Complete Vercel AI Masterclass

Explore retrieval augmented generation (rag) and its role in supplying up-to-date data to large language models; learn how data containers, vectors, and open versus closed resources influence accuracy.

Explore how vector embeddings convert text into multi-dimensional vectors, cluster words by similarity, and store them in vector databases like Pinecone or ChromaDB to improve responses and reduce token size.

Explore Vercel AI, an open-source framework built on Next.js that lets developers integrate LLM models, RAG, and embeddings with a single Vercel AI SDK, enabling streaming generation.

Set up the Versal ai sdk to integrate multiple ai models through a single gateway endpoint, install and configure the ai package, and run a quick framework-agnostic integration.

Discover how streaming text with the streamText method delivers AI responses incrementally, word by word or line by line, and how to loop through partial outputs for readable terminal results.

Explore how to generate structured outputs for AI prompts, using arrays of objects with ingredients, instructions, and serve sections, and validate schemas with VersaLiAi and Zod or JSON schema.

Compare partial output streaming with text streaming to improve AI response quality, and learn to deliver final at-once responses using process.stdout.write.

Learn about inbuilt tool calling properties, including strict, tool choice, and auto, none, or required modes, to ensure your model follows the defined schema and handles real-time data.

Learn how to implement multi-step tool calling with an approval gate in the Varsalya SDK, using tools like getWeather and sendEmail, with needApproval to await user permission.

Build an askApproval function with readline to capture user input and return a boolean. Enable a three-step tool workflow (getWeather, sendEmail) with mandatory user approval and a unique approval id.

Integrate text embeddings by using aisdk to convert text into multi-dimensional vectors with OpenAI's text embedding 3 small, adjust dimensions, and inspect embeddings and usage tokens in code.

Explore the rag workflow for document-based answering, from converting text into chunks and embeddings to storing vectors in a vector database and retrieving context with cosine similarity or euclidean distance.

Explore V0, an AI agent-based platform acting as an AI pair programmer beyond generative models, and review pricing, including a free plan with $5 credits and seven messages per day.

Preview your app, edit code in a VS Code–style sandbox, connect to Neon for accounts, sessions, and todos, and use design mode to apply commits and UI changes with diffs.

Develop a feature-rich chatbot project showcasing tool calling, file and image uploads, knowledge base with document chunking and embedding into a vector database.

Create a next.js project named chatbot-app, open it in VS Code, and organize code by adding a root lib folder for helper functions following best practices.

Implement a TypeScript text chunking utility for RAG pipelines, defining chunk size and overlap, cleaning input, splitting by punctuation and newlines, and building connected chunks.

Design and implement a local vector store to hold embeddings in vectorstore.ts, using cosine similarity to retrieve relevant chunks and prepare for migrating to a hosted database.

Create api/chat, api/document, and api/upload routes with route.ts, implement a post chat endpoint using streamText, system prompt, convert to model message, and set max duration to avoid vercel timeouts.

Create document upload api routes in route.ts, implementing get to list documents and delete by id using a vector store, and return structured json with next server responses.

Define upload routes for knowledge base files, validate text/plain and text/markdown up to 5 mb, extend duration to 60 seconds, and process with chunking, embedding, vector store, and sdk ui.

Build and connect a responsive chat interface using ai-sdk-react’s useChat in chat.tsx, wire to api/chat, and integrate tool calls, tailwind styling, and client-server communication.

Builds a knowledge base interface by fetching documents with a React API, managing state with useCallback and useEffect, and enabling md or txt uploads and deletions in Next.js.

Show how to render AI messages with message.tsx, including text, images, and tool cards for knowledge base, weather, and email. Connect knowledge base and chat components on the landing page.

Deploy the application on Vercel by importing the git repo and setting OpenAI keys. Verify ai features and tool calling; external hosting is needed for the vector database.

Complete vercel ai masterclass closes by showing how to create a VersaLEI project, cover core concepts, and migrate a local database to production using NEON, serverless postgres, and pgVector.