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import chromadb | |
from chromadb import Documents, EmbeddingFunction, Embeddings | |
from transformers import AutoModel | |
import json | |
from numpy.linalg import norm | |
import sqlite3 | |
import urllib.request | |
from django.conf import settings | |
import Levenshtein | |
# this module act as a singleton class | |
import os | |
os.environ['HF_HOME'] = 'models/' | |
class JinaAIEmbeddingFunction(EmbeddingFunction): | |
def __init__(self, model): | |
super().__init__() | |
self.model = model | |
def __call__(self, input: Documents) -> Embeddings: | |
embeddings = self.model.encode(input) | |
return embeddings.tolist() | |
# instance of embedding_model | |
embedding_model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en', | |
trust_remote_code=True, | |
cache_dir='models') | |
# instance of JinaAIEmbeddingFunction | |
ef = JinaAIEmbeddingFunction(embedding_model) | |
# list of topics | |
topic_descriptions = json.load(open("topic_descriptions.txt")) | |
topics = list(dict.keys(topic_descriptions)) | |
embeddings = [embedding_model.encode(topic_descriptions[key]) for key in topic_descriptions] | |
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) | |
def lev_sim(a,b): return Levenshtein.distance(a,b) | |
def choose_topic(summary): | |
embed = embedding_model.encode(summary) | |
topic = "" | |
max_sim = 0. | |
for i,key in enumerate(topics): | |
sim = cos_sim(embed,embeddings[i]) | |
if sim > max_sim: | |
topic = key | |
max_sim = sim | |
return topic | |
def authors_list_to_str(authors): | |
"""input a list of authors, return a string represent authors""" | |
text = "" | |
for author in authors: | |
text+=author+", " | |
return text[:-3] | |
def authors_str_to_list(string): | |
"""input a string of authors, return a list of authors""" | |
authors = [] | |
list_auth = string.split("and") | |
for author in list_auth: | |
if author != "et al.": | |
authors.append(author.strip()) | |
return authors | |
def chunk_texts(text, max_char=400): | |
""" | |
Chunk a long text into several chunks, with each chunk about 300-400 characters long, | |
but make sure no word is cut in half. | |
Args: | |
text: The long text to be chunked. | |
max_char: The maximum number of characters per chunk (default: 400). | |
Returns: | |
A list of chunks. | |
""" | |
chunks = [] | |
current_chunk = "" | |
words = text.split() | |
for word in words: | |
if len(current_chunk) + len(word) + 1 >= max_char: | |
chunks.append(current_chunk) | |
current_chunk = " " | |
else: | |
current_chunk += " " + word | |
chunks.append(current_chunk.strip()) | |
return chunks | |
def trimming(txt): | |
start = txt.find("{") | |
end = txt.rfind("}") | |
return txt[start:end+1].replace("\n"," ") | |
# crawl data | |
def extract_tag(txt,tagname): | |
return txt[txt.find("<"+tagname+">")+len(tagname)+2:txt.find("</"+tagname+">")] | |
def get_record(extract): | |
id = extract_tag(extract,"id") | |
updated = extract_tag(extract,"updated") | |
published = extract_tag(extract,"published") | |
title = extract_tag(extract,"title").replace("\n ","").strip() | |
summary = extract_tag(extract,"summary").replace("\n","").strip() | |
authors = [] | |
while extract.find("<author>")!=-1: | |
author = extract_tag(extract,"name") | |
extract = extract[extract.find("</author>")+9:] | |
authors.append(author) | |
pattern = '<link title="pdf" href="' | |
link_start = extract.find('<link title="pdf" href="') | |
link = extract[link_start+len(pattern):extract.find("rel=",link_start)-2] | |
return [id, updated, published, title, authors, link, summary] | |
def crawl_exact_paper(title,author,max_results=3): | |
authors = authors_list_to_str(author) | |
records = [] | |
url = 'http://export.arxiv.org/api/query?search_query=ti:{title}+AND+au:{author}&max_results={max_results}'.format(title=title,author=authors,max_results=max_results) | |
url = url.replace(" ","%20") | |
try: | |
arxiv_page = urllib.request.urlopen(url,timeout=100).read() | |
xml = str(arxiv_page,encoding="utf-8") | |
while xml.find("<entry>") != -1: | |
extract = xml[xml.find("<entry>")+7:xml.find("</entry>")] | |
xml = xml[xml.find("</entry>")+8:] | |
extract = get_record(extract) | |
topic = choose_topic(extract[6]) | |
records.append([topic,*extract]) | |
return records | |
except Exception as e: | |
return "Error: "+str(e) | |
def crawl_arxiv(keyword_list, max_results=100): | |
baseurl = 'http://export.arxiv.org/api/query?search_query=' | |
records = [] | |
for i,keyword in enumerate(keyword_list): | |
if i ==0: | |
url = baseurl + 'all:' + keyword | |
else: | |
url = url + '+OR+' + 'all:' + keyword | |
url = url+ '&max_results=' + str(max_results) | |
url = url.replace(' ', '%20') | |
try: | |
arxiv_page = urllib.request.urlopen(url,timeout=100).read() | |
xml = str(arxiv_page,encoding="utf-8") | |
while xml.find("<entry>") != -1: | |
extract = xml[xml.find("<entry>")+7:xml.find("</entry>")] | |
xml = xml[xml.find("</entry>")+8:] | |
extract = get_record(extract) | |
topic = choose_topic(extract[6]) | |
records.append([topic,*extract]) | |
return records | |
except Exception as e: | |
return "Error: "+str(e) | |
# This class act as a module | |
class ArxivChroma: | |
""" | |
Create an interface to arxivdb, which only support query and addition. | |
This interface do not support edition and deletion procedures. | |
""" | |
client = None | |
model = None | |
collection = None | |
def connect(table="arxiv_records", name="arxivdb/"): | |
ArxivChroma.client = chromadb.PersistentClient(name) | |
ArxivChroma.model = embedding_model | |
ArxivChroma.collection = ArxivChroma.client.get_or_create_collection(table, | |
embedding_function=JinaAIEmbeddingFunction( | |
model = ArxivChroma.model | |
)) | |
def query_relevant(keywords, query_texts, n_results=3): | |
""" | |
Perform a query using a list of keywords (str), | |
or using a relavant string | |
""" | |
contains = [] | |
for keyword in keywords: | |
contains.append({"$contains":keyword.lower()}) | |
return ArxivChroma.collection.query( | |
query_texts=query_texts, | |
where_document={ | |
"$or":contains | |
}, | |
n_results=n_results, | |
) | |
def query_exact(id): | |
ids = ["{}_{}".format(id,j) for j in range(0,10)] | |
return ArxivChroma.collection.get(ids=ids) | |
def add(crawl_records): | |
""" | |
Add crawl_records (list) obtained from arxiv_crawlers | |
A record is a list of 8 columns: | |
[topic, id, updated, published, title, author, link, summary] | |
Return the final length of the database table | |
""" | |
for record in crawl_records: | |
embed_text = """ | |
Topic: {}, | |
Title: {}, | |
Summary: {} | |
""".format(record[0],record[4],record[7]) | |
chunks = chunk_texts(embed_text) | |
ids = [record[1][21:]+"_"+str(j) for j in range(len(chunks))] | |
paper_ids = [{"paper_id":record[1][21:]} for _ in range(len(chunks))] | |
ArxivChroma.collection.add( | |
documents = chunks, | |
metadatas=paper_ids, | |
ids = ids | |
) | |
return ArxivChroma.collection.count() | |
def close_connection(): | |
pass | |
# This class act as a module | |
class ArxivSQL: | |
table = "arxivsql" | |
con = None | |
cur = None | |
def connect(name="db.sqlite3"): | |
ArxivSQL.con = sqlite3.connect(name, check_same_thread=False) | |
ArxivSQL.cur = ArxivSQL.con.cursor() | |
def query(title="", author=[], threshold = 15): | |
if len(author)>0: | |
query_author= " OR ".join([f"authors LIKE '%{a}%'" for a in author]) | |
else: | |
query_author= "True" | |
# Execute the query | |
query = f"select * from {ArxivSQL.table} where {query_author}" | |
results = ArxivSQL.cur.execute(query).fetchall() | |
if len(title) == 0: | |
return results | |
else: | |
sim_score = {} | |
for row in results: | |
row_title = row[2] | |
row_id = row[0] | |
score = lev_sim(title, row_title) | |
if score < threshold: | |
sim_score[row_id] = score | |
sorted_results = sorted(sim_score.items(), key=lambda x: x[1]) | |
return ArxivSQL.query_id(sorted_results) | |
def query_id(ids=[]): | |
try: | |
if len(ids) == 0: | |
return None | |
query = "select * from {} where id in (".format(ArxivSQL.table) | |
for id in ids: | |
query+="'"+id+"'," | |
query = query[:-1] + ")" | |
result = ArxivSQL.cur.execute(query) | |
return result.fetchall() | |
except Exception as e: | |
print(e) | |
print("Error query: ",query) | |
def add(crawl_records): | |
""" | |
Add crawl_records (list) obtained from arxiv_crawlers | |
A record is a list of 8 columns: | |
[topic, id, updated, published, title, author, link, summary] | |
Return the final length of the database table | |
""" | |
results = "" | |
for record in crawl_records: | |
try: | |
query = """insert into arxivsql values("{}","{}","{}","{}","{}","{}","{}")""".format( | |
record[1][21:], | |
record[0], | |
record[4].replace('"',"'"), | |
authors_list_to_str(record[5]), | |
record[2][:10], | |
record[3][:10], | |
record[6] | |
) | |
ArxivSQL.cur.execute(query) | |
ArxivSQL.con.commit() | |
except Exception as e: | |
results+=str(e) | |
results+="\n" + query + "\n" | |
finally: | |
return results |