HyperCrawl

Impressive Performance Metrics

• 890+ Trusted Users: A growing user base that relies on Exthalpy's capabilities.

• 85% Reduced Latency: Significant improvement in response times.

• 88% Less Computation: Efficient use of computational resources.

• 178K+ Queries Processed: Demonstrating the platform's robust processing capacity.

Advanced Retrieval Techniques

Asynchronous Retrieval

Exthalpy employs parallel processing to request multiple webpages simultaneously, significantly speeding up data collection.

Data Preprocessing & Merging

High concurrency settings allow the crawler to handle multiple tasks at once, enhancing overall efficiency.

Local Embedding Setup

Utilizes HyperLLM to reduce resource consumption by reusing existing connections.

Dense Vector Semantic Retrieval

Implements memory of visited URLs to avoid redundant processing, saving time and resources.

Historical Dataset Management

Versatile design allows operation in various environments like Google Colab or Jupyter notebook without event loop issues.

Flexible Deployment Options

• Cloud or Local Deployment: Available as both an API and an open-source Python library.

• Easy Access: Accessible via API or installable through pip.

Community-Driven Development

Provides resources and community support for users to learn and maximize the use of Exthalpy.

What is Exthalpy's mission?

Exthalpy is dedicated to building the future of fast LLMs. As part of HyperLLM, they aim to develop infrastructure for future Language Models that require fewer computational resources while outperforming existing models.

How can I start using Exthalpy?

You can sign up for free and start using Exthalpy immediately. It's designed to be easy, free, and simple to use.

What makes Exthalpy different from other AI platforms?

Exthalpy stands out with its focus on retrieval-first AI applications, significantly reduced latency and computation requirements, and the ability to process a large number of queries efficiently.