RAG for structured data: benefits, challenges, examples, & more

What this post added

This post introduces structured RAG, explaining its benefits, challenges, and implementation. It details how structured RAG works with organized data, the retrieval process using SQL queries and metadata, and data injection into prompts using formats like JSON. The post highlights benefits such as reduced hallucinations, real-time updates, and better control over output, while also discussing challenges like mapping semantic queries to structured ones and maintaining data freshness. It outlines implementation steps including ingestion, tagging, vectorization, hybrid search, and LLM connection, and specifically mentions Meilisearch's capability to index structured records and provide filtered semantic search. The post also provides examples of structured RAG use cases like BI assistants and customer service help, and differentiates it from knowledge graphs and data lakes.