Introduction:

Multi-Tenancy in Retriever-Augmented Generation (RAG) systems is crucial for maintaining data security and privacy by ensuring that each user only accesses documents they have indexed. This approach prevents unauthorized cross-referencing of private information between different users, which is especially important for sensitive or proprietary data. The system uses metadata to tie documents to specific users, enabling the retriever to filter and access only relevant documents during queries. The blog post provides a detailed guide on building a Multi-Tenancy RAG system using LlamaIndex, demonstrating how to manage data ingestion, document indexing, and querying processes. By implementing user-specific metadata and query engines, the system maintains data integrity and confidentiality, as illustrated through examples with users Jerry and Ravi. The article also introduces a MultiTenancyRAGPack for practical exploration, offering a Streamlit interface for hands-on experience.

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