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.

What are Rig's main features?

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.

Which LLM providers does Rig support?

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.

How do you create a simple agent or prompt a model using Rig?

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.

What kinds of AI workflows can Rig handle?

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.

Why choose Rig over other AI libraries?

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.

Where can I find documentation and examples?

Comprehensive API documentation is available on docs.rs under the rig-core package. GitHub repositories and examples are also provided for getting started.

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Rig

4.6

• 143 views

Rig is a Rust library helping developers build AI applications with Large Language Models (LLMs). It provides a consistent API for various LLM providers, making integration simple. Developers choose Rig for its efficient, type-safe tools and modular design, ensuring powerful and reliable AI systems.

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Overview Features Use Cases FAQ Integrations

About Rig

Who It's For

This tool helps Rust developers build applications with Large Language Models (LLMs). It's for those creating efficient, full-stack AI systems. Developers valuing type safety, clear APIs, and modular design will find Rig very useful.

What You Get

Rig gives you a consistent API to work with various LLM providers like OpenAI and Cohere. You get pre-built, modular components for complex AI systems such as Retrieval-Augmented Generation (RAG) and multi-agent setups. It also includes easy tools for vector stores and embeddings, key for smart search.

How It Works

Rig uses Rust's safety and strong type system to ensure your AI code is correct before it runs, avoiding errors. Its asynchronous design efficiently handles many tasks at once. You set up a client, create an "agent" for an AI model, then send prompts and manage responses smoothly.

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Features & Capabilities

⚙️ Core AI Capabilities & Integration

Provider Agnostic API

Offers a consistent API to easily integrate with various LLM providers, minimizing vendor lock-in.

Modular AI System Components

Provides pre-built, modular components for implementing complex AI systems like RAG and multi-agent setups.

Integrated Vector Store Support

Includes built-in support for vector stores to enable efficient similarity search and retrieval.

Embedding APIs

Offers user-friendly APIs for managing embeddings, vital for semantic search and content-based recommendations.

⚡ Performance & Type Safety

High Performance with Rust

Utilizes Rust's zero-cost abstractions and memory safety for efficient LLM operations.

Type-Safe LLM Interactions

Leverages Rust's strong type system to ensure compile-time correctness in LLM interactions.

Async-First Design

Employs an asynchronous design for optimal resource utilization and enhanced performance.

🚀 Development & Production Readiness

Rust Ecosystem Integration

Provides seamless integration with popular Rust ecosystem tools like Tokio and Serde.

Extensible Modular Architecture

Offers a modular design for easy customization and extension of AI applications.

Comprehensive Error Handling

Features robust error handling with custom error types for reliable production systems.

Integrated Tracing & Logging

Includes built-in support for tracing and logging to aid in debugging and monitoring.

Use Cases

Building High-Performance Retrieval-Augmented Generation (RAG) Systems

Developers often face challenges in building efficient and contextually accurate AI applications that rely on external data for Retrieval-Augmented Generation (RAG). Rig addresses this by providing modular components for RAG, integrated vector store support for efficient similarity search, and leverages Rust's performance and memory safety to create fast, reliable, and context-aware AI systems.

Enterprise SoftwareFor: AI Engineers

Streamlining Multi-Provider LLM Application Development

Integrating and managing multiple Large Language Model (LLM) providers can lead to complex codebases, vendor lock-in, and inconsistent API calls. Rig offers a consistent, unified API across various LLM providers like OpenAI and Cohere, simplifying integration and enabling seamless switching or simultaneous use of different models to reduce development overhead.

B2B SaaSFor: Software Architects

Ensuring Production-Ready and Type-Safe AI Agent Deployment

Deploying complex AI agents and advanced workflows requires high reliability, error prevention, and robust architecture. Rig ensures production readiness through Rust's strong type system for compile-time correctness, a modular and async-first design, and comprehensive error handling, allowing developers to build robust, scalable AI systems.

FinTechFor: Senior AI Developers

Powering Efficient Semantic Search and Content Recommendation

Creating intelligent search and recommendation features requires effective handling of embeddings and fast, accurate similarity search across large datasets. Rig simplifies this by offering easy-to-use APIs for working with embeddings and built-in support for various vector stores, enabling developers to build high-performance semantic search and content-based recommendation engines.

E-commerceFor: Data Scientists

Frequently asked questions

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Rig

4.6

• 143 views

Visit Website

Overview Features Use Cases FAQ Integrations