Update app.py

app.py

@@ -97,37 +97,21 @@ def transform_query(query: str) -> str:
 def query_hybrid_search(query: str, client: QdrantClient, collection_name: str, dense_model: OptimumEncoder, sparse_model: SparseTextEmbedding):
     dense_embeddings = dense_model.embed_query(transform_query(query))[0]
     sparse_embeddings = list(sparse_model.query_embed(query))[0]
-    
-    return client.query_batch_points(
+
+    return client.query_points(
         collection_name=collection_name,
-        requests=[
-            QueryRequest(
-                prefetch=Prefetch(query=sparse_embeddings.as_object(), using="text-sparse", limit=10),
-                with_vector=False,
-                with_payload=True,
-                query=FusionQuery(fusion=Fusion.DBSF),
-                limit=3,
-                offset=0,
-                filter=Filter(must_not=[
-                    HasIdCondition(has_id=st.session_state.filter_ids)
-                ])
-            ),
-            QueryRequest(
-                prefetch=Prefetch(query=dense_embeddings, using="text-dense", limit=10),
-                with_vector=False,
-                with_payload=True,
-                query=FusionQuery(fusion=Fusion.DBSF),
-                limit=3,
-                offset=0,
-                filter=Filter(must_not=[
-                    HasIdCondition(has_id=st.session_state.filter_ids)
-                ])
-            )
-        ]
+        prefetch=[
+            Prefetch(query=sparse_embeddings.as_object(), using="text-sparse", limit=10),
+            Prefetch(query=dense_embeddings, using="text-dense", limit=10)
+        ],
+        query=FusionQuery(fusion=Fusion.DBSF),
+        with_vector=False,
+        with_payload=True
+        limit=3
     )
 
 def main(query: str, client: QdrantClient, collection_name: str, tokenizer: AutoTokenizer, llm: vllm.LLM, dense_model: OptimumEncoder, sparse_model: SparseTextEmbedding):
-    scored_points = query_hybrid_search(query, client, collection_name, dense_model, sparse_model)[0].points
+    scored_points = query_hybrid_search(query, client, collection_name, dense_model, sparse_model).points
 
     docs = [(scored_point.payload['text'], scored_point.payload['metadata']) for scored_point in scored_points]
     contents, metadatas = [list(t) for t in zip(*docs)]