Semlib: LLM-Powered Data Processing

Hi HN!

I've been thinking a lot about semantic data processing recently. A lot of the attention in AI has been on agents and chatbots (e.g., Claude Code or Claude Desktop), and I think semantic data processing is not well-served by such tools (or frameworks designed for implementing such tools, like LangChain).

As I was working on some concrete semantic data processing problems and writing a lot of Python code (to call LLMs in a for loop, for example, and then adding more and more code to do things like I/O concurrency and caching), I wanted to figure out how to disentangle data processing pipeline logic from LLM orchestration. Functional programming primitives (map, reduce, etc.), common in data processing systems like MapReduce/Flume/Spark, seemed like a natural fit, so I implemented semantic versions of these operators. It's been pretty effective for the data processing tasks I've been trying to do.

This blog post shares some more details on the story here and elaborates what I like about this approach to semantic data processing. It also covers some of the related work in this area (like DocETL from Berkeley's EPIC Data Lab, LOTUS from Stanford and Berkeley, and Palimpzest from MIT's Data Systems Group).

Like a lot of my past work, the software itself isn't all that fancy; but it might change the way you think!

The software is open-source at https://github.com/anishathalye/semlib. I'm very curious to hear the Hacker News community's thoughts!