Update app.py

app.py

@@ -42,11 +42,12 @@ def invoke(openai_api_key, use_rag, prompt):
             vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR)
         rag_chain = RetrievalQA.from_chain_type(llm, retriever = vector_db.as_retriever(search_kwargs = {"k": 3}), return_source_documents = True, chain_type_kwargs = {"prompt": CHAIN_PROMPT})
         result = rag_chain({"question": prompt})
+        result = result["result"]
     else:
         chain = LLMChain(llm = llm, prompt = CHAIN_PROMPT)
         result = chain.run({"context": "", "question": prompt})
     #print(result)
-    return result["result"]
+    return result
 
 description = """<strong>Overview:</strong> The app demonstrates how to use a Large Language Model (LLM) with Retrieval Augmented Generation (RAG) on external data 
                  (in this case a YouTube video, but it could be PDFs, URLs, or other structured/unstructured private/public