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'''
This script is used to generate the most relevant papers of a given title.
- Search for as many as possible references. For 10~15 keywords, 10 references each.
- Sort the results from most relevant to least relevant.
- Return the most relevant using token size.
Note: we do not use this function in auto-draft function. It has been integrated in that.
'''
import os.path
import json
from utils.references import References
from section_generator import keywords_generation # section_generation_bg, #, figures_generation, section_generation
import itertools
from gradio_client import Client
def generate_raw_references(title, description="",
bib_refs=None, tldr=False, max_kw_refs=10,
save_to="ref.bib"):
# load pre-provided references
ref = References(title, bib_refs)
# generate multiple keywords for searching
input_dict = {"title": title, "description": description}
keywords, usage = keywords_generation(input_dict)
keywords = list(keywords)
comb_keywords = list(itertools.combinations(keywords, 2))
for comb_keyword in comb_keywords:
keywords.append(" ".join(comb_keyword))
keywords = {keyword:max_kw_refs for keyword in keywords}
print(f"keywords: {keywords}\n\n")
ref.collect_papers(keywords, tldr=tldr)
# paper_json = ref.to_json()
with open(save_to, "w") as f:
json.dump(paper_json, f)
return save_to, ref # paper_json
def generate_top_k_references(title, description="",
bib_refs=None, tldr=False, max_kw_refs=10, save_to="ref.bib", top_k=5):
json_path, ref_raw = generate_raw_references(title, description, bib_refs, tldr, max_kw_refs, save_to)
json_content = ref_raw.to_json()
client = Client("https://shaocongma-evaluate-specter-embeddings.hf.space/")
result = client.predict(
title, # str in 'Title' Textbox component
json_path, # str (filepath or URL to file) in 'Papers JSON (as string)' File component
top_k, # int | float (numeric value between 1 and 50) in 'Top-k Relevant Papers' Slider component
api_name="/get_k_relevant_papers"
)
with open(result) as f:
result = json.load(f)
return result
if __name__ == "__main__":
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
title = "Using interpretable boosting algorithms for modeling environmental and agricultural data"
description = ""
save_to = "paper.json"
save_to, paper_json = generate_raw_references(title, description, save_to=save_to)
print("`paper.json` has been generated. Now evaluating its similarity...")
k = 5
client = Client("https://shaocongma-evaluate-specter-embeddings.hf.space/")
result = client.predict(
title, # str in 'Title' Textbox component
save_to, # str (filepath or URL to file) in 'Papers JSON (as string)' File component
k, # int | float (numeric value between 1 and 50) in 'Top-k Relevant Papers' Slider component
api_name="/get_k_relevant_papers"
)
with open(result) as f:
result = json.load(f)
print(result)
save_to = "paper2.json"
with open(save_to, "w") as f:
json.dump(result, f) |