Rig
rig.rs
Rig helps Rust developers build AI apps with Large Language Models.
Описание
Build AI agents in Rust · Rig is a Rust library for building LLM-powered applications and AI agents — one unified API across 20+ providers, with type-safe tools, structured output, and production-ready performance. · Add Rig in one line · Simple, powerful APIs · One interface. Every provider. · What developers build with Rig
Rig is an open-source Rust framework for creating LLM-powered applications. It offers a consistent API across different LLM providers, advanced AI workflow abstractions, and type-safe interactions. Rig enables developers to build everything from simple chatbots to complex RAG systems and multi-agent setups with ease and efficiency.
Возможности
Unified LLM interface
Advanced AI workflow abstractions
Type-safe LLM interactions
Seamless vector store integration
Flexible embedding support
High-performance Rust implementation
Сценарии использования
Building AI agents for specific tasks (e.g., flight search, research assistance)
Implementing RAG (Retrieval-Augmented Generation) systems
Creating multi-agent AI applications
Developing chatbots and conversational AI
Constructing semantic search and recommendation systems
Частые вопросы
Rig is a Rust library designed to help developers build full-stack AI applications utilizing Large Language Models (LLMs) with an emphasis on portability, modularity, and lightweight design.
Rig's main features include full support for LLM completion and embedding workflows, common abstractions over various LLM providers like OpenAI and Cohere, vector store integrations such as MongoDB and in-memory for embeddings and retrieval, minimal boilerplate code required to integrate LLMs in applications, support for advanced AI workflows like Retrieval-Augmented Generation (RAG), and efficient, thread-safe memory management leveraging Rust's safety.
Rig abstracts over several LLM providers including OpenAI and Cohere using common API traits to allow easy switching or integration of multiple providers in the same project.
You create a client from environment variables and build an agent for a specific model (e.g., GPT-4). Then, you send prompts asynchronously and handle responses. This code requires the OPENAI_API_KEY environment variable.
Rig supports complex workflows including completion and embedding requests, Retrieval-Augmented Generation (RAG) systems that combine retrieval and generation, and audio generation workflows via abstracted providers.
Rig is chosen over other AI libraries because it is developer-friendly with clear API design and good documentation, offers a unified API across providers which simplifies multi-provider or provider-switching projects, provides efficient memory and thread management suited for concurrent requests, and is extensible for customization and complex AI agent creation.
Comprehensive API documentation is available on docs.rs under the rig-core package. GitHub repositories and examples are also provided for getting started.
Характеристики
| Тип | Агент |
| Категория | Инфраструктура и MLOps |
| Цена | бесплатно (от $0/мес) |
| Платформа | Только API |
| Системы | api, web |
| Для кого | Individual |
| Язык сайта | en |
| Рейтинг | 0.00 (0 отзывов) |
| Просмотры | 757 |
| Запуск | 2025-01-12 |
