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.
Enable code_executor and artifact_generator by default in chats (added to
DEFAULT_CHAT_TOOLS) — they load via the synthetic-id path user- and
conversation-scoped like scheduler, and persist artifacts (no user_tools FK).
Make read_document an internal agent-builtin flagged workflow_only so it appears
only in the workflow builder, not the classic agent picker or the Add-Tool
catalog (reuses the builtin synthetic-id path; still run-scoped and authz-gated).
Per decision, default-on code_executor runs sandboxed code without an approval
prompt (the sandbox is the trust boundary); this is documented in settings and
the threat model, and multi-tenant deployments should add per-tenant isolation
(Daytona / gVisor / egress policy).
Add code execution as an attack surface and an isolation threat: untrusted
LLM-authored code runs in the sandbox, and the self-hosted Jupyter runner is a
single trust domain (shared container/uid). Record the mitigations (approval
gating, state passed as data not code, scrubbed kernel secrets, 0700 workspaces,
network-layer egress) and that per-tenant isolation means the Daytona
per-session-VM backend or running the runner under gVisor.
Alex