How to AI: RAG (Retrieval Augmented Generation) feat. danGPT

Опубликовано: 31 Март 2025
на канале: Tejas Kumar

705

Links
https://x.com/tejaskumar_
https://github.com/tejasq/dangpt
https://dangpt.vercel.app
https://datastax.com/

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In this video, we explore Retrievable Augmented Generation (RAG) with Artificial Intelligence (AI), focusing on the integration of embeddings models and vector stores for optimization. We provide an in-depth look at how AI can leverage embeddings models and vector stores to enhance RAG's capabilities, making it a must-watch for anyone interested in AI, embeddings models, RAG, and vector stores optimization.

We start by introducing the basics of RAG and its significance in the realm of AI, explaining how RAG functions in conjunction with embeddings models and vector stores to generate responses that are not only relevant but also contextually rich. The video delves into the mechanics of embeddings models, illustrating their role in transforming textual data into numerical vectors that AI systems can understand and process efficiently.

Following this, we explore the concept of vector stores and their importance in storing and retrieving these embeddings in a way that optimizes the performance of RAG systems. We discuss various strategies for optimizing vector stores to ensure fast and accurate retrieval of information, which is crucial for the effective functioning of RAG with AI.

We talk about the role of RAG with AI for building danGPT, which was a hobby project incorporating embeddings models and a vector store, AstraDB.

Key topics covered in this video include:
Introduction to Retrievable Augmented Generation (RAG) with AI
Understanding embeddings models in AI
The role of vector stores in optimizing RAG

This video is ideal for developers, researchers, and enthusiasts keen on harnessing the power of RAG with AI, embeddings models, and vector stores for their AI applications. Whether you're new to RAG or looking to deepen your understanding of embeddings models and vector stores optimization, this tutorial offers valuable insights and techniques to enhance your AI projects.