[Online] RAGged Edge Box: A Personal AI-Powered Document Search System

One of the most popular embodiments of Generative AI are information retrieval (IR) augmented generation (RAG). Such systems use an information retrieval engine (based on semantic embeddings or keyword search) and then use a Large Language Model (LLM) to extract answers to a given query.

These systems require a large amount of computation and are usually implemented in the cloud which presents data privacy issues.

In this talk we will present The RAGged Edge Box project in which basic embedding systems and small local LLMs are packaged inside a multi-platform virtual machine (VirtualBox). The system provides a Web interface that runs locally and allows access to the RAG functionality in a completely private manner. The neural networks run on a ONNX runtime and do not require a GPU. RAG code is implemented in PHP and is easy to modify, requiring a much smaller execution environment than a Python alternative.

https://textualization.com/ragged/

Outline

• RAGged Edge Box demo
• AI Concepts (RAG, LLMs, Embeddings)
• RAG Concepts (IR, chunk, prompt)
• RAGged Edge Box (concept, advantages)
• RAGged Edge Box Architecture
• Enabling Technology Bits (ONNX, PHP Semantic Search)
• Extension Points
• VM Packaging
• RAGged Edge Box as a Platform

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How to Join the Webinar
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You can join via your browser (no app download required). Use Chrome or Firefox. Pre-register for the webinar:
https://www.bigmarker.com/neo4j/Data-Umbrella-Webinar

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Video Recording
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This event will be recorded and placed on our YouTube. We usually have it up within 24 hours of the event. Subscribe to our YT and set your notifications: https://www.youtube.com/c/DataUmbrella/

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Time
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16:00 UTC, 9am PT / 12pm ET/ 7pm EAT/ 9:30pm IST

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Additional Details
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Talk Level: Beginner
Pre-reqs: None
Prep Work: None
Resources: https://textualization.com/ragged/
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