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/semantic/ann_index.py

@@ -508,6 +508,10 @@ class ANNIndex:
 class BinaryANNIndex:
     """Binary vector ANN index using Hamming distance for fast coarse retrieval.
 
+    .. deprecated::
+        This class is deprecated. Use :class:`codexlens.search.binary_searcher.BinarySearcher`
+        instead, which provides faster memory-mapped search with centralized storage.
+
     Optimized for binary vectors (256-bit / 32 bytes per vector).
     Uses packed binary representation for memory efficiency.
 
@@ -553,6 +557,14 @@ class BinaryANNIndex:
                 "Install with: pip install codexlens[semantic]"
             )
 
+        import warnings
+        warnings.warn(
+            "BinaryANNIndex is deprecated. Use codexlens.search.binary_searcher.BinarySearcher "
+            "instead for faster memory-mapped search with centralized storage.",
+            DeprecationWarning,
+            stacklevel=2
+        )
+
         if dim <= 0 or dim % 8 != 0:
             raise ValueError(
                 f"Invalid dimension: {dim}. Must be positive and divisible by 8."