> It moves RAG away from approximate "semantic vibes" and toward explicit reasoning about where information lives. That clarity can help teams trust outputs and debug workflows more effectively.
Wasn't this a feature of RAGs, though? That they could match semantics instead of structure, while us mere balls of flesh need to rely on indexes. I'd be interested in benchmarks of this versus traditional vector-based RAGs, is something to that effect planned?
In their gitHub repo’s readme, they show a benchmark on FinanceBench and found that PageIndex-based retrieval significantly outperforms vector-based methods. I’ve noticed that in domain-specific documents, where all the text has similar “semantic vibes,” non-vector methods like PageIndex can be more useful. In contrast, for use cases like recommendation systems, you might actually need a semantic-vibe search.
RAG is just finding the right context for your question.
Embedding based RAG is fast and conceptually accurate, but very poor for high complexity tasks. Agentic RAG is higher quality, but much higher compute and latency cost. But often worth it for complex situations.