Building a Secure Multi-Tenancy RAG System: Step-by-Step Guide

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Learn how to build a secure and efficient Multi-Tenancy RAG system using LlamaIndex! This step-by-step guide covers everything from data ingestion and metadata tagging to creating user-specific query engines. Perfect for ensuring data privacy and security in AI workflows, with practical examples.

Multi-tenancy enables a system to securely serve multiple users by isolating their data and ensuring queries only access user-specific information. It's essential for privacy, scalability, and efficient resource sharing in AI systems like RAG pipelines.

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Here are 5 key takeaways:

🌐 Understanding Multi-Tenancy in RAG: Multi-Tenancy ensures data isolation, where each user can only access their indexed documents, maintaining confidentiality and security.

🏷️ Metadata for User Separation: Incorporating user-specific metadata in documents ensures that queries are restricted to user-relevant data, preventing unauthorized access.

🛠️ Step-by-Step Implementation: From data ingestion to querying, the tutorial showcases the full pipeline for setting up a Multi-Tenancy RAG system with LlamaIndex.

🔍 Custom Query Engines: Separate query engines for different users use metadata filters, ensuring precise and user-specific information retrieval.

📚 Practical Demonstrations: The video demonstrates indexing, metadata tagging, and querying with real-world examples like LLMCompiler and Dense X Retrieval papers.

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[Links]

Google Colab - https://colab.research.google.com/gis...

LlamaIndex blog - https://www.llamaindex.ai/blog/buildi...

Groq api key - https://console.groq.com/keys
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[Timestamps]

0:00 Introduction
0:25 What is Multi Tenancy Problem
2:19 How to solve Multi Tenancy Problem
5:15 Hands on Demo
15:20 Like, Share & Subscribe

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