mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-09 00:10:10 +01:00
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
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
import os, json
|
|
from urllib.parse import urlparse
|
|
from .utils import tokenize, ada_v2_cost
|
|
from .substack_utils import fetch_all_publications, only_valid_publications, get_content, append_meta
|
|
from alive_progress import alive_it
|
|
|
|
# Example substack URL: https://swyx.substack.com/
|
|
def substack():
|
|
author_url = input("Enter the substack URL of the author you want to collect: ")
|
|
if(author_url == ''):
|
|
print("Not a valid author.substack.com URL")
|
|
exit(1)
|
|
|
|
source = urlparse(author_url)
|
|
if('substack.com' not in source.netloc or len(source.netloc.split('.')) != 3):
|
|
print("This does not appear to be a valid author.substack.com URL")
|
|
exit(1)
|
|
|
|
subdomain = source.netloc.split('.')[0]
|
|
publications = fetch_all_publications(subdomain)
|
|
valid_publications = only_valid_publications(publications)
|
|
|
|
if(len(valid_publications)==0):
|
|
print("There are no public or free preview newsletters by this creator - nothing to collect.")
|
|
exit(1)
|
|
|
|
print(f"{len(valid_publications)} of {len(publications)} publications are readable publically text posts - collecting those.")
|
|
|
|
totalTokenCount = 0
|
|
transaction_output_dir = f"../server/storage/documents/substack-{subdomain}"
|
|
if os.path.isdir(transaction_output_dir) == False:
|
|
os.makedirs(transaction_output_dir)
|
|
|
|
for publication in alive_it(valid_publications):
|
|
pub_file_path = transaction_output_dir + f"/publication-{publication.get('id')}.json"
|
|
if os.path.exists(pub_file_path) == True: continue
|
|
|
|
full_text = get_content(publication.get('canonical_url'))
|
|
if full_text is None or len(full_text) == 0: continue
|
|
|
|
full_text = append_meta(publication, full_text)
|
|
item = {
|
|
'id': publication.get('id'),
|
|
'url': publication.get('canonical_url'),
|
|
'thumbnail': publication.get('cover_image'),
|
|
'title': publication.get('title'),
|
|
'subtitle': publication.get('subtitle'),
|
|
'description': publication.get('description'),
|
|
'published': publication.get('post_date'),
|
|
'wordCount': publication.get('wordcount'),
|
|
'pageContent': full_text,
|
|
}
|
|
|
|
tokenCount = len(tokenize(full_text))
|
|
item['token_count_estimate'] = tokenCount
|
|
|
|
totalTokenCount += tokenCount
|
|
with open(pub_file_path, 'w', encoding='utf-8') as file:
|
|
json.dump(item, file, ensure_ascii=True, indent=4)
|
|
|
|
print(f"[Success]: {len(valid_publications)} scraped and fetched!")
|
|
print(f"\n\n////////////////////////////")
|
|
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.")
|
|
print(f"////////////////////////////\n\n")
|
|
exit(0)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|