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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Experience complete privacy with AnyLLM, an all-in-one application that integrates any LLM, document, and agent directly on your desktop. This desktop solution only interacts with the services you choose, allowing it to function entirely offline without the need for an internet connection. You're not restricted to a single LLM provider; instead, you can select from enterprise options like GPT-4, customize your own model, or utilize open-source alternatives such as Llama and Mistral. Your business relies on a variety of formats, including PDFs and Word documents, and with AnyLLM, you can seamlessly incorporate them all into your workflow. The application is pre-configured with sensible defaults for your LLM, embedder, and storage, ensuring your privacy is prioritized right from the start. AnyLLM is available for free on desktop or can be self-hosted through our GitHub repository. For those seeking a hassle-free experience, AnyLLM offers cloud hosting starting at $50 per month, tailored for businesses or teams that require the robust capabilities of AnyLLM without the burden of technical management. With its user-friendly design and flexibility, AnyLLM stands out as a powerful tool for enhancing productivity while maintaining control over your data.

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.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
Codestral
GPT-4
Google Colab
JavaScript
Jupyter Notebook
Le Chat
Llama 2
Llama 3.1
Llama 3.2
Llama 3.3
Mathstral
Ministral 3B
Ministral 8B
Mistral AI
Mistral Large
Mistral NeMo
Mixtral 8x22B
Pixtral Large
React

Integrations

Amazon Web Services (AWS)
Codestral
GPT-4
Google Colab
JavaScript
Jupyter Notebook
Le Chat
Llama 2
Llama 3.1
Llama 3.2
Llama 3.3
Mathstral
Ministral 3B
Ministral 8B
Mistral AI
Mistral Large
Mistral NeMo
Mixtral 8x22B
Pixtral Large
React

Pricing Details

$50 per month
Free Trial
Free Version

Pricing Details

Free
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

AnythingLLM

Website

anythingllm.com

Vendor Details

Company Name

HyperCrawl

Website

hypercrawl.hyperllm.org

Product Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

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