𝗔𝘀𝘆𝗻𝗰 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴 𝗜𝘀 𝗕𝗲𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗥𝗔𝗚 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻
RAG systems often fail because of stale data. The page changes but your index stays the same. Your AI then gives wrong answers with high confidence.
Many people try to fix this with simple synchronous scrapers. You fetch a page, extract data, and update your vector store. This approach creates problems in production.
The main issues with synchronous scraping:
- Page loads take a long time due to JavaScript or cookie banners.
- Your API waits for the scraper to finish, which slows down your users.
- You run out of memory or open sockets when running tasks in parallel.
- Errors like timeouts or rate limits are hard to manage.
Async scraping uses a submit, poll, and retrieve flow. You submit a task, get a job ID, and check for the result later. This keeps your application fast.
How to build a reliable ingestion pipeline:
- Separate scraping from request handling. Your app should not wait for a browser to load.
- Store job states in a database. Track the URL, status, and errors.
- Use content hashes. If the page content has not changed, do not re-embed it. This saves money and time.
- Use dead-letter queues. If a job fails three times, stop retrying. Move it to a visible list so you can fix it.
- Validate your data. Use a schema to check the extracted data before it reaches your vector store. An empty string is worse than a failed job.
Async scraping works best for background updates and scheduled refreshes. It is not for real-time needs where a user waits for a fresh page.
If a user needs data immediately, show them cached content and update the index in the background.
Optional learning community: https://t.me/GyaanSetuAi