Update app.py

app.py

@@ -50,6 +50,28 @@ def query_vectara(question, chat_history, uploaded_file):
     api_endpoint = "https://api.vectara.io/v1/query"
     return f"{upload_status}\n\nResponse from Vectara API: {response.text}"
 
+# from vectara_retriever import VectaraRetriever  # Assuming VectaraRetriever is in vectara_retriever.py
+
+# Function to interact with Vectara API
+# def query_vectara(question, chat_history, uploaded_file):
+#    # Handle file upload to Vectara
+#
+#    # Get the user's message from the chat history
+#    user_message = chat_history[-1][0]
+#
+#    # Create a VectaraRetriever instance
+#    retriever = VectaraRetriever(index=your_vectara_index)  # Replace with your VectaraIndex instance
+#
+#    # Create a QueryBundle with the user's message
+#    query_bundle = QueryBundle(query_str=user_message)
+#
+#    # Retrieve the top k most similar nodes
+#    top_k_nodes = retriever._retrieve(query_bundle)
+#
+#    # Format the nodes for display
+#    responses = "\n".join([f"{node.node.text} (score: {node.score})" for node in top_k_nodes])
+#
+#    return f"{upload_status}\n\nResponse from Vectara API: {responses}"
     
 # Create a Gradio ChatInterface with a text input, a file upload input, and a text output
 iface = gr.Interface(