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AI Search Application

Description

This is an AI-powered search application built with Svelte and the Vercel AI SDK, providing an efficient and customizable search experience. It uses Tavily, a powerful tool built for integrating Vercel AI SDK functionality, and is configured to work with Claude as the default AI model. The application is flexible and can also support other AI models with minimal adjustments.

This project delivers a fast, intelligent, and modern AI search solution that can be deployed on Vercel, leveraging scalable and serverless capabilities to handle various use cases.

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Tech Stack

Core Technologies

1. Svelte: A highly reactive and lightweight JavaScript framework, enabling faster development and reactivity.
2. Vercel AI SDK: Used to integrate AI-based functionalities seamlessly into the application.
3. Tavily: Search engine api.
4. DaisyUI: CSS component library for Tailwind CSS

   AI Models

• Deepseek: The default AI model used for the search functionality.
• Claude: Another example model which is included
• Extensible with Other Models: The AI integration is not limited to Claude; other models can be configured as per the requirements.

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Features

• Powerful AI Search: Delivers intelligent, precise, and quick search capabilities.
• Extensibility: Switch from Claude to other AI models based on project requirements.
• Modern Tech Stack: Built using Svelte and optimized for Vercel deployments.
• Serverless Scalability: Deployed on Vercel for fast, serverless infrastructure handling.

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Prerequisites

Before setting up the project, ensure the following requirements are met:

1. Node.js is installed on your system. (Recommended: Latest LTS version)
2. A Vercel account is ready for deployment. You can also use another deploment methods.
3. An API key for accessing the Tavily AI SDK and a API Key for your AI Model.
4. If you want to work with credits, set up a redis database

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Installation

1. Clone the Repository

git clone https://github.com/tguelcan/sengine
cd ai-search-app

2. Install Dependencies

After navigating to the project directory, install the required dependencies:

npm install

3. Configure Environment Variables

Duplicate the .env.example file and rename it to .env. Then configure your environment variables:

# Required API Key for Tavily
TVLY_API_KEY=<your-tavily-api-key>

# REDIS_URL is required for credit use if desired.
KV_URL=<your-redis-db-url>

# - AI MODELS
ANTHROPIC_API_KEY=<your-anthropic-key>
# OR
DEEPSEEK_API_KEY=<your-deepseek-key>
# OR other like openai...

Make sure to replace <your-tavily-api-key> with your actual Tavily API key. Additional variables, if needed, are listed in the .env.example file.

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Getting Started

Start the development server locally:

npm run dev

The application will be available at: http://localhost:5173

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Building for Production

To create a production-ready build of your application:

npm run build

You can preview the production build using:

npm run preview

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Environment Variables

The application uses environment variables to manage API keys and other sensitive information. Below is the required variable:

• TVLY_API_KEY: API key for accessing Tavily and the Vercel AI SDK.

Ensure all required variables are properly configured in the .env file before running or deploying the application.

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Extending AI Models

Although Claude is the default model, this project supports other AI models via the Vercel AI SDK. To switch or add models, extend the integration in the src/lib files as needed.

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Contributing

Contributions to the project are welcome! To contribute:

1. Fork the Repository on GitHub.

2. Create a feature branch:

   git checkout -b feature/your-feature-name
   

3. Commit your changes and push to the branch.

4. Submit a pull request.

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License

This project is licensed under the MIT License.

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Support

For any issues or inquiries, feel free to open an issue or contact the repository owner directly.