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.
Remove the integration tests that launch a real Jupyter Kernel Gateway or hit
Daytona (artifact_generator / code_executor / workflow-code-node e2e, the gateway
integration test, and the Daytona live test) — these belong in a separate e2e PR
with proper harnessing. The ephemeral-Postgres integration tests (artifacts
repository, run-scoped + input-document authz) stay: they run in CI without any
external service and hold the cross-tenant coverage. Trim the now-unused
test-only deps (jupyter-kernel-gateway, ipykernel, websocket-client); keep
jupyter-client for the retained kernelspec-resolution test.
Add an artifact generator tool with create, edit, and rewrite actions that
render presentations, documents, spreadsheets, and PDFs from a validated JSON
spec by running a fixed program in the sandbox (the spec travels as data, so
its contents can never execute). The spec is stored as the source of truth on
each artifact version; edits apply a JSON merge-patch and re-render, appending
a new version while earlier versions stay intact.
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.
* feat: enhance modal functionality and configuration handling
- Updated WrapperModal to improve click outside detection for closing the modal.
- Refactored ToolConfig to utilize ConfigFieldSpec for better configuration management.
- Added validation and dynamic handling of configuration fields in ToolConfig.
- Introduced reconnect functionality for MCP tools in the Tools component.
- Enhanced user experience with improved error handling and loading states.
- Updated types for better type safety and clarity in configuration requirements.
* refactor: reorganize imports and improve conditional formatting
* fix: revert API_URL to use backend service name in docker-compose
* feat: add MCP auth status endpoint and integrate into user service and tools
* feat: implement logging for Brave, Postgres, and Telegram tools; add transport sanitization and credential extraction for MCP
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Co-authored-by: Alex <a@tushynski.me>
Alex