HyperCrawl

HyperCrawl is the first web crawler designed for Large Language Models (LLM) and Retrieval Augmented Generative Models (RAG). By introducing a variety of advanced methods, it significantly reduces the crawling time of domain names and improves the efficiency of the retrieval process. HyperCrawl is part of HyperLLM and is committed to building the infrastructure for future LLM models that require fewer computing resources and outperform any existing models.

Demand group:

Machine Learning Engineer

data scientist

Example of usage scenario:

Datasets for building large language models

Provide fast data retrieval services for RAG applications

In the field of education, help researchers collect academic resources

Product features:

Asynchronous I/O: request multiple web pages at the same time to improve efficiency

Concurrency management: high concurrency settings, processing multiple tasks at the same time

Efficient resource handling: reuse existing connections and reduce resource consumption

Visit URL tracking: avoid repeated visits and processing of the same page

Nested event loop support: adaptable to different environments, such as Google Colab or Jupyter notebooks

HyperAPI: Use HyperCrawl anywhere via API

Python core library: As an open source Python library, free to use

Usage tutorial:

1. Visit HyperCrawl official website and register a free account

2. Read the documentation to understand the basic usage of HyperCrawl

3. Install the HyperCrawl Python library through Pip

4. Use HyperAPI to integrate HyperCrawl in web projects

5. Set up concurrency management and configure crawler parameters

6. Start the crawler and start data collection and retrieval

7. Monitor the running status of the crawler to ensure that the data is accurate