mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-06-24 07:40:07 +02:00
139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
import os, json, tempfile
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from urllib.parse import urlparse
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from requests_html import HTMLSession
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from langchain.document_loaders import UnstructuredHTMLLoader
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from .link_utils import append_meta
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from .utils import tokenize, ada_v2_cost
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# Example Channel URL https://tim.blog/2022/08/09/nft-insider-trading-policy/
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def link():
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print("[NOTICE]: The first time running this process it will download supporting libraries.\n\n")
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fqdn_link = input("Paste in the URL of an online article or blog: ")
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if(len(fqdn_link) == 0):
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print("Invalid URL!")
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exit(1)
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session = HTMLSession()
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req = session.get(fqdn_link)
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if(req.ok == False):
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print("Could not reach this url!")
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exit(1)
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req.html.render()
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full_text = None
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with tempfile.NamedTemporaryFile(mode = "w") as tmp:
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tmp.write(req.html.html)
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tmp.seek(0)
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loader = UnstructuredHTMLLoader(tmp.name)
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data = loader.load()[0]
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full_text = data.page_content
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tmp.close()
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link = append_meta(req, full_text, True)
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if(len(full_text) > 0):
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source = urlparse(req.url)
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output_filename = f"website-{source.netloc}-{source.path.replace('/','_')}.json"
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output_path = f"./outputs/website-logs"
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transaction_output_filename = f"article-{source.path.replace('/','_')}.json"
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transaction_output_dir = f"../server/documents/website-{source.netloc}"
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if os.path.isdir(output_path) == False:
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os.makedirs(output_path)
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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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full_text = append_meta(req, full_text)
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tokenCount = len(tokenize(full_text))
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link['pageContent'] = full_text
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link['token_count_estimate'] = tokenCount
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with open(f"{output_path}/{output_filename}", 'w', encoding='utf-8') as file:
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json.dump(link, file, ensure_ascii=True, indent=4)
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with open(f"{transaction_output_dir}/{transaction_output_filename}", 'w', encoding='utf-8') as file:
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json.dump(link, file, ensure_ascii=True, indent=4)
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else:
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print("Could not parse any meaningful data from this link or url.")
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exit(1)
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print(f"\n\n[Success]: article or link content fetched!")
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print(f"////////////////////////////")
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print(f"Your estimated cost to embed this data using OpenAI's text-embedding-ada-002 model at $0.0004 / 1K tokens will cost {ada_v2_cost(tokenCount)} using {tokenCount} tokens.")
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print(f"////////////////////////////")
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exit(0)
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def links():
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links = []
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prompt = "Paste in the URL of an online article or blog: "
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done = False
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while(done == False):
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new_link = input(prompt)
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if(len(new_link) == 0):
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done = True
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links = [*set(links)]
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continue
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links.append(new_link)
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prompt = f"\n{len(links)} links in queue. Submit an empty value when done pasting in links to execute collection.\nPaste in the next URL of an online article or blog: "
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if(len(links) == 0):
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print("No valid links provided!")
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exit(1)
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totalTokens = 0
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for link in links:
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print(f"Working on {link}...")
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session = HTMLSession()
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req = session.get(link)
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if(req.ok == False):
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print(f"Could not reach {link} - skipping!")
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continue
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req.html.render()
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full_text = None
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with tempfile.NamedTemporaryFile(mode = "w") as tmp:
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tmp.write(req.html.html)
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tmp.seek(0)
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loader = UnstructuredHTMLLoader(tmp.name)
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data = loader.load()[0]
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full_text = data.page_content
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tmp.close()
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link = append_meta(req, full_text, True)
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if(len(full_text) > 0):
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source = urlparse(req.url)
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output_filename = f"website-{source.netloc}-{source.path.replace('/','_')}.json"
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output_path = f"./outputs/website-logs"
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transaction_output_filename = f"article-{source.path.replace('/','_')}.json"
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transaction_output_dir = f"../server/documents/website-{source.netloc}"
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if os.path.isdir(output_path) == False:
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os.makedirs(output_path)
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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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full_text = append_meta(req, full_text)
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tokenCount = len(tokenize(full_text))
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link['pageContent'] = full_text
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link['token_count_estimate'] = tokenCount
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totalTokens += tokenCount
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with open(f"{output_path}/{output_filename}", 'w', encoding='utf-8') as file:
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json.dump(link, file, ensure_ascii=True, indent=4)
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with open(f"{transaction_output_dir}/{transaction_output_filename}", 'w', encoding='utf-8') as file:
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json.dump(link, file, ensure_ascii=True, indent=4)
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else:
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print(f"Could not parse any meaningful data from {link}.")
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continue
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print(f"\n\n[Success]: {len(links)} article or link contents fetched!")
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print(f"////////////////////////////")
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print(f"Your estimated cost to embed this data using OpenAI's text-embedding-ada-002 model at $0.0004 / 1K tokens will cost {ada_v2_cost(totalTokens)} using {totalTokens} tokens.")
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print(f"////////////////////////////")
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exit(0) |