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AUTORAG
AutoML for RAG — automatically find the optimal pipeline for your data
Apache-2.0
ABOUT
Building a RAG system requires choosing from dozens of possible module combinations — retrievers, rerankers, chunk sizes, embedding models, and generators — and the optimal combination varies by dataset and use case. Manually testing all permutations is tedious, slow, and error-prone. AutoRAG automates this with AutoML-style evaluation, running controlled experiments across module combinations, scoring results, and identifying the pipeline that delivers the best performance for your specific data and requirements.
INSTALL
pip install AutoRAGINTEGRATION GUIDE
1. Automatically evaluate and select the optimal RAG pipeline configuration for enterprise document Q&A systems without manual trial-and-error
2. Benchmark different embedding models, retrievers, and generators against your proprietary dataset to maximize retrieval accuracy and answer quality
3. Continuously optimize production RAG pipelines by re-running evaluations when new modules, models, or chunking strategies become available
4. Support academic research with reproducible, data-driven RAG pipeline optimization experiments across standardized benchmarks
TAGS
ragautomlevaluationoptimizationbenchmarkpipelinepython