feat: AI quality improvements - caching, logging, fallback, and comprehensive tests - #531
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Akatenvictor merged 4 commits intoSep 28, 2026
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…ioBitsStellar#422) - Implement in-memory LRU cache with configurable size and TTL - Support track-based and metadata-hash-based cache keys - Add cache statistics for monitoring (hits, misses, hit rate) - Integrate cache into songQualityFilter with opt-in enablement - Automatic eviction of oldest entries when capacity is reached - Periodic pruning of expired entries Part of AI Song Quality Filter (Mastra AI + NVIDIA) initiative
…ioBitsStellar#427, AudioBitsStellar#425) AudioBitsStellar#427 - Comprehensive logging for AI analysis pipeline: - Add configurable logging with custom logger support - Log API requests, responses, cache hits/misses - Log errors and fallback activations - Default to console.log when enabled AudioBitsStellar#425 - Fallback behavior when NVIDIA API is unavailable: - Return review-status assessment when API fails - Customizable fallback scores and verdicts - Graceful degradation for network errors, timeouts, invalid responses - Configurable with enableFallback option Also integrates caching into the quality filter with opt-in support. Part of AI Song Quality Filter (Mastra AI + NVIDIA) initiative
…AudioBitsStellar#428) Add 47 test cases covering: qualityAnalysisCache.test.ts (19 tests): - Basic cache operations (get, set, has, delete, clear) - LRU eviction behavior - TTL-based expiration - Cache statistics (hits, misses, hit rate) - Metadata hash support - Global singleton pattern songQualityFilter.test.ts (28 tests): - Basic quality assessment and normalization - Caching integration (AudioBitsStellar#422) - Logging functionality (AudioBitsStellar#427) - Fallback behavior (AudioBitsStellar#425) - Error handling and API integration - Custom configurations Coverage: 98.24% (cache), 92.39% (filter) Part of AI Song Quality Filter (Mastra AI + NVIDIA) initiative
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@onyinyechinwokwu-success Great news! 🎉 Based on an automated assessment of this PR, the linked Wave issue(s) no longer count against your application limits. You can now already apply to more issues while waiting for a review of this PR. Keep up the great work! 🚀 |
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Summary
This PR implements comprehensive improvements to the AI Song Quality Filter pipeline, addressing four related issues in the Stellar Wave Program.
Changes
Issue #422: Add caching of quality analysis results per track
analyzeSongQualityfunctionIssue #425: Add fallback behavior if NVIDIA API is unavailable
reviewverdict with informative reasonsIssue #427: Implement logging for AI analysis pipeline
enableLoggingoptionIssue #428: Write unit tests for quality scoring logic
Test Evidence
All 28 unit tests pass successfully:
Documentation
Closes
Closes #422
Closes #425
Closes #427
Closes #428
Part of the AI Song Quality Filter (Mastra AI + NVIDIA) initiative for AudioBlock