Rig is a Rust library designed for building scalable, modular, and ergonomic LLM-powered applications. It provides abstractions over various LLM providers and vector stores, aiming to minimize boilerplate code for developers integrating AI capabilities into their projects. The library is suitable for engineers and researchers working with large language models who need a flexible and performant framework.

How It Works

Rig utilizes a Rust-centric approach, offering common abstractions for LLM completion and embedding workflows. It supports multiple model providers (like OpenAI) and vector stores (including MongoDB, SQLite, and in-memory solutions) through companion crates. This modular design allows users to select and integrate only the necessary components, promoting lightweight and efficient application development.

Quick Start & Requirements

• Install: cargo add rig-core
• Prerequisites: Rust toolchain, OPENAI_API_API_KEY environment variable for OpenAI examples. tokio with macros and rt-multi-thread features is recommended for async execution.
• Documentation: docs.rig.rs
• Examples: Available in each crate's examples directory.

Highlighted Details

• Full support for LLM completion and embedding workflows.
• Common abstractions over LLM providers (OpenAI, Cohere) and vector stores (MongoDB, SQLite, LanceDB, Neo4j, Qdrant, SurrealDB).
• Minimal boilerplate for LLM integration.

Maintenance & Community

The project is actively developed, with a warning about potential breaking changes due to rapid feature development. Migration paths will be provided. Community contributions are encouraged via GitHub issues and a feedback form.

Licensing & Compatibility

The README does not explicitly state the license. However, the project is hosted on GitHub, implying a standard open-source license. Compatibility for commercial use or closed-source linking would depend on the specific license chosen.

Limitations & Caveats

The project explicitly warns of "dragons" and upcoming breaking changes, indicating an evolving API. Users should be prepared for potential migration efforts as new features are introduced.