* fix(moonshot): preserve reasoning_content on Pydantic Message objects in multi-turn tool calls
The condition 'reasoning_content not in msg' doesn't work correctly for
Pydantic Message objects because they don't support the 'in' operator
like dicts do. This caused reasoning_content to be stripped from
assistant messages in multi-turn conversation history.
Changed the condition to use msg.get('reasoning_content') instead,
which works correctly for both dicts and Pydantic models.
Fixes#23765
* added newline eof
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update tests/test_litellm/llms/moonshot/test_moonshot_chat_transformation.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Simplify assertions in test_moonshot_chat_transformation
Removed redundant assertions for non-assistant messages.
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Co-authored-by: BillionClaw <267901332+BillionClaw@users.noreply.github.com>
Co-authored-by: Aarish Alam <arishalam121@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
- Set output_cost_per_second to 0.0 (was 0.0001) for whisper-1 and
azure/whisper-1: transcription is billed on input duration only,
not output duration
- Fix cost_per_second() in openai/cost_calculation.py: change elif to if
so input_cost_per_second is evaluated independently of output_cost_per_second,
and remove the erroneous completion_cost = 0.0 assignment that masked
any previously-set output cost
- Add TestCostPerSecondArithmetic unit tests covering both cost fields,
the None-guard, and zero-duration edge case
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
The Gemini REST API documents the embedding task type parameter as
camelCase `taskType`. The existing transformation functions convert
`dimensions` to `outputDimensionality` but miss the parallel
`task_type` to `taskType` conversion. This adds that conversion to
both `transform_openai_input_gemini_content` (batchEmbedContents path)
and `transform_openai_input_gemini_embed_content` (embedContent path).
Fixes#24190
Add Google AI Studio (gemini/) and Vertex AI (vertex_ai/) as
supported providers for prompt caching. Same cache_control format
as Anthropic works - LiteLLM translates it to Google's context
caching API under the hood.
Includes SDK + proxy examples for both providers.
Non-streaming paths call _process_hidden_params_and_response_cost; streaming
assembles the full response later and skipped that, so litellm_params.metadata
lacked hidden_params (e.g. response_cost for OTEL/OpenSearch).
- Add _merge_hidden_params_from_response_into_metadata and call it from
success_handler and async_success_handler after cost is set, before
_build_standard_logging_payload.
- Unit tests for merge helper.
Tests: pytest tests/test_litellm/litellm_core_utils/test_litellm_logging.py
Made-with: Cursor
Addresses Greptile feedback about missing integration tests for PUT/DELETE
when invoked by mcp_server_manager role. Adds tests for edit success/403,
delete success with team cleanup/403, and the _remove_mcp_server_from_team
helper directly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace raw prisma_client.db.litellm_teamtable.update with
handle_update_object_permission from team_endpoints (follows
established helper-function pattern)
- Extract _auto_assign_mcp_server_to_team and
_remove_mcp_server_from_team helpers for reuse and testability
- Update tests to mock at the correct boundaries
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The /v1/messages/count_tokens endpoint was hardcoding the Bedrock runtime
URL, ignoring api_base and aws_bedrock_runtime_endpoint settings. This
aligns it with invoke/converse handlers by using the existing
get_runtime_endpoint() method for consistent endpoint resolution.
Signed-off-by: stias <seokjun.yang@mycraft.kr>
* fix: resolve recursion in OVHCloud get_supported_openai_params (#24111)
Root cause: OVHCloudChatConfig.get_supported_openai_params() called
get_model_info() which called back into get_supported_openai_params(),
causing infinite recursion and falling back to no tools support.
Made-with: Cursor
* fix: use .get() for TypedDict + don't hardcode function_calling support
Made-with: Cursor
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Co-authored-by: themavik <themavik@users.noreply.github.com>
AntD v5 static message API doesn't render without an App wrapper or
useMessage() context holder. Mirrors the existing notification pattern
by adding message.useMessage() to AntdGlobalProvider and routing all
calls through a new MessageManager module.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
