The rise of GraphRAG has shown how important structure is for building reliable AI systems, but many teams run into the heavy lift of managing a full graph ontology or data representation. The BM25-based Document RAG Agent offers a middle ground. It keeps the clarity and grounding that people like in GraphRAG, but without the operational overhead… And it avoids the problems that show up in pure VectorRAG pipelines, where meaning is flattened and explainability disappears. This teaser walks through why structure matters, how BM25 fits into the picture, and why many teams are looking for alternatives that give them more control over retrieval.

These ideas fit neatly into the larger trend of teams pushing for trustworthy AI. More organizations want systems that can show their work, document their decisions, and operate in a predictable way. Anyone tracking the movement around AI governance, compliance, or domain-aware retrieval has probably seen the excitement around GraphRAG. The BM25-based Document RAG Agent (or what I am calling DocumentRAG) offers a practical option that captures many of those benefits while staying lightweight.

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