Creating a Chat Tool Using OpenAI Models and Pinecone

Course details

Embeddings and vector databases allow developers to create tools that can retrieve knowledge from custom documents and use it to form more accurate and dynamic conversations. But while cutting-edge AI models like ChatGPT can generate useful conversational responses to many different kinds of queries, the replies are always limited to the data that was available when the model was last trained. In this course, instructor Guil Hernandez offers an overview of text embeddings, vector databases, and retrieval-augmented generation (RAG) to elevate and optimize your AI learning journey. Along the way, test out your new skills in the exercise challenges provided at the end of each section.

Learning objectives
Discover embeddings in the context of generative AI.
Set up and store embeddings in a vector database.
Utilize best practices related to retrieval-augmented generation (RAG).
Generate conversational responses from data retrieved from a vector database using OpenAI models and Pinecone.

Concepts

Introduction

Building context-aware AI tools with custom knowledge bases
What you should know about AI and JavaScript
Set up the project

Text Embeddings

The importance of embeddings in generative AI
Create embeddings with OpenAI
Pair embeddings with related text
Text chunking for embeddings
Challenge - Chunk text and create embeddings
Solution - Chunk text and create embeddings

Vector Databases

The power of vector databases
Set up a vector database with Pinecone
Store embeddings in Pinecone
What is semantic search
Send queries to Pinecone
Challenge - Insert and retrieve data from Pinecone
Solution - Insert and retrieve data from Pinecone

Conversational Responses with OpenAI and Pinecone

Retrieval-augmented generation (RAG)
Create replies and manage messages with OpenAI
Form a reply from multiple database matches
Enhance the AI's context awareness
Handling errors

Conclusion

Next steps