cgpt 5

app.py

@@ -1,8 +1,8 @@
 import streamlit as st
 import os
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceBgeEmbeddings
-from langchain.llms import Together
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_together import Together
 from langchain.prompts import ChatPromptTemplate, PromptTemplate
 from langchain.schema import format_document
 from typing import List
@@ -12,11 +12,11 @@ import time
 
 # Load the embedding function
 model_name = "BAAI/bge-base-en"
-encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+encode_kwargs = {'normalize_embeddings': True}
 embedding_function = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
 
 # Load the LLM
-llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.2, max_tokens=19096, top_k=10, together_api_key=os.environ['pilotikval'], streaming=True)
+llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.2, max_tokens=19096, top_k=10, together_api_key=os.environ['pilotikval'])
 
 msgs = StreamlitChatMessageHistory(key="langchain_messages")
 memory = ConversationBufferMemory(chat_memory=msgs)