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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 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
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 + "\n\n" + document['question'] + ": " + 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})
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