This enables Oracle Cloud Infrastructure (OCI) GenAI authentication via the UI
by allowing users to paste their PEM private key content directly into a
multiline textarea field.
Changes:
- Add `textarea` field type to UI component system
- Configure OCI provider with proper credential fields (oci_key, oci_user,
oci_fingerprint, oci_tenancy, oci_region, oci_compartment_id)
- Handle PEM content newline normalization (\\n -> \n, \r\n -> \n)
- Use OCIError for consistent error handling
Previously OCI only supported file-based authentication (oci_key_file), which
doesn't work for UI-based model configuration. This adds support for inline
PEM content via the new oci_key field.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude <noreply@anthropic.com>
* docs vertex tts
* place vertex ai types in file
* use VertexAITextToSpeechConfig
* use vertex_voice_dict
* refactor docs
* docs vertex ai chirp
* TestVertexAITextToSpeechConfig
* new provider vertex ai chirp3
* test_litellm_speech_vertex_ai_chirp
* add vertex_ai/chirp cost trackign
- Add model identifier to FLASH_IMAGE_PREVIEW_MODEL_IDENTIFIERS
- Add imageSize parameter support (1K, 2K, 4K) with GeminiImageSize type
- Add tests for imageSize parameter transformation
- Update documentation with new model
* Add imageConfig parameter support for Vertex AI to enable gemini-2.5-flash-image model requirements
* Add test for imageConfig parameter support in Vertex AI Gemini transformation
* fix(utils.py): support non default params for audio transcription
allows passing provider specific params straight through on transcription calls
* fix(gpt_transformation.py): fix o_series model routing
call _transform_request on async event
* refactor: refactor tests
* test(test_azure_chat_o_series_transformation.py): add unit test for azure o series error
* test: update test
* test: update json
* fix: fix mutiple keyword error
* feat: Make gemini accept the openai parameter parallel_tool_calls
When mapping, allow the parameter: True because that is the
intrinsic behavior of Gemini. Allow False, but reject if there
are multiple tools because there's no actual equivalent in Gemini.
fixes#9686
ref: issues/9686
* chore: cleanup and move test_vertex.py down to tests/litellm
as suggested in https://github.com/BerriAI/litellm/pull/11125#discussion_r2105905871
* fix: add flag for disabling use_aiohttp_transport
* feat: add _create_async_transport
* feat: fixes for transport
* add httpx-aiohttp
* feat: fixes for transport
* refactor: fixes for transport
* build: fix deps
* fixes: test fixes
* fix: ensure aiohttp does not auto set content type
* test: test fixes
* feat: add LiteLLMAiohttpTransport
* fix: fixes for responses API handling
* test: fixes for responses API handling
* test: fixes for responses API handling
* feat: fixes for transport
* fix: base embedding handler
* test: test_async_http_handler_force_ipv4
* test: fix failing deepeval test
* fix: add YARL for bedrock urls
* fix: issues with transport
* fix: comment out linting issues
* test fix
* test: XAI is unstable
* test: fixes for using respx
* test: XAI fixes
* test: XAI fixes
* test: infinity testing fixes
* docs(config_settings.md): document param
* test: test_openai_image_edit_litellm_sdk
* test: remove deprecated test
* bump respx==0.22.0
* test: test_xai_message_name_filtering
* test: fix anthropic test after bumping httpx
* use n 4 for mapped tests (#11109)
* fix: use 1 session per event loop
* test: test_client_session_helper
* fix: linting error
* fix: resolving GET requests on httpx 0.28.1
* test fixes proxy unit tests
* fix: add ssl verify settings
* fix: proxy unit tests
* fix: refactor
* tests: basic unit tests for aiohttp transports
* tests: fixes xai
---------
Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com>
* 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
* 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
* 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
* feat(key_management_endpoints.py): add validation checks for migrating key to team
Ensures requests with migrated key can actually succeed
Prevent migrated keys from failing in prod due to team missing required permissions
* fix(mistral/): fix image url handling for mistral on async call
* fix(key_management_endpoints.py): improve check for running team validation on key update
- docs/my-website/docs/providers/lm_studio.md: add Structured Output section with JSON schema and Pydantic examples
- litellm/llms/lm_studio/chat/transformation.py: extend map_openai_params to handle `response_format` mappings (`json_schema`, `json_object`) and move them to optional_params
- litellm/utils.py: include `LM_STUDIO` in `supports_response_schema` list
- tests/litellm/llms/lm_studio/test_lm_studio_chat_transformation.py: add tests for Pydantic model and dict-based JSON schema handling
Co-authored-by: Earl St Sauver <estasuver@gmail.com>
* fix(openai/gpt_transformation.py): handle missing filename for openai file data call
* fix(openai/gpt_transformation.py): clean handling for sync + async pdf url transformation flows
Fixes https://github.com/BerriAI/litellm/issues/10820
* build(model_prices_and_context_window.json): add 'supports_pdf_input' for all openai models which have 'vision' support
Follows openai guidelines
* feat(bedrock/chat): support cache pointing tool calls on Bedrock
Closes https://github.com/BerriAI/litellm/pull/10613
* fix: fix linting error
If the client sets the `labels` field in the request to the LiteLLM:
- pass the `labels` field to the Vertex AI backend
If the client sets the `metadata` field in the request to the LiteLLM:
- if the `labels` field is not set, fill it with `metadata` key/value
pairs for all string values
* fix(duration_parser.py): support `mo` unit
* test(test_key_management_endpoints.py): add test confirming generate_key_helper_fn uses predictable budgets
Closes https://github.com/BerriAI/litellm/issues/10800
* fix(anthropic/chat/transformation.py): add tool use cost tracking
* fix(anthropic/): refactor how hosted tool usage tracking is done
keep it separate from prompt / completion token details
* fix(anthropic/): add web search tool cost tracking
accurate cost tracking
* feat(anthropic/chat/transformation.py): map openai 'web_search_options' param to anthropic hosted tool
Allows calling anthropic web search in same format as openai
* feat(anthropic/chat/transformation.py): support unified anthropic 'web_search_options' param
Allows calling anthropic's web search tool in the openai format
* feat(anthropic/chat/transformation.py): map openai 'search_context_size' to anthropic 'max_uses' param
Translate search effort across both providers
* fix: mark web_search_options param as supported by openai + azure
* fix: fix linting error
* fix: fix linting errors
* fix: fix linting error
* fix: check if usage hasattr
* fix: pass web search options param
* fix(main.py): use base model instead of user model if given
Fixes https://github.com/BerriAI/litellm/issues/10760
* feat(azure/image_generation/__init__.py): make azure image gen check more robust
Fixes https://github.com/BerriAI/litellm/issues/10760
* fix(user_api_key_auth.py): support bearer token auth for `x-litellm-api-key` header
Fixes earlier regression on vertex ai passthrough auth
* fix(user_api_key_auth.py): refactor get api key into separate function
enables easier testing
* fix: cleanup
* fix: fix linting error
* fix: cleanup
* test: update tests
* Add new model provider Novita AI (#7582)
* feat: add new model provider Novita AI
* feat: use deepseek r1 model for examples in Novita AI docs
* fix: fix tests
* fix: fix tests for novita
* fix: fix novita transformation
* ci: fix ci yaml
* fix: fix novita transformation and test (#10056)
---------
Co-authored-by: Jason <ggbbddjm@gmail.com>
* Fixed Json.dumps in JSON Schema Validation Error
* Added Response Schema to Ollama chat for structured response
* Added Test cases
* refactor(ollama): remove redundant response_format check
The response_format parameter conversion is already handled in utils.py's
get_optional_params function, making the duplicate check in ollama_chat.py
unnecessary. This change removes the redundant code while maintaining the
same functionality.
* Azure LLM: fix passing through of azure_ad_token_provider parameter
* add test
---------
Co-authored-by: Clara Luise Pohland <clara-luise.pohland@telekom.de>
* Add support for nscale provider
* Add image generation support and fix unit tests
* Add docs for nscale
* Fix unit test import issues
* Minor doc improvement
* Remove redundant null tokens from model cost map
* Address PR review comments for doc updates
* Revert changes to large text
