lotus

Yeah, that was just a design choice that I made: I wanted a library that worked with `Iterator`s, felt more lightweight to me / fit my immediate needs better. I'm personally not a huge fan of Pandas DataFrames for certain applications. LOTUS (by Liana Patel et al., folks from Stanford and Berkeley; https://arxiv.org/abs/2407.11418) extends Pandas DataFrames with semantic operators, you could check out their open-source library: https://github.com/lotus-data/lotus Semlib does batch requests, that was one of the primary motivations (I wanted to solve some concrete data processing tasks, started using the OpenAI API directly, then started calling LLMs in a for-loop, then wanted concurrency...). Semlib lets you set `max_concurrency` when you construct a session, and then many of the algorithms like `map` and `sort` take advantage of I/O concu

You might enjoy Lotus: https://github.com/lotus-data/lotus 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 data