From review of #2800:
- systemd `enable --now` starts nothing when the unit is already active, so a
second `up --native` kept the old ExecStart and left the API on its previous
port. start enables and then restarts, as the launchd path already did by
booting the job out first.
- An explicit `home` now wins over XDG_CONFIG_HOME, which is what callers pass
it for.
- The ExecStart program must be a real executable: when `docsgpt` is not on
PATH, sys.argv[0] is accepted only if it can be run, and otherwise the
failure is raised before any unit is written.
- `up --native` over a directory holding a Docker install now refuses and says
how to proceed, instead of starting native services beside containers that
down, status and uninstall would no longer see.
- Docs: without a terminal only --postgres-uri is required, and the Windows
fallback names `docsgpt beat`, which the worker cannot embed there.
SystemdServices was the least covered part of the module and cannot be run on
this machine, so it now has tests for install, start, stop, remove, is_running
and a failing systemctl.
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.
Docker-Deploying gains a `docsgpt up` section, Pip-Install and Upgrading
describe the ~/.docsgpt/server data home, and the changelog covers both.
docker-image-verify.yml installs the wheel and runs `docsgpt up`, `status`,
a second `up` that must keep the secrets, and `uninstall --purge` against
the image it built. The standalone Compose file maps host.docker.internal
to the host gateway, so a model server on a Linux host is reachable the way
`docsgpt up` suggests.
- Ship tiktoken's cl100k_base inside the package and build the encoding
from it, so token counting never downloads anything.
- Default EMBEDDINGS_CACHE_DIR to <data home>/models instead of FastEmbed's
temp dir, and read tokenizer.json and repo metadata from that cache, so
a model downloads once and survives reboots.
- TTS_PROVIDER=none and STT_PROVIDER=none switch the speech features off:
the endpoints return 404, audio files fail to ingest with a clear
message, /api/config reports tts_available/stt_available, and the UI
hides the Speak and microphone buttons.
- Drop the Google Fonts Roboto import from the web UI.
- prefetch-models fills the cache the app reads; verify-offline checks the
packaged encoding.
- Docs: new Air-Gapped Deployment guide, settings and cache notes.
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.
- pipx: replacing the CUDA torch inside the pipx environment needs
pip's --force-reinstall (pipx inject, with or without --force, leaves a
satisfied requirement alone), and --no-deps so torch's dependencies are not
reinstalled from the PyTorch index, which carries older copies of them.
- Upgrading: the embedded Milvus and LanceDB default paths now resolve under
the checkout (or DOCSGPT_HOME) instead of the start directory; the note
names the old location and the settings to pin.
- 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.
Alex