- A tool continuation refused for usage now releases the resume claim it
took; before, retries got a 409 until the stale claim was reverted.
- Agent traffic is any row with an agent key or an agent id, so keyless
agents and workflow nodes count toward the agent bucket, not direct.
- The user quota modal discards responses for a previously opened user.
- The usage meter shows every limited bucket, not only 'all'.
- Restore the class separator on the analytics stat card that a formatter
run removed, and align the OpenRouter DeepSeek description with its rates.
Admins read and set the instance default, team allowances and user
overrides under /api/admin/quotas. A user's endpoint also returns the
limits those layers resolve to, the layer each came from and the usage
against them. The overview lists catalog models used this period that have
no price, since a cost limit cannot see them. Every write is audited.
GET /api/user/quota gives a user their own limited buckets, usage and reset
time without naming the policies behind them; any valid token may call it.
docsgpt/quotas resolves a user's effective token and cost limits from the
policy rows that apply to them: their own override, then the most generous
allowance among their teams, then the instance default, then any defaults a
registered provider supplies. Each budget resolves on its own, and a team
counts once however many memberships the user holds in it.
QuotaService compares those limits with the user's token_usage totals over
the current QUOTA_PERIOD window (calendar-aligned, UTC, computed at read
time). It fails open, and skips the usage query for unlimited users.
sum_tokens_in_range now skips the scheduler's per-run rollup rows, whose
tokens are already on the run's per-call rows, so the per-agent 24h limit
and the admin total no longer count scheduled spend twice.
Workflow node LLMs carry the workflow agent's id, so their usage rows are
attributed to the agent instead of landing with a user id only.
usage_totals returns a user's tokens and cost since a window start, split
by interactive and agent-key traffic.
Migration 0033 adds quota_policies (instance default, team per-member
allowance, user override; a token budget and a USD budget per row) and
token_usage.cost. The column is added IF NOT EXISTS so a database that
already carries it upgrades cleanly.
Every usage row now records the call's USD cost from the model catalog;
bring-your-own models are recorded at $0.
POST /api/user/tokens/<id>/regenerate swaps the secret of an existing token
in place: name, scopes and restrictions stay, the old secret stops matching
at once, and the expiry is reset. The lifetime defaults to the one the token
was last issued with (clamped to today's policy) or to expires_in_days when
given. An expired token can be renewed this way; a revoked one cannot. It is
session only like the rest of token management, and writes a pat_regenerated
audit event. regenerated_at records the rotation.
A resource restriction was checked on ids in the request, not on what the
addressed row pulls in or belongs to. Closed:
- Workflow writes for tokens restricted on sources, tools or prompts (a graph
names those inside its nodes), and attaching a workflow to an agent unless
the token is restricted on workflows too.
- Chat for tools-restricted tokens (chat executes tools; rejected at token
creation as well), and agent-less chat for tokens restricted on prompts or
workflows.
- conversation_id on chat: it must belong to the agent being run, or to no
agent for agent-less chat. Otherwise the server continued, appended to, or
resumed pending tool calls of another agent's conversation.
- Schedules for tokens restricted on anything but agents; schedule-id routes
for every restricted token.
- Conversations and analytics for every restricted token, not only
agent-restricted ones.
Also: create, first publish and adopt return the agent API key masked to a
token without agents:keys; token ids must be canonical UUIDs (urn:uuid: gave
a 500); an expired token is reported as expired; token creation takes a
per-user advisory lock so the cap cannot be raced; admin revoke-sessions
writes a pat_revoked event per token. The UI drops a row whose revoke returns
404 and does not offer a tools restriction next to chat:run.
The ASGI message events route now accepts the same scopes as its Flask
sibling (conversations:read or chat:run) through a shared constant. A token
request that fails routing gets Flask's 404/405 instead of a 403. Creating a
token retires an expired token that still held the name. allowed_ids uses
is_pat instead of a bare literal. PAT_ENABLED is documented as the master
switch it is: turning it off stops every existing token from authenticating.
The docs explain that sources are matched by name (oldest wins) and point CI
flows at sources upload --replace.
A personal_access_tokens table (migration 0032) holds scoped user-level API
credentials. Only the SHA-256 of the secret is stored, like device session
tokens. Lookups exclude revoked and expired tokens and the tokens of
deactivated users. PAT_* settings cover the feature switch, default and
maximum lifetime, the operator opt-in for non-expiring tokens and the
per-user cap.
Graph retrieval tied plain vector search at best and never beat it. Measured
across five corpora, the bottleneck was seeding, not the graph: the walk
started from nodes whose embeddings were computed from bare entity names, and
a whole question shares almost nothing with a name like "Quill".
Extraction now embeds each node from "name (type): description" and each
relationship as the fact it asserts ("Alder streams_to Quill: ..."), stored on
a new nullable graph_edges.fact_embedding column that ensure_vector_schema adds
in place. Entity names are canonicalised (case, punctuation, word breaks and a
cautious plural) so "VECTOR_STORE" and "vector stores" land on one node. Extraction calls run
concurrently (GRAPHRAG_EXTRACTION_WORKERS, default 8) while embedding and graph
writes stay serial on the task thread, so ordering and idempotency are
unchanged; that measured 8.4x faster with identical output.
Retrieval gains per-source options, stored under retrieval.graph and read live
at query time:
- seed_strategy: start from matching entities (default) or matching
relationships, which can reach an entity the question never names;
- passage_nodes (on): walk the source's passages alongside entities, with
PageRank damping 0.5 instead of 0.85;
- blend_vector (on): fuse the graph ranking with the source's vector ranking
by reciprocal rank.
The defaults are the measured-best configuration. Through GraphRAGRetriever,
the new seeding moved recall@4 from 0.41 to 0.68 on a multi-hop corpus and
from 0.50 to 1.00 on the docs corpus, and regressed none of the corpora
measured. Existing graphs keep name-only embeddings until rebuilt.
About 85 call sites read a setting as getattr(settings, "NAME", fallback),
each carrying its own copy of the default. Every one of those names is a
field with a default on the model, so the fallback could never apply to
the real settings object; it only masked drift. Two had drifted:
- OPENAI_PROMPT_CACHE_KEY defaults to True on the model but the reader
fell back to False, and two test stubs relied on that.
- SharePoint's MICROSOFT_AUTHORITY fallback to
https://login.microsoftonline.com/<tenant> never fired, because the
attribute always exists (as None), so MSAL got authority=None. The
connector now derives the tenant authority when the setting is unset,
as its test always assumed.
Four places read EMBEDDINGS_KEY straight from os.environ, skipping the
"None"/"" normalisation the model applies; they read the setting now.
Test stubs that replaced a module's settings with a SimpleNamespace list
every setting the code under test reads.
SAGEMAKER_REGION, SAGEMAKER_ACCESS_KEY and SAGEMAKER_SECRET_KEY survive
only as a fallback for the S3_* credentials. They carry
Field(deprecated=...) now, so any read emits a DeprecationWarning naming
the replacement and the generated reference shows the notice. The S3
store is the one sanctioned reader; it silences that warning locally
because it already logs its own operator-facing one when the fallback
is actually used.
DEFAULT_MAX_HISTORY was referenced nowhere. RETRIEVERS_ENABLED was read by
no code at all, while two docs pages described it as an enforced
allow-list; both the setting and those claims are removed.
The connector OAuth popup posted the session token to window.opener with
a '*' target origin, so any page that opened the popup received it. An
attacker with an account on a multi-user deployment could start a flow for
their own pending session, get a victim to finish the provider consent, and
receive a token backed by the victim's Drive/SharePoint/Confluence tokens.
- Post popup results only to allowed frontend origins: the callback origin,
OIDC_FRONTEND_URL, the new CONNECTOR_ALLOWED_ORIGINS, and localhost:5173
when the callback runs on a loopback host.
- Render the success page from the callback itself so the token never
appears in a URL; callback-status ignores session_token/user_email params.
- ConnectorAuth accepts messages only from the popup it opened, on the
callback origin reported by /api/connectors/auth.
- /api/connectors/disconnect requires auth and only deletes the caller's
session.
- /api/connectors/sync and /api/remote reject session tokens the caller
does not own.
Fixes#2766
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.
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.
Alex