Replace the sandbox Docling extractor with read_document, backed by the in-process
backend parser (the same one ingestion uses) and offloaded to a dedicated
'parsing' Celery queue so it can run on GPU-capable workers with predictable RAM.
The tool resolves the input ref under the run-scoped gate, enqueues the parse,
and awaits it with a timeout (degrading to an error rather than hanging); the
worker independently re-resolves the artifact through the same gate and never
trusts a raw path. Untrusted files get the upload path's safeguards (extension
whitelist, size cap, sanitized temp file, cleanup). Options: output
(markdown/text/structured/chunks), ocr, pages, engine, max_chars, include_tables,
persist, json_schema. The workflow native-file 'extract' fallback now uses the
same worker path, so document parsing no longer needs the sandbox and works on
every backend.
Also fixes the branch's periodic-task test (the sandbox reaper made it 12) and
points the dev and e2e Celery workers at the parsing queue.
An agent node can list input_documents (short refs like A1, a literal "*" for all
inputs, or state variables holding refs produced by an upstream node). The engine
resolves each through the run-scoped artifact gate and feeds it to the node's LLM
natively when the model supports the format (PDFs included via the provider's
image synthesis), otherwise extracts it to text with Docling. A file_passing
policy (auto/native/extract) and per-node count + per-file byte caps bound cost;
the native decision uses the same supported-types source the provider handler
uses, so a document is never silently dropped. Bytes never enter run state.
Alex