Release v1.0.0 - First Production Release! #3
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Benchmarking Features: - Created scripts/benchmark.py for performance testing - Support 4 backends (pgvector, astradb, milvus, chroma) - Support 2 embedding providers (OpenAI, Gemini) - Test 7 operations: bulk/individual create, vector/metadata search, Query DSL, update, delete - Added --skip-slow flag to skip cloud backends for faster testing - Smart Query DSL optimization: 4 operators for slow backends, 10 for fast - Performance summary shows tested vs skipped backends - Comprehensive markdown reports with detailed metrics Engine Enhancements: - Added VectorEngine.drop_collection() method for cleanup - Fixed DEFAULT_COLLECTION_NAME to use api_settings.VECTOR_COLLECTION_NAME Architecture Improvements: - Enhanced ABC base class with unified initialization - Improved adapter collection name handling - Better error reporting in benchmarks Documentation: - Added benchmarking section to README.md (102 lines) - Created docs/benchmarking.md (385 lines complete guide) - Updated docs/contributing.md with benchmarking workflow - Added usage examples and best practices Testing: - All 365 unit tests passing - Updated tests for collection name changes - 40% overall coverage (core components 70-100%) Breaking Changes: - Removed DEFAULT_COLLECTION_NAME class constant (use settings instead)
Version: 1.0.0 (0.1.3 → 1.0.0) Status: Production Ready ✅ Production Readiness: - 365 tests passing (100% success rate) - 40% overall coverage, core components 70-100% - 19 PgVector integration tests verified - Comprehensive benchmarking and documentation Removed Beta Status: - No longer in beta - Production-ready and stable - Removed all beta warnings from README - Updated badges to v1.0.0 Recommended Backends: - ChromaDB Cloud - Best for cloud deployments - PgVector - Best for self-hosted/on-premise - Both verified with production-grade performance
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🚀 CrossVector v1.0.0 - Production Release
This PR merges the v1.0.0 production release.
Checklist
See full details in CHANGELOG.md