feat: Enhance embedding generation and search capabilities

- Added pre-calculation of estimated chunk count for HNSW capacity in `generate_dense_embeddings_centralized` to optimize indexing performance.
- Implemented binary vector generation with memory-mapped storage for efficient cascade search, including metadata saving.
- Introduced SPLADE sparse index generation with improved handling and metadata storage.
- Updated `ChainSearchEngine` to prefer centralized binary searcher for improved performance and added fallback to legacy binary index.
- Deprecated `BinaryANNIndex` in favor of `BinarySearcher` for better memory management and performance.
- Enhanced `SpladeEncoder` with warmup functionality to reduce latency spikes during first-time inference.
- Improved `SpladeIndex` with cache size adjustments for better query performance.
- Added methods for managing binary vectors in `VectorMetadataStore`, including batch insertion and retrieval.
- Created a new `BinarySearcher` class for efficient binary vector search using Hamming distance, supporting both memory-mapped and database loading modes.

codex-lens/src/codexlens/config.py

@@ -25,6 +25,7 @@ SPLADE_DB_NAME = "_splade.db"
 # Dense vector storage names (centralized storage)
 VECTORS_HNSW_NAME = "_vectors.hnsw"
 VECTORS_META_DB_NAME = "_vectors_meta.db"
+BINARY_VECTORS_MMAP_NAME = "_binary_vectors.mmap"
 
 log = logging.getLogger(__name__)