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/src/codexlens/rerank/local.py

@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from codexlens.config import Config
+from .base import BaseReranker
+
+
+class FastEmbedReranker(BaseReranker):
+    """Local reranker backed by fastembed TextCrossEncoder."""
+
+    def __init__(self, config: Config) -> None:
+        self._config = config
+        self._model = None
+
+    def _load(self) -> None:
+        if self._model is None:
+            from fastembed.rerank.cross_encoder import TextCrossEncoder
+            self._model = TextCrossEncoder(model_name=self._config.reranker_model)
+
+    def score_pairs(self, query: str, documents: list[str]) -> list[float]:
+        self._load()
+        results = list(self._model.rerank(query, documents))
+        scores = [0.0] * len(documents)
+        for r in results:
+            scores[r.index] = float(r.score)
+        return scores