OSRepos

Griptape: Modular Python Framework for AI Agents and Workflows

Griptape is a modular Python framework designed to simplify the development of generative AI applications. It provides a flexible set of abstractions for working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and various other AI components. With its structured approach, Griptape enables developers to build sophisticated AI agents and workflows efficiently.

RouteLLM: Optimize LLM Costs and Maintain Quality with Intelligent Routing

RouteLLM is a powerful framework designed to serve and evaluate LLM routers, enabling significant cost savings without compromising response quality. It intelligently routes simpler queries to cheaper models while maintaining high performance, offering a drop-in replacement for existing OpenAI clients or a compatible server. This solution helps balance the dilemma of LLM deployment costs versus model capabilities.

Memoripy: An AI Memory Layer for Context-Aware Applications

Memoripy is a Python library designed to provide an AI memory layer for context-aware applications. It offers both short-term and long-term storage, semantic clustering, and optional memory decay. This robust tool helps AI systems manage and retrieve relevant information efficiently, supporting various LLM APIs like OpenAI and Ollama.

minimaxir-simpleaichat

RAGChecker: A Fine-grained Framework for Diagnosing RAG Systems

RAGChecker is an advanced automatic evaluation framework developed by Amazon Science, specifically designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It offers a comprehensive suite of metrics and tools for in-depth analysis of RAG performance. This framework empowers developers and researchers to thoroughly evaluate and enhance their RAG systems with precision.

rerankers: Unified API for Reranking and Cross-Encoder Models

rerankers is a lightweight, low-dependency Python library that provides a unified API for various reranking and cross-encoder models. It simplifies the integration of different reranking approaches into retrieval architectures, offering a consistent interface for diverse models like cross-encoders, RankGPT, T5, and API-based rerankers. This library aims to make reranking more accessible and easier to implement for developers.