nest_scan
Scan a folder to extract and classify files into a SQLite Nest DB. Use dry-run to preview counts before writing, with local-only inference by default.
Instructions
Walk a drop folder, extract + classify its files, and write a canonical SQLite Nest DB. Returns structure only (counts by source status and fragment type) — never file content.
dry_run=True (default): classify and report counts WITHOUT writing the DB — inspect what a dump would become before committing it. dry_run=False writes. use_embed uses an Ollama embedding model when present (falls back to regex offline); use_llm escalates the uncertain tail to a text/vision model.
Inference stays on this machine by default. It is NOT unconditional: the
seams post to $OLLAMA_HOST, and if that points off-box this tool requires the
operator's standing consent.cloud_llm and denies without it. Classification
sends document bodies, so where that host points is a privacy decision, not a
performance one. (This docstring used to promise "nothing leaves the machine"
flatly, which was true of the default and false of the variable.)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| owner | No | ||
| app_id | Yes | ||
| folder | Yes | ||
| db_path | No | ||
| dry_run | No | ||
| use_llm | No | ||
| use_embed | No |