Coord is a Go library designed to simplify interactions with various AI services, providing a unified interface for Large Language Models (LLMs), Text-to-Speech (TTS) systems, and Embedding models.
This allows developers to seamlessly integrate and utilize different AI services without grappling with the complexities of each provider's specific APIs and requirements.
- Unified Interface: Interact with LLMs, TTS, and Embedding models using a consistent API, reducing code complexity and learning curves.
- Abstraction: Coord handles the intricacies of model communication, data formatting, and result processing, letting you focus on your application logic.
- Flexibility: Easily switch between different LLM, TTS, or Embedding providers without significant code changes.
Coord is ideal for a wide range of AI-powered applications, including:
- Chatbots and Conversational AI: Build interactive chatbots that leverage the power of LLMs for natural language understanding and generation.
- Content Generation: Generate high-quality text, articles, summaries, and more using various LLM providers.
- Speech Synthesis: Integrate natural-sounding speech into your applications with support for different TTS engines.
- Semantic Search and Recommendation: Utilize embedding models to power features like semantic search, similarity comparisons, and personalized recommendations.
- Provides a standardized way to interact with various LLMs.
- Supports streaming responses, chat history management, and function calling for enhanced interaction design.
- Offers a unified interface for text-to-speech synthesis.
- Supports different audio formats (MP3, WAV, OGG, etc.) for flexible output.
- Simplifies working with embedding models for text representation.
- Supports various embedding tasks, including semantic similarity, classification, and clustering.
- Installation:
go get -u github.com/lemon-mint/coord - Documentation: https://pkg.go.dev/github.com/lemon-mint/coord
- Examples: Explore the examples directory for practical implementations.
Contributions to Coord are welcome! Please submit issues or pull requests to help improve and expand the library.