HyperLLM Howto

HyperLLM appears to be a project or platform related to large language models, but there is insufficient information to provide a detailed description of its features or capabilities.
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How to Use HyperLLM

Install HyperCrawl: HyperCrawl is available as both an API and a Python library. Install the Python library, which is open-source and free to use.
Import and initialize HyperCrawl: Import the HyperCrawl library in your Python project and initialize it with your desired configuration settings.
Set concurrency: Set a high concurrency value to allow the crawler to handle multiple tasks simultaneously, which speeds up the process.
Define crawl targets: Specify the websites or web pages you want HyperCrawl to crawl and extract data from.
Configure extraction rules: Define rules for what type of data you want to extract from the crawled pages (e.g. text, links, images).
Start the crawl: Initiate the crawling process using the HyperCrawl API or library functions.
Process extracted data: Once crawling is complete, process and analyze the extracted data as needed for your specific use case.
Integrate with LLM: Use the crawled and processed data as input for large language models (LLMs) to generate insights or perform other NLP tasks.

HyperLLM FAQs

HyperCrawl is the first web crawler designed specifically for LLM and RAG applications. It aims to boost the retrieval process by eliminating crawl time of domains and uses advanced methods to build retrieval engines.

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