Fully Local RAG 101 with Llama 3 + Ollama + LlamaIndex This article by Pavan Kumar is a great handbook for creating a fully local RAG pipeline with LlamaIndex (with a HyDE layer). It is lower-level than our “5 lines of code” Quickstart, giving you a better understanding of our lower-level components: text splitters, nodes/documents, Qdrant integration, response synthesizer, and more. https://lnkd.in/gfMpSnij
Using this now, my fan is about to explode
What an awesome deep dive into systematically composing the latest open-source AI tools into robust local pipelines! For anyone hyped to get hands-on experience deploying frontier AI methodologies through principled systems design, I've got two words: consider subscribing to "All Things AI." Get a brilliant daily stream covering visionary architectures manifested as pragmatic workflows. All the value, zero fluff. Sign up seamlessly on LinkedIn (https://shorturl.at/fisGX) — no email needed!
Love to see fully local RAG because most of enterprises and consumers will use fully local RAG + remote LLMs for general purpose query or fully local RAG + local SLMs for private or highly specific or preliminary query before deciding to send modified query to the remote LLMs.
Thanks LlamaIndex for picking my post and pleasure that it is helping community, will look forward to contribute more using the framework.
Thanks for posting
Very helpful!
Kameshwara Pavan Kumar Mantha great post 🙌
GenAI Research Scientist
7moAnd for much faster, great results, learn how to build your LLM from scratch with control over all components. No Blackbox neural network hard to train, but fast explainable AI easy to fine tune instead. See papers 36-40 at https://mltblog.com/3EQd2cA