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import streamlit as st | |
from streamlit_lottie import st_lottie | |
def faq(): | |
st.markdown( | |
""" | |
# FAQ | |
## How does Document Q&A Bot work? | |
When you upload a document (in Pdf, word, csv or txt format), it will be divided into smaller chunks | |
and stored in a special type of database called a vector index | |
that allows for semantic search and retrieval. | |
When you ask a question, our Q&A bot will first look through the document chunks and find the | |
most relevant ones using the vector index. Then, it will use open-source LLM model named Google Palm | |
and will provide the final answer. | |
## Is my data safe? | |
Yes, your data is safe. Our bot does not store your documents or | |
questions. All uploaded data is deleted after you close the browser tab. | |
## Why does it take so long to index my document? | |
Since, this is a sample QA bot project that uses open-source model | |
and doesn't have much resource capabilities like GPU, it may take time | |
to index your document based on the size of the document. | |
## Are the answers 100% accurate? | |
No, the answers are not 100% accurate. | |
But for most use cases, our QA bot is very accurate and can answer | |
most questions. Always check with the sources to make sure that the answers | |
are correct. | |
""" | |
) | |
def sidebar(): | |
with st.sidebar: | |
st.markdown("## Google Palm") | |
st.success('API key already provided!', icon='✅') | |
st.markdown( | |
"## How to use QA bot\n" | |
"1. Upload a pdf, docx, or a txt file📄\n" | |
"2. Ask questions about the document💬\n" | |
) | |
# st.session_state["OPENAI_API_KEY"] = api_key_input | |
st.markdown("---") | |
st.markdown("# About") | |
st.markdown( | |
"🤖 QA bot allows you to ask questions about your " | |
"documents and get accurate answers with citations. " | |
) | |
st.markdown("Created by [Krishna Kumar](https://www.linkedin.com/in/krishna-kumar-yadav-726831105/)") | |
st.markdown("---") | |
faq() | |