Semlib: LLM-powered Data Processing (anishathalye.com)

Semlib is a new Python library designed to harness large language models (LLMs) for building structured data processing and analysis pipelines. Unlike traditional coding approaches, Semlib lets developers define familiar functional programming operations—like map, reduce, sort, and filter—using natural language descriptions. It then handles the complexity behind the scenes, including prompt engineering, concurrency, caching, and cost management. This structured orchestration enables higher-quality outputs from LLMs by breaking down complex tasks into manageable semantic steps, overcoming limitations of single-shot model queries, especially with large or diverse datasets. This approach is significant for the AI/ML community as it offers a practical, flexible framework to integrate LLMs with conventional programming, enabling more reliable and scalable semantic data workflows. Semlib’s design supports concurrency to reduce latency, allows model choice for cost efficiency, and even facilitates sensitive data processing with open-source models. A compelling case study showcased its effectiveness in automating performance review synthesis, where Semlib-generated summaries outperformed manual efforts and generic LLM prompts, demonstrating improved accuracy and context management. Compared to academic systems like DocETL, LOTUS, and Palimpzest, Semlib prioritizes usability and real-world adoption via a Python API well-suited for interactive environments like Jupyter notebooks. By combining semantic LLM-powered operations with traditional algorithms, Semlib offers a powerful, developer-friendly toolset that advances the practical application of LLMs beyond conversational use cases into structured, robust data processing pipelines.