Update app.py

app.py

@@ -8,11 +8,15 @@ from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTex
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain import VectorDBQA
 from langchain.llms import AzureOpenAI
+from langchain.chains import RetrievalQA
+from langchain.chat_models import AzureChatOpenAI
+
+# from langchain.chat_models import AzureChatOpenAI
 
 os.environ["OPENAI_API_TYPE"] = openai.api_type = "azure"
-os.environ["OPENAI_API_VERSION"] = openai.api_version = "2022-12-01"
+os.environ["OPENAI_API_VERSION"] = openai.api_version = "2023-03-15-preview"
 os.environ["OPENAI_API_BASE"] = openai.api_base = "https://openai-endpoint.openai.azure.com/"
-openai.api_key = os.environ["OPENAI_API_KEY"]
+openai.api_key = os.environ["OPENAI_API_KEY"] = "b83d692637df4e339298f24790a2dcb6"
 
 
 def upload_pdf(file, pdf_text, embeddings, vectorstore, azure_embeddings, qa, progress = gr.Progress(track_tqdm=True)):
@@ -52,7 +56,8 @@ def upload_pdf(file, pdf_text, embeddings, vectorstore, azure_embeddings, qa, pr
         documents=texts,
         embeddings=embeddings,
         metadatas=[{"source": "source"} for text in texts])
-    qa = VectorDBQA.from_chain_type(llm= AzureOpenAI(deployment_name="davinci003", model_name="text-davinci-003"), chain_type="stuff", vectorstore=vectorstore)
+    qa = RetrievalQA.from_chain_type(llm= AzureChatOpenAI(deployment_name="Bartos", model_name='gpt-35-turbo' ), chain_type="stuff", retriever=vectorstore.as_retriever())
+    # qa = RetrievalQA.from_chain_type(llm= AzureOpenAI(deployment_name="davinci003", model_name="text-davinci-003"), chain_type="stuff", vectorstore=vectorstore)
 
     return pdf_text, pdf_text, embeddings, vectorstore, azure_embeddings, qa, gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
 
@@ -63,8 +68,9 @@ def add_text(chatstate, query, qa):
 
     return chatstate, chatstate, qa
 
-with gr.Blocks(css="footer {visibility: hidden}") as demo:
+with gr.Blocks(css="footer {visibility: hidden}", title='PDF - Q&A') as demo:
     qa = pdf_text = embeddings = vectorstore = azure_embeddings = gr.State([])
+
     with gr.Row(visible=False) as chat_row:
         chatbot = gr.Chatbot()
     with gr.Row(visible=False) as submit_row:
@@ -72,9 +78,6 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
     chatstate = gr.State([])
     text.submit(add_text, [chatstate, text, qa], [chatbot, chatstate, qa])
 
-    
-
-
     # set state
     with gr.Column() as upload_column:
 
@@ -83,7 +86,8 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
         output_text = gr.TextArea()
         upload_btn.click(upload_pdf, inputs=[file, pdf_text, embeddings, vectorstore, azure_embeddings, qa], outputs=[output_text, pdf_text, embeddings, vectorstore, azure_embeddings, qa, chat_row, submit_row, upload_column])
 
-
+    with gr.Row():
+        gr.Markdown("`now with GPT-3.5 Turbo`")
 
 
 demo.launch(enable_queue=True)