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import streamlit as st
import pandas as pd
from streamlit_gsheets import GSheetsConnection
def load_data():
GSheetsConnection = "https://docs.google.com/spreadsheets/d/1Ui9gZoSKxSIW0B7fG8ryuN0nhkVhAckFxl2hWf6CaJQ/edit?usp=sharing"
conn = st.connection("gsheets", type=GSheetsConnection)
df = conn.read()
return df
def case_insensitive_search(data, query, column):
if query:
return data[data[column].str.lower().str.contains(query.lower())]
return data
def display_table(data, rows_per_page=10):
container = st.container()
with container:
height = min(40 + rows_per_page * 38, 800)
st.dataframe(data, height=height)
def main():
st.title("Multihop-RAG Benchmark 💡")
data = load_data()
st.sidebar.header("Search Options")
chat_model_query = st.sidebar.text_input("Chat Model")
embedding_model_query = st.sidebar.text_input("Embedding Model")
chunk_query = st.sidebar.text_input("Chunk")
frame_query = st.sidebar.text_input("Framework")
if chat_model_query:
data = case_insensitive_search(data, chat_model_query, 'chat_model')
if embedding_model_query:
data = case_insensitive_search(data, embedding_model_query, 'embedding_model')
if chunk_query:
data = case_insensitive_search(data, chunk_query, 'chunk')
if frame_query:
data = case_insensitive_search(data, frame_query, 'framework')
st.write("Displaying results across different frameworks, embedding models, chat models, and chunks.")
st.info("Retrieval Stage: MRR@10 and Hit@10; Response Stage: Accuracy ")
display_table(data)
st.sidebar.header("Citation")
st.sidebar.info(
"Please cite this dataset as:\n"
"Tang, Yixuan, and Yi Yang. MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries. ArXiv, 2024, /abs/2401.15391."
)
st.markdown("---")
st.caption("For citation, please use: 'Tang, Yixuan, and Yi Yang. MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries. ArXiv, 2024, /abs/2401.15391. '")
st.markdown("---")
st.caption("For results self-reporting, please send an email to [email protected]")
if __name__ == "__main__":
main() |