Build a Generative AI Chatbot Using a Vector Database for Custom Data (RAG)
This video walks through the process of building a generative AI chatbot that leverages Retrieval Augmented Generation (RAG) to query OpenAI's Large Language Model using a vector database. Viewers will learn how to set up this system, encode custom data, and test both the vector database and the resulting chatbot functionality.
Description
This video demonstrates how to use Retrieval Augmented Generation (RAG) to query OpenAI's Large Language Model (LLM) using a Vector database. This technique leverages the language understanding and summarization capabilities of Generative AI while introducing semantic understanding of our own data.
In this video I walk through building a chatbot using OpenAI GPT models, Pinecone, and Python Notebooks.
This video is a walk-through of a post available at https://robkerr.ai/generative-ai-chatbot-grounding-data-vector-text/
Quick Links:
0:00 Introduction
0:52 Setup
1:55 Source Data
3:14 Pinecone Index
6:43 Vector Embeddings
6:53 Encoding Content
9:54 Testing Vector Database
12:13 Testing Chatbot