Update app.py

app.py

@@ -90,6 +90,33 @@ for _, row in df.iterrows():
     case_docs.append(Document(text=text, metadata=meta))
 logger.info(f"Loaded {len(case_docs)} documents from {CSV_PATH}.")
 
+# --- ADD THIS CODE SNIPPET ---
+
+# Define and configure the embedding model
+EMBED_MODEL_ID = "BAAI/bge-small-en-v1.5"
+embed_model = HuggingFaceEmbedding(
+    model_name=EMBED_MODEL_ID
+)
+
+# Set the global embedding model for Llama-Index
+Settings.embed_model = embed_model
+logger.info(f"✅ Set global embedding model to {EMBED_MODEL_ID}")
+
+# --- END OF SNIPPET ---
+
+# --- CELL 4: Load prebuilt Vector Index ---
+vector_store = QdrantVectorStore(
+    client=qdrant,
+    collection_name=COLLECTION_NAME,
+    prefer_grpc=False
+)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+# This line is now fixed to use the correct API method
+index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
+logger.info("✅ Loaded existing VectorStoreIndex from Qdrant")
+
+
 # --- CELL 4: Load prebuilt Vector Index ---
 vector_store = QdrantVectorStore(
     client=qdrant,