osr-forge 0.1.0

Convert tabletop adventure module PDFs into playable osrlib Adventure documents.

osr-forge

Convert tabletop adventure module PDFs into playable osrlib Adventure documents: an LLM-assisted extraction pipeline, deterministic map-geometry synthesis, a human correction loop built on an overrides file, and validation against the real osrlib models.

Standalone package + CLI. Consumers need only its artifacts — adventure.json, report.json, overrides.yaml, SVG map previews — or its CLI, regardless of their own tech stack.

Status: released — install from PyPI, docs at https://mmacy.github.io/osr-forge/:

uv pip install osr-forge     # or: pip install osr-forge

The package runs the whole pipeline — preprocessing, the survey (chunked past survey_max_pages) and content extraction stages, monster resolution against the osrlib catalog, deterministic geometry synthesis (doors and secret doors on the stating room's wall, stair and trapdoor transitions from keyed and level-shaped links, guessed landings flagged), and assembly — plus the human correction loop (overrides application, rerun/resume, the playability lint with its smoke delve, cost estimation) and the eval harness that keeps extraction quality a measured number (tools/eval/). The roadmap's phase plans live in docs/ beside the specification.

The library entry points are convert(), assemble(), check(), and estimate(); the osrforge console script wraps them:

osrforge estimate my-module.pdf           # preprocess only; rough token/cost estimate
osrforge convert my-module.pdf            # full pipeline into ./my-module.forge
osrforge assemble --workdir my-module.forge   # stage caches + overrides → artifacts, pure
osrforge check --workdir my-module.forge      # validate_adventure + the playability lint
osrforge preview --workdir my-module.forge    # regenerate the SVG maps only
osrforge rerun assemble --workdir my-module.forge   # resume any stage through assemble

Recording sessions and live verification runs are driven via tools/extract/run_extraction.py (see tools/extract/README.md); the on-demand eval harness — corpus, scorer, and scoreboard — lives in tools/eval/ (see tools/eval/README.md). The harness also measures your own retail modules: a private corpus directory side-loads with --corpus DIR (integrity via a local hash sidecar, truth authored per tools/eval/AUTHORING.md), and an explicit publish step copies aggregate-only scores — never module text — onto the committed BYOM scoreboard.

The correction loop

Conversion produces a draft — every gap and guess called out in report.json — and corrections live in overrides.yaml, never in hand-edits to generated output (a fresh convert leaves a commented template there). The loop:

1. Read report.json (flags, findings, the monsters summary) and eyeball previews/*.svg against the printed map.
2. Edit overrides.yaml: monster remaps, per-area field replacement, area adds and removes, geometry (cells, edges, entrance, transitions), town/module metadata. Every entry carries a reason, and every entry must take effect — a typo'd address fails assembly loudly instead of silently doing nothing.
3. osrforge assemble && osrforge check — re-assembly is pure and instant (no model calls), and check exits 0 once validation passes and no error-severity finding remains.
4. Repeat until publishable.

Settings changes on an existing workdir go through rerun --set — for example, osrforge rerun preprocess --set 'blank_page_renders=[21]' blanks a render Azure's content filter rejects, or osrforge rerun assemble --set unresolved_fallback=omit flips the stand-in policy without re-rolling the model.

Development quickstart

Requires Python ≥ 3.14 and uv.

uv sync
uv run pytest

The full check suite, as CI runs it:

uv run ruff format --check && uv run ruff check && uv run pyright && uv run pytest

Tests use no network — model interactions replay from recorded fixtures. The only live-network activity in the repo is manual: the Foundry capability spike (tools/spike/) and the extraction runner (tools/extract/).

Pipeline settings

ConversionSettings holds the deterministic knobs, echoed into each workdir's run.json:

Knob	Default	Meaning
render_dpi	150	Page-render resolution (a legibility knob, not a cost knob — see docs/foundry-capabilities.md)
max_pages	200	Source page-count guardrail
max_source_bytes	100 MiB	Source file-size guardrail
blank_page_renders	()	Page numbers whose renders are emitted as blank white PNGs (text layer still extracted) — the content-safety-filter workaround; each blanked page is flagged page_unreadable in the report
content_batch_pages	8	Content-pass batch size in pages (floor 2)
survey_max_pages	50	The survey chunk size — the service's measured 50-images-per-request cap: a source at or under it surveys in one request; a larger source surveys in page windows of this size, merged before normalization
monster_fuzzy_threshold	0.85	Monster resolution's fuzzy-tier auto-accept floor, pinned against measured catalog pairs
monster_llm_top_k	8	Candidate templates offered per name in the monster-resolution LLM tier
custom_monsters	emit	Whether the monsters stage runs the stat-block pass feeding custom-template emission: emit gives unresolved names the module's own creatures; off skips the per-unresolved-name model spend and keeps the draft SRD-catalog-pure (monsters-stage-owned — toggling it re-runs monsters, including its LLM tier)
unresolved_fallback	best-effort	Where resolution or parsing came up empty and no usable stat block exists: flagged level-band monster stand-ins and unguarded-treasure rolls (best-effort), or leave the gap (omit)

On an existing workdir, change a knob with rerun --set KEY=VALUE: the update lands in the run.json settings echo before the chain runs, and a knob owned by a stage upstream of the rerun stage is rejected with the stage to rerun instead.

Provider configuration

The Azure AI Foundry adapter reads its connection from OSRFORGE_FOUNDRY_* environment variables (an osr-forge-specific prefix, deliberately not AZURE_OPENAI_*, to avoid colliding with other tools' conventions):

Variable	Azure meaning	Required
OSRFORGE_FOUNDRY_ENDPOINT	The Azure OpenAI resource endpoint (https://<resource>.openai.azure.com)	yes
OSRFORGE_FOUNDRY_DEPLOYMENT	The model deployment name	yes
OSRFORGE_FOUNDRY_API_KEY	The API key. Omit to use Entra ID via DefaultAzureCredential, which needs the osr-forge[entra] extra	no

Licensing

Package code is MIT (see LICENSE). osr-forge ships no game content: osrlib's OGL data stays in osrlib, and test assets live under tests/assets/ outside the built distribution, each with its provenance and license documented in tests/assets/README.md.

These fences govern what this repository redistributes, not what you convert: bringing your own purchased, non-redistributable module is the primary use case. Conversion runs locally and everything derived from your module stays in your own workdir — nothing is shared unless you share it.