Explore a comprehensive 36-minute video featuring a live code review of the Pinecone Vercel starter template and Retrieval Augmented Generation (RAG). Dive deep into building an AI chatbot less prone to hallucination, starting with an explanation of RAG and its importance in Generative AI applications. Follow along as the hosts step through the code, explaining major components, TypeScript benefits, and the implementation of a recursive web crawler. Learn about converting documents to embeddings, vector databases, and practical RAG applications. Gain insights into reading TypeScript signatures, working with OpenAIStream responses, and initializing the Pinecone client. Understand the getContext method, metadata handling, and the intricacies of the Crawler component. Perfect for those learning about Generative AI, building chatbots, or seeking to improve AI accuracy with proprietary data.
Live Code Review - Pinecone Vercel Starter Template and Retrieval Augmented Generation
Overview
Syllabus
0:00 - What is a Vercel template?
1:18 - How to find the template
1:51 - What is hallucination?
2:56 - Example of hallucination in a Generative AI application
5:18 - What is Retrieval Augmented Generation? Diagram
10:17 - How you can modify and deploy this template yourself
Start of the live stream recording
12:04 - Reading TypeScript signatures
14:20 - OpenAIStream responses
15:55 - What are embeddings? How do we get them?
19:55 - Initializing the Pinecone client API key & environment
21:29 - Getting matches from Pinecone vector database
23:02 - The getContext method and how it works / Metadata
26:48 - Converting the user's query into embeddings
27:40 - Summarizing what we have built so far
28:59 - The Crawler component - how it works - HTML semantics
33:26 - Zack presses Roie on how this Crawler was REALLY built :
Taught by
Pinecone
