Let’s Build a RAG Agent Using Phi-Data in Just 20 Lines of Code — Open Source & Local! 🚀

Author(s): Anoop Maurya

Originally published on Towards AI.

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As we wrap up 2024, let’s celebrate the power of AI with one last project for the year: building a RAG (Retrieval-Augmented Generation) agent! 🎉 These agents combine the brilliance of large language models with custom knowledge bases, making them incredibly effective at answering questions with pinpoint accuracy. Today, using Phi-Data, an open-source framework, we’ll create a RAG agent in just 20 lines of code! Let’s make it count and start the new year empowered by innovation. Happy New Year to everyone! 🥳

This agent will leverage:

Ollama’s llama-3.2 model for generating responses.Nomic-Embed-Text for creating embeddings.pgvector- as the vector database.

And the best part? It runs locally. Let’s dive in! 🌍

Clap 50 times — each one helps more than you think! 👏Follow me here on Medium and subscribe for free to catch my latest posts. 🫶Let’s connect on LinkedIn, check out my projects on GitHub, and stay in touch on Twitter!If you found this project useful, don’t forget to ⭐ the repo on GitHub. It helps others find it too!

Imagine having a virtual assistant that can access specific, up-to-date knowledge directly from your own documents or custom… Read the full blog for free on Medium.

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Published via Towards AI