Update app.py

app.py

@@ -40,10 +40,12 @@ def invoke(openai_api_key, use_rag, prompt):
         text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500, chunk_overlap = 150)
         splits = text_splitter.split_documents(docs)
         vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR)
-    #else:
-    #    vector_db = Chroma(persist_directory = CHROMA_DIR, embedding_function = OpenAIEmbeddings())
-    llm = ChatOpenAI(model_name = MODEL_NAME, openai_api_key = openai_api_key, temperature = 0)
-    qa_chain = RetrievalQA.from_chain_type(llm, retriever = vector_db.as_retriever(search_kwargs = {"k": 3}), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
+        llm = ChatOpenAI(model_name = MODEL_NAME, openai_api_key = openai_api_key, temperature = 0)
+        qa_chain = RetrievalQA.from_chain_type(llm, retriever = vector_db.as_retriever(search_kwargs = {"k": 3}), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
+    else:
+        #vector_db = Chroma(persist_directory = CHROMA_DIR, embedding_function = OpenAIEmbeddings())
+        llm = ChatOpenAI(model_name = MODEL_NAME, openai_api_key = openai_api_key, temperature = 0)
+        qa_chain = RetrievalQA.from_chain_type(llm, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
     result = qa_chain({"query": prompt})
     #print(result)
     return result["result"]