Update app.py

app.py

@@ -1,12 +1,12 @@
 from pymongo import MongoClient
-# error since Jan 2024, from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain_community.embeddings.openai import OpenAIEmbeddings
-# error since Jan 2024, from langchain.vectorstores import MongoDBAtlasVectorSearch
-from langchain_community.vectorstores import MongoDBAtlasVectorSearch
-# error since Jan 2024, from langchain.document_loaders import DirectoryLoader
-from langchain_community.document_loaders import DirectoryLoader
-# error since Jan 2024, from langchain.llms import OpenAI
-from langchain_community.llms import OpenAI
+from langchain.embeddings import OpenAIEmbeddings
+#from langchain_community.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import MongoDBAtlasVectorSearch
+#from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+from langchain.document_loaders import DirectoryLoader
+#from langchain_community.document_loaders import DirectoryLoader
+from langchain.llms import OpenAI
+#from langchain_community.llms import OpenAI
 from langchain.chains import RetrievalQA
 import gradio as gr
 from gradio.themes.base import Base
@@ -41,7 +41,7 @@ def query_data(query,openai_api_key,mongo_uri):
     # If it's not specified (for example like in the code below),
     # then the default OpenAI model used in LangChain is OpenAI GPT-3.5-turbo, as of August 30, 2023
     
-    llm = OpenAI(openai_api_key=openai_api_key, temperature=0, model_name='gpt-4-1106-preview')
+    llm = OpenAI(openai_api_key=openai_api_key, temperature=0)
 
 
     # Get VectorStoreRetriever: Specifically, Retriever for MongoDB VectorStore.
@@ -51,7 +51,7 @@ def query_data(query,openai_api_key,mongo_uri):
     # Load "stuff" documents chain. Stuff documents chain takes a list of documents,
     # inserts them all into a prompt and passes that prompt to an LLM.
 
-    qa = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=retriever, model_name='gpt-4-1106-preview')
+    qa = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=retriever)
 
     # Execute the chain