AkariAsai/self-rag

Health v2 · maintenance only

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Adoption not part of the score

2421 stars · 227 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Original implementation of Self-RAG, a framework that trains a language model to adaptively retrieve passages on demand and critique its own generations via special reflection tokens. It includes training code, inference scripts, and pretrained 7B/13B Llama2-based models released for the ICLR 2024 paper.

Use cases

• run self-reflective retrieval-augmented generation with adaptive retrieval
• improve factuality of LLM outputs with reflection tokens
• reproduce the Self-RAG ICLR 2024 paper experiments
• fine-tune a language model to decide when to retrieve passages
• generate answers with citations and self-critique
• compare Self-RAG against standard RAG and ChatGPT baselines

When to choose

• you want adaptive on-demand retrieval instead of always retrieving fixed passages
• you need controllable generation with factuality and citation critique
• you want to reproduce or extend the Self-RAG research
• you can run Llama2-scale models on GPUs with vLLM

When to avoid

• you need a production-ready plug-and-play RAG pipeline with connectors
• you cannot host 7B/13B models locally
• you want a maintained library with frequent updates and long-term support
• you need retrieval-augmented generation without model fine-tuning

Facets

library · maturity maintenance

rag llm-inference machine-learning search-engine large-language-models artificial-intelligence python self-rag reflection-tokens research-code llama2 vllm factuality adaptive-retrieval retrieval-augmented-generation natural-language-processing gpu linux

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