Build a Generative AI Chatbot Using a Vector Database for Custom Data (RAG)
In this video we'll build a generative AI chatbot that can answer questions based on your own custom data. We'll walk through how to use Retrieval Augmented Generation (RAG) to query OpenAI's LLM using a vector database, showing you how to set up the system and test the chatbot end-to-end.
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