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Description
HyperCrawl is an innovative web crawler tailored specifically for LLM and RAG applications, designed to create efficient retrieval engines. Our primary aim was to enhance the retrieval process by minimizing the time spent crawling various domains. We implemented several advanced techniques to forge a fresh ML-focused approach to web crawling. Rather than loading each webpage sequentially (similar to waiting in line at a grocery store), it simultaneously requests multiple web pages (akin to placing several online orders at once). This strategy effectively eliminates idle waiting time, allowing the crawler to engage in other tasks. By maximizing concurrency, the crawler efficiently manages numerous operations at once, significantly accelerating the retrieval process compared to processing only a limited number of tasks. Additionally, HyperLLM optimizes connection time and resources by reusing established connections, much like opting to use a reusable shopping bag rather than acquiring a new one for every purchase. This innovative approach not only streamlines the crawling process but also enhances overall system performance.
Description
WebCrawlerAPI serves as an effective solution for developers aiming to streamline the processes of web crawling and data extraction. It features a user-friendly API that allows users to obtain content from various websites in formats such as text, HTML, or Markdown, which is particularly beneficial for training artificial intelligence models or conducting data-driven operations. With an impressive success rate of 90% and an average crawling duration of 7.3 seconds, this API adeptly navigates challenges including the management of internal links, elimination of duplicates, JavaScript rendering, counteracting anti-bot measures, and accommodating large-scale data storage. Furthermore, it integrates smoothly with a range of programming languages, such as Node.js, Python, PHP, and .NET, enabling developers to initiate projects with minimal code. In addition to these features, WebCrawlerAPI automates the data cleaning process, guaranteeing high-quality results for subsequent usage. Converting HTML into structured text or Markdown can involve intricate parsing rules, and effectively managing multiple crawlers across various servers adds another layer of complexity. Thus, WebCrawlerAPI emerges as an essential resource for developers focused on efficient and effective web data extraction.
API Access
Has API
API Access
Has API
Integrations
JavaScript
Python
.NET
Amazon Web Services (AWS)
Docker
Google Colab
HTML
Jupyter Notebook
Markdown
Node.js
Integrations
JavaScript
Python
.NET
Amazon Web Services (AWS)
Docker
Google Colab
HTML
Jupyter Notebook
Markdown
Node.js
Pricing Details
Free
Free Trial
Free Version
Pricing Details
$2 per month
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
HyperCrawl
Website
hypercrawl.hyperllm.org
Vendor Details
Company Name
WebCrawlerAPI
Country
United States
Website
webcrawlerapi.com