Why Ontology? (Advanced)
The TextQL Ontology is a robust semantic layer for data modeling, that allows for conveniently attaching additional context to data. A key benefit of the ontology is that it allows for deterministic SQL writing, once the agent has identified data sources of interest.
Here are some key benifits:
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Attaching Context at the Representation Level: The TextQL ontology allows users to attach context at the level of tables, columns, metrics and links. This allows for capturing the nuances of real world datasets, and improves agent responses and workflows. Added context can also eliminate uncertainty in the agent’s planning process, resulting in more efficient and streamlined operation.
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Robust Data Model: On setup, the user’s data may be conveniently transforming and linked through the TextQL ontology. Given a perceived task, links allow the agent to search across the users data, and devise constellations of relevant columns across tables.
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Deterministic SQL Compilation: A key feature of the TextQL Insights Agent is its ability to write SQL to a client’s data-warehouse, and load data to a Python Sandbox Environment. Importantly, SQL code is deterministically compiled by the TextQL ontology. TextQL does not use Language models directly for the writing of SQL. Using this approach, via the ontology, removes the possibility of LLM hallucinations and significantly improves the efficacy of data loading and selection.