Fixes#19508
The cost calculation for gpt-image-1.5 was not including image tokens
from output_tokens_details, causing costs to be underreported
(e.g., $0.046 instead of $0.14).
Root cause: The OpenAI image generation API uses Responses API naming
(input_tokens, output_tokens, output_tokens_details) but the cost
calculator expected Chat Completions API naming (prompt_tokens,
completion_tokens, completion_tokens_details).
Changes:
- convert_dict_to_response.py: Map Responses API fields to Chat
Completions API fields and convert dicts to wrapper objects
- cost_calculator.py: Use usage directly if already transformed,
avoiding double transformation that lost the wrapper objects
- Added test for gpt-image-1.5 output image token cost calculation
* Enhance error handling for num_retries in Router class to support string values. Add test case to verify conversion from string to int for deployment num_retries.
* Refactor Router class for improved readability by formatting long lines and enhancing exception handling tests for num_retries. Ensure consistent style in test cases for better maintainability.
* Update exception handling for num_retries in Router class to suppress mypy warnings. Add type ignore comment for clarity in type conversion from string to int.
* added support for nova grounding for amazon nova model
* added citations support
* added integration tests
* removing test file
* refactor: Use web_search_options for Nova grounding instead of system_tool
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Co-authored-by: Juhie <juhiechandra@gmail.com>
Co-authored-by: Juhie <75068056+juhiechandra@users.noreply.github.com>
The websearch interception handler was passing internal flags like
`_websearch_interception_converted_stream` to the follow-up LLM request.
This caused "Extra inputs are not permitted" errors from providers like
Bedrock that use strict Pydantic validation.
Fix: Filter out all kwargs starting with `_websearch_interception` prefix
before making the follow-up anthropic_messages.acreate() call.
The Azure Responses API uses a different schema (flattened) for tools compared to the standard OpenAI/Azure Chat Completions API (nested). This caused a `BadRequestError` when users passed standard tool definitions.
Changes:
- Implemented tool flattening logic in `AzureOpenAIResponsesAPIConfig.transform_responses_api_request`.
- Added comprehensive unit tests in test_azure_transformation.py to verify nested-to-flat transformation, pass-through of flat tools, and immutability.
- Ensures cross-provider compatibility for tool definitions.
Fixes#19523
* Consolidated change
* fix(prompt_security): update message processing to persist sanitized files and filter for API calls
* fix per krrishdholakia suggestion
Forward static_headers from /mcp-rest/test/* routes into the MCP client so headers are present during session.initialize() and tool discovery.
Also add a shared merge_mcp_headers() helper to keep header precedence consistent and ensure OpenAPI-to-MCP generated tools include static_headers.
Tests:
- pytest tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py
- pytest tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py -k register_openapi_tools_includes_static_headers
Fixes#19341
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
