Commit Graph

7 Commits

Author SHA1 Message Date
Brian K 244a266c06 chore: switch CI to uv, remove uv.lock from gitignore (#885)
* chore: declare pytest as a uv dev dependency

The contributing guide currently tells contributors `pip install pytest`
as a separate step, and CI does the same. Move pytest into PEP 735
`[dependency-groups]` so it's declared in pyproject.toml and `uv sync`
installs it by default (no `--with` workaround, no separate install
line). Update CI to use astral-sh/setup-uv + `uv sync` + `uv run pytest`,
and refresh the Contributing section of the README to match.

`[dependency-groups]` is the right home (vs `[project.optional-dependencies]`)
because pytest is dev-only and shouldn't appear in the published wheel's
optional features list alongside things like `pdf` or `mcp`.

* remove uv.lock from gitignore
2026-05-28 14:38:31 +01:00
Safi 03dd794a80 Add v8 to CI branch list
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-15 00:09:16 +01:00
Safi 913feca6a6 CI: add v5-v7 branches, workflow_dispatch trigger, sql extra 2026-05-04 18:27:33 +01:00
Safi 4b87e67ff3 Add v4 to CI branch triggers
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-10 16:01:14 +01:00
Safi aed4a981a1 v3: MIT license, multi-platform packaging and CI 2026-04-06 16:06:31 +01:00
Safi 4ba8198c7c v2.1: PyPI release and CI for v2 branch 2026-04-06 16:06:31 +01:00
Safi 64e07abd98 docs: CI, architecture guide, worked examples, README fixes
- Add GitHub Actions CI workflow (Python 3.10 and 3.12)
- Add CI badge to README
- Add ARCHITECTURE.md: pipeline overview, module table, schema, how to
  add a language extractor, security summary
- Move eval reports from tests/ to worked/httpx/ and worked/mixed-corpus/
- Fix README: test count 163→212, language table (13 languages via
  tree-sitter), extract.py description, worked examples links

benchmark: 8.8x token reduction on nanoGPT + minGPT + micrograd

- Run AST extraction on 29 Python files across 3 Karpathy repos
- 177 nodes, 246 edges, 17 communities (Leiden)
- 8.8x avg token reduction vs naive full-corpus context stuffing
- Notable: micrograd cleanly splits into engine/nn communities;
  nanoGPT model vs training loop correctly separated
- Honest: stdlib import noise flagged, config isolates documented

benchmark: 71.5x token reduction on mixed corpus (code+papers+images)

Full run: nanoGPT+minGPT+micrograd + 5 research papers + 4 images
285 nodes, 340 edges, 53 communities
Average BFS query: 1,726 tokens vs 123,488 naive (71.5x)
Code-only (AST) sub-benchmark: 8.8x on 13k-word corpus
2026-04-06 16:06:31 +01:00