Spaces:
Sleeping
Sleeping
File size: 2,253 Bytes
6da9c31 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import os
import pandas as pd
import streamlit as st
from langchain_community.vectorstores.faiss import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
st.set_page_config(page_title="ICLR2025 Paper Search", layout="wide")
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
@st.cache_resource
def create_vector_store(
vector_store_path: str,
embedding_model_name: str,
) -> FAISS:
embedding_model = HuggingFaceEmbeddings(model_name=embedding_model_name)
vector_store = FAISS.load_local(
folder_path=vector_store_path,
embeddings=embedding_model,
allow_dangerous_deserialization=True,
)
return vector_store
def grab_topk(
input_text: str,
vector_store: FAISS,
top_k: int,
) -> pd.DataFrame:
retriever = vector_store.as_retriever(search_kwargs={"k": top_k + 1})
relevant_docs = retriever.get_relevant_documents(input_text)
abstracts = list()
titles = list()
urls = list()
for relevant_doc in relevant_docs:
content = relevant_doc.page_content
url = content.split("<BEGIN_URL>")[-1].split("<END_URL>")[0]
abstract = content.split("\\n")[-1].split("<BEGIN_URL>")[0]
title = content.split("\\n")[0]
abstracts.append(abstract + "...")
titles.append(title)
urls.append(url)
return pd.DataFrame({"title": titles, "abstract": abstracts, "url": urls})
if __name__ == "__main__":
vector_store_path = "db"
embedding_model_name = "intfloat/multilingual-e5-large-instruct"
vector_store = create_vector_store(
vector_store_path,
embedding_model_name,
)
st.markdown("## ICLR2025")
st.markdown("- list of papers (https://iclr.cc/Downloads/2025)")
st.markdown(
"- repository (https://github.com/ohashi3399/paper-sonar?tab=readme-ov-file)"
)
input_text = st.text_input(
"query",
"",
placeholder="Enter the keywords you are interested in...",
)
top_k = st.number_input("top_k", min_value=1, value=10, step=1)
if st.button("検索"):
stripped_input_text = input_text.strip()
df = grab_topk(
stripped_input_text,
vector_store,
top_k,
)
st.table(df)
|