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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)
@st.cache_resource
def get_vectordb():
embedding = OpenAIEmbeddings(openai_api_key=os.getenv("openai_token"))
return Chroma(persist_directory="./chroma_db", embedding_function=embedding)
vectordb = get_vectordb()
# # 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=0.7,
openai_api_key=openai_api_key)
qa_chain = RetrievalQA.from_chain_type(
llm,
retriever=vectordb.as_retriever(search_kwargs={"k": 5})
)
# Streamed response emulator
def response_generator(prompt):
response = qa_chain({"query": prompt})['result']
for word in response.split():
yield word + " "
time.sleep(0.05)
st.title("Technical Support Chatbot")
# 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("Enter your question here"):
# 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 = st.write_stream(response_generator(prompt))
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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