* fix: make HTTPHandler mockable in OIDC secret manager tests
- Add _get_oidc_http_handler() factory function to make HTTPHandler
easily mockable in tests
- Update test_oidc_github_success to patch factory function instead
of HTTPHandler directly
- Update Google OIDC tests for consistency
- Fixes test_oidc_github_success failure where mock was bypassed
This change allows tests to properly mock HTTPHandler instances used
for OIDC token requests, fixing the test failure where the mock was
not being used.
* fix: patch base_llm_http_handler method directly in container tests
- Use patch.object to patch container_create_handler method directly
on the base_llm_http_handler instance instead of patching the module
- Fixes test_provider_support[openai] failure where mock wasn't applied
- Also fixes test_error_handling_integration with same approach
The issue was that patching 'litellm.containers.main.base_llm_http_handler'
didn't work because the module imports it with 'from litellm.main import',
creating a local reference. Using patch.object patches the method on the
actual object instance, which works regardless of import style.
* fix: resolve flaky test_openai_env_base by clearing cache
- Add cache clearing at start of test_openai_env_base to prevent cache pollution
- Ensures no cached clients from previous tests interfere with respx mocks
- Fixes intermittent failures where aiohttp transport was used instead of httpx
- Test-only change with low risk, no production code modifications
Resolves flaky test marked with @pytest.mark.flaky(retries=3, delay=1)
Both parametrized versions (OPENAI_API_BASE and OPENAI_BASE_URL) now pass consistently
* test: add explicit mock verification in test_provider_support
- Capture mock handler with 'as mock_handler' for explicit validation
- Add assert_called_once() to verify mock was actually used
- Ensures test verifies no real API calls are made
- Follows same pattern as test_openai_env_base validation
* fix(proxy): use return value from CustomLogger.async_post_call_success_hook
Previously the return value was ignored for CustomLogger callbacks,
preventing users from modifying responses. Now the return value is
captured and used to replace the response (if not None), consistent
with CustomGuardrail and streaming iterator hook behavior.
Fixes issue with custom_callbacks not being able to inject data into
LLM responses.
* fix(proxy): also fix async_post_call_streaming_hook to use return value
Previously the streaming hook only used return values that started with
"data: " (SSE format). Now any non-None return value is used, consistent
with async_post_call_success_hook and streaming iterator hook behavior.
Added tests for streaming hook transformation.
---------
Co-authored-by: Gabriele Michelli <michelligabriele0@gmail.com>
- Add @lru_cache decorator to get_model_info() and _cached_get_model_info_helper()
- Update _invalidate_model_cost_lowercase_map() to clear these caches when model_cost changes
- Update test to call cache invalidation after modifying litellm.model_cost
Reduces get_model_cost_information from 46% to <1% of request handling time.
* Fix: SSO user roles are not updated for existing users
Fixes#19620
* Refactor: Remove redundant user_info retrieval in SSOAuthenticationHandler
* Test: add new tests for user creation and updates in get_user_info_from_db
The websearch interception handler was passing internal flags like
`_websearch_interception_converted_stream` to the follow-up LLM request.
This caused "Extra inputs are not permitted" errors from providers like
Bedrock that use strict Pydantic validation.
Fix: Filter out all kwargs starting with `_websearch_interception` prefix
before making the follow-up anthropic_messages.acreate() call.
The Azure Responses API uses a different schema (flattened) for tools compared to the standard OpenAI/Azure Chat Completions API (nested). This caused a `BadRequestError` when users passed standard tool definitions.
Changes:
- Implemented tool flattening logic in `AzureOpenAIResponsesAPIConfig.transform_responses_api_request`.
- Added comprehensive unit tests in test_azure_transformation.py to verify nested-to-flat transformation, pass-through of flat tools, and immutability.
- Ensures cross-provider compatibility for tool definitions.
Fixes#19523
