gleanyard

Documentation: https://kausalflow.github.io/gleanyard/

Gleanyard collects structured data from recurring web sources into a schema you define.

Gleanyard is early and under active development. The design is in the v2 design doc; the package holds only what every collection repo must get right the same way, and everything that varies per project lives in that project's own repo.

A project repo

gleanyard.yaml        # optional project settings
schema.yaml           # the output schema every record must meet
sources/<id>.yaml     # one file per source: delivery + extract
mappings/<id>.json    # crawl4ai selector schemas for selectors mode

See examples/events for a worked example.

A source file keeps two settings apart: delivery says how data arrives (html, ical, json, rss, csv, pdf, sitemap) and extract says how it maps to the schema (fields, jsonld, selectors, llm). The extraction mode is pinned per source; scheduled runs never switch it on their own.

Schema validation

Every record is normalized and checked against schema.yaml:

• a missing or invalid required field rejects the record;
• a value outside a field's scope (dates before today, a postcode pattern, an enum) rejects the record, so wrong or irrelevant pages show up at once;
• an invalid optional field is dropped and reported as a warning;
• a key that is not in the schema rejects the record.

Rejected records go to review, never to the output.

Commands

gleanyard validate [PROJECT] [--json]   # check schema and sources, with file:line fixes
gleanyard json-schema source            # JSON Schema for editors and AI tools
gleanyard collect [PROJECT] [-s ID] [--from-cache] [--json]
gleanyard export [PROJECT] [--json]     # rights-filtered public export

collect fetches each enabled source with the bounded fetcher (same hosts only, robots.txt, request delay, request and byte limits), keeps raw bodies in a content-addressed cache with conditional requests, and writes records and rejections under data/. --from-cache re-extracts the last run with no network. export checks the current source rights: a field is exported only if it is in public_fields and its class is allowed (fact, protected_text, image), and personal fields are never exported.

Releasing

Publishing to PyPI is automatic through trusted publishing (no API token).

1. Bump version in pyproject.toml and merge to main.
2. Create a GitHub release with tag v<version>, e.g. v0.3.0.
3. The Publish to PyPI workflow builds the package and uploads it.