- Add clarifying comments to test assertions
- Query prisma DB instead of in-memory router to avoid stale state
- Prevents incorrect deletion of old public name when siblings exist
Made-with: Cursor
Guard should_include_deployment fallback to only return deployments
matching the requested team_id, preventing public-name collisions
from leaking deployments across teams
Made-with: Cursor
- Add None guard for original_model_name in _add_team_model_to_db
- Remove stale old public name when renaming team model
- Add comment clarifying team deployment early-return priority
Made-with: Cursor
Remove temporary fire-emoji router logs used for local verification while keeping team sibling deployment routing behavior unchanged.
Made-with: Cursor
Remove team model_alias rewrites and resolve team deployments by team_public_model_name with team_id so sibling deployments stay in the routing candidate pool, with explicit logs showing candidate selection before load balancing.
Made-with: Cursor
Use a deterministic internal model_name for team-scoped deployments so sibling deployments with the same public model share a routing group. This makes team alias writes idempotent and preserves multi-deployment failover/load balancing behavior.
Made-with: Cursor
- Add cancel/retrieve overrides in AzureOpenAIFineTuningAPI to normalize responses
- Expand _AZURE_STATUS_MAP to handle all known Azure statuses
- Add "pending" to OpenAIFileObject.status allowed values
- Fix async test mock to return awaitable LiteLLMFineTuningJob
- Add test_openai_file_object_accepts_pending_status
Made-with: Cursor
- Move trainingType injection to AzureOpenAIFineTuningAPI handler
- Guard normalization with is_azure flag to only apply to Azure responses
- Override acreate_fine_tuning_job in Azure handler to use is_azure=True
- Update test to directly test _ensure_training_type method
- Add test for OpenAI unchanged behavior
Made-with: Cursor
- Default trainingType=1 for Azure when omitted to avoid misleading "base model does not support fine-tuning" error
- Normalize Azure FineTuningJob responses (pending→queued, null fields→defaults) to match OpenAI schema
- Add pending status support to OpenAIFileObject for Azure file uploads
- Add test coverage for trainingType default and response normalization
Made-with: Cursor
Addresses Greptile review feedback: replace direct litellm.model_cost
lookup with the standard _supports_factory infrastructure used by
supports_reasoning, supports_native_streaming, etc.
- Add supports_native_structured_output() utility in litellm/utils.py
- Add supports_native_structured_output field to ModelInfoBase type
- Wire field into _get_model_info_helper return dict
- Delegate from Bedrock _supports_native_structured_outputs to utility
- Add field to JSON schema validator in test_utils.py
Main has two duplicate keys for vertex_ai/gemini-embedding-2-preview.
Our JSON round-trip collapsed them to the second (text-only) entry, but
PR #23599 intentionally keeps the first (multimodal pricing) entry.
Restore the multimodal entry to avoid conflicts.
Wrap cost-map-dependent tests in try/finally to restore
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] and litellm.model_cost,
preventing test-ordering sensitivity.
Integration tested 28/28 (10 sync + 10 streaming + extras) on the
native outputConfig.textFormat path in us-west-2. deepseek.v3.2 does
not support native structured output (Bedrock returns 400).
Integration testing confirmed gemma-3 (4b/12b/27b) ignores the JSON
schema and returns free text, and nemotron-nano (9b/12b) errors with
"Tool calling is not supported in streaming mode" even on sync calls.
Remove the flag so these models fall back to the tool-call approach.
Also fix test assertions to match (nemotron-nano-3-30b is supported,
gemma-3 and nemotron-nano-12b are not).
Move the source of truth for which Bedrock models support native structured
outputs (outputConfig.textFormat) from a hardcoded substring set
(BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS) to the cost JSON via a new
"supports_native_structured_output" flag. This makes it possible to add
support for new models (including Claude Sonnet 4.6, which was missing)
by updating the JSON alone, with no code changes needed.
Pin all pip install commands to exact versions and SHA-pin all GitHub
Actions to prevent supply chain attacks. Remove snok/install-poetry
in favor of direct pip install. Delete orphaned load test scripts.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
