Add engine abstraction

src/vector_search_mcp/engine/qdrant_engine.py

@@ -0,0 +1,79 @@
+from collections.abc import Sequence
+from typing import final, override
+
+from qdrant_client import AsyncQdrantClient, models
+
+from ..config import Settings
+from ..models import SearchRow, Condition, Match, MatchAny, MatchExclude
+from .base_engine import BaseEngine
+
+__all__ = ["QdrantEngine"]
+
+
+@final
+class QdrantEngine(BaseEngine[list[models.ScoredPoint], models.Filter]):
+    def __init__(self) -> None:
+        self.settings = Settings()  # type: ignore[reportCallArgs]
+        self.client = AsyncQdrantClient(
+            url=self.settings.url, api_key=self.settings.api_key
+        )
+
+    @override
+    def transform_conditions(
+        self, conditions: list[Condition] | None
+    ) -> models.Filter | None:
+        if not conditions:
+            return None
+
+        filters: list[models.Condition] = []
+
+        for condition in conditions:
+            if isinstance(condition, Match):
+                filters.append(
+                    models.FieldCondition(
+                        key=condition.key,
+                        match=models.MatchValue(value=condition.value),
+                    )
+                )
+            elif isinstance(condition, MatchAny):
+                filters.append(
+                    models.FieldCondition(
+                        key=condition.key, match=models.MatchAny(any=condition.any)
+                    )
+                )
+            elif isinstance(condition, MatchExclude):
+                filters.append(
+                    models.FieldCondition(
+                        key=condition.key,
+                        match=models.MatchExcept(**{"except": condition.exclude}),
+                    )
+                )
+
+        return models.Filter(must=filters)
+
+    @override
+    def transform_response(self, response: list[models.ScoredPoint]) -> list[SearchRow]:
+        return [
+            SearchRow(chunk_id=str(point.id), score=point.score, payload=point.payload)
+            for point in response
+            if point.payload is not None
+        ]
+
+    @override
+    async def run_similarity_query(
+        self,
+        embedding: Sequence[float] | models.NamedVector,
+        collection: str,
+        limit: int = 10,
+        conditions: models.Filter | None = None,
+        threshold: float | None = None,
+    ) -> list[models.ScoredPoint]:
+        return await self.client.search(
+            collection_name=collection,
+            query_vector=embedding,
+            query_filter=conditions,
+            limit=limit,
+            with_payload=True,
+            with_vectors=False,
+            score_threshold=threshold,
+        )