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/integration/conftest.py

@@ -0,0 +1,108 @@
+import pytest
+import numpy as np
+import tempfile
+from pathlib import Path
+
+from codexlens.config import Config
+from codexlens.core import ANNIndex, BinaryStore
+from codexlens.embed.base import BaseEmbedder
+from codexlens.rerank.base import BaseReranker
+from codexlens.search.fts import FTSEngine
+from codexlens.search.pipeline import SearchPipeline
+
+# Test documents: 20 code snippets with id, path, content
+TEST_DOCS = [
+    (0, "auth.py", "def authenticate(user, password): return check_hash(password, user.hash)"),
+    (1, "auth.py", "def authorize(user, permission): return permission in user.roles"),
+    (2, "models.py", "class User: def __init__(self, name, email): self.name = name; self.email = email"),
+    (3, "models.py", "class Session: token = None; expires_at = None"),
+    (4, "middleware.py", "def auth_middleware(request): token = request.headers.get('Authorization')"),
+    (5, "utils.py", "def hash_password(password): import bcrypt; return bcrypt.hashpw(password)"),
+    (6, "config.py", "DATABASE_URL = os.environ.get('DATABASE_URL', 'sqlite:///db.sqlite3')"),
+    (7, "search.py", "def search_users(query): return User.objects.filter(name__icontains=query)"),
+    (8, "api.py", "def get_user(request, user_id): user = User.objects.get(id=user_id)"),
+    (9, "api.py", "def create_user(request): data = request.json(); user = User(**data)"),
+    (10, "tests.py", "def test_authenticate(): assert authenticate('admin', 'pass') is not None"),
+    (11, "tests.py", "def test_search(): results = search_users('alice'); assert len(results) > 0"),
+    (12, "router.py", "app.route('/users', methods=['GET'])(list_users)"),
+    (13, "router.py", "app.route('/login', methods=['POST'])(login_handler)"),
+    (14, "db.py", "def get_connection(): return sqlite3.connect(DATABASE_URL)"),
+    (15, "cache.py", "def cache_get(key): return redis_client.get(key)"),
+    (16, "cache.py", "def cache_set(key, value, ttl=3600): redis_client.setex(key, ttl, value)"),
+    (17, "errors.py", "class AuthError(Exception): status_code = 401"),
+    (18, "errors.py", "class NotFoundError(Exception): status_code = 404"),
+    (19, "validators.py", "def validate_email(email): return '@' in email and '.' in email.split('@')[1]"),
+]
+
+DIM = 32  # Use small dim for fast tests
+
+
+def make_stable_vec(doc_id: int, dim: int = DIM) -> np.ndarray:
+    """Generate a deterministic float32 vector for a given doc_id."""
+    rng = np.random.default_rng(seed=doc_id)
+    vec = rng.standard_normal(dim).astype(np.float32)
+    vec /= np.linalg.norm(vec)
+    return vec
+
+
+class MockEmbedder(BaseEmbedder):
+    """Returns stable deterministic vectors based on content hash."""
+
+    def embed_single(self, text: str) -> np.ndarray:
+        seed = hash(text) % (2**31)
+        rng = np.random.default_rng(seed=seed)
+        vec = rng.standard_normal(DIM).astype(np.float32)
+        vec /= np.linalg.norm(vec)
+        return vec
+
+    def embed_batch(self, texts: list[str]) -> list[np.ndarray]:
+        return [self.embed_single(t) for t in texts]
+
+    def embed(self, texts: list[str]) -> list[np.ndarray]:
+        """Called by SearchPipeline as self._embedder.embed([query])[0]."""
+        return self.embed_batch(texts)
+
+
+class MockReranker(BaseReranker):
+    """Returns score based on simple keyword overlap."""
+
+    def score_pairs(self, query: str, documents: list[str]) -> list[float]:
+        query_words = set(query.lower().split())
+        scores = []
+        for doc in documents:
+            doc_words = set(doc.lower().split())
+            overlap = len(query_words & doc_words)
+            scores.append(float(overlap) / max(len(query_words), 1))
+        return scores
+
+
+@pytest.fixture
+def config():
+    return Config.small()  # hnsw_ef=50, hnsw_M=16, binary_top_k=50, ann_top_k=20, rerank_top_k=10
+
+
+@pytest.fixture
+def search_pipeline(tmp_path, config):
+    """Build a full SearchPipeline with 20 test docs indexed."""
+    embedder = MockEmbedder()
+    binary_store = BinaryStore(tmp_path / "binary", dim=DIM, config=config)
+    ann_index = ANNIndex(tmp_path / "ann.hnsw", dim=DIM, config=config)
+    fts = FTSEngine(tmp_path / "fts.db")
+    reranker = MockReranker()
+
+    # Index all test docs
+    ids = np.array([d[0] for d in TEST_DOCS], dtype=np.int64)
+    vectors = np.array([embedder.embed_single(d[2]) for d in TEST_DOCS], dtype=np.float32)
+
+    binary_store.add(ids, vectors)
+    ann_index.add(ids, vectors)
+    fts.add_documents(TEST_DOCS)
+
+    return SearchPipeline(
+        embedder=embedder,
+        binary_store=binary_store,
+        ann_index=ann_index,
+        reranker=reranker,
+        fts=fts,
+        config=config,
+    )