The backend import package is now docsgpt, the name it will carry on PyPI;
application was far too generic to install into anyone's site-packages.
git mv plus a mechanical rewrite of every import, dotted string and path
reference: 734 Python files, the compose files, Dockerfile, workflows, docs,
setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage
config, .gitignore. Behaviour is unchanged.
Kept for one release:
- A top-level application package whose meta-path finder resolves
application.x.y to the already-imported docsgpt.x.y object, so old imports
and entry points (celery -A application.app.celery,
uvicorn application.asgi:asgi_app) keep working with a FutureWarning.
- Celery registers every application.* task name as an alias of its
docsgpt.* task on start-up, so messages queued by the previous release still
run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries
the previous release wrote are left unread instead of firing twice.
The backend image builds from the repository root (docker build -f
docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore
allow-lists docsgpt/ and application/ and keeps caches, local data, .env
files, the sample index files and the Dockerfile out. Compose and the image
workflows point at the new context.
Let workflow runs consume and produce documents end to end: bridge uploaded
attachments into run-scoped artifacts so nodes receive the input documents
(with a per-run cap and server-computed size/sha256, and the run row pre-created
so produced artifacts are authorized during the run); emit the run id to the
client and add a builder panel that lists, previews, and downloads a run's
artifacts; and allow attaching documents to a Preview run via the existing
upload flow.
Also fixes issues a compliance workflow surfaced: attachment ownership now keys
on the raw identity instead of a sanitized one (the sanitized form could not be
read back and could collide across users); workflow code nodes read prior state
from a state.json data file instead of templating it into the program, so
untrusted document content can never be interpolated into executed code;
structured node output wrapped in code fences is recovered; and the live
speech-to-text ownership check compares the raw identity.
Add a code workflow node that runs code in the run-scoped sandbox session and
writes produced files as artifact references into workflow state, passing them
by reference (only id and metadata, never bytes) so downstream nodes and CEL
conditions can branch on them. Add an artifacts.* templating namespace that
resolves those references to metadata via a run-scoped lookup, available to
both the workflow engine and the prompt renderer. Extract the sandbox-to-
artifact persistence into a shared helper reused by the code node and the
code_executor tool.
Alex