Update app.py

app.py

@@ -103,11 +103,19 @@ def submit_message(prompt, prompt_template, temperature, max_tokens, context_len
 
 # completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
 
-        completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" )
-        completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever())
-        query = str(system_prompt + history[-context_length*2:] +  [prompt_msg])
-        completion = completion.run(query)
+        #completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" )
+        #completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever())
+        #query = str(system_prompt + history[-context_length*2:] +  [prompt_msg])
+        #completion = completion.run(query)
         # from https://blog.devgenius.io/chat-with-document-s-using-openai-chatgpt-api-and-text-embedding-6a0ce3dc8bc8
+
+        completion = ChatVectorDBChain.from_llm(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), vectordb, return_source_documents=True)
+        query = str(system_prompt + history[-context_length*2:] +  [prompt_msg])
+        completion = completion({"question": query, "chat_history": history[-context_length*2:]})
+
+
+
+
         
         history.append(prompt_msg)
         history.append(completion.choices[0].message.to_dict())