Migration 0038 moves every stored secret (OAuth tokens, MCP OAuth
tokens and client registrations, API keys) into the connection's
encrypted envelope, links API-key tools to one connection per distinct
credential, allows several accounts per provider, and adds
credential_mode to sources and tools. OAuth MCP tools keep resolving
each member's own token, as they did before.
docsgpt.connectors.service is now the only reader of OAuth tokens:
get_valid_token_info refreshes under a row lock and persists rotated
refresh tokens, and a revoked grant flags the connection, pauses its
sources and notifies the owner. Loaders build from a connection
(BaseConnectorLoader.from_connection), so scheduled sync covers Drive,
SharePoint and Confluence sources with no browser. S3 and Reddit keys
stay on the connection instead of in remote_data.
New endpoints: POST /api/connections, /setup, /reconnect,
/picker-token, /claim, DELETE /api/connections/<id>, per-action
permissions and MCP refresh-tools. Upload, file listing, sync and
validate-session take a connection_id; session tokens keep working for
this release. The tool executor reads credentials from the resolved
connection (owner or member mode) and pauses on a Connect card when a
connection needs signing in. docsgpt connectors reencrypt rewrites
stored credentials after a key rotation.
`docsgpt up --native` installs services meant to outlive the shell. Development
wants the opposite, and until now it meant three terminals from the guide:
uvicorn, celery, and vite.
`docsgpt dev` runs this checkout's API and worker as children of one terminal,
both restarting when a file is saved, their output interleaved and labelled, and
Ctrl-C stopping them together. `--ui` adds the Vite dev server, `--mock-llm`
runs the bundled mock model so no API key is needed, and `--no-worker` leaves
the worker to your editor's debugger. Celery has no reloader of its own, so the
worker is wrapped in watchfiles when it is installed, and runs plain when it is
not.
Alongside it, the commands a dev loop keeps reaching for:
- `docsgpt doctor` checks what usually breaks a new setup: PostgreSQL answering
and its schema matching this version, Redis answering, a model provider being
configured, and the port being free.
- `docsgpt restart [api|worker]` bounces services without rewriting settings or
rerunning migrations, which `down` plus `up` did.
- `docsgpt logs -f` follows a native install instead of telling you to run
`tail -f` yourself.
- `docsgpt env set` applies itself to a running native install rather than
asking you to run `docsgpt up` again to change one value.
Two bugs found on the way, both older than this change:
- `docsgpt api --reload` watched the working directory, which in a checkout is
178,425 files: .venv, node_modules, and the indexes/ and inputs/ the app
writes to while ingesting, so the server restarted itself mid-request. It
watches the package now — 1,217 files.
- The VS Code "Flask Debugger" ran `flask run`, which serves only the WSGI app:
/mcp, the SSE streams and artifact downloads 404 under it. The guide warned
about this in prose while the debug config did it anyway. It runs uvicorn on
the ASGI app now, like production.
Run DocsGPT without Docker: the API and the worker each become a service
on the machine itself, a launchd agent on macOS and a systemd user unit
on Linux, pointed at a PostgreSQL and a Redis that already run.
`docsgpt up --native --postgres-uri ... --redis-url ...` writes the same
.env a Docker install uses, applies the migrations and starts both
services. status, logs, down and uninstall work on a native install the
same way they do on a Docker one, and never touch the database or Redis:
they were the user's to begin with.
One Redis URL covers the broker, the result backend and the cache on
three consecutive databases, starting at the one the URL names, so a
Redis that already holds something else can be shared.
Windows has neither service manager, so native mode refuses it and says
what to do instead.
`docsgpt backup` writes one archive holding a pg_dump of the database, a tar
of each data volume and a manifest of what it came from; `docsgpt restore`
puts it back over an install. The settings file is left out unless
--with-settings asks for it, since it holds the install's secrets, and a
backup taken with a newer DocsGPT is refused without --force.
The volume tars go through the image the install already runs, so a backup
pulls nothing extra, and compose calls can now redirect stdout and stdin so
the dump never passes through this process.
`docsgpt up` copies the standalone Compose file shipped with this package
version into the stack directory (~/.docsgpt/server by default), writes its
.env and starts the stack on the images of the same version. A first run
asks who should reach DocsGPT (this computer, the network with a token, or a
domain with HTTPS) and which model provider to use; flags answer the same
questions for scripts. Re-running keeps secrets and settings and moves the
image tag, and the database password is only generated for a new database.
Also: down, status, logs, token, open, env, upgrade (uv tool installs
upgrade themselves and run `up` again) and uninstall (keeps settings and
data unless --purge). The commands import no Flask, Celery or settings.
The wheel carries deployment/docker-compose-standalone.yaml as
docsgpt/deploy/docker-compose.yaml; the sdist includes the source file.
Outside a checkout the data home was the working directory, so running
`docsgpt api` from another folder silently used different settings and data.
It is now ~/.docsgpt/server (/opt/docsgpt for root on Linux); DOCSGPT_HOME
and a checkout still take precedence. The API and worker commands create
the home and point out a .env left in the working directory.
pip install docsgpt now brings the web UI with it: `docsgpt api` serves the
API and the UI on one port.
- scripts/build_frontend.sh builds the frontend into docsgpt/static
(gitignored) the way the frontend image does: .env.development as the
production baseline, and index.html loading /config.js ahead of the
bundle. hatch admits the directory into the wheel and the sdist through
`artifacts`; the package workflows run the script before `uv build` and
fail if the wheel lacks the UI. The backend image keeps ignoring it.
- docsgpt/ui.py serves the build in front of Flask: files as they are,
hashed assets immutable, Flask's own path prefixes (taken from its URL map,
so new blueprints need no registration) passed through, every other GET
rendered as index.html for the client-side router. /config.js is generated
per request with VITE_API_HOST and VITE_BASE_URL set to the page's origin,
VITE_* environment variables winning. SERVE_UI=false leaves the API alone.
- docsgpt api configures gunicorn in code (gunicorn.app.base.Application)
instead of rewriting sys.argv, so the SIGUSR2 re-exec that gunicorn uses
for zero-downtime upgrades runs the docsgpt console script again and
works; verified with a live handover.
- Docs: the pip page says the UI is included, that DOCSGPT_HOME and
DOCSGPT_ENV_FILE are process environment variables rather than .env
entries, and the settings page describes SERVE_UI.
- The image pins DOCSGPT_HOME=/app: it ships no checkout, so the data home
no longer depends on the working directory.
- api, worker, beat and migrate print the data home and env file they
resolved, so an API and a worker started from different directories show
it.
- The worker passes -Q only when asked; a bare worker consumes every
configured queue, which honours EMBEDDINGS_QUEUE and DOCUMENT_PARSE_QUEUE.
- The worker runs through celery.start and returns its exit code; click
usage errors print usage and exit 2 instead of a traceback.
- Windows: solo pool and no embedded scheduler (celery rejects -B there),
with a pointer to the new `docsgpt beat` command, which runs the
scheduler on its own.
- prefetch_models and verify_offline parse their arguments, so --help is
help rather than a model name.
- A DOCSGPT_ENV_FILE that is not a file raises instead of booting with
defaults.
- docsgpt api binds 127.0.0.1 by default, like gunicorn and uvicorn do;
--host 0.0.0.0 exposes it. The docs say so.
- The embedded Milvus and LanceDB defaults derive from the data home, so
they follow DOCSGPT_HOME like the faiss indexes and uploads do. A checkout
run from its root and the Docker image resolve to the same paths as before.
- The docs and the pyproject comment describe the CPU torch install as two
steps (torch and torchvision from the PyTorch CPU index first, then the
docling extra): pip picks the highest version across indexes, so
--extra-index-url only yields the CPU build while that index keeps pace
with PyPI.
- AGENTS.md separates DOCSGPT_HOME (moves the data home) from
DOCSGPT_ENV_FILE (selects the .env file); the docs example uses a password
placeholder.
pip install docsgpt (extras: docling, milvus) installs the backend with a
docsgpt command: api, worker, migrate, prefetch-models, verify-offline,
reembed. Second step of the PyPI work after the package rename.
- hatchling build; the version comes from docsgpt/version.py. The wheel is
the docsgpt package with the data it reads at runtime (prompts, model
catalogs, seed config, alembic.ini and migrations) and without the
Dockerfile, the exported requirements, the sample index and local runtime
data. The application import alias stays checkout-only. uv sync installs
the package editable now that [tool.uv] package = false is gone.
- docsgpt/cli.py: api (gunicorn + BoundedDrainUvicornWorker with the image's
flags, --reload for uvicorn), worker (Celery worker with beat embedded,
--no-beat/-Q/--concurrency/--pool, solo pool on macOS), migrate, and
argument pass-through to the maintenance scripts. --help imports no app.
- docsgpt/core/paths.py: runtime data lives in a data home (DOCSGPT_HOME,
else the checkout, else cwd); DOCSGPT_ENV_FILE overrides the env file.
Settings, the dotenv load, LocalStorage and the internal upload route use
it instead of "three directories above this file", which is site-packages
for an installed package. A checkout and the Docker image behave as before.
- [project] dependencies are compatible ranges so the package installs next
to other packages; uv.lock resolves to the same versions and the exported
requirements files are unchanged.
- package-build.yml builds and checks the wheel on PRs and installs it into
a clean venv; pypi-publish.yml publishes on a published release through
trusted publishing (environment pypi), or to TestPyPI on a manual run.
- Docs: Deploying -> Install with pip. AGENTS.md notes the package.
arc53-machine