Agentic Adaptive RAG
What it is
A Python framework built on LangGraph that implements an adaptive Retrieval-Augmented Generation (RAG) pipeline. It dynamically routes queries to local vector stores, web search, or direct LLM generation based on complexity, incorporating self-correcting loops for relevance grading and hallucination detection.
The problem it solves
Traditional RAG systems often fail on complex queries or produce hallucinated answers because they use a static retrieval strategy and lack mechanisms to verify the quality of generated responses.
What you could use it for
- Building enterprise Q&A bots that need to verify answers against internal documents
- Creating research assistants that can switch between offline knowledge bases and live web data
- Developing LLM applications that require automated quality assurance and hallucination mitigation
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