Project Repo Intro

Your application uses the following stack:

• Next.js – full-stack React framework by Vercel
• AI SDK – LLM interaction and streaming
• Gemini 2.5 Flash – default model (easily swappable)
• JSON files – data persistence (no database setup required)
• Real-time chat interface with memory management and email search

Steps To Complete

Understand the Project Structure

• Run pnpm install to install all dependencies

• Run pnpm dev to start the development server

• Open localhost:3000 in your browser to see the UI

Explore what's already built: the chat interface on the left, the memories section with the plus button, and the data tab at the bottom.

Explore the Email Dataset

• Navigate to data/emails.json and open it

This contains 547 pre-generated emails for a fictional person named Sarah Chen. The data includes varying lengths, topics, and automated content to feel realistic.

• Use the UI's data tab to search through the emails

This lets you understand the dataset and test retrieval algorithms without calling the LLM.

Review the Main Application File

• Open app/page.tsx (the main front page)

This is where the app loads chats and memories, and renders the sidebar, top bar, and chat component.

• Understand the basic persistence hooks

Look for functions like loadChats(), saveChats(), and createChats(). These all write to a single JSON file instead of a database.

Study the Core Chat Logic

• Open app/api/chat/route.ts

This is the most important file in the codebase. The POST function here is where most of your work will happen.

• Find the createUIMessageStream() wrapper

This handles generating chat titles, appending messages, and persisting data.

• Locate the streamText() call

This is where the LLM generates responses and writes them to the UI message stream. Currently using Gemini 2.5 Flash, but you can switch models here.

Understand the Component Architecture

• Explore the component files that make up the UI

Review the sidebar component, top bar component, and chat component to see how the interface is structured.

• Check how the useChat() hook is being used

This hook manages chat state and persistence - it's what you learned about in the AI SDK crash course.

Explore the Persistence Layer

• Open the persistence layer functions (look for loadChats, saveChats, etc.)

Understand that these currently use JSON file operations, but could easily be swapped out for Postgres or similar in production.

• Note what would need to change to move to a real database

This will help you understand the architecture's flexibility for production deployments.