{"id":12512,"date":"2025-01-13T12:20:42","date_gmt":"2025-01-13T20:20:42","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=12512"},"modified":"2025-04-29T12:43:04","modified_gmt":"2025-04-29T12:43:04","slug":"multi-index-rag-apps","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/multi-index-rag-apps\/","title":{"rendered":"Build Multi-Index Advanced RAG Apps"},"content":{"rendered":"\n

Welcome to Lesson 12 of 12 in our free course series, LLM Twin: Building Your Production-Ready AI Replica. You\u2019ll learn how to use LLMs, vector DVs, and LLMOps best practices to design, train, and deploy a production ready \u201cLLM twin\u201d of yourself. This AI character will write like you, incorporating your style, personality, and voice into an LLM. For a full overview of course objectives and prerequisites, start with Lesson 1.<\/em><\/p>\n\n\n\n

Lessons<\/strong><\/p>\n\n\n\n

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  1. An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin<\/a><\/span><\/li>\n\n\n\n
  2. Your Content is Gold: I Turned 3 Years of Blog Posts into an LLM Training<\/a><\/span><\/li>\n\n\n\n
  3. I Replaced 1000 Lines of Polling Code with 50 Lines of CDC Magic<\/a><\/span><\/li>\n\n\n\n
  4. SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG \u2014 in Real-Time!<\/a><\/span><\/li>\n\n\n\n
  5. The 4 Advanced RAG Algorithms You Must Know to Implement<\/a><\/span><\/li>\n\n\n\n
  6. Turning Raw Data Into Fine-Tuning Datasets<\/a><\/span><\/li>\n\n\n\n
  7. 8B Parameters, 1 GPU, No Problems: The Ultimate LLM Fine-tuning Pipeline<\/a><\/span><\/li>\n\n\n\n
  8. The Engineer\u2019s Framework for LLM & RAG Evaluation<\/a><\/span><\/li>\n\n\n\n
  9. Beyond Proof of Concept: Building RAG Systems That Scale<\/a><\/span><\/li>\n\n\n\n
  10. The Ultimate Prompt Monitoring Pipeline<\/a><\/span><\/li>\n\n\n\n
  11. [Bonus] Build a scalable RAG ingestion pipeline using 74.3% less code<\/a><\/li>\n\n\n\n
  12. [Bonus] Build Multi-Index Advanced RAG Apps<\/a><\/li>\n<\/ol>\n\n\n\n
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    This article will teach you how to implement multi-index structures<\/strong> for building advanced RAG systems.<\/strong><\/p>\n\n\n\n

    To implement<\/strong> our multi-index collections and queries<\/strong>, we will leverage Superlinked<\/a>, a vector compute engine highly optimized for working with vector data, offering solutions for ingestion, embedding, storing and retrieval.<\/p>\n\n\n\n

    To better understand how Superlinked<\/a> queries work, we will gradually present how to build a complex query that uses two vector indexes, adds filters based on the metadata extracted using an LLM, and returns only the top K most similar documents to reduce network I\/O overhead.<\/p>\n\n\n\n

    Ultimately, we will dig into how Superlinked can help us implement and optimize various advanced RAG methods, such as query expansion, self-query, filtered vector search and rerank.<\/p>\n\n\n\n

    As this article is part of the LLM Twin course<\/em>, before we start, here is some essential context you have to know to move along with this lesson (which you can read independently if you want to<\/em><\/strong>):<\/p>\n\n\n\n