mirror of
https://github.com/Mintplex-Labs/anything-llm.git
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9f33b3dfcb
* Updates for Linux for frontend/server * frontend/server docker * updated Dockerfile for deps related to node vectordb * updates for collector in docker * docker deps for ODT processing * ignore another collector dir * storage mount improvements; run as UID * fix pypandoc version typo * permissions fixes
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import os, json
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from urllib.parse import urlparse
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from .utils import tokenize, ada_v2_cost
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from .medium_utils import get_username, fetch_recent_publications, append_meta
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from alive_progress import alive_it
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# Example medium URL: https://medium.com/@yujiangtham or https://davidall.medium.com
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def medium():
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print("[NOTICE]: This method will only get the 10 most recent publishings.")
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author_url = input("Enter the medium URL of the author you want to collect: ")
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if(author_url == ''):
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print("Not a valid medium.com/@author URL")
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exit(1)
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handle = get_username(author_url)
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if(handle is None):
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print("This does not appear to be a valid medium.com/@author URL")
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exit(1)
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publications = fetch_recent_publications(handle)
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if(len(publications)==0):
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print("There are no public or free publications by this creator - nothing to collect.")
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exit(1)
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totalTokenCount = 0
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transaction_output_dir = f"../server/storage/documents/medium-{handle}"
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if os.path.isdir(transaction_output_dir) == False:
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os.makedirs(transaction_output_dir)
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for publication in alive_it(publications):
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pub_file_path = transaction_output_dir + f"/publication-{publication.get('id')}.json"
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if os.path.exists(pub_file_path) == True: continue
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full_text = publication.get('pageContent')
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if full_text is None or len(full_text) == 0: continue
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full_text = append_meta(publication, full_text)
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item = {
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'id': publication.get('id'),
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'url': publication.get('url'),
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'title': publication.get('title'),
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'published': publication.get('published'),
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'wordCount': len(full_text.split(' ')),
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'pageContent': full_text,
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}
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tokenCount = len(tokenize(full_text))
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item['token_count_estimate'] = tokenCount
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totalTokenCount += tokenCount
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with open(pub_file_path, 'w', encoding='utf-8') as file:
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json.dump(item, file, ensure_ascii=True, indent=4)
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print(f"[Success]: {len(publications)} scraped and fetched!")
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print(f"\n\n////////////////////////////")
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print(f"Your estimated cost to embed all of this data using OpenAI's text-embedding-ada-002 model at $0.0004 / 1K tokens will cost {ada_v2_cost(totalTokenCount)} using {totalTokenCount} tokens.")
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print(f"////////////////////////////\n\n")
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exit(0)
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