chore: adjust dir structure

src/langbot/pkg/rag/knowledge/services/retriever.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from . import base_service
+from ....core import app
+from ....provider.modelmgr.requester import RuntimeEmbeddingModel
+from ....entity.rag import retriever as retriever_entities
+
+
+class Retriever(base_service.BaseService):
+    def __init__(self, ap: app.Application):
+        super().__init__()
+        self.ap = ap
+
+    async def retrieve(
+        self, kb_id: str, query: str, embedding_model: RuntimeEmbeddingModel, k: int = 5
+    ) -> list[retriever_entities.RetrieveResultEntry]:
+        self.ap.logger.info(
+            f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}"
+        )
+
+        query_embedding: list[float] = await embedding_model.requester.invoke_embedding(
+            model=embedding_model,
+            input_text=[query],
+            extra_args={},  # TODO: add extra args
+        )
+
+        vector_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k)
+
+        # 'ids' shape mirrors the Chroma-style response contract for compatibility
+        matched_vector_ids = vector_results.get('ids', [[]])[0]
+        distances = vector_results.get('distances', [[]])[0]
+        vector_metadatas = vector_results.get('metadatas', [[]])[0]
+
+        if not matched_vector_ids:
+            self.ap.logger.info('No relevant chunks found in vector database.')
+            return []
+
+        result: list[retriever_entities.RetrieveResultEntry] = []
+
+        for i, id in enumerate(matched_vector_ids):
+            entry = retriever_entities.RetrieveResultEntry(
+                id=id,
+                metadata=vector_metadatas[i],
+                distance=distances[i],
+            )
+            result.append(entry)
+
+        return result