots information, improving the output format.69934b0 -
Refactor RecommendationService to remove filtering logic
Doanh Van Vu- Removed the filter construction and fallback mechanism from the recommendation process, simplifying the query to Pinecone by applying no filters. - Updated logging to reflect the changes, ensuring clarity in the absence of filters during mentor queries. - Enhanced the overall readability and maintainability of the code by eliminating unnecessary complexity in the filtering logic.7083cd1 -
Implement fallback filtering in RecommendationService for mentor queries
Doanh Van Vu- Added logic to handle cases where no mentors are found with strict filters (rating/availability) by introducing a fallback mechanism that relaxes these constraints. - Enhanced logging to provide detailed information about the filtering process and fallback attempts, improving traceability and debugging capabilities. - Updated the filter construction to include logging for each filter applied based on mentee data.f879018 -
Enhance logging in PineconeService for query results and statistics
Doanh Van Vu- Updated the logging mechanism in the PineconeService to provide detailed information about each mentor result, including metadata when available. - Added statistics logging for score metrics (min, max, avg) after query completion to improve performance monitoring and analysis.ff5d801 -
Add test for recommend mentors endpoint and enhance logging in services
Doanh Van Vu- Introduced a new test script `test_api.py` to validate the recommend mentors API endpoint. - Enhanced logging in `embedding_service.py`, `pinecone_service.py`, `recommendation_service.py`, `reranker_service.py`, and `scoring.py` to include timing information for various operations, improving traceability and performance monitoring. - Improved error handling and logging messages for better clarity and debugging support.6a14fa9 -
Initial commit of the MentorMe AI Recommendation Server, including core application structure, environment configuration, Docker setup, and essential services for mentor recommendations and health checks.
Doanh Van Vu1904012