* feat(gemini): add opt-in support for responseJsonSchema
Add support for Gemini's native responseJsonSchema parameter which uses
standard JSON Schema format instead of OpenAPI-style responseSchema.
Benefits of responseJsonSchema (Gemini 2.0+ only):
- Standard JSON Schema format (lowercase types)
- Supports additionalProperties for stricter validation
- Better compatibility with Pydantic's model_json_schema()
- No propertyOrdering required
Usage:
```python
response_format={
"type": "json_schema",
"json_schema": {"schema": {...}},
"use_json_schema": True # opt-in
}
```
This is backwards compatible - existing code continues to use
responseSchema by default.
Closes#16340
* docs: add documentation for use_json_schema parameter
Document the new use_json_schema option for Gemini 2.0+ models
in the JSON Mode documentation.
* refactor(gemini): use responseJsonSchema by default for Gemini 2.0+
Remove opt-in flag `use_json_schema` and automatically detect model version:
- Gemini 2.0+: uses responseJsonSchema (standard JSON Schema, supports additionalProperties)
- Gemini 1.5: uses responseSchema (OpenAPI format, legacy)
This follows LiteLLM's philosophy of abstracting provider differences -
users write the same code regardless of model version.
* test(vertex): update json_schema tests to accept both responseSchema formats
Gemini 2.x+ uses responseJsonSchema while Gemini 1.x uses responseSchema.
Update tests to accept both formats since litellm now auto-selects based
on model version.
Users were getting "does not support parameters: ['tools']" errors when
using lowercase model names (e.g., "qwen/qwen3-next-80b-a3b-thinking")
because the model cost map has mixed-case keys and the lookup was
case-sensitive.
Added _get_model_cost_key() helper that tries exact match first (O(1)),
then falls back to case-insensitive search if not found.
extract model id from vertex ai passthrough routes that follow the pattern:
/vertex_ai/*/models/{model_id}:*
the model extraction now handles vertex ai routes by regex matching the model
segment from the url path, which allows proper model identification for
authentication and authorization in proxy pass-through endpoints.
adds comprehensive test coverage for vertex ai model extraction including:
- various vertex api versions (v1, v1beta1)
- different locations (us-central1, asia-southeast1)
- model names with special suffixes (gemini-1.5-pro, gemini-2.0-flash)
- precedence verification (request body model over url)
- non-vertex route isolation
* feat(custom_llm): add image_edit and aimage_edit support
Add support for image_edit and aimage_edit methods in CustomLLM class,
allowing users to implement custom image editing providers.
Changes:
- Add image_edit() and aimage_edit() methods to CustomLLM base class
- Add custom provider detection in litellm.image_edit() function
- Add tests for sync and async image_edit with custom handlers
* docs: add image_edit to CustomLLM documentation
- Add /v1/images/edits to supported routes
- Add Image Edit section with example
- Update Custom Handler Spec with image_edit methods
- Filter skip_mcp_handler and other internal params in fallback_utils.py before calling acompletion
Fixes issue where internal parameters were being passed to provider APIs causing errors
- Remove deployment field from GCS bucket logger test metadata
Fixes model name mismatch where deployment field was overriding the model in logging
- Update Bedrock Titan test to use non-deprecated model (titan-text-express-v1)
Fixes test failure due to deprecated amazon.titan-text-lite-v1 model
- Add @pytest.mark.flaky(retries=3, delay=1) decorator to handle intermittent Anthropic API failures
- Add error handling to skip test when Anthropic API returns InternalServerError
- Prevents false test failures due to external API 500 errors
Sameer Kankute