feat: add search engine interface with embedding and filtering

src/companion/rag/search.py

@@ -0,0 +1,47 @@
+from typing import Any
+
+from companion.rag.embedder import OllamaEmbedder
+from companion.rag.vector_store import VectorStore
+
+
+class SearchEngine:
+    def __init__(
+        self,
+        vector_store: VectorStore,
+        embedder_base_url: str,
+        embedder_model: str,
+        embedder_batch_size: int,
+        default_top_k: int,
+        similarity_threshold: float,
+        hybrid_search_enabled: bool,
+        keyword_weight: float = 0.3,
+        semantic_weight: float = 0.7,
+    ):
+        self.vector_store = vector_store
+        self.embedder = OllamaEmbedder(
+            base_url=embedder_base_url,
+            model=embedder_model,
+            batch_size=embedder_batch_size,
+        )
+        self.default_top_k = default_top_k
+        self.similarity_threshold = similarity_threshold
+        self.hybrid_search_enabled = hybrid_search_enabled
+        self.keyword_weight = keyword_weight
+        self.semantic_weight = semantic_weight
+
+    def search(
+        self,
+        query: str,
+        top_k: int | None = None,
+        filters: dict[str, Any] | None = None,
+    ) -> list[dict[str, Any]]:
+        k = top_k or self.default_top_k
+        query_embedding = self.embedder.embed([query])[0]
+        results = self.vector_store.search(query_embedding, top_k=k, filters=filters)
+        if self.similarity_threshold > 0 and results:
+            results = [
+                r
+                for r in results
+                if r.get("_distance", float("inf")) <= self.similarity_threshold
+            ]
+        return results