Build a Next.JS Answer Engine with Vercel AI SDK, Groq, Mistral, Langchain, OpenAI, Brave & Serper

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
    

• Install Prerequisites
  • Navigate into the cloned directory:

    cd answr
    

  • Install the required dependencies using npm or yarn:

    npm install
    

    or

    yarn install
    

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!