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.
Wrap a storage read in a context manager (close the file handle), add a comment
on the expected SandboxCapacityError in the churn test, and unify the
artifacts-routes test on a single import style. Also annotate the intentional
per-session sandbox workspace path with # nosec B108 (controlled dir inside the
runner container, not an insecure shared temp file).
Add runtime governance for the sandbox and artifact store: a per-process
concurrent-session cap with least-recently-used eviction of idle sessions and a
periodic idle reaper (Celery beat), so sandbox kernels do not accumulate. The
session manager performs all backend start/stop outside its lock and tears down
the captured handle, so eviction never closes a concurrently re-opened session.
Add per-user artifact quotas (count, total bytes, and per-file size) enforced at
persistence time as a soft cap, and best-effort cleanup of per-render scratch
directories in the sandbox workspace.
Introduce a pluggable CodeSandbox abstraction with a SandboxManager and a
Jupyter Kernel Gateway backend: the app is a client of a single always-on
runner that executes code in stateful in-process kernels (no child-container
spawning, no docker socket). Sessions bind to a conversation or workflow run
with an agent-selectable TTL clamped by a global cap; execution enforces a
wall-clock deadline with interrupt-on-timeout and capped output, and file
transfer is workspace-contained with size and integrity checks. Adds a
docsgpt-sandbox docker-compose service (resource-capped, read-only, internal
network) plus settings, with a real local-gateway integration test.
Alex