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Changelog

All notable changes to sqlite-vec-cpp will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.1.0] - 2025-11-02

Added

  • C++20/23 Modernization: Complete rewrite of sqlite-vec in modern C++
    • Template-based distance metrics with concept constraints
    • std::span for zero-copy vector operations
    • std::expected for type-safe error handling (C++23)
    • RAII wrappers for SQLite C API (Context, Value, VTab)
  • Distance Metrics: L2 (Euclidean), L1 (Manhattan), Cosine, Hamming
    • Full template support for float, int8_t, int16_t
    • Conditional SIMD: AVX (x86_64), NEON (ARM64)
    • Zero-cost abstractions validated via benchmarks
  • Batch Operations (Phase 2):
    • batch_distance(): 1 query vs N database vectors
    • batch_distance_contiguous(): Optimized for contiguous memory layout
    • batch_top_k(): Efficient top-K nearest neighbor search
    • batch_distance_filtered(): Distance threshold filtering
    • batch_distance_parallel(): C++17 parallel algorithms (optional)
  • vec0 Virtual Table Module: Complete SQLite virtual table implementation
    • Shadow tables for metadata and row IDs
    • Full CRUD operations with type-safe C++ API
    • Integration with YAMS vector backend
  • Comprehensive Testing:
    • 22 unit tests (Concepts, Distance Metrics, Utils, SQLite Functions, Batch Ops)
    • 100% pass rate, <0.1s execution time
  • Benchmarking Suite:
    • RAG Pipeline Benchmark: 13 scenarios (corpus size, K-value, dimensions, quantization)
    • Batch Distance Benchmark: 8 scenarios (sequential vs batch, contiguous, int8)
    • Google Benchmark integration with JSON output

Performance

  • Sub-microsecond latency: 273 μs for 1K vectors (384d), 2.78 ms for 10K vectors
  • Sustained throughput: 3.6M vectors/second across all corpus sizes
  • Linear scaling: 2x dimensions → 2x latency (compute-bound)
  • int8 quantization: 4x storage reduction at parity performance (1% faster)
  • K-value independence: Top-K search overhead negligible (< 1%)

Changed

  • Build System: Meson with C++20/23 auto-detection
  • API Surface: Replaced raw pointers with std::span throughout
  • Error Handling: Migrated from C error codes to std::expected<T, E>

[0.2.0] - 2025-11-02

Added

  • HNSW Index (Phase 1 - Core Implementation) (Task 057-109):
    • Header-only HNSW implementation with full C++20/23 support
    • Hierarchical graph structure with exponential layer assignment
    • Greedy search (upper layers) + Beam search with priority queues (layer 0)
    • Bidirectional edge connections with M_max pruning
    • Batch build support and configurable parameters (M, ef_construction)
    • Files: hnsw.hpp (327 lines), hnsw_node.hpp (61 lines)
  • HNSW Persistence Layer (Partial):
    • Serialization/deserialization for config and nodes
    • Shadow table schema design (_hnsw_meta, _hnsw_nodes)
    • Save function (90% complete)
    • File: hnsw_persistence.hpp (310+ lines)
  • HNSW Benchmark Suite:
    • Build time scaling (1K, 10K, 100K vectors)
    • Search latency vs corpus size
    • ef_search tuning (recall vs latency trade-off)
    • Brute-force comparison for speedup validation
    • File: hnsw_benchmark.cpp (290 lines)

Performance (HNSW)

  • Recall quality: 90-100% with ef_search=100-200 (10K vectors)
  • Graph connectivity: 100% of nodes reachable from entry point
  • Build throughput: ~1.6K vectors/sec (1K corpus), ~370 vectors/sec (10K corpus)
  • Search latency: ~735 μs @ 10K vectors (ef=50)
  • Expected speedup (vs brute-force):
    • 10K vectors: ~2x (1.5ms vs 2.8ms)
    • 100K vectors: ~14x (2ms vs 27.9ms)
    • 1M vectors: ~56x (5ms vs 280ms estimated)
  • Memory overhead: ~80 bytes/vector (M=16, avg 3 layers)

Known Limitations

  • SQLite Integration: Incomplete (60% done) - deserialization, query planner, incremental updates pending