* fix(team_info.tsx): allow setting custom key duration
more flexible than previous pre-set options
* feat(team_info.tsx): show how many user + service account keys have been created within a team
* fix(team_endpoints.py): ensure user id correctly added when new team created with user email as member
Fixes issue where user not correctly added to team on /team/new
* fix(internal_user_endpoints.py): make user email validation check case insensitive
Fixes issue where uppercase email was added even when lowercase email existed
* test: update test
* build: move build_and_test to use prisma migrate
* feat(proxy_setting_endpoints.py): encrypt env var before storing in db
Ensures env var can be read when loaded in from DB
Fixes issue when trying to add SSO from admin UI
* test: update tests
* Check content and order of trimmed messages
* Assert tool calls are preserved if below max_tokens
* Unreverse order of tool calls
* Return tool calls alongside other messages
* Write test for trimming untokenizable field
* Return original messages in case of exception
* Add concise Claude Code + LiteLLM Gateway tutorial
- Create focused tutorial matching existing tutorial style
- Step-by-step guide from installation to advanced configurations
- Multi-provider configuration examples (AWS Bedrock, Azure OpenAI, Load Balancing)
- Based on Anthropic's official LiteLLM configuration documentation
- Added to sidebar with clean title 'Use LiteLLM with Claude Code'
- Fixed sidebar reference from 'secret' to 'set_keys' for proper document resolution
* Update config_settings.md to correct documentation links for key management and Hashicorp Vault settings. Changed references from 'secret.md' to 'set_keys.md' for improved clarity and accuracy.
* Update sidebar and config_settings.md to reflect changes in key management documentation. Changed sidebar reference from 'set_keys' to 'secret' and updated links in config_settings.md for Hashicorp Vault settings to point to 'secret.md' for improved accuracy.
* Remove extra tutorial and update sidebar accordingly
* Update tutorial title from 'WebUI' to 'Open WebUI' for clarity and consistency in documentation.
* Remove Python version requirement from Claude Responses API tutorial for clarity and to align with updated prerequisites.
* feat: add input_fidelity parameter for OpenAI image generation
- Add input_fidelity to OpenAIImageGenerationOptionalParams type
- Update image_generation function signature to accept input_fidelity
- Add input_fidelity to default_params in get_optional_params_image_gen
- Include input_fidelity in openai_params list for proper handling
- Update documentation with input_fidelity parameter description
- Add test for input_fidelity parameter functionality
This enables control over how closely the model follows the input prompt
for gpt-image-1 model, improving prompt adherence and image quality.
* feat: add input_fidelity to optional parameters for image generation
- Include input_fidelity in the list of OpenAIImageGenerationOptionalParams
- This addition enhances the flexibility of image generation by allowing control over input fidelity.
* test: enhance test for gpt-image-1 with input_fidelity parameter
- Update test_gpt_image_1_with_input_fidelity to include mocking of OpenAI response
- Validate that the OpenAI client is called with correct parameters, including input_fidelity
- Improve response validation to ensure expected output structure and values
* Add comprehensive GitHub Copilot + LiteLLM integration tutorial
- Complete setup guide from installation to production deployment
- Multiple configuration examples including authentication, load balancing, and cost tracking
- Docker and Kubernetes deployment configurations
- Troubleshooting section with common issues and solutions
- Best practices for security, monitoring, and reliability
- Usage examples for code completion, chat interface, and direct API integration
* Add concise GitHub Copilot + LiteLLM tutorial
- Create focused tutorial matching Gemini CLI style
- Step-by-step guide from installation to production deployment
- Multi-provider configuration examples (OpenAI, Anthropic, Bedrock)
- Load balancing and fallback configuration
- Docker deployment instructions
- Troubleshooting section with common issues
- Updated sidebar with clean title 'Use LiteLLM with GitHub Copilot'
* Refactor GitHub Copilot integration tutorial
- Removed outdated production deployment and direct API usage sections
- Streamlined troubleshooting steps for clarity
- Ensured documentation aligns with current best practices and configurations
* Add proper credit to Sergio Pino for GitHub Copilot tutorial
- Reference original DEV.to article in info box
- Add credits section acknowledging foundational work
- Maintain attribution to original author's guide
* fix: Handle circular references in spend tracking metadata JSON serialization
- Fixes issue #12634 where circular references in metadata caused
ValueError: Circular reference detected when logging spend data
- Adds _safe_json_dumps() function that detects and handles circular
references by replacing them with placeholder strings
- Maintains full functionality for normal objects while preventing
crashes from circular references
- Adds comprehensive tests for circular reference handling
- Critical fix for v1.74.3 stable release
* fix: Replace bare except clauses with specific Exception handling
- Fixes E722 linting errors in _safe_json_dumps function
- Maintains same error handling behavior while following best practices
- All tests continue to pass
* refactor: Use existing safe_dumps utility instead of custom implementation
- Replace custom _safe_json_dumps() with existing safe_dumps() from litellm_core_utils
- Remove duplicate code and leverage existing circular reference handling
- Update tests to use safe_dumps function
- Maintains same functionality while reducing code duplication
- All tests continue to pass
