Update app.py

app.py

@@ -696,14 +696,19 @@ if __name__ == '__main__':
                 dense_embeddings = dense_model.embed_documents(documents)
                 sparse_embeddings = list(sparse_model.embed(documents, 32))
 
-                client.upsert(
-                    collection_name,
-                    make_points(
-                        documents,
-                        [{'source': full_path}.copy() for _ in range(len(documents))],
-                        dense_embeddings,
-                        sparse_embeddings
-                    )
+                client.upload_collection(
+                    collection_name=collection_name,
+                    ids=weakDict.all_ids(),
+                    payload={
+                        'texts': documents,
+                        'metadatas':
+                    },
+                    vectors={
+                        'text-dense': dense_embeddings,
+                        'text-sparse': sparse_embeddings
+                    },
+                    parallel=4,
+                    max_retries=3
                 )
 
                 documents_ingested()