MultiHop-RAG / app.py
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import streamlit as st
import pandas as pd
def load_data():
return pd.read_csv("benchmark_data.csv")
def case_insensitive_search(data, query, column):
if query:
return data[data[column].str.lower().str.contains(query.lower())]
return data
def main():
st.title("Multihop-RAG Benchmark 💡")
data = load_data()
# 筛选条件
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')
# 创建多级列索引
columns = [
("Settings", "Framework"),
("Settings", "Chat Model"),
("Settings", "Embedding Model"),
("Settings", "Chunk"),
("Retrieval Metrics", "MRR@10"),
("Retrieval Metrics", "Hit@10"),
("Response Metrics", "Accuracy")
]
# 选择并设置多级列索引
data = data[['framework', 'chat_model', 'embedding_model', 'chunk', 'MRR@10', 'Hit@10', 'Accuracy']]
data.columns = pd.MultiIndex.from_tuples(columns)
# 展示数据
st.dataframe(data.style.set_table_styles([{
'selector': 'th',
'props': [('background-color', '#f4f4f4'), ('font-weight', 'bold')]
}]), height=600)
if st.sidebar.checkbox("Show Metrics Distribution"):
st.subheader("Metrics Distribution")
st.bar_chart(data[['MRR@10', 'Hit@10', 'Accuracy']])
# 引用
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."
)
if __name__ == "__main__":
main()