Langchain and RAG

What will I learn?

Learn how to harness the power of Retrieval Augmented Generation (RAG) through this hands-on guide using Langchain, KDB.AI, and various Large Language Models (LLMs). RAG is an advanced prompt engineering technique that enables LLMs to access real-time, contextually accurate information from external knowledge bases, enhancing their ability to provide up-to-date and relevant responses.

Agenda

In this tutorial, we’ll cover the process of setting up RAG with LangChain, from loading text data to KDB.AI and performing Retrieval Augmented Generation using OpenAI and HuggingFace LLMs.

Who is this suitable for?

Aimed at complete beginners to KDB.AI. No prior experience required but python knowledge will make it easier to follow along.

To access your own KDB.AI instance as shown in the video simply click the blue button above or else signup here.

The Jupyter notebook shown in the video is available to download here. Note, you will need to install the requirements.txt file in the repository to make sure yo have all the relevant Python packages needed to run the code.