Implement search and reranking functionality with FTS and embedding support

- Added BaseReranker abstract class for defining reranking interfaces.
- Implemented FastEmbedReranker using fastembed's TextCrossEncoder for scoring document-query pairs.
- Introduced FTSEngine for full-text search capabilities using SQLite FTS5.
- Developed SearchPipeline to integrate embedding, binary search, ANN indexing, FTS, and reranking.
- Added fusion methods for combining results from different search strategies using Reciprocal Rank Fusion.
- Created unit and integration tests for the new search and reranking components.
- Established configuration management for search parameters and models.

codex-lens-v2/tests/unit/test_config.py

@@ -0,0 +1,31 @@
+from codexlens.config import Config
+
+
+def test_config_instantiates_no_args():
+    cfg = Config()
+    assert cfg is not None
+
+
+def test_defaults_hnsw_ef():
+    cfg = Config.defaults()
+    assert cfg.hnsw_ef == 150
+
+
+def test_defaults_hnsw_M():
+    cfg = Config.defaults()
+    assert cfg.hnsw_M == 32
+
+
+def test_small_hnsw_ef():
+    cfg = Config.small()
+    assert cfg.hnsw_ef == 50
+
+
+def test_custom_instantiation():
+    cfg = Config(hnsw_ef=100)
+    assert cfg.hnsw_ef == 100
+
+
+def test_fusion_weights_keys():
+    cfg = Config()
+    assert set(cfg.fusion_weights.keys()) == {"exact", "fuzzy", "vector", "graph"}