* attempt to implement the passthrough feature
* Formatting and small change
* Fix formatting
* Format test file
---------
Co-authored-by: Xiaohan Fu <xiaohan@grayswan.ai>
* Use auth key name if there are no app id in in headers or in extra_data
* use key alias instead of key name
* Fix
* last priority key alias
* Fix
* Add tests
* fix(spend-logs): trim logged response strings
- route spend-log responses through the existing string sanitizer so oversized base64/text fields are truncated before persistence
- add unit tests covering the truncation path and the feature flag
Note: embeddings-specific truncation (numeric vectors) is still pending and will be handled separately.
* remove unnecessary comment
* add: sanitization unit test for embeddings
* fix: simplify sanatization logic
I overcomplicated a simple change for lack of understanding, fixed.
* Add support for vector store files endpoints (#16490)
* Add base code for vector store integration
* fix azure related tests and linting error
* fix mypy errors
* Add vector store files documentation
* fix mapped tests
* Add bytedance and ideogram support in fal ai (#16636)
* Add fal ai flux pro v1.1 support (#16578)
* Add fal ai flux pro v1.1 support
* Add tests and docs
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Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
* add NewModelGroupRequest
* add endpoint for create_model_group
* fix model_access_group_management_router
* add UpdateModelGroupRequest, info and delete
* fix model management tag
* fix validate_models_exist
* fix get_all_access_groups_from_db
* test_create_duplicate_access_group_fails
* test fixes
* fix working create access groups
* fix access group management endpoints
* add is db model checks for model access groups
* Refactor proxy embeddings to use shared processor
- allow ProxyBaseLLMRequestProcessing to accept the aembedding route so embeddings requests reuse the base pipeline hooks
- route embeddings requests through base_process_llm_request, sharing logging, hook execution, retries, and header handling with chat/responses
- tighten token array decoding logic by using router deployment lookups and the unified error handler
* Fix: Correctly process embedding requests with token arrays
The `test_embedding_input_array_of_tokens` test was failing due to a regression that caused embedding requests with token arrays to be processed incorrectly. This prevented the `aembedding` function from being called as expected.
This was caused by a combination of three distinct issues:
1. In `litellm/proxy/common_request_processing.py`, the `function_setup` utility was called with `aembedding` as the `original_function` for embedding routes. This has been corrected to `embedding` to ensure proper request setup.
2. In `litellm/proxy/proxy_server.py`, a `TypeError` occurred because the `get_deployment` method was called with the `model_name` keyword argument instead of the expected `model_id`. This has been corrected. Additionally, the check for token arrays was improved to validate that all elements in the input subarray are integers.
3. In `litellm/proxy/litellm_pre_call_utils.py`, the check for the `enforced_params` enterprise feature was too strict. It blocked valid requests even when the `enforced_params` list was empty. The condition has been adjusted to trigger the check only for non-empty lists.
Finally, the `test_embedding_input_array_of_tokens` assertion was updated to be more robust. The previous `assert_called_once_with` was overly strict, causing failures when unrelated internal parameters were added to the function call. The test now first asserts that `aembedding` is called and then separately verifies the `model` and `input` arguments. This makes the test more resilient to future changes without sacrificing its ability to catch regressions.
* test: align proxy embedding assertions
Update the embedding proxy test to match the new request pipeline: keep the data the proxy builds, expect the extra control kwargs, let the post-call hook return the actual response, and assert the normalized 'embeddings' hook type. This proves the refactor still forwards metadata and returns the mocked payload.
* Update proxy exception test
The proxy now forwards additional kwargs (request_timeout, litellm_call_id, litellm_logging_obj) to llm_router.aembedding. The test needs to accept these to match the real call signature and keep validating the error path instead of the kwargs list.
* testing: unsure of this change
I don't remember why I changed this, will revert and see if any tests fail since the manual test isn't failing without it.
* fix: remove unrelated change
This change was not related to the embeddings refactor and actually belonged to a different branch.
* Use end user budget instead of key budget when creating new team
* Fixed implementation to use user's max budget from the UserTable instead of EndUserTable
Fixes#16470
Video generation endpoints (/v1/videos, /videos/{video_id}, etc.) were
incorrectly restricted to proxy_admin role only. These routes are now
added to openai_routes list, making them accessible to internal_user
role as they should be - video generation is a legitimate user feature,
not a management/admin operation.
Changes:
- Added 8 video route patterns to LiteLLMRoutes.openai_routes in _types.py
- Added comprehensive tests verifying internal_user and virtual key access
- All existing route permission tests continue to pass
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude <noreply@anthropic.com>
* fix: ensure end user endpoints use "handle_exception_on_proxy" correctly
* test 404 on info and update for non-existent user
* test 404 for no customer found
* fix 404 handling for customer endpoints
* test_error_schema_consistency
* test_customer_endpoints_error_schema_consistency
Derek Duenas