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import pandas as pd |
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import arxiv |
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import requests |
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from pinecone import Pinecone, ServerlessSpec |
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import logging |
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import os |
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script_dir = os.path.dirname(os.path.abspath(__file__)) |
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os.chdir(script_dir) |
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def get_zotero_ids(api_key, library_id, tag): |
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base_url = 'https://api.zotero.org' |
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suffix = '/users/'+ library_id +'/items?tag='+ tag |
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header = {'Authorization': 'Bearer '+ api_key} |
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request = requests.get(base_url + suffix, headers= header) |
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return [data['data']['archiveID'].replace('arXiv:', '') for data in request.json()] |
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def get_arxiv_papers(ids = None, category = None, comment = None): |
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logging.getLogger('arxiv').setLevel(logging.WARNING) |
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client = arxiv.Client() |
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if category is None: |
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search = arxiv.Search( |
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id_list= ids, |
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max_results= len(ids), |
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) |
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else : |
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if comment is None: |
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custom_query = f'cat:{category}' |
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else: |
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custom_query = f'cat:{category} AND co:{comment}' |
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search = arxiv.Search( |
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query = custom_query, |
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max_results= 15, |
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sort_by= arxiv.SortCriterion.SubmittedDate |
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) |
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if ids is None and category is None: |
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raise ValueError('not a valid query') |
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df = pd.DataFrame({'Title': [result.title for result in client.results(search)], |
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'Abstract': [result.summary.replace('\n', ' ') for result in client.results(search)], |
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'Date': [result.published.date().strftime('%Y-%m-%d') for result in client.results(search)], |
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'id': [result.entry_id for result in client.results(search)]}) |
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if ids: |
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df.to_csv('arxiv-scrape.csv', index = False) |
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return df |
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def get_hf_embeddings(api_key, df): |
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title_abs = [title + '[SEP]' + abstract for title,abstract in zip(df['Title'], df['Abstract'])] |
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API_URL = "https://api-inference.huggingface.co/models/malteos/scincl" |
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headers = {"Authorization": f"Bearer {api_key}"} |
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response = requests.post(API_URL, headers=headers, json={"inputs": title_abs, "options": {"wait_for_model": False}}) |
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if response.status_code == 503: |
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response = requests.post(API_URL, headers=headers, json={"inputs": title_abs, "options": {"wait_for_model": True}}) |
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embeddings = response.json() |
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return embeddings, len(embeddings[0]) |
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def upload_to_pinecone(api_key, index, namespace, embeddings, dim, df): |
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input = [{'id': df['id'][i], 'values': embeddings[i]} for i in range(len(embeddings))] |
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pc = Pinecone(api_key = api_key) |
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if index in pc.list_indexes().names(): |
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while True: |
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logging.warning(f'Index name : {index} already exists.') |
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return f'Index name : {index} already exists' |
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pc.create_index( |
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name=index, |
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dimension=dim, |
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metric="cosine", |
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deletion_protection="disabled", |
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spec=ServerlessSpec( |
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cloud='aws', |
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region='us-east-1' |
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) |
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) |
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index = pc.Index(index) |
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return index.upsert(vectors=input, namespace=namespace) |
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def get_new_papers(df): |
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df_main = pd.read_csv('arxiv-scrape.csv') |
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df.reset_index(inplace=True) |
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df.drop(columns=['index'], inplace=True) |
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union_df = df.merge(df_main, how='left', indicator=True) |
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df = union_df[union_df['_merge'] == 'left_only'].drop(columns=['_merge']) |
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if df.empty: |
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return 'No New Papers Found' |
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else: |
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return df |
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def recommend_papers(api_key, index, namespace, embeddings, df, threshold): |
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pc = Pinecone(api_key = api_key) |
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if index in pc.list_indexes().names(): |
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index = pc.Index(index) |
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else: |
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raise ValueError(f"{index} doesnt exist. Project isnt initialized properly") |
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results = [] |
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score_threshold = threshold |
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for i,embedding in enumerate(embeddings): |
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query = embedding |
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result = index.query(namespace=namespace,vector=query,top_k=3,include_values=False) |
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sum_score = sum(match['score'] for match in result['matches']) |
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if sum_score > score_threshold: |
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results.append(f"Paper-URL : [{df['id'][i]}]({df['id'][i]}) with score: {sum_score / 3} <br />") |
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if results: |
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return '\n'.join(results) |
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else: |
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return 'No Interesting Paper' |
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