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import os
import streamlit as st
import random
import time
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import DataFrameLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
# Get OpenAI setup
openai_api_key = os.getenv("openai_token")
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
# Setup vector database
persist_directory = './chroma_db'
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0,
openai_api_key=openai_api_key)
qa_chain = RetrievalQA.from_chain_type(
llm,
retriever=vectordb.as_retriever()
)
# Conversation memory
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
from langchain.chains import ConversationalRetrievalChain
retriever=vectordb.as_retriever()
qa_memory = ConversationalRetrievalChain.from_llm(
llm,
retriever=vectordb.as_retriever(),
memory=memory
)
# Streamed response emulator
def response_generator(prompt):
response = qa_memory({"question": prompt})['result']
# Fake streaming
for word in response.split():
yield word + " "
time.sleep(0.05)
st.title("Simple chat")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
if prompt := st.chat_input("What is up?"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
response = qa_memory({"question": prompt})['result']
st.write(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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