* refactor: migrate utils.py lazy imports to registry pattern
- Refactored utils.py __getattr__ to use cached registry pattern (similar to __init__.py)
- Added UTILS_MODULE_NAMES tuple and _UTILS_MODULE_IMPORT_MAP to _lazy_imports_registry.py
- Added _get_utils_globals() helper function to _lazy_imports.py
- Added _lazy_import_utils_module() handler function for utils module lazy imports
- Updated _get_lazy_import_registry() to include utils module lazy imports
- Removed redundant _get_utils_globals() from utils.py (now in _lazy_imports.py)
- Added comprehensive tests for utils module lazy imports in test_lazy_imports.py
This refactoring:
- Reduces code duplication (from 670+ lines to ~10 lines in __getattr__)
- Improves maintainability (new lazy imports just need registry entry)
- Maintains consistency with __init__.py lazy import pattern
- All existing functionality preserved and tested
* Fix NameError: get_coroutine_checker not defined in check_coroutine function
* Fix lazy loading for get_coroutine_checker in function_setup and check_coroutine
- Add lazy loading for get_coroutine_checker at start of function_setup to ensure all calls use lazy-loaded version
- Fix check_coroutine function to use lazy loading pattern via getattr
- All direct calls to get_coroutine_checker() now properly use lazy import mechanism
- Keep **kwargs approach (no exec()) for security
- Integrate path traversal validation from main branch
- Add URL encoding for path parameters
- Merge both test suites (edge cases + security tests)
- All 14 tests passing
Fix metric name inconsistency for litellm_remaining_requests_metric
and litellm_remaining_tokens_metric. The factory received names
without the _metric suffix, causing _is_metric_enabled to fail when
users configured these metrics in prometheus_metrics_config.
Fixes#18221
Signed-off-by: majiayu000 <1835304752@qq.com>
Fix Ollama_chatException "illegal base64 data at input byte 4" error
when using images with ollama_chat provider. Ollama expects pure base64
data, not the full data URL format (data:image/png;base64,...).
Fixes#18338
Signed-off-by: majiayu000 <1835304752@qq.com>
Add support for Z.AI GLM-4.7, latest flagship model with enhanced reasoning capabilities.
Changes:
- Add zai/glm-4.7 to model pricing with /bin/bash.60/M input, .20/M output
- Add cached input pricing (/bin/bash.11/M) for GLM-4.7
- Add supports_reasoning flag to enable thinking parameter
- Update ZAIChatConfig to support thinking parameter for models with reasoning
- Update documentation with GLM-4.7 as latest flagship model
- Add cached input column to pricing table (GLM-4.7 only)
- Add tests for GLM-4.7 reasoning support and cost calculation
- Update all examples to use GLM-4.7
Model specifications:
- Context: 200K input, 128K output
- Supports: reasoning, function calling, tool choice, prompt caching
- Pricing: Same as GLM-4.6 with cache support
See: https://docs.z.ai/guides/llm/glm-4.7
Add the output_text convenience property to ResponsesAPIResponse that
aggregates all output_text items from the output list, matching the
OpenAI SDK's Response.output_text behavior.
The property iterates through output items, collects text content from
message-type outputs, and returns them concatenated into a single
string. Returns empty string if no output_text content exists.
Handles both dict and Pydantic model access patterns for compatibility
with different output formats.
Fixes#18470🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: yurekami <yurekami@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Fixes#18599
When OpenAI models (gpt-5-nano, o1-*, o3-*) and other providers return
reasoning_tokens in completion_tokens_details but don't provide text_tokens,
LiteLLM was incorrectly calculating costs using only reasoning_tokens,
ignoring the remaining completion tokens.
Changes:
- Modified generic_cost_per_token() in llm_cost_calc/utils.py to calculate
text_tokens as: completion_tokens - reasoning_tokens - audio_tokens - image_tokens
when text_tokens is not explicitly provided
- Added comprehensive test case test_reasoning_tokens_without_text_tokens_gpt5_nano()
to verify all completion_tokens are billed correctly
Example:
- completion_tokens: 977
- reasoning_tokens: 768
- Before: only 768 tokens billed (21% less)
- After: all 977 tokens billed correctly
Affected models:
- OpenAI: gpt-5-nano, o1-*, o3-*
- Perplexity: sonar-reasoning*
- Any model returning reasoning_tokens without text_tokens
- /v2/model/info now returns {"data": []} when llm_router is None or model_list is empty
- /model_group/info now returns {"data": []} when llm_model_list is None or empty
- Fixes UI crash on fresh installs with STORE_MODEL_IN_DB=True
- Added 4 unit tests for empty model list scenarios
* fix(cost_calculator): correct gpt-image-1 cost calculation using token-based pricing (#13847)
gpt-image-1 uses token-based pricing (like chat models), not pixel-based pricing
like DALL-E. The old code was calculating incorrect costs by treating it as DALL-E.
Changes:
- Update model pricing JSON with correct token-based costs for gpt-image-1
- Add dedicated cost calculator for OpenAI gpt-image models
- Route gpt-image-1 to token-based calculator in cost router
- Add comprehensive tests for the new calculator
* refactor: simplify gpt-image-1 cost calculator using responses API helper
Reuse _transform_response_api_usage_to_chat_usage and generic_cost_per_token
for gpt-image-1 cost calculation since ImageUsage has the same spec as
ResponseAPIUsage.
* Allow get_nested_value dot notation to support escaping for Kubernetes JWT Support
* Add support for team and org alias fields, add docs, tests
* Fix lint issue with max statements in handle jwt logic
