utils/references.py

# Each `paper` is a dictionary containing:
#       (1) paper_id (2) title (3) authors (4) year (5) link (6) abstract (7) journal (8) embeddings
#
# Generate references:
#   `Reference` class:
#       1. Read a given .bib file to collect papers; use `search_paper_abstract` method to fill missing abstract.
#       2. Given some keywords; use Semantic Scholar API to find papers.
#       3. Generate bibtex from the selected papers. --> to_bibtex()
#       4. Generate prompts from the selected papers: --> to_prompts()
#               A sample prompt: {"paper_id": "paper summary"}

# todo: (1) citations & citedby of provided papers:
#       load the pre-defined papers; use S2 to find all related works
#       add all citations to `bib_papers`
#       add all citedby to `bib_papers`
#       use Semantic Scholar to find their embeddings
#       (2) separate references:
#       divide references into different groups to reduce the tokens count
#       for generating different paragraph of related works, use different set of references
from typing import Dict, List
import requests
import re
import bibtexparser
import random
from scholarly import scholarly
from scholarly import ProxyGenerator
import tiktoken
import itertools, uuid, json
from gradio_client import Client
import time
import numpy as np
from numpy.linalg import norm


URL = "https://model-apis.semanticscholar.org/specter/v1/invoke"
MAX_BATCH_SIZE = 16
MAX_ATTEMPTS = 20

######################################################################################################################
# Some basic tools
######################################################################################################################
def evaluate_cosine_similarity(v1, v2):
    try:
        return np.dot(v1, v2)/(norm(v1)*norm(v2))
    except ValueError:
        return 0.0

def chunks(lst, chunk_size=MAX_BATCH_SIZE):
    """Splits a longer list to respect batch size"""
    for i in range(0, len(lst), chunk_size):
        yield lst[i : i + chunk_size]

def embed(papers):
    embeddings_by_paper_id: Dict[str, List[float]] = {}
    for chunk in chunks(papers):
        # Allow Python requests to convert the data above to JSON
        response = requests.post(URL, json=chunk)

        if response.status_code != 200:
            raise RuntimeError("Sorry, something went wrong, please try later!")

        for paper in response.json()["preds"]:
            embeddings_by_paper_id[paper["paper_id"]] = paper["embedding"]

    return embeddings_by_paper_id

def get_embeddings(paper_title, paper_description):
    output = [{"title": paper_title, "abstract": paper_description, "paper_id": "target_paper"}]
    emb_vector = embed(output)["target_paper"]
    target_paper = output[0]
    target_paper["embeddings"] = emb_vector
    return target_paper

def get_top_k(papers_dict, paper_title, paper_description, k=None):
    target_paper = get_embeddings(paper_title, paper_description)
    papers = papers_dict # must include embeddings

    # if k < len(papers_json), return k most relevant papers
    # if k >= len(papers_json) or k is None, return all papers
    max_num_papers = len(papers)
    if k is None:
        k = max_num_papers
    num_papers = min(k, max_num_papers)

    # evaluate the cosine similarity for each paper
    target_embedding_vector = target_paper["embeddings"]

    for k in papers:
        v = papers[k]
        embedding_vector = v["embeddings"]
        cos_sim  = evaluate_cosine_similarity(embedding_vector, target_embedding_vector)
        papers[k]["cos_sim"] = cos_sim

    # return the best k papers
    sorted_papers = {k: v for k, v in sorted(papers.items(), key=lambda x: x[1]["cos_sim"], reverse=True)[:num_papers]}
    for key in sorted_papers:
        sorted_papers[key].pop("embeddings", None)
    return sorted_papers

def remove_newlines(serie):
    # This function is applied to the abstract of each paper to reduce the length of prompts.
    serie = serie.replace('\n', ' ')
    serie = serie.replace('\\n', ' ')
    serie = serie.replace('  ', ' ')
    serie = serie.replace('  ', ' ')
    return serie


def search_paper_abstract(title):
    pg = ProxyGenerator()
    success = pg.FreeProxies()  # pg.ScraperAPI("921b16f94d701308b9d9b4456ddde155")
    if success:
        try:
            scholarly.use_proxy(pg)
            # input the title of a paper, return its abstract
            search_query = scholarly.search_pubs(title)
            found_paper = next(search_query)
        except:
            return ""
    else:
        return ""
        # raise RuntimeError("ScraperAPI fails.")
    return remove_newlines(found_paper['bib']['abstract'])


def load_papers_from_bibtex(bib_file_path):
    with open(bib_file_path) as bibtex_file:
        bib_database = bibtexparser.load(bibtex_file)
    if len(bib_database.entries) == 0:
        return []
    else:
        bib_papers = []
        for bibitem in bib_database.entries:
            # Add each paper to `bib_papers`
            paper_id = bibitem.get("ID")
            title = bibitem.get("title")
            if title is None:
                continue
            journal = bibitem.get("journal")
            year = bibitem.get("year")
            author = bibitem.get("author")
            abstract = bibitem.get("abstract")
            if abstract is None:
                abstract = search_paper_abstract(title)
            result = {
                "paper_id": paper_id,
                "title": title,
                "link": "",
                "abstract": abstract,
                "authors": author,
                "year": year,
                "journal": journal
            }
            bib_papers.append(result)
        return bib_papers

# `tokenizer`: used to count how many tokens
tokenizer_name = tiktoken.encoding_for_model('gpt-4')
tokenizer = tiktoken.get_encoding(tokenizer_name.name)


def tiktoken_len(text):
    # evaluate how many tokens for the given text
    tokens = tokenizer.encode(text, disallowed_special=())
    return len(tokens)


######################################################################################################################
# Semantic Scholar (SS) API
######################################################################################################################
def ss_search(keywords, limit=20, fields=None):
    # space between the  query to be removed and replaced with +
    if fields is None:
        fields = ["title", "abstract", "venue", "year", "authors", "tldr", "embedding", "externalIds"]
    keywords = keywords.lower()
    keywords = keywords.replace(" ", "+")
    url = f'https://api.semanticscholar.org/graph/v1/paper/search?query={keywords}&limit={limit}&fields={",".join(fields)}'
    # headers = {"Accept": "*/*", "x-api-key": constants.S2_KEY}
    headers = {"Accept": "*/*"}

    response = requests.get(url, headers=headers, timeout=30)
    return response.json()


def _collect_papers_ss(keyword, counts=3, tldr=False):
    def externalIds2link(externalIds):
        # Sample externalIds:
        #   "{'MAG': '2932819148', 'DBLP': 'conf/icml/HaarnojaZAL18', 'ArXiv': '1801.01290', 'CorpusId': 28202810}"
        if externalIds:
            # Supports ArXiv, MAG, ACL, PubMed, Medline, PubMedCentral, DBLP, DOI
            # priority: DBLP > arXiv > (todo: MAG > CorpusId > DOI > ACL > PubMed > Mdeline > PubMedCentral)
            # DBLP
            dblp_id = externalIds.get('DBLP')
            if dblp_id is not None:
                dblp_link = f"dblp.org/rec/{dblp_id}"
                return dblp_link
            # arXiv
            arxiv_id = externalIds.get('ArXiv')
            if arxiv_id is not None:
                arxiv_link = f"arxiv.org/abs/{arxiv_id}"
                return arxiv_link
            return ""
        else:
            # if this is an empty dictionary, return an empty string
            return ""

    def extract_paper_id(last_name, year_str, title):
        pattern = r'^\w+'
        words = re.findall(pattern, title)
        # return last_name + year_str + title.split(' ', 1)[0]
        try:
            output = last_name + year_str + words[0]
        except IndexError:
            output = last_name + year_str + title[:4]
        return output

    def extract_author_info(raw_authors):
        authors = [author['name'] for author in raw_authors]

        authors_str = " and ".join(authors)
        try:
            last_name = authors[0].split()[-1]
            last_name = last_name.replace("'", "")
        except IndexError:
            last_name = "ma"
        # pattern = r'^\w+'
        # last_name = re.findall(pattern, authors[0])
        return authors_str, last_name

    def parse_search_results(search_results_ss):
        if len(search_results_ss) == 0:
            return []

        # turn the search result to a list of paper dictionary.
        papers_ss = []
        for raw_paper in search_results_ss:
            if raw_paper["abstract"] is None:
                continue

            authors_str, last_name = extract_author_info(raw_paper['authors'])
            year_str = str(raw_paper['year'])
            title = raw_paper['title']

            # some journal may contain &; replace it. e.g. journal={IEEE Power & Energy Society General Meeting}
            journal = raw_paper['venue'].replace("&", "\\&")
            if not journal:
                journal = "arXiv preprint"

            paper_id = extract_paper_id(last_name, year_str, title).lower()
            link = externalIds2link(raw_paper['externalIds'])

            if tldr and raw_paper['tldr'] is not None:
                abstract = raw_paper['tldr']['text']
            else:
                abstract = remove_newlines(raw_paper['abstract'])

            # some papers have no embeddings; handle this case
            embeddings_dict = raw_paper.get('embedding')
            if embeddings_dict is None:
                continue
            else:
                embeddings = raw_paper['embedding']['vector']
            result = {
                "paper_id": paper_id,
                "title": title,
                "abstract": abstract,
                "link": link,
                "authors": authors_str,
                "year": year_str,
                "journal": journal,
                "embeddings": embeddings
            }
            papers_ss.append(result)
        return papers_ss

    raw_results = ss_search(keyword, limit=counts)
    if raw_results is not None:
        search_results = raw_results.get("data")
        if search_results is None:
            search_results = []
    else:
        search_results = []
    results = parse_search_results(search_results)
    return results


######################################################################################################################
# References Class
######################################################################################################################

class References:
    def __init__(self, title, load_papers=None, keyword="customized_refs", description=""):
        if load_papers is not None:
            self.papers = {keyword: load_papers_from_bibtex(load_papers)}
        else:
            self.papers = {}
        self.title = title
        self.description = description

    def load_papers(self, bibtex, keyword):
        self.papers[keyword] = load_papers_from_bibtex(bibtex)

    def generate_keywords_dict(self):
        keywords_dict = {}
        for k in self.papers:
            keywords_dict[k] = len(self.papers[k])
        return keywords_dict

    def collect_papers(self, keywords_dict, tldr=False):
        """
        Collect as many papers as possible

        keywords_dict:
            {"machine learning": 5, "language model": 2};
            the first is the keyword, the second is how many references are needed.
        """
        keywords = list(keywords_dict)
        comb_keywords = list(itertools.combinations(keywords, 2))
        for comb_keyword in comb_keywords:
            keywords.append(" ".join(comb_keyword))
        for key in keywords:
            self.papers[key] = _collect_papers_ss(key, 10, tldr)
        # print("Collected papers: ", papers)
        # for key, counts in keywords_dict.items():
        #     self.papers[key] = _collect_papers_ss(key, counts, tldr)

    def to_bibtex(self, path_to_bibtex="ref.bib"):
        """
        Turn the saved paper list into bibtex file "ref.bib". Return a list of all `paper_id`.
        """
        # todo:
        #   use embeddings to evaluate; keep top k relevant references in papers
        #   send (title, .bib file) to evaluate embeddings; recieve truncated papers
        papers = self._get_papers(keyword="_all")

        l = len(papers)
        print(f"{l} papers will be added to `ref.bib`.")
        # clear the bibtex file
        with open(path_to_bibtex, "w", encoding="utf-8") as file:
            file.write("")

        bibtex_entries = []
        paper_ids = []
        seen = set()
        for paper in papers:
            if paper["paper_id"] in seen:
                continue
            else:
                seen.add(paper["paper_id"])
            bibtex_entry = f"""@article{{{paper["paper_id"]},
          title = {{{paper["title"]}}},
          author = {{{paper["authors"]}}}, 
          journal={{{paper["journal"]}}}, 
          year = {{{paper["year"]}}}, 
          url = {{{paper["link"]}}}
        }}"""
            bibtex_entries.append(bibtex_entry)
            paper_ids.append(paper["paper_id"])
            # Save the generated BibTeX entries to a file
            with open(path_to_bibtex, "a", encoding="utf-8") as file:
                file.write(bibtex_entry)
                file.write("\n\n")
                # print(f'{paper["paper_id"]} has been added to `ref.bib`.')
        return paper_ids

    def _get_papers(self, keyword="_all"):
        if keyword == "_all":
            papers = []
            for k, v in self.papers.items():
                papers = papers + v
        else:
            papers = self.papers["keyword"]
        return papers

    def to_prompts(self, keyword="_all", max_tokens=2048):
        # `prompts`:
        #   {"paper1_bibtex_id": "paper_1_abstract", "paper2_bibtex_id": "paper2_abstract"}
        #   this will be used to instruct GPT model to cite the correct bibtex entry.

        # two steps:
        #   1. Sort everything from most relevant to less relevant
        #   2. Add paper to prompts until max_tokens
        json_path = str(uuid.uuid1()) + ".json"
        papers_json = self.to_json()
        with open(json_path, "w") as f:
            json.dump(papers_json, f)

        try:
            # Use external API to obtain the most relevant papers
            title = self.title
            description = self.description
            result = get_top_k(papers_json, title, description)
            # 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
            #     50,  # 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)
            result = [item for key, item in result.items()]
        except Exception as e:
            print(f"Error occurs during calling external API: {e}\n")
            print("Use default method instead!")
            result = self._get_papers(keyword)
        prompts = {}
        tokens = 0
        for paper in result:
            abstract = paper.get("abstract")
            if abstract is not None and isinstance(abstract, str):
                prompts[paper["paper_id"]] = paper["abstract"]
                tokens += tiktoken_len(paper["abstract"])
            else:
                prompts[paper["paper_id"]] = " "
            if tokens >= max_tokens:
                break
        return prompts

    def to_json(self, keyword="_all"):
        papers = self._get_papers(keyword)
        papers_json = {}
        for paper in papers:
            papers_json[paper["paper_id"]] = paper
        return papers_json


if __name__ == "__main__":
    # testing search results
    print("================Testing `ss_search`================")
    r = ss_search("Deep Q-Networks", limit=1)  # a list of raw papers
    if r['total'] > 0:
        paper = r['data'][0]
        # print(paper)

    # resting References
    print("================Testing `References`================")
    refs = References(title="Super Deep Q-Networks")
    keywords_dict = {
        "Deep Q-Networks": 5,
        "Actor-Critic Algorithms": 4,
        "Exploration-Exploitation Trade-off": 3
    }
    print("================Testing `References.collect_papers`================")
    refs.collect_papers(keywords_dict, tldr=True)
    for k in refs.papers:
        papers = refs.papers[k]  # for each keyword, there is a list of papers
        print("keyword: ", k)
        for paper in papers:
            print(paper["paper_id"])

    print("================Testing `References.to_bibtex`================")
    refs.to_bibtex()

    print("================Testing `References.to_json`================")
    papers_json = refs.to_json()  # this json can be used to find the most relevant papers
    with open("papers.json", "w", encoding='utf-8') as text_file:
        text_file.write(f"{papers_json}")

    print("================Testing `References.to_prompts`================")
    prompts = refs.to_prompts()
    print(prompts)

    # bib = "test.bib"
    # refs.load_papers(bib, "variance-reduction rl")
    # print(refs.papers)
    #
    # prompts = refs.to_prompts()
    # for k in prompts:
    #     print(f"{k}: {prompts[k]}\n")