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import streamlit as st
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
from langchain.chains.retrieval_qa.base import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain_community.chat_message_histories.streamlit import (
StreamlitChatMessageHistory,
)
from langchain_community.chat_models.openai import ChatOpenAI
from calback_handler import PrintRetrievalHandler, StreamHandler
from chat_profile import ChatProfileRoleEnum
from document_retriever import configure_retriever
LLM_MODEL = "gpt-3.5-turbo"
st.set_page_config(
page_title="InkChatGPT: Chat with Documents",
page_icon="📚",
initial_sidebar_state="collapsed",
menu_items={
"Get Help": "https://x.com/vinhnx",
"Report a bug": "https://github.com/vinhnx/InkChatGPT/issues",
"About": """InkChatGPT is a simple Retrieval Augmented Generation (RAG) application that allows users to upload PDF documents and engage in a conversational Q&A, with a language model (LLM) based on the content of those documents.
GitHub: https://github.com/vinhnx/InkChatGPT""",
},
)
# Hide Header
# st.markdown(
# """<style>.stApp [data-testid="stToolbar"]{display:none;}</style>""",
# unsafe_allow_html=True,
# )
# Setup memory for contextual conversation
msgs = StreamlitChatMessageHistory()
with st.sidebar:
with st.container():
col1, col2 = st.columns([0.2, 0.8])
with col1:
st.image(
"./assets/app_icon.png",
use_column_width="always",
output_format="PNG",
)
with col2:
st.header(":books: InkChatGPT")
# chat_tab,
documents_tab, settings_tab = st.tabs(
[
# "Chat",
"Documents",
"Settings",
]
)
with settings_tab:
openai_api_key = st.text_input("OpenAI API Key", type="password")
if len(msgs.messages) == 0 or st.button("Clear message history"):
msgs.clear()
msgs.add_ai_message("""
Hi, your uploaded document(s) had been analyzed.
Feel free to ask me any questions. For example: you can start by asking me `'What is this book about?` or `Tell me about the content of this book!`'
""")
with documents_tab:
uploaded_files = st.file_uploader(
label="Select files",
type=["pdf", "txt", "docx"],
accept_multiple_files=True,
disabled=(not openai_api_key),
)
if not openai_api_key:
st.info("🔑 Please Add your **OpenAI API key** on the `Settings` to continue.")
if uploaded_files:
result_retriever = configure_retriever(uploaded_files)
if result_retriever is not None:
memory = ConversationBufferMemory(
memory_key="chat_history",
chat_memory=msgs,
return_messages=True,
)
# Setup LLM and QA chain
llm = ChatOpenAI(
model=LLM_MODEL,
api_key=openai_api_key,
temperature=0,
streaming=True,
)
chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=result_retriever,
memory=memory,
verbose=False,
max_tokens_limit=4000,
)
avatars = {
ChatProfileRoleEnum.HUMAN: "user",
ChatProfileRoleEnum.AI: "assistant",
}
for msg in msgs.messages:
st.chat_message(avatars[msg.type]).write(msg.content)
if user_query := st.chat_input(
placeholder="Ask me anything!",
disabled=(not openai_api_key and not result_retriever),
):
st.chat_message("user").write(user_query)
with st.chat_message("assistant"):
retrieval_handler = PrintRetrievalHandler(st.empty())
stream_handler = StreamHandler(st.empty())
response = chain.run(user_query, callbacks=[retrieval_handler, stream_handler])
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