app.py

import json
import os

import sqlalchemy
import sqlite_vss
import streamlit as st
import streamlit.components.v1 as components
from langchain import OpenAI
from langchain.callbacks import get_openai_callback
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.embeddings import GPT4AllEmbeddings
from sqlalchemy import event

from chat_history import insert_chat_history, insert_chat_history_articles
from css import load_css
from custom_pgvector import CustomPGVector
from message import Message

CONNECTION_STRING = "sqlite:///data/sorbobot.db"

st.set_page_config(layout="wide")

st.title("Sorbobot - Le futur de la recherche scientifique interactive")

chat_column, doc_column = st.columns([2, 1])


def connect() -> sqlalchemy.engine.Connection:
    engine = sqlalchemy.create_engine(CONNECTION_STRING)

    @event.listens_for(engine, "connect")
    def receive_connect(connection, _):
        connection.enable_load_extension(True)
        sqlite_vss.load(connection)
        connection.enable_load_extension(False)

    conn = engine.connect()
    return conn

conn = connect()


def initialize_session_state():
    if "history" not in st.session_state:
        st.session_state.history = []
    if "token_count" not in st.session_state:
        st.session_state.token_count = 0
    if "conversation" not in st.session_state:
        embeddings = GPT4AllEmbeddings()

        db = CustomPGVector(
            embedding_function=embeddings,
            table_name="article",
            column_name="abstract_embedding",
            connection=conn,
        )

        retriever = db.as_retriever()

        llm = OpenAI(
            temperature=0,
            openai_api_key=os.environ["OPENAI_API_KEY"],
            model="text-davinci-003",
        )

        memory = ConversationBufferMemory(
            output_key="answer", memory_key="chat_history", return_messages=True
        )
        st.session_state.conversation = ConversationalRetrievalChain.from_llm(
            llm=llm,
            retriever=retriever,
            verbose=True,
            memory=memory,
            return_source_documents=True,
        )


def on_click_callback():
    with get_openai_callback() as cb:
        human_prompt = st.session_state.human_prompt
        llm_response = st.session_state.conversation(human_prompt)
        st.session_state.history.append(Message("human", human_prompt))
        st.session_state.history.append(
            Message(
                "ai", llm_response["answer"], documents=llm_response["source_documents"]
            )
        )
        st.session_state.token_count += cb.total_tokens
        history_id = insert_chat_history(conn, human_prompt, llm_response["answer"])
        insert_chat_history_articles(conn, history_id, llm_response["source_documents"])


load_css()
initialize_session_state()

with chat_column:
    chat_placeholder = st.container()
    prompt_placeholder = st.form("chat-form")
    information_placeholder = st.empty()

    with chat_placeholder:
        for chat in st.session_state.history:
            div = f"""
                <div class="chat-row 
                    {'' if chat.origin == 'ai' else 'row-reverse'}">
                    <img class="chat-icon" src="./app/static/{
                        'ai_icon.png' if chat.origin == 'ai' 
                                    else 'user_icon.png'}"
                        width=32 height=32>
                    <div class="chat-bubble
                    {'ai-bubble' if chat.origin == 'ai' else 'human-bubble'}">
                        &#8203;{chat.message}
                    </div>
                </div>
            """
            st.markdown(div, unsafe_allow_html=True)

        for _ in range(3):
            st.markdown("")

    with prompt_placeholder:
        st.markdown("**Chat**")
        cols = st.columns((6, 1))
        cols[0].text_input(
            "Chat",
            value="Hello bot",
            label_visibility="collapsed",
            key="human_prompt",
        )
        cols[1].form_submit_button(
            "Submit",
            type="primary",
            on_click=on_click_callback,
        )

    information_placeholder.caption(
        f"""
    Used {st.session_state.token_count} tokens \n
    Debug Langchain conversation: 
    {st.session_state.conversation.memory.buffer}
    """
    )

    components.html(
        """
    <script>
    const streamlitDoc = window.parent.document;

    const buttons = Array.from(
        streamlitDoc.querySelectorAll('.stButton > button')
    );
    const submitButton = buttons.find(
        el => el.innerText === 'Submit'
    );

    streamlitDoc.addEventListener('keydown', function(e) {
        switch (e.key) {
            case 'Enter':
                submitButton.click();
                break;
        }
    });
    </script>
    """,
        height=0,
        width=0,
    )

with doc_column:
    if len(st.session_state.history) > 0:
        st.markdown("**Source documents**")
        for doc in st.session_state.history[-1].documents:
            doc_content = json.loads(doc.page_content)

            expander = st.expander(doc_content["title"])
            expander.markdown("**" + doc_content["doi"] + "**")
            expander.markdown(doc_content["abstract"])
            expander.markdown("**Authors** : " + doc_content["authors"])
            expander.markdown("**Keywords** : " + doc_content["keywords"])
            expander.markdown("**Distance** : " + str(doc_content["distance"]))