feat: add search engine interface with embedding and filtering

tests/test_search.py

@@ -0,0 +1,34 @@
+import tempfile
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+from companion.rag.search import SearchEngine
+from companion.rag.vector_store import VectorStore
+
+
+@patch("companion.rag.search.OllamaEmbedder")
+def test_search_returns_results(mock_embedder_cls):
+    mock_embedder = MagicMock()
+    mock_embedder.embed.return_value = [[1.0, 0.0, 0.0, 0.0]]
+    mock_embedder_cls.return_value = mock_embedder
+
+    with tempfile.TemporaryDirectory() as tmp:
+        store = VectorStore(uri=tmp, dimensions=4)
+        store.upsert(
+            ids=["a"],
+            texts=["hello world"],
+            embeddings=[[1.0, 0.0, 0.0, 0.0]],
+            metadatas=[{"source_file": "a.md", "source_directory": "docs"}],
+        )
+        engine = SearchEngine(
+            vector_store=store,
+            embedder_base_url="http://localhost:11434",
+            embedder_model="dummy",
+            embedder_batch_size=32,
+            default_top_k=5,
+            similarity_threshold=0.0,
+            hybrid_search_enabled=False,
+        )
+        results = engine.search("hello")
+        assert len(results) == 1
+        assert results[0]["source_file"] == "a.md"