app.py

import streamlit as st
from pyvi.ViTokenizer import tokenize
from src.services.generate_embedding import generate_embedding
import pymongo
import time
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
import os

# Connect DB
client = pymongo.MongoClient(
    "mongodb+srv://rag:[email protected]/?retryWrites=true&w=majority&appName=RAG"
)
db = client.rag
collection = db.pdf


def stream_response(answer: str):
    for word in answer.split(" "):
        yield word + " "
        time.sleep(0.03)


# 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"], unsafe_allow_html=True)


def retriveByIndex(idxs):
    docs = collection.find({"index": {"$in": idxs}})
    content = ""
    for doc in docs:
        content = content + " " + doc["page_content"]
    return content


def generateAnswer(context: str, question: str):
    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "user","""Trả lời câu hỏi của người dùng dựa vào thông tin có trong thẻ <context> </context> được cho bên dưới. Nếu context không chứa những thông tin liên quan tới câu hỏi, thì đừng trả lời và chỉ trả lời là "Tôi không biết". <context> {context} </context> Câu hỏi: {question}""",
            ),
        ]
    )
    messages = prompt.invoke({"context": context, "question": question});
    print(messages)
    chat = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0.8)
    response = chat.invoke(messages)
    return response.content


# React to user input
if prompt := st.chat_input(""):
    tokenized_prompt = tokenize(prompt)

    # 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)

    embedding = generate_embedding(tokenized_prompt)
    results = collection.aggregate(
        [
            {
                "$vectorSearch": {
                    "queryVector": embedding,
                    "path": "page_content_embedding",
                    "numCandidates": 5,
                    "limit": 5,
                    "index": "vector_index",
                }
            }
        ]
    )

    allIndx = []
    for document in results:
        idx = document["index"]
        allIndx.append(idx)
        allIndx.append(idx + 1)
        allIndx.append(idx + 2)
        allIndx.append(idx + 3)
    print(allIndx)

    context = retriveByIndex(allIndx)
    answer = generateAnswer(context, question=prompt)
    with st.chat_message("assistant"):
        st.markdown(answer, unsafe_allow_html=True)

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": answer})