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| import streamlit as st | |
| from pyvi.ViTokenizer import tokenize | |
| from src.services.generate_embedding import generate_embedding | |
| import pymongo | |
| import time | |
| from src.indexing import indexData, SHEET_ID, SHEET_NAME | |
| from langchain_openai import ChatOpenAI | |
| from langchain.prompts import ChatPromptTemplate | |
| import os | |
| # Connect DB | |
| client = pymongo.MongoClient( | |
| "mongodb+srv://rag:p9vojYc9fafYwxE9@rag.xswi7nq.mongodb.net/?retryWrites=true&w=majority&appName=RAG" | |
| ) | |
| db = client.rag | |
| collection = db.questionAndAnswers | |
| with st.expander('Dataset'): | |
| col1 , col2 = st.columns(2) | |
| with col1: | |
| st.markdown( | |
| """ | |
| <div style="display:flex; gap: 16px; align-items: center"> | |
| <a style="font-size: 14px" | |
| href="https://docs.google.com/spreadsheets/d/1MKB6MHgL_lrPB1I69fj2VcVrgmSAMLVNZR1EwSyTSeA/edit#gid=0">Link | |
| question & answers</a> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| with col2: | |
| if st.button('Re-train'): | |
| placeholder = st.empty() | |
| placeholder.empty() | |
| placeholder.write('Training ...') | |
| indexData(SHEET_ID, SHEET_NAME) | |
| placeholder.write('Completed') | |
| def generateAnswer(context: str, question: str): | |
| prompt = ChatPromptTemplate.from_messages( | |
| [ | |
| ( | |
| "user","""Trả lời câu hỏi dựa trên thông tin trong thẻ <context>. Mỗi cặp câu hỏi và trả lời được ngăn cách bằng dấu <enter/>. Câu hỏi và trả lời được phân tách bằng dấu <space/>. | |
| Nếu không có thông tin liên quan trong context, chỉ trả lời "Tôi không biết". | |
| Câu trả lời phải đầy đủ thông tin, nhấn mạnh vào những điểm chính từ thông tin trong context. | |
| <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 | |
| 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) | |
| # 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": tokenized_prompt}) | |
| # Display user message in chat message container | |
| with st.chat_message("user"): | |
| st.markdown(tokenized_prompt) | |
| embedding = generate_embedding(tokenized_prompt) | |
| results = collection.aggregate( | |
| [ | |
| { | |
| "$vectorSearch": { | |
| "queryVector": embedding, | |
| "path": "question_embedding", | |
| "numCandidates": 10, | |
| "limit": 10, | |
| "index": "vector_index", | |
| } | |
| } | |
| ] | |
| ) | |
| posibleQuestions = "" | |
| context = "" | |
| question = "" | |
| index = 0 | |
| for document in results: | |
| posibleQuestions = posibleQuestions + f"<li>{document['question']}</li>" | |
| context =context + "<enter/>" + document['question'] + "<space/> " + document['answer'] | |
| if index == 0: | |
| question = document["question"] | |
| index = index + 1 | |
| posibleQuestions = f"""<ol> <p style="font-weight: 600">Câu hỏi liên quan: </p> {posibleQuestions}</ol>""" | |
| answer = generateAnswer(context, prompt); | |
| response = f"""<p>{answer}</p> | |
| {posibleQuestions} | |
| """ | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| st.markdown(response, unsafe_allow_html=True) | |
| # st.markdown(f"""<p style="font-weight: 600">Question: {question}</p>""", unsafe_allow_html=True) | |
| # st.write_stream(stream_response(answer)) | |
| # st.markdown(posibleQuestions, unsafe_allow_html=True) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |