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fix(chroma): recover from corrupted persistent DB by cleaning and retrying init
sethmcknightb3b90ec -
fix(chroma): use get_or_create_collection to prevent race conditions
sethmcknightfix(onnx): explicitly specify onnx model filename15f6c83 -
Refactor test cases for improved readability and consistency
sethmcknight- Simplified multi-line strings in test cases to single-line format for better readability. - Consolidated test case structures by removing unnecessary line breaks. - Updated assertions to be more concise and maintainable. - Added new integration tests for search caching and embedding warmup functionality. - Ensured all tests maintain consistent formatting and style across the test suite.159faf0 -
Implement PostgreSQL with pgvector as ChromaDB alternative (#88)
Seth McKnight* feat: Implement PostgreSQL with pgvector as ChromaDB alternative - Add PostgresVectorService with full pgvector integration - Create PostgresVectorAdapter for ChromaDB compatibility - Update config to support vector storage type selection - Add factory pattern for seamless backend switching - Include migration script with data optimization - Add comprehensive tests for PostgreSQL implementation - Update dependencies and environment configuration - Expected memory reduction: 300-350MB (from 400MB+ to 50-150MB) This enables deployment on Render's 512MB free tier by using persistent PostgreSQL storage instead of in-memory ChromaDB. * Add pgvector init script, update migration docs, and test adjustments * feat: Default to postgres and automate DB init * feat: migrate vector store from ChromaDB to PostgreSQL with pgvector - Replace in-memory ChromaDB with persistent PostgreSQL + pgvector - Add ONNX model quantization for reduced memory footprint - Implement PostgresVectorAdapter wit…9988b25
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Postgres vector migration (#83)
Seth McKnight* feat: Implement PostgreSQL with pgvector as ChromaDB alternative - Add PostgresVectorService with full pgvector integration - Create PostgresVectorAdapter for ChromaDB compatibility - Update config to support vector storage type selection - Add factory pattern for seamless backend switching - Include migration script with data optimization - Add comprehensive tests for PostgreSQL implementation - Update dependencies and environment configuration - Expected memory reduction: 300-350MB (from 400MB+ to 50-150MB) This enables deployment on Render's 512MB free tier by using persistent PostgreSQL storage instead of in-memory ChromaDB. * Add pgvector init script, update migration docs, and test adjustmentsdca679b -
Add memory diagnostics endpoints and logging enhancements (#80)
Seth McKnight* feat(memory): add diagnostics endpoints, periodic & milestone logging, force-clean; fix flake8 E501 * fix: update .gitignore, add chromadb files, enforce cpu for embeddings, add test mocks * Fix test suite: update FakeEmbeddingService to support default arguments and type annotations, resolve monkeypatching errors, and ensure fast, reliable test runs with CPU-only embedding. All tests passing. Move all imports to top and break long lines for flake8 compliance. * feat: enable memory logging and tracking; update requirements to include psutil * Add render memory monitoring, memory checkpoints and tests fixes; wrap long lines to satisfy linters * fix(memory): include label in /memory/force-clean response for test compatibility Ensure the force-clean endpoint returns the submitted label at the top level of the JSON response so tests and integrations can read it. * fix(ci): robust error handling for LLM configuration errors - Add custom LLMConfigurationError exception for specific…0a7f9b4
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Comprehensive memory optimizations and embedding service updates (#76)
Seth McKnight +1: Copilot* Comprehensive memory optimizations and embedding service updates (#74) * feat: Disable embedding generation on startup * feat: Complete memory optimization for Render free tier - Fix critical bug: Change default embedding model to paraphrase-albert-small-v2 - Add pre-built embeddings database (98 chunks, 768-dim) - Optimize Gunicorn config for single worker + threads - Reduce batch sizes for memory efficiency - Add Python memory optimization env vars - Disable startup embedding generation - Add build_embeddings.py script for local database rebuilding - Update Makefile with build-embeddings target Expected memory savings: ~300MB from model change + startup optimization * feat: Add comprehensive memory monitoring and optimization - Add memory monitoring utilities with usage tracking and cleanup - Implement memory-aware service loading with MemoryManager - Add enhanced health endpoint with memory status reporting - Optimize Gunicorn config with reduced connection limits and freque…f75da29 -
Refactor embedding model and enhance validation features (#71)
Seth McKnight +1: Copilot* refactor: Update embedding model configuration and enhance embedding service initialization * chore: Remove obsolete binary files from chroma_db directory * feat: Implement embedding validation on app startup and enhance VectorDatabase methods * feat: Optimize embedding model for memory efficiency and update related documentation * refactor: Enhance embedding validation and logging during app startup * Update src/app_factory.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>f88b1d2
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style: Fix code formatting and linting issues for CI/CD compliance
Tobias Pasquale- Fix black code formatting across all Python files - Fix isort import ordering - Remove unused imports (pytest, pathlib, numpy, Union) - Fix line length issues (split long lines) - Remove unused variables in tests - Fix whitespace and end-of-file issues - Address flake8 linting requirements All changes maintain functionality while ensuring CI/CD pipeline passes.7793bb6 -
feat: Complete Phase 2A Foundation Layer - ChromaDB + Embeddings
Tobias Pasquale- Add ChromaDB vector database integration (src/vector_store/) - Add HuggingFace embedding service (src/embedding/) - Implement comprehensive test suite (25 new tests) - Add integration tests for end-to-end workflow - Update dependencies (chromadb, sentence-transformers) - Add vector database configuration to config.py - Create CHANGELOG.md for development tracking Test Results: 45/45 passing (100% success rate) Components: VectorDatabase + EmbeddingService fully integrated Performance: Model caching, batch processing, <100ms operations Quality: TDD approach, comprehensive error handling, full documentation Phase 2A Status: ✅ COMPLETED - Foundation ready for Phase 2Bafecdc5