Hypercrawl : An efficient web crawler designed for LLM and RAG applications

Hypercrawl

Development & Tools Research Tools #web scraping #machine learning #data retrieval #efficient #open-source Fresh Picks Paid

Overview :

HyperCrawl is the first web crawler designed for LLM (Large Language Models) and RAG (Retrieval Augmented Generation) applications, aiming to develop powerful indexing engines. By introducing various advanced methods, it significantly reduces the time required to crawl domains and improves the efficiency of the retrieval process. HyperCrawl is part of HyperLLM, dedicated to building the future infrastructure for LLMs that require fewer computational resources and outperform existing models.

Target Users :

HyperCrawl is suitable for machine learning engineers and data scientists who need to quickly and reliably collect and retrieve large amounts of web data to support their research and development work.

Total Visits： 0

Website Views ： 50.5K

Use Cases

Building datasets for large language models.

Providing fast data retrieval services for RAG applications.

In the education field, helping researchers collect academic resources.

Features

Asynchronous I/O: Simultaneously requests multiple web pages, boosting efficiency.

Concurrent Management: High concurrency settings handle multiple tasks concurrently.

Efficient Resource Utilization: Reuses existing connections to minimize resource consumption.

URL Access Tracking: Avoids repeated visits and processing of the same page.

Nested Event Loop Support: Adapts to different environments, such as Google Colab or Jupyter notebooks.