Vertex AI / Gemini uses Pydantic's model_json_schema() which omits
additionalProperties: False (Gemini rejects it). The test expected
the same schema for all providers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The patch.object with new_callable=AsyncMock can behave inconsistently
across Python versions, causing mock_response.status_code to return a
MagicMock instead of the assigned value. Direct assignment is simpler
and more reliable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The CompletionTokensDetailsWrapper type now includes video_tokens field,
but this test's expected dict was not updated to include it.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Gemini API requires role="user" on function_response content blocks
(added in commit 273cf12afa), but these tests were never updated to match.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a "Reset Spend" button to the key detail view so proxy admins and team
admins can immediately reset a key's spend to $0, unblocking keys that have
hit their budget limit without waiting for the next scheduled budget reset.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Fix object_team_id + object_key_hash combining incorrectly as OR — each
filter now adds an AND clause wrapping an internal OR over before_value
and updated_values, so both conditions must be satisfied simultaneously
- Rename helper to _build_json_field_or_condition to reflect its purpose
- Remove allTeams from AuditLogsProps and its call site in index.tsx
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Validate anchor is "created_at" in enforced_file_expires_after (matching
user-provided path). Add key existence validation to batch endpoint for
enforced_batch_output_expires_after.
Cleanup per review: this class attribute is no longer used after the
__next__ refactor to queue-based approach.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two independent fixes for pre-existing test failures on main:
1. Anthropic streaming: The sync __next__ method used a simple
holding_chunk pattern that lost chunks when multiple events needed
to be returned. Refactored to use the same chunk_queue approach as
the async __anext__ method. Also fixed tests that used ModelResponse
(which defaults finish_reason to 'stop') instead of ModelResponseStream.
2. Azure GPT-5.1 logprobs: The base OpenAI class includes logprobs for
gpt-5.1+ models, but Azure hasn't verified support for gpt-5.1.
Added explicit removal of logprobs/top_logprobs for gpt-5.1 (non-5.2)
models in the Azure config.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace **kwargs with explicit tools and system parameters to match
the BaseTokenCounter.count_tokens abstract method signature.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- OpenAICredentials.api_base is always non-None due to fallback default,
so type it as str instead of Optional[str]
- Remove unreachable elif vertex_ai block in create_file(); vertex_ai is
already handled by ProviderConfigManager via provider_config path
Suppress noisy error log fired every cron tick when spend log cleanup
is simply not configured. _should_delete_spend_logs already logs the
specific reason at the right level (info for None, warning for
invalid value), so the redundant blanket error log in
cleanup_old_spend_logs is removed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Verifies that vertex_ai gemini models route to
aiplatform.googleapis.com instead of
generativelanguage.googleapis.com, preventing
regressions if the branch ordering changes.
- Only release distributed lock in finally if it was actually acquired;
prevents spurious Redis release_lock calls on early returns
- Treat bare integer maximum_spend_logs_retention_period as days (e.g. 3 → "3d")
instead of silently failing with a ValueError
- Elevate "Skipping cleanup" log from info to error so misconfigured
retention settings are visible without verbose logging
- Add tests for all three fixes
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Move get_openai_credentials() to litellm/llms/openai/common_utils.py
and get_azure_credentials() to litellm/llms/azure/common_utils.py
so they can be reused by batches/main.py and other modules.
Signatures now take individual params instead of GenericLiteLLMParams.
