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
- Add TYPES_NAMES tuple and _lazy_import_types function in _lazy_imports.py
- Remove direct import of GuardrailItem from __init__.py
- Add lazy loading handler in __getattr__ to dispatch type imports
- Add type stub for GuardrailItem in TYPE_CHECKING block
- Add test_types_lazy_imports test to verify lazy loading works
- Use from __future__ import annotations for forward reference support
- Follows same pattern as DOTPROMPT_NAMES and LLM_CONFIG_NAMES for consistency
- Add LLM_CONFIG_NAMES tuple and _lazy_import_llm_configs function in _lazy_imports.py
- Remove direct import of AmazonConverseConfig from __init__.py
- Add lazy loading handler in __getattr__ to dispatch LLM config imports
- Add type stub for AmazonConverseConfig in TYPE_CHECKING block
- Add test_llm_config_lazy_imports test to verify lazy loading works
- Follows same pattern as DOTPROMPT_NAMES for consistency
- Remove 20 deprecated/unavailable Groq models from registry
- Add groq/meta-llama/llama-guard-4-12b (new safety model)
- Add supports_vision to Llama 4 models (maverick, scout)
- Update Groq documentation with current model list
- Clean up test file references to deprecated models
Fixes#18043
Fixes#17477
Guardrails couldn't access request headers (like User-Agent) on Bedrock
pass-through endpoints because headers were only stored in
data["proxy_server_request"]["headers"] but not in data["metadata"]["headers"]
where guardrails typically look for them.
This fix adds headers to metadata in add_litellm_data_to_request() so
guardrails can access User-Agent, API keys, and other header-based checks
on all endpoints including Bedrock pass-through.
Test added to verify headers are available in metadata for guardrails.
- Add 'mask' action to SUPPORTED_ON_FLAGGED_ACTIONS
- Automatically sanitizes sensitive content using masked_session_messages
- Allows requests to proceed with masked content instead of blocking
- Add MCP call support
- Add pre_mcp_call and during_mcp_call to supported_event_hooks
- Verify mcp_call is supported in call_type Literal types
- Control exception details based on config
- Conditionally include scanners/evidence in exceptions based on
include_scanners and include_evidence settings
- Reduces payload size when detailed exception info isn't needed
- Add comprehensive test coverage
- Tests for masking functionality
- Tests for conditional exception details
- Tests for MCP call support
- Update documentation
- Add Mask section explaining masking functionality
- Clarify exception details control
All changes maintain backward compatibility.
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
