* fix(auto_router): bill the routing embedding to the caller's key and team
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(auto_router): validate the forwarded caller metadata with a pydantic model
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Always serialize object_permission.agents and agent_access_groups in
the team update payload so removing the last agent in the dashboard
sends an explicit empty array instead of omitting the key, which the
backend merge treats as no change
Resolves LIT-6861
Co-authored-by: yassin <yassin@berri.ai>
Without the auto_router feature in the signed enterprise license a proxy may hold
one complexity router with classifier_type heuristic_v2 across config.yaml and the
DB; with it the limit is lifted. The ceiling is derived once from LicenseCheck and
handed to the Router, which refuses the extra router at registration. config.yaml
over the limit refuses to start, and /model/new, /model/update and
PATCH /model/{id}/update refuse the write with a 403 before touching the DB.
Expiry follows the existing max_users/max_teams pattern: judged when the
license is verified, not on every call, and a verify that rejects the license
(expired or unreadable) leaves no signed payload behind. The rollback after a
failed upsert re-admits state that was already serving, so it is exempt from the
ceiling: an edit that fails, including one refused by a ceiling that has since
tightened, leaves the router serving its previous configuration.
A write that leaves a row on heuristic_v2 under a limited license runs in one
transaction that takes a Postgres advisory lock before counting the DB rows plus
this proxy's config.yaml routers, so concurrent writes on any pod cannot both
claim the sole slot and no surplus row is ever persisted.
Only the row insert runs under that lock: the team model bookkeeping, which
needs a second pool connection, runs after the transaction has committed.
PATCH /model/{id}/update follows the same order as create: the row is written
through the slot first and the team's model list is updated only afterwards, so
a refused write leaves the team as it was.
The slot transaction bypasses the repository's publish-on-write, so it
publishes the config change once after commit, as delete_team_models does.
425e3069b9 added Router.get_configured_mode but only exercised it
through create_model_info_response, which the router coverage gate does
not count. The code-quality workflow has been failing on staging and on
every open PR since.
* fix(vector_stores): only list vector stores the caller was granted
/vector_store/list returned every managed vector store with no team_id to any
key, and let a dashboard session see stores created from the dashboard because
every session shares the litellm-dashboard team id. Non-admin listings now show
a store only when the key or one of the caller's real teams is allowlisted for
it via object_permission.vector_stores, or the team owns it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vector_stores): keep a dashboard session key's own grants when the user has no teams
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
CI unit shards load the price map from main at import, so a model that only
exists on the branch never reaches open_ai_chat_completion_models and
cost_per_token cannot infer its provider. Refresh the sets after swapping in
the local map so the tier pricing cases resolve gpt-6-astra before merge
OpenAI documents low, medium, high, xhigh, and max for gpt-6-astra, with no none level, so the entry stops advertising none and starts advertising max.
Adds the OpenAI gpt-6-astra entry to both price files with standard, flex, priority (fast mode), batch, and above-272K long-context rates, and regression tests covering each tier and the batch rates.
A five-token response can complete before the cancel lands, which put the
test back on the "Cannot cancel a completed response" path it used to
swallow. Ask for a long generation so the cancel always beats completion,
and assert the cancelled status unconditionally
