LAiSER

LAiSER (Leveraging Artificial Intelligence for Skill Extraction & Research) is a Python package that reads free text — job postings, course descriptions, syllabi — and returns the skills, knowledge areas and tasks it describes, each matched to an entry in a published taxonomy.

The point of the matching step is comparability. Two job postings that say "Python scripting" and "writing Python code" should end up pointing at the same taxonomy skill, so the results can be counted, joined and compared across employers, programs and datasets.

How it works

For every row in an input table, LAiSER runs four stages:

1. Extraction. The document text is placed into a prompt for its input type (job description or syllabus) and sent to a language model, which returns candidate skills — and, if requested, the knowledge and tasks behind them.
2. Parsing and deduplication. The model's response is parsed into a list of phrases. Exact duplicates are removed, then near-duplicates are collapsed using embedding similarity.
3. Taxonomy alignment. Each phrase is embedded and matched to its closest entry in bundled FAISS indexes of taxonomy entries. Matches above a similarity threshold are kept, up to top_k per concept type.
4. Output. Matches are returned as one table with a row per match: the phrase as extracted, the taxonomy entry it matched, which taxonomy that entry comes from, and the similarity score.

The language model can be a hosted API or a model that runs on your own machine — see Model providers. The taxonomy indexes ship with the package, so alignment runs locally either way.

Bundled taxonomies

Source	allowed_sources value	Covers
ESCO — European Skills, Competences, Qualifications and Occupations	esco	skills, knowledge, tasks
O*NET — US Occupational Information Network	onet	skills, knowledge, tasks
UK Skills Classification	ukos	skills, knowledge, tasks
Open Skills Network	osn	skills

Details and entry counts are on the Taxonomies page.

Where to go next

• Installation — install from PyPI, with or without GPU support.
• Quickstart — a complete run, including one that needs no API key.
• Usage — every option of extract_concepts and what the output columns mean.
• Cookbook notebooks — end-to-end analyses you can open in Google Colab.
• API reference — generated from the source.