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src/embeddings/vector_store.py

@@ -242,7 +242,10 @@ class QdrantVectorStore:
         for res in response.points:
             docs.append(res.payload.get("page_content", ""))
             metadatas.append({k:v for k,v in res.payload.items() if k != "page_content"})
-            distances.append(res.score) 
+            # Chroma returns distance (lower is better), Qdrant returns score (higher is better).
+            # Retriever expects distance and calculates 1-distance.
+            # So we return 1-score matches Chroma distance (approx).
+            distances.append(1 - res.score) 
             
         return {
             "documents": [docs],