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AI Response Analysis Dashboard

A Next.js application for analyzing responses from various AI language models. The initial implementation focuses on analyzing emoji usage patterns across different response styles, but the architecture is designed to be extensible for other types of analysis.

Features

  • Support for multiple LLM providers (Anthropic, Google, OpenAI)
  • Configurable analysis parameters
  • Interactive data visualization
  • Extensible architecture for different types of analysis
  • Real-time response processing and analysis

Getting Started

Prerequisites

  • Node.js 18.0.0 or later
  • npm or yarn
  • API keys for the LLM providers you want to use

Installation

  1. Clone the repository:

    git clone <repository-url>
cd emoji-testing

  2. Install dependencies:

    npm install

  3. Create a .env.local file in the root directory with your API keys:

    NEXT_PUBLIC_ANTHROPIC_API_KEY=your_anthropic_key
NEXT_PUBLIC_GOOGLE_API_KEY=your_google_key
NEXT_PUBLIC_OPENAI_API_KEY=your_openai_key

  4. Start the development server:

    npm run dev

  5. Open http://localhost:3000 in your browser.

Project Structure

src/
├── app/                    # Next.js app directory
├── components/            
│   ├── analysis/          # Analysis-specific components
│   └── dashboard/         # Dashboard components
├── lib/
│   ├── analysis/          # Analysis implementations
│   ├── llm/               # LLM provider implementations
│   └── types/             # TypeScript type definitions

Adding New Analyses

To add a new type of analysis:

  1. Define your analysis configuration in src/lib/analysis/:

    export const myAnalysisConfig: AnalysisConfig = {
  name: 'My Analysis',
  description: 'Description of what this analysis does',
  models: [...],
  promptCategories: [...],
  promptVariables: [...],
  responseAttributes: [...],
  promptFunction: (categories, variable) => {...},
};

  2. Update the dashboard components to support your new analysis type.

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.