LLM Data Pipelines and Extraction

Residential Proxies for AI Data Collection Workflows

Collect permitted public web data for training, evaluation, and RAG pipelines with request rotation, session controls, location targeting, and measurable retry limits.

High-Throughput Async Scraping Pipeline

Leverage Python asyncio and httpx to collect thousands of web pages per minute with per-request residential IP rotation.

example.py

import asyncio
import httpx

PROXY_URL = "http://user-youraccount-rotate:password@proxy.bytesflows.com:31112"

async def fetch_target(client: httpx.AsyncClient, url: str):
    try:
        response = await client.get(url, timeout=10.0)
        print(f"[{response.status_code}] Fetched {len(response.text)} bytes from {url}")
        return response.text
    except Exception as e:
        print(f"Failed to scrape {url}: {e}")

async def run_batch_extraction(urls: list[str]):
    async with httpx.AsyncClient(proxy=PROXY_URL, verify=False) as client:
        tasks = [fetch_target(client, u) for u in urls]
        return await asyncio.gather(*tasks)

if __name__ == "__main__":
    target_list = ["https://httpbin.org/html"] * 20
    asyncio.run(run_batch_extraction(target_list))

Validate your AI data collection workflow

Run a controlled test, measure useful output and transferred traffic, then choose a plan that fits the validated workload.