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- Implemented BinaryEmbeddingBackend for fast coarse filtering using 256-dimensional binary vectors. - Developed DenseEmbeddingBackend for high-precision dense vectors (2048 dimensions) for reranking. - Created CascadeEmbeddingBackend to combine binary and dense embeddings for two-stage retrieval. - Introduced utility functions for embedding conversion and distance computation. chore: Migration 010 - Add multi-vector storage support - Added 'chunks' table to support multi-vector embeddings for cascade retrieval. - Included new columns: embedding_binary (256-dim) and embedding_dense (2048-dim) for efficient storage. - Implemented upgrade and downgrade functions to manage schema changes and data migration.
21 KiB
21 KiB