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Are we hitting the limits of standard vector search for data agents?

Summary: Grazia Frontoso shares insights on the limitations of using vector databases and embedding models for text-to-SQL agents, noting that these approaches struggle with complex business logic due to a lack of understanding of specific business contexts. They reference an inspiring video by Databricks co-founders about a self-improving knowledge graph that performs better by weighing definitions based on authority and usage patterns. Grazia Frontoso invites thoughts on handling subjective metrics and whether a graph-based ontology layer is essential for achieving text-to-SQL accuracy, expressing curiosity about how others are bridging the semantic context gap.
AI Summary

Most of us have realized that simply throwing schema metadata into a vector database or embedding model doesn't stop text-to-SQL agents from hallucinating complex business logic. If an agent doesn't understand your specific business context, it fails.

This video by Databricks co-fonders is truly inspiring: https://www.youtube.com/watch?v=Yp_u1NpbkJg Instead of a flat semantic layer, it talks about an active, self-improving knowledge graph that weighs definitions based on authority and actual usage patterns (almost like a PageRank for metadata). It got an 84.5% first-attempt accuracy rate on complex workloads by separating the core ontology from the execution runtime.

For those of you building in production right now:

  1. How are you handling the mapping of highly subjective metrics (like calculating "churn" or "net active ARR") without hardcoding endless custom rules?

  2. Do you think a graph-based ontology layer is the only real path to enterprise-grade text-to-SQL accuracy?

Curious to hear your thoughts on how you’re solving the semantic context gap.

1 comment
Abdelhalim Dadouche
2 weeks, 6 days ago

Thanks for sharing!

Grazia Frontoso
3 weeks, 3 days ago