Build a Next.JS Answer Engine with Vercel AI SDK, Groq, Mistral, Langchain, OpenAI, Brave & Serper
Table of Contents
Introduction
In this tutorial, you will learn how to build a Perplexity-style Large Language Model (LLM) answer engine using Next.js and various cutting-edge technologies like Vercel AI SDK, Groq, Mistral AI, Langchain, OpenAI, Brave, and Serper. This guide will take you through both frontend and backend development to create a comprehensive answer engine from scratch.
Step 1: Setting Up the Project
- Clone the Repository
- Open your terminal and run the following command to clone the project repository:
git clone https://git.new/answr
- Open your terminal and run the following command to clone the project repository:
- Install Prerequisites
- Navigate into the cloned directory:
cd answr
- Install the required dependencies using npm or yarn:
ornpm install
yarn install
- Navigate into the cloned directory:
Step 2: Acquiring Necessary API Keys
- Get API Keys
- Sign up for accounts on the required platforms (OpenAI, Brave, Serper, etc.).
- Access the API keys from the respective dashboard and keep them handy.
- Ensure you have keys for:
- OpenAI (for embeddings)
- Brave (for web searches)
- Serper (for search engine API)
Step 3: Diving into Backend Development
- Start with Actions
- Set up your backend to handle requests and connect to the APIs.
- Create an actions file (e.g.,
actions.js
) to define functions for:- Fetching data from OpenAI.
- Integrating Groq's inference API.
- Making calls to Brave and Serper for search functionalities.
- Example function to fetch embeddings:
async function fetchEmbeddings(text) { const response = await fetch('https://api.openai.com/v1/embeddings', { method: 'POST', headers: { 'Authorization': `Bearer YOUR_API_KEY`, 'Content-Type': 'application/json' }, body: JSON.stringify({ input: text }) }); return response.json(); }
Step 4: Exploring Frontend Development
- Components and State Management
- Build the frontend using React components.
- Use state management (like Context API or Redux) to manage application state.
- Create components for:
- User input
- Displaying answers
- Loading states
- Ensure each component is reusable and modular.
Step 5: Quick Overview of Custom Components
- Create Custom Components
- Develop custom components to enhance UI/UX.
- Implement features like:
- Dynamic input fields for queries.
- Response display area that formats data from the backend.
- Use CSS or a UI framework for styling to make the application visually appealing.
Conclusion
You have now set up a Perplexity-style LLM answer engine using Next.js and integrated various powerful APIs. Key steps included setting up your project, acquiring API keys, developing backend actions, and creating a responsive frontend. Moving forward, experiment with additional features such as advanced search capabilities or integrating other AI models. Enjoy building and enhancing your application!