Piconix - F1 Data Analysis Service

A NestJS-based service that provides Formula One data analysis using OpenAI and Llama AI models.

Overview

Piconix is a specialized service that combines the power of OpenAI's GPT-4 and Llama AI to provide intelligent analysis of Formula One data. The service processes natural language queries about F1 data and returns concise, accurate responses.

Features

• Natural language processing of F1-related queries
• Integration with OpenAI GPT-4 for initial query processing
• Llama AI integration for data extraction and response refinement
• Structured data handling with Prisma ORM
• Docker support for easy deployment

Services

OpenAI Service

The OpenAIService provides integration with OpenAI's API, offering the following capabilities:

• Chat Completion: Process natural language queries using GPT-4
• Database Query: Extract structured data from the database based on user questions
• Audio Transcription: Convert speech to text using Whisper
• Text-to-Speech: Convert text responses to speech

Example usage:

@Injectable()
export class YourService {
  constructor(private openAIService: OpenAIService) {}

  async processQuery(question: string) {
    const result = await this.openAIService.fetchDbAnswer({ userQuestion: question });
    // Process result...
  }
}

Llama Service

The LlamaService provides integration with Llama AI, specializing in:

• Chat Completion: Process queries using Llama's language model
• JSON Answer Extraction: Extract and format answers from structured data
• Response Validation: Ensure response quality and format

Example usage:

@Injectable()
export class YourService {
  constructor(private llamaService: LlamaService) {}

  async getAnswer(question: string, data: any) {
    const result = await this.llamaService.extractJsonAnswer({
      userQuestion: question,
      result: data
    });
    // Process result...
  }
}

Prerequisites

• Node.js (v16 or higher)
• Docker and Docker Compose (for containerized deployment)
• OpenAI API key
• Llama API key

Environment Variables

Create a .env file in the root directory with the following variables:

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key

# Llama Configuration
LLAMA_API_KEY=your_llama_api_key
LLAMA_API_URL=https://api.llama.com/v1

# Database Configuration (if using Prisma)
DATABASE_URL=your_database_url

Installation

1. Clone the repository:

git clone [repository-url]
cd piconix

2. Install dependencies:

npm install

3. Set up the database (if using Prisma):

npx prisma generate
npx prisma migrate dev

Development

Start the development server:

npm run dev

The server will start in watch mode, automatically reloading when changes are detected.

Building

Build the project:

npm run build

Running in Production

Start the production server:

npm run start:prod

Docker Deployment

Build and run using Docker Compose:

docker-compose up --build

API Usage

The service provides endpoints for F1 data queries. Here's an example of how to use it:

// Using OpenAI Service
const openaiResponse = await openAIService.fetchDbAnswer({
    userQuestion: "Who won the Monaco Grand Prix in 2023?"
});

// Using Llama Service
const llamaResponse = await llamaService.extractJsonAnswer({
    userQuestion: "Who won the Monaco Grand Prix in 2023?",
    result: openaiResponse.data
});

Testing

Run the test suite:

# Unit tests
npm run test

# E2E tests
npm run test:e2e

# Test coverage
npm run test:cov

Project Structure

src/
├── llama/           # Llama AI integration
│   ├── llama.ts     # LlamaService implementation
│   └── types.ts     # Llama-specific types
├── openai/          # OpenAI integration
│   ├── openai.service.ts  # OpenAIService implementation
│   ├── functions.ts       # OpenAI function definitions
│   └── types.ts          # OpenAI-specific types
├── prisma/          # Database schema and migrations
└── test/            # Test files

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.