A developer has implemented semantic search in SQLite using binary embeddings and Hamming distance, enabling hybrid search without external vector databases. The approach combines SQLite's FTS5 extension for text search with binary embeddings that reduce storage from 4KiB to 128 bytes per document while using fast bit operations instead of floating-point arithmetic.

Binary embeddings quantize each dimension to a single bit, trading accuracy for dramatic storage and speed benefits. The Hamming distance metric counts differing bit positions between vectors, with modern CPUs offering dedicated popcount instructions for efficient computation. Testing on 1 million rows showed 35ms for hybrid search including sorting, or 28ms for distance computation alone on an Apple M4 chip.

The implementation uses a SQLite extension written in C that registers a custom hamming_distance function. The code processes 64-bit chunks using XOR and popcount operations, handling any remaining bytes efficiently. While the approach requires scanning every row without indexing, the O(n) simplicity proved sufficient for the developer's needs, avoiding the complexity of HNSW or IVF indexing.