{"id":12497,"date":"2025-01-13T12:18:28","date_gmt":"2025-01-13T20:18:28","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=12497"},"modified":"2025-04-29T12:16:08","modified_gmt":"2025-04-29T12:16:08","slug":"refactoring-rag-retrieval","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/refactoring-rag-retrieval\/","title":{"rendered":"Build a scalable RAG ingestion pipeline using 74.3% less code"},"content":{"rendered":"\n

Welcome to Lesson 11 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.<\/a><\/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
    \n\n\n\n

    Lessons 11<\/strong> and 12<\/strong> are part of a bonus series<\/strong> in which we will take the advanced RAG system from the LLM Twin course<\/strong> (written in LangChain) and refactor it using Superlinked<\/a>, a framework specialized in vector computing for information retrieval.<\/p>\n\n\n\n

    In Lesson 11 (this article), we will learn to build a highly scalable, real-time RAG feature pipeline that ingests multi-data categories into a Redis vector database.<\/p>\n\n\n\n

    More concretely we will take the ingestion pipeline implemented in Lesson 4 and swap the chunking, embedding, and vector DB logic with Superlinked.<\/p>\n\n\n\n

    You don\u2019t have to read Lesson 4 to read this article. We will give enough context to make sense of it.<\/em><\/p>\n\n\n\n

    In the 12th lesson<\/strong>, we will use Superlinked to implement a multi-index query strategy and further optimize the advanced RAG retrieval module (initially built in Lesson 5<\/strong>).<\/p>\n\n\n\n

    The value of this article lies in understanding how easy it is to build complex advanced RAG systems using Superlinked.<\/em><\/p>\n\n\n\n

    Using Superlinked<\/strong>, we reduced the number of RAG-related lines of code by 74.3%. Powerful, right?<\/em><\/p>\n\n\n\n

    By the end of this article, you will learn<\/strong> to build a production-ready feature pipeline built in Superlinked that:<\/p>\n\n\n\n