File size: 15,372 Bytes
8e698eb
70e35a5
8e698eb
 
 
 
70e35a5
8e698eb
 
 
fb8c051
70e35a5
d18c569
 
 
 
70e35a5
d18c569
 
70e35a5
fb8c051
 
8e698eb
c304855
8e698eb
 
2dc9347
 
 
ae239a7
fb8c051
8ef9348
a7f1695
3a7ead9
a7f1695
3a7ead9
8e698eb
3a7ead9
 
 
 
 
 
8ef9348
8e698eb
 
d18c569
70e35a5
64fee17
 
 
 
 
 
 
70e35a5
b730fc0
 
70e35a5
8e698eb
 
 
 
 
 
 
 
 
 
70e35a5
8e698eb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2dc9347
 
 
 
 
d18c569
2dc9347
 
 
 
d18c569
 
a7f1695
3a7ead9
a7f1695
3a7ead9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8ef9348
 
3a7ead9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8ef9348
 
 
295e94f
 
 
 
 
3a7ead9
 
 
 
 
a7f1695
 
c9efba3
295e94f
a7f1695
 
 
3a7ead9
 
8ef9348
70e35a5
 
 
3a7ead9
8e698eb
8ef9348
3a7ead9
 
 
 
 
 
70e35a5
8ef9348
 
3a7ead9
 
70e35a5
3a7ead9
 
70e35a5
3a7ead9
 
 
 
365213e
 
c9efba3
 
 
 
 
3a7ead9
 
 
8e698eb
3a7ead9
 
 
70e35a5
 
3a7ead9
8e698eb
 
8ef9348
3a7ead9
 
70e35a5
 
 
3a7ead9
 
 
 
 
d18c569
a7f1695
8ef9348
a7f1695
8ef9348
fb8c051
2dc9347
365213e
d18c569
365213e
 
 
70e35a5
 
 
 
 
 
 
 
 
 
 
fb8c051
2dc9347
 
fb8c051
 
 
 
2dc9347
 
 
 
ae239a7
2dc9347
 
7d01fc4
2dc9347
 
70e35a5
2dc9347
fb8c051
 
 
365213e
 
 
d18c569
fb8c051
 
 
 
 
 
 
c9efba3
fb8c051
c9efba3
 
 
 
fb8c051
 
 
 
 
 
 
 
 
 
 
4e7254f
 
fb8c051
 
d18c569
70e35a5
 
 
 
 
 
 
 
d18c569
fb8c051
 
 
2dc9347
 
 
 
 
 
 
 
 
 
d18c569
2dc9347
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fb8c051
2dc9347
 
c42190b
 
 
 
 
 
2dc9347
 
a6aecff
 
d18c569
70e35a5
 
 
 
 
 
 
a6aecff
c9efba3
2dc9347
c9efba3
 
 
 
70e35a5
c9efba3
2dc9347
 
 
 
 
 
 
 
 
 
d18c569
2dc9347
 
 
70e35a5
2dc9347
 
8e698eb
2dc9347
d18c569
 
2dc9347
 
 
c9efba3
2dc9347
 
 
 
 
 
 
 
 
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
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
# 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

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


######################################################################################################################
# Some basic tools
######################################################################################################################
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"):
        if load_papers is not None:
            self.papers = {keyword: load_papers_from_bibtex(load_papers)}
        else:
            self.papers = {}
        self.title = title

    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))
        print("Keywords: ", keywords)
        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")

        # 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")
        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
            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")