- 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.
Adds nested exception handling in add_files() and _migrate_fts_to_external()
to catch and log rollback failures. Uses exception chaining to preserve both
transaction and rollback errors, preventing silent database inconsistency.
Solution-ID: SOL-1735385400010
Issue-ID: ISS-1766921318981-10
Task-ID: T1
Add fallback validation to detect dead threads missed by
threading.enumerate(), ensuring all stale connections are cleaned.
Solution-ID: SOL-1735392000002
Issue-ID: ISS-1766921318981-3
Task-ID: T2
