It takes all the core relational operators and makes an easy semantic version of each as a python dataframe library extension . Each call ends up being a 'model' point in case you also want to do fancier things later like more learning based approaches. Afaict, snowflake and friends are moving in this direction for their cloud SQLs as well.
We ended up doing something similar for louie.ai , where you use AI notebooks/dashboards/APIs (ex: MCP) to talk to your data (splunk, databricks, graph db, whatever), and it'll figure out symbolic + semantic operators based on the context. Super helpful in practice.
My 80% case here is:
- semantic map: "get all the alerts from splunk index xyz, add a column flagging anything suspicious and another explaining why" <--- generates an enriched dataframe
- semantic map => semantic reduce: "... then summarize what you found" <--- then tells you about it in natural text
It takes all the core relational operators and makes an easy semantic version of each as a python dataframe library extension . Each call ends up being a 'model' point in case you also want to do fancier things later like more learning based approaches. Afaict, snowflake and friends are moving in this direction for their cloud SQLs as well.
We ended up doing something similar for louie.ai , where you use AI notebooks/dashboards/APIs (ex: MCP) to talk to your data (splunk, databricks, graph db, whatever), and it'll figure out symbolic + semantic operators based on the context. Super helpful in practice.
My 80% case here is:
- semantic map: "get all the alerts from splunk index xyz, add a column flagging anything suspicious and another explaining why" <--- generates an enriched dataframe
- semantic map => semantic reduce: "... then summarize what you found" <--- then tells you about it in natural text