Rig

Rig is a framework for building large language model applications, featuring a multi-provider client and a workflow builder for retrieval-augmented generation systems. It serves as an orchestrator for creating autonomous agents that can maintain conversation state and execute complex tasks through custom prompting and plugins.

The project provides standardized interfaces for both completion and embedding model providers, allowing for unified request and response patterns across different engines. It also includes a vector database integration layer that defines a common interface for indexing and retrieving high-dimensional embeddings across various storage backends.

Its broader capabilities cover generative AI workflows for multimedia content production and tools for unstructured data extraction, including sentiment analysis and text classification. The framework supports modular composition, enabling the integration of third-party plugins and custom provider implementations.

Features

LLM Application Frameworks - Serves as a comprehensive framework for building LLM-powered applications and agentic RAG systems.

Autonomous AI Agents - Provides a framework for building autonomous agents that leverage knowledge bases and model wrappers to solve complex tasks.

AI Agent Development - Provides a complete environment for creating autonomous entities that use knowledge bases and maintain conversation state.

Autonomous Agent Orchestration - Offers a framework for building autonomous agents that maintain state and execute multi-step workflows.

Agentic Retrieval Workflows - Ships a system for constructing retrieval-augmented generation workflows and agent constructs using specialized clients.

RAG Pipelines - Implements retrieval-augmented generation systems by connecting vector databases and language models to process unstructured data.

Multi-Provider Abstractions - Implements a unified interface that abstracts multiple completion and embedding model providers.

Multi-Model AI Orchestrators - Manages multiple completion and embedding model providers through a single unified interface to standardize requests.

Retrieval Augmented Generation Pipelines - Combines vector store retrieval with language model prompting to ground AI responses in external knowledge bases.

LLM Completion Interfaces - Standardizes request and response formats across multiple AI model providers to enable a single API call for different engines.

Stateful Agent Orchestration - Enables the development of AI agents that maintain conversation state and handle multi-turn streaming.

Vector Database Integrations - Provides a standardized interface to route embedding queries across various high-dimensional vector database backends.

Unified Model Wrappers - Provides a standardized interface to unify request and response patterns across multiple completion and embedding model providers.

Custom Provider Implementations - A framework for extending model capabilities by implementing specific traits for custom model providers or vector stores.

Provider Abstraction Layers - Defines common traits for language models and vector stores to allow swapping backends without changing application logic.

AI Pipeline Compositions - Wraps model providers and retrieval clients into reusable components to build complex AI pipelines.

Artificial Intelligence - Library for building modular, scalable LLM-powered agents.

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Features

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