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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
import pandas as pd
from sklearn.model_selection import train_test_split
# Download dataset
file_path = "dataset-tickets-multi-lang-4-20k.csv"
df = pd.read_csv(file_path)
# Pre-processing of the dataset to prepare for VectorDB creation
df = df[df['language'] == 'en']
non_string_body = df[~df['body'].apply(lambda x: isinstance(x, str))].index
non_string_answers = df[~df['answer'].apply(lambda x: isinstance(x, str))].index
non_string_ids = non_string_body.union(non_string_answers)
df = df.drop(index=non_string_ids)
df['q_and_a'] = 'Question: ' + df['body'] + ' Answer: ' + df['answer']
df_train, df_holdout = train_test_split(df, test_size=0.2, random_state=42)
# Setup of chromadb database
persist_directory = './chroma_db'
loader = DataFrameLoader(
df_train,
page_content_column="q_and_a")
documents = loader.load()
# Get OpenAI setup
openai_api_key = os.getenv("openai_token")
# Cache the creation of chroma_db so it only runs at app startup
@st.cache_resource
def get_vectordb():
embedding = OpenAIEmbeddings(openai_api_key=os.getenv("openai_token"))
return Chroma.from_documents(
documents=documents,
embedding=embedding,
persist_directory=persist_directory)
vectordb = get_vectordb()
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})
)
# Emulate a streamed response
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)
with st.chat_message("assistant"):
response = st.write_stream(response_generator(prompt))
st.session_state.messages.append({"role": "assistant", "content": response}) |