lesgo

app.py

@@ -51,7 +51,7 @@ def process_llm_response(llm_response):
 def get_answer(question):
     llm_response = qa_chain(question)
     response = process_llm_response(llm_response)
-    return response["result"]
+    return response["result"], response["sources"]
 
 # @app.route('/question', methods=['POST'])
 # def answer():
@@ -137,18 +137,24 @@ qa_chain = RetrievalQA.from_chain_type(llm=turbo_llm,
                               retriever=retriever, 
                               return_source_documents=True)
 qa_chain.combine_documents_chain.llm_chain.prompt.messages[0].prompt.template= """
-Use only the following pieces of context and think step by step to answer. Answer the users question only if they are related to the context given.
+Use only the following pieces of context. Answer the users question only if they are related to the context given.
 If you don't know the answer, just say that you don't know, don't try to make up an answer. Make your answer very detailed and long. 
 Use bullet points to explain when required. 
 Use only text found in the context as your knowledge source for the answer. 
 ----------------
 {context}"""
 
-
 def getanswer(question):
     llm_response = qa_chain(question)
     response = process_llm_response(llm_response)
     return response
 
-iface = gr.Interface(fn=getanswer, inputs="text", outputs="text")
-iface.launch()
+# iface = gr.Interface(fn=getanswer, inputs="text", outputs="text")
+# iface.launch()
+
+demo = gr.Interface(
+    fn=getanswer,
+    inputs=["text"],
+    outputs=["text", "text"],
+)
+demo.launch()