* feat(audit_logging_endpoints.py): expose list endpoint to show all audit logs
make it easier for user to retrieve individual endpoints
* feat(enterprise/): add audit logging endpoint
* feat(audit_logging_endpoints.py): expose new GET `/audit/{id}` endpoint
make it easier to retrieve view individual audit logs
* feat(key_management_event_hooks.py): correctly show the key of the user who initiated the change
* fix(key_management_event_hooks.py): add key rotations as an audit log event
'
* test(test_audit_logging_endpoints.py): add simple unit testing for audit log endpoint
* fix: testing fixes
* fix: fix ruff check
* fix: improve health check logic by deep copying model list on each iteration
* test: add async test for background health check reflecting model list changes
* fix: validate health check interval before executing background health check
* fix: specify type for health check results dictionary
* fix: cleanup print statement
* feat(managed_files.py): add auth check on managed files
Implemented for file retrieve + delete calls
* feat(files_endpoints.py): support returning files by model name
enables managed file support
* feat(managed_files/): filter list of files by the ones created by user
prevents user from seeing another file
* test: update test
* fix(files_endpoints.py): list_files - always default to provider based routing
* build: add new table to prisma schema
* feat(user_api_key_auth.py): (enterprise) allow user to enable custom auth + litellm api key auth
makes it easy to migrate to proxy
* fix(proxy/_types.py): allow setting 'spend' for new customer
* fix(customer_endpoints.py): fix updating max budget on `/customer/update`
Fixes https://github.com/BerriAI/litellm/issues/6920
* test(test_customer_endpoints.py): add unit tests for customer update endpoint
* fix: fix linting error
* fix(custom_auth_auto.py): fix ruff check
* fix(customer_endpoints.py): fix documentation
* Add handling and verification for 'usage' field in OpenRouter chat transformations and streaming responses.
* Ensure consistent response ID by using valid ID from any chunk.
* Remove redundant comments from OpenRouter chat transformation tests and logic.
* Remove this from here as I'm opening a new pr
* Reverting space
* Remove redundant assertions from OpenRouter chat transformation test
* feat(helm): Add loadBalancerClass support for LoadBalancer services
Adds the ability to specify a loadBalancerClass when using LoadBalancer service type.
This enables integration with custom load balancer implementations like Tailscale.
* fixup! feat(helm): Add loadBalancerClass support for LoadBalancer services
With tzdata installed, the environment variable `TZ` will be respected by Python's datetime module. This means that users can specify the timezone they want LiteLLM to use.
Co-authored-by: Simon Stone <sipreuss@gmail.com>
* Add LiteLLM Managed file support for `retrieve`, `list` and `cancel` finetuning jobs (#11033)
* feat: initial commit adding managed file support to fine tuning endpoints
* feat(fine_tuning/endpoints.py): working call to openai finetuning route
Uses litellm managed files for finetuning api support
* feat(fine-tuning/main.py): refactor to use LiteLLMFineTuningJob pydantic object
includes 'hidden_params'
* fix: initial commit adding unified finetuning id support
return a unified finetuning id we can use to understand which deployment to route the ft request to
* test: fix test
* feat(managed_files.py): return unified finetuning job id on create finetuning job
enables retrieve, delete to work with litellm managed files
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(fine_tuning_endpoints/endpoints.py): add managed files support to list finetuning jobs
* feat(finetuning_endpoints/main): add managed files support for retrieving ft job
Makes it easier to control permissions for ft endpoint
* LiteLLM Managed Files - Enforce validation check if user can access finetuning job (#11034)
* feat: initial commit adding managed file support to fine tuning endpoints
* feat(fine_tuning/endpoints.py): working call to openai finetuning route
Uses litellm managed files for finetuning api support
* feat(fine-tuning/main.py): refactor to use LiteLLMFineTuningJob pydantic object
includes 'hidden_params'
* fix: initial commit adding unified finetuning id support
return a unified finetuning id we can use to understand which deployment to route the ft request to
* test: fix test
* feat(managed_files.py): return unified finetuning job id on create finetuning job
enables retrieve, delete to work with litellm managed files
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(fine_tuning_endpoints/endpoints.py): add managed files support to list finetuning jobs
* feat(finetuning_endpoints/main): add managed files support for retrieving ft job
Makes it easier to control permissions for ft endpoint
* feat(managed_files.py): store create fine-tune / batch response object in db
storing this allows us to filter files returned on list based on what user created
* feat(managed_files.py): Ensures users can't retrieve / modify each others jobs
* fix: fix check
* fix: fix ruff check errors
* test: update to handle testing
* fix: suppress linting warning - openai 'seed' is none on azure
* test: update tests
* test: update test
* feat: initial commit adding managed file support to fine tuning endpoints
* feat(fine_tuning/endpoints.py): working call to openai finetuning route
Uses litellm managed files for finetuning api support
* feat(fine-tuning/main.py): refactor to use LiteLLMFineTuningJob pydantic object
includes 'hidden_params'
* fix: initial commit adding unified finetuning id support
return a unified finetuning id we can use to understand which deployment to route the ft request to
* test: fix test
* feat(managed_files.py): return unified finetuning job id on create finetuning job
enables retrieve, delete to work with litellm managed files
* test: update test
* fix: fix linting error
* fix: fix ruff linting error
* test: fix check
* fix: handle dict objects in Anthropic streaming response
Fix issue where dictionary objects in Anthropic streaming responses
were not properly converted to SSE format strings before being yielded,
causing AttributeError: 'dict' object has no attribute 'encode'
* fix: refactor Anthropic streaming response handling
- Added STREAM_SSE_DATA_PREFIX constant in constants.py
- Created return_anthropic_chunk helper function for better maintainability
- Using safe_dumps from safe_json_dumps.py for improved JSON serialization
- Added unit test for dictionary object handling in streaming response
* fix: correct patch path in anthropic_endpoints test
