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.

• 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

.
├── 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

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

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

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

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

[Insert License Information]

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

For support, please [Insert Support Information]

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