An open-source AutoML-style framework for evaluating and optimizing retrieval-augmented generation pipelines by automatically testing combinations of chunking, embedding, retrieval, and generation strategies.
AutoRAG — Automated RAG Pipeline Optimization
An open-source AutoML-style framework for evaluating and optimizing retrieval-augmented generation pipelines by automatically testing combinations of chunking, embedding, retrieval, and generation strategies.
Instalación lista para agent
Este activo puede instalarse después de elegir el runtime, revisar el plan y ejecutar el comando correspondiente.
npx -y tokrepo@latest install a0982619-5530-11f1-9bc6-00163e2b0d79 --target codexEjecutar después de confirmar el plan con dry-run.
Discusión
Activos relacionados
RAG Best Practices — Production Pipeline Guide 2026
Comprehensive guide to building production RAG pipelines. Covers chunking strategies, embedding models, vector databases, retrieval techniques, evaluation, and common pitfalls with code examples.
Haystack — AI Orchestration for Search & RAG
Open-source AI orchestration framework by deepset. Build production RAG pipelines, semantic search, and agent workflows with modular components. 25K+ GitHub stars.
Auto-Sklearn — Automated Machine Learning with Scikit-Learn
Auto-Sklearn is an AutoML toolkit that automatically selects scikit-learn algorithms and tunes hyperparameters using Bayesian optimization, meta-learning, and ensemble construction to build high-accuracy models.
LightRAG — Graph-Enhanced Retrieval-Augmented Generation
LightRAG integrates knowledge graphs into the RAG pipeline, enabling both low-level entity retrieval and high-level thematic search for more accurate and context-rich LLM responses.