* feat(litellm_content_filter.py): add support for content filtering categories
make it easy for proxy admin to prevent messages about violence, self harm or illegal weapons going through litellm
* feat: initial commit adding bias detection
allows admin to block inappropriate content about sexual orientation, etc.
* refactor: simplify content_filter.py
use a more exhaustive set of keywords, instead of guessing at potential phrases user can use
* feat(content_filter.py): add new denied topics for in-built content filter guardrails
allow user to automatically block content relating to certain categories from being sent to the LLML
* refactor(content-filter): document new params to litellm content filter
* feat(ui/): litellm content filter - select content categories on ui
* docs: update documentation
* docs(litellm_content_filter.md): document new content filters
* feat: initial commit adding support for inappropriate images via litellm content filter
* feat(content_filter.py): support blocking images containing blocked content
prevent images which contain disallowed content from being sent to the llm api
* docs(litellm_content_filter.md): document new image capabilities of litellm_content_filter
* fix: fix expected error code
The _add_tag_to_deployment function was directly modifying the
deployment's litellm_params in memory and writing it back to the
database, which caused encrypted API keys and other sensitive fields
to be lost. This fix retrieves the model from the database first,
preserves all existing fields including encrypted ones, adds only the
new tag to the tags array, and updates the database with the modified
params while keeping encrypted fields intact.
Added comprehensive unit tests covering preservation of encrypted
fields, handling of both string and dict litellm_params formats,
duplicate tag prevention, and error handling for missing models.
Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude <noreply@anthropic.com>
Claude models on Azure AI were incorrectly using AzureAIStudioConfig,
causing tool calls to fail with invalid_request_error because tools
remained in OpenAI format instead of being transformed to Anthropic format.
extract model id from vertex ai passthrough routes that follow the pattern:
/vertex_ai/*/models/{model_id}:*
the model extraction now handles vertex ai routes by regex matching the model
segment from the url path, which allows proper model identification for
authentication and authorization in proxy pass-through endpoints.
adds comprehensive test coverage for vertex ai model extraction including:
- various vertex api versions (v1, v1beta1)
- different locations (us-central1, asia-southeast1)
- model names with special suffixes (gemini-1.5-pro, gemini-2.0-flash)
- precedence verification (request body model over url)
- non-vertex route isolation
