ScrapegraphLib Examples

This repository contains example implementations and usage patterns for ScrapegraphLib across various AI model providers and platforms. It serves as a comprehensive resource for developers looking to integrate different AI models into their applications using ScrapegraphLib.

🌟 Features

• Examples for 15+ AI providers including:
  • OpenAI
  • Anthropic
  • Google (VertexAI & GenAI)
  • Azure OpenAI
  • Mistral
  • Together AI
  • Hugging Face Hub
  • AWS Bedrock
  • OneAI
  • Moonshot
  • Nemotron
  • Groq
  • Fireworks
  • Deepseek
  • Ernie
  • Local Models

📁 Repository Structure

.
├── anthropic/          # Claude model examples
├── azure/              # Azure OpenAI integration examples
├── bedrock/           # AWS Bedrock implementation examples
├── deepseek/          # Deepseek AI model examples
├── ernie/             # Ernie model integration
├── fireworks/         # Fireworks AI examples
├── google_genai/      # Google GenerativeAI examples
├── google_vertexai/   # Google VertexAI implementation
├── groq/              # Groq model examples
├── huggingfacehub/    # Hugging Face Hub integrations
├── local_models/      # Examples for running local models
├── mistral/           # Mistral AI implementation examples
├── moonshot/          # Moonshot AI examples
├── nemotron/          # Nemotron model examples
├── oneapi/            # OneAPI integration examples
├── openai/            # OpenAI GPT models examples
├── together/          # Together AI implementation
├── benchmarks/        # Performance benchmarking tools
├── integrations/      # Additional integration examples
├── model_instance/    # Model instance configurations
├── single_node/       # Single node deployment examples
└── extras/            # Additional utilities and examples

🚀 Getting Started

Each directory contains specific examples for different AI providers. To get started:

1. Choose the AI provider you want to work with
2. Navigate to the corresponding directory
3. Follow the README instructions in that directory for setup and usage

📊 Benchmarks

Performance benchmarks are available in the benchmarks/ directory, comparing:

• Response times
• Token usage
• Cost analysis
• Hardware performance comparisons

Sample Benchmark Results

Local Models (Mistral on Ollama with nomic-embed-text)

Hardware	Example 1	Example 2
Macbook pro 14' m1	11.60s	26.61s
Macbook pro 16' m2 max	8.05s	12.17s

🛠 Prerequisites

• Python 3.8+
• Required API keys for specific providers
• Docker (for containerized examples)
• Ollama (for local model examples)

📝 License

[Insert License Information]

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📫 Support

For support, please [Insert Support Information]

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Note: This repository contains examples for ScrapegraphLib. For the main library documentation, please visit [Insert Main Library Link]