- 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
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.
2025-12-16 08:49:03 +05:30
Ishaan JaffGitHubCopilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
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
* prioritized bedrock guardrail blocking by removing early return based on masking flags
When mask_request_content: true or mask_response_content: true, the method immediately returning False.
The Result: Even when Bedrock Guardrails returned action: "BLOCKED" for dangerous content, LiteLLM would not raise an exception and allowing the content through the response.
So removed that early condition which will return true for the blocked actions based on guardrails.
* Added test case for bedrock guardrail block content precedence
* fix(gemini): use JSON instead of form-data for image edit requests
Gemini's image edit API expects JSON body, not multipart/form-data.
The handler was sending form-encoded data which caused 400 errors:
"Invalid JSON payload received. Unexpected token."
Changes:
- Add use_multipart_form_data() method to BaseImageEditConfig (default True)
- Modify image_edit_handler to use json= when use_multipart_form_data() is False
- Override use_multipart_form_data() in GeminiImageEditConfig to return False
* test(gemini): add test for use_multipart_form_data
Claude 3.7 Sonnet's default max_output_tokens is 64000, not 128000.
The 128K output limit requires the beta header 'output-128k-2025-02-19'.
This fixes the integration test failure where requests with max_tokens=128000
were being rejected by the Anthropic API.
Fixes test_multiturn_tool_calls in test_anthropic_responses_api.py
* feat(custom_llm): add image_edit and aimage_edit support
Add support for image_edit and aimage_edit methods in CustomLLM class,
allowing users to implement custom image editing providers.
Changes:
- Add image_edit() and aimage_edit() methods to CustomLLM base class
- Add custom provider detection in litellm.image_edit() function
- Add tests for sync and async image_edit with custom handlers
* docs: add image_edit to CustomLLM documentation
- Add /v1/images/edits to supported routes
- Add Image Edit section with example
- Update Custom Handler Spec with image_edit methods
* fix(unified_guardrails.py): send all chunks on completion of final stream
* feat(generic_guardrail_api.py): handle tool call response on streaming LLM responses
* fix(anthropic/chat/guardrail_translation): initial commit adding anthropic tool response streaming guardrails
enables guardrail checks on tool response from llm's to work via `/v1/messages`
* feat(anthropic/): working guardrail checks on tool response from LLMs
ensures guardrail checks on anthropic /v1/messages works as expected
* feat(responses/guardrail_translation): support tool call response guardrails on streaming for /v1/responses
ensures complete coverage of tool call responses
* refactor(openai.py): refactor to use consistent pydantic model for responses api tool response on streaming
enables non-openai model tool call response to work correctly with guardrail checks on /v1/responses
* test: update tests
* fix: fix linting error
* fix: fix failing tests
* fix: fix import errors
* fix(openai/chat/guardrail_transformation): fix final chunk returned on streaming
* fix(anthropic): use dynamic max_tokens based on model
When users don't specify max_tokens in requests to Anthropic models,
LiteLLM now uses the correct max_output_tokens value from the model
pricing JSON instead of a hardcoded 4096.
This fixes truncated responses for Claude 3.5+ models which support
higher output limits (8192 for Claude 3.5, 128k for Claude 3.7, etc.)
Fixes#8835
* fix(anthropic): restore env var support for backwards compatibility
Keep DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS as fallback when model is not
found in JSON, allowing users to configure via environment variable.
Add direct Stability AI REST API support for image generation endpoints.
This enables using Stability's SD3, SD3.5, and Stable Image models via
LiteLLM's OpenAI-compatible interface.
Changes:
- Add STABILITY provider to LlmProviders enum
- Create StabilityImageGenerationConfig with multipart/form-data support
- Add OpenAI size to Stability aspect_ratio mapping
- Register provider in ProviderConfigManager
- Add 9 Stability models to model_prices_and_context_window.json
- Add documentation at docs/providers/stability.md
- Add 25 unit tests
Supported models:
- stability/sd3, sd3-large, sd3-large-turbo, sd3-medium
- stability/sd3.5-large, sd3.5-large-turbo, sd3.5-medium
- stability/stable-image-ultra, stable-image-core
Fixes#15337
Perplexity API returns pre-calculated costs in `usage.cost.total_cost`
that include the `request_cost` (fixed per-request fee). LiteLLM was
ignoring this and calculating costs manually, resulting in ~27x
underreporting (e.g., $0.0002 vs actual $0.006).
Changes:
- Use `usage.cost.total_cost` from Perplexity response when available
- Fall back to manual calculation if cost object not present
- Add tests for both behaviors
