Implement SPLADE sparse encoder and associated database migrations

- Added `splade_encoder.py` for ONNX-optimized SPLADE encoding, including methods for encoding text and batch processing.
- Created `SPLADE_IMPLEMENTATION.md` to document the SPLADE encoder's functionality, design patterns, and integration points.
- Introduced migration script `migration_009_add_splade.py` to add SPLADE metadata and posting list tables to the database.
- Developed `splade_index.py` for managing the SPLADE inverted index, supporting efficient sparse vector retrieval.
- Added verification script `verify_watcher.py` to test FileWatcher event filtering and debouncing functionality.

codex-lens/src/codexlens/config.py

@@ -103,6 +103,15 @@ class Config:
                                    # For litellm: model name from config (e.g., "qwen3-embedding")
     embedding_use_gpu: bool = True  # For fastembed: whether to use GPU acceleration
 
+    # SPLADE sparse retrieval configuration
+    enable_splade: bool = True  # Enable SPLADE as default sparse backend
+    splade_model: str = "naver/splade-cocondenser-ensembledistil"
+    splade_threshold: float = 0.01  # Min weight to store in index
+    splade_onnx_path: Optional[str] = None  # Custom ONNX model path
+
+    # FTS fallback (disabled by default, available via --use-fts)
+    use_fts_fallback: bool = False  # Use FTS instead of SPLADE
+
     # Indexing/search optimizations
     global_symbol_index_enabled: bool = True  # Enable project-wide symbol index fast path
     enable_merkle_detection: bool = True  # Enable content-hash based incremental indexing