Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents
We introduce Agentic AutoRAG, an LLM-agent optimizer for multi-objective RAG hyperparameter optimization with retrieval-versus-generation failure attribution.
Key points
- Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge.
- However, configuring a pipeline is an expensive hyperparameter optimization problem over many interacting choices, from chunking and embedding model to reranking and generation.
- Existing optimizers, from greedy search to Bayesian optimization, reduce each trial to an aggregate score and search without modeling why a configuration performed as it did, even though the retrieved chunks already provide evidence about whether each failure occurred during retrieval or after it.
- It proposes configurations scored on a frozen exam from the corpus: after each trial a Diagnoser attributes each failed question to retrieval or generation, and a Proposer, grounded in a knowledge base of model rankings and pricing, selects the next configuration, weighing accuracy against cost to trace a Pareto frontier.
Sources (1)
- [1]Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 02:38 PM
We introduce Agentic AutoRAG, an LLM-agent optimizer for multi-objective RAG hyperparameter optimization with retrieval-versus-generation failure attribution.
Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge.
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