feat: Add multi-type embedding backends for cascade retrieval

- 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.

codex-lens/src/codexlens/config.py

@@ -131,6 +131,16 @@ class Config:
     reranker_model: str = "cross-encoder/ms-marco-MiniLM-L-6-v2"
     reranker_top_k: int = 50
 
+    # Cascade search configuration (two-stage retrieval)
+    enable_cascade_search: bool = False  # Enable cascade search (coarse + fine ranking)
+    cascade_coarse_k: int = 100  # Number of coarse candidates from first stage
+    cascade_fine_k: int = 10  # Number of final results after reranking
+    cascade_strategy: str = "binary"  # "binary" (fast binary+dense) or "hybrid" (FTS+SPLADE+Vector+CrossEncoder)
+
+    # RRF fusion configuration
+    fusion_method: str = "rrf"  # "simple" (weighted sum) or "rrf" (reciprocal rank fusion)
+    rrf_k: int = 60  # RRF constant (default 60)
+
     # Multi-endpoint configuration for litellm backend
     embedding_endpoints: List[Dict[str, Any]] = field(default_factory=list)
     # List of endpoint configs: [{"model": "...", "api_key": "...", "api_base": "...", "weight": 1.0}]