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Overview

This section of the documentation has downloadable Jupyter notebooks that demonstrate various aspects of Semlib's functionality.

  • Disneyland Reviews Synthesis. This notebook analyzes tens of thousands of Disneyland reviews to produce a list of top complaints, ordered by frequency, with citations. The notebook demonstrates a sophisticated multi-stage processing pipeline that includes map, reduce, apply, and non-semantic operations.
  • arXiv Paper Recommendations. This notebook analyzes the latest papers published on arXiv and surfaces reading recommendations based on your interests. The notebook demonstrates a pipeline that includes semantic filter and sort operations.
  • Airline Support Report. This notebook analyzes a large number of airline support tickets and produces a report summarizing complaints. The notebook is directly translated from a DocETL example, allowing for a direct comparison between the systems.
  • Resume Filtering. This notebook analyzes PDF resumes, extracts structured data, and filters the resumes according to a criteria. The notebook demonstrates processing PDFs using an open-source PDF-to-Markdown conversion tool, using Semlib with local models using Ollama, and using a series of maps to implement a model cascade in Semlib.