ingest/README.md

Knowledge Ingestion

Turns the sources in ../docs/KNOWLEDGE-SOURCES.md into Chief Engineer knowledge. Two kinds, matching how each source actually carries information:

Output	From	Goes to	Used as
Reference facts	Prusa .ini, Klipper .cfg, Marlin .h (data only)	data/references.jsonl	injected into the prompt as a Material Reference block
Candidate lessons	research distilled into env-keyed JSONL rows	the ledger (source="ingested")	retrieved exactly like seed/earned lessons

Local distiller (deterministic, runs offline)

uv run python -m ingest.run          # uses ingest/samples/
uv run python -m ingest.run --dir /path/to/your/marlin_klipper_prusa_files

Replace ingest/samples/* with your real configs and a *lessons*.jsonl of research-distilled rows. The sample files are illustrative and clearly marked.

License-safe: we ingest profile data (INI/cfg/#define values). We never import or link OrcaSlicer/PrusaSlicer code (AGPL-3.0).

Heavy datasets + fine-tune → Modal (modal_app.py)

ablam/gcode (>6GB) and 3DTimeDataset/3DTime are too big for the Space; they run on Modal. modal_app.py is a stub to be replaced by Kyle's MCP-hackathon ingestion code (which already parses training data for multi-agent use). Off the critical demo path; this is where the Modal bonus + the frontier fine-tune live.

uv pip install modal datasets      # not in the Space requirements
modal token set
modal run ingest/modal_app.py::sample_gcode