AutoRAG and fastRAG
AutoRAG and fastRAG are complementary tools: AutoRAG provides automated evaluation and optimization of RAG pipelines through hyperparameter tuning and experiment management, while fastRAG focuses on efficient runtime execution of retrieval and generation components, so they address different stages of the RAG development lifecycle and can be used together.
About AutoRAG
Marker-Inc-Korea/AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
Provides end-to-end RAG pipeline optimization through YAML-driven configuration, encompassing document parsing, semantic chunking, and QA dataset generation with support for multiple parsing/chunking strategies simultaneously. Uses grid-search and metric-driven evaluation across retriever-generator combinations to identify optimal module configurations, with results tracked in a dashboard for deployment-ready pipeline export. Integrates with LlamaIndex, LangChain, and local embedding models, supporting both cloud APIs (OpenAI) and GPU-accelerated inference for custom models.
About fastRAG
IntelLabs/fastRAG
Efficient Retrieval Augmentation and Generation Framework
**Technical Summary:** Built on Haystack v2, fastRAG provides optimized RAG components including ColBERT with PLAID indexing for token-level late interaction, Fusion-in-Decoder for multi-document generation, and REPLUG for improved decoding. It integrates with Intel hardware acceleration (IPEX, Optimum-Intel, Optimum-Habana) and alternative inference backends like ONNX Runtime, OpenVINO, and Llama-CPP, enabling efficient LLM inference on Xeon processors and Gaudi accelerators. The framework bundles quantized embedders, sparse rerankers, and vector stores (FAISS, Qdrant, Elasticsearch) as drop-in Haystack components.
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