* Optimize _get_model_cost_key to avoid expensive scans
- Remove expensive O(n) scan fallback that was causing 42.87% CPU overhead
- Only scan when size mismatch detected (O(1) check)
- Add warning in docstring: Only O(1) lookup operations are acceptable
- Clean up comments to be more concise
- Keep stale entry rebuild for pop() case (only triggers when stale entry found)
This fixes the performance issue where the scan was being triggered on every
failed lookup, causing severe CPU overhead during router operations.
* Add code quality check to enforce O(1) operations in _get_model_cost_key
- Add check_get_model_cost_key_performance.py to statically analyze _get_model_cost_key
- Detects O(n) operations (loops, comprehensions, problematic function calls)
- Recursively checks called functions to find nested O(n) operations
- Allows conditional O(n) rebuilds in helper functions (_rebuild_model_cost_lowercase_map, _handle_stale_map_entry_rebuild, _handle_new_key_with_scan)
* Integrate _get_model_cost_key performance check into CI pipeline
- Add check_get_model_cost_key_performance.py to check_code_and_doc_quality job
- Ensures O(1) requirement is enforced in CI to prevent performance regressions
* Remove unused performance test and clean up utils.py
- Remove test_get_model_info_performance.py (no longer needed)
- Remove extra blank line in utils.py
* Document allowed helper functions and exception process in _get_model_cost_key
- Add documentation listing allowed helper functions with O(n) operations
- Explain why these are acceptable (conditionally called)
- Add instructions for adding new exceptions to check_get_model_cost_key_performance.py
* Fix docstring detection and type checker error in performance check
- Add proper docstring tracking to skip docstring content (fixes false positive for 'map' in docstring)
- Add None check for docstring_quote to fix type checker error
- Restore _handle_new_key_with_scan to allowed_helpers list
* Remove check_get_model_cost_key_performance from CI pipeline
- Temporarily remove the performance check from CI to avoid blocking builds
* Restore performance check and remove memory leak tests from CI
- Add back check_get_model_cost_key_performance.py to CI pipeline
- Remove memory_leak_tests job that was causing port conflicts
* Remove extra blank line in CI config
* do not fallback to token counter if disable_token_counter is enabled, and return errors instead
* add exceptions and exception utils to map the same as /v1/chat/completions
* use safe_json_loads
* fix(text_completion): support token IDs (list of integers) as prompt
Add support for passing token IDs (list of integers) to the text_completion
endpoint for OpenAI-compatible providers (openai, azure, vllm, etc.).
Fixes#17118
* test(text_completion): replace live test with mock test for token IDs
Move token IDs test from local_testing to test_litellm with mocks
per PR review feedback.
* feat(bedrock): add OpenAI-compatible service_tier parameter translation
Translates OpenAI's service_tier parameter (string) to Bedrock's
serviceTier format (object with type field).
* docs(bedrock): add OpenAI-compatible service_tier parameter documentation
Document the automatic translation from OpenAI-style service_tier
parameter to Bedrock's native serviceTier format.
* feat(bedrock): add service_tier to response when present
According to OpenAI's API documentation, when service_tier is sent in the
request, it should be returned in the response. This commit implements
this behavior for Bedrock Converse API to maintain compatibility with
OpenAI's API.
Changes:
- Added serviceTier field to ConverseResponseBlock type definition
- Moved ServiceTierBlock definition before ConverseResponseBlock to fix
type reference order
- Added response transformation to map Bedrock serviceTier (object) to
OpenAI service_tier (string format)
- Added 4 new tests for response transformation with service_tier
The service_tier is only added to the response when present in Bedrock's
response, maintaining backward compatibility.
Fixes#18137
Similar to the fix for web_search_tool_result (#17746, #17798), this PR
preserves web_fetch_tool_result blocks in multi-turn conversations.
Changes:
- Add handling for web_fetch_tool_result in transformation.py (non-streaming)
- Add capture of web_fetch_tool_result in handler.py (streaming)
- Fix streaming tool arguments bug where empty input {} was prepended to
actual arguments by using empty string instead of str({})
- Add unit tests for web_fetch_tool_result handling
The OCI adapter now accepts both string and object formats for image_url:
- String: "image_url": "https://example.com/image.png"
- Object: "image_url": {"url": "https://example.com/image.png"}
This fixes compatibility with OpenAI Vision API format.
* fix(gemini): prevent negative text_tokens with explicit caching (#18750)
## Problem
When using Gemini with explicit caching (especially with images),
text_tokens would become negative (e.g., -3327) due to incorrectly
subtracting total cached_tokens from modality-specific text_tokens.
## Root Cause
The old code did:
```python
text_tokens = text_tokens - cached_tokens # 737 - 4064 = -3327
```
This was wrong because:
- cached_tokens includes ALL modalities (text + image + audio + video)
- text_tokens only contains text
- Subtracting total from specific caused negative values
## Solution
Parse cacheTokensDetails to get per-modality cached token breakdown:
```python
if "cacheTokensDetails" in usage_metadata:
cached_text_tokens = parse from cacheTokensDetails["TEXT"]
text_tokens = text_tokens - cached_text_tokens # Correct!
```
Now we subtract cached tokens per modality, preventing negatives.
## Changes
- Parse cacheTokensDetails field from Gemini response
- Calculate non-cached tokens per modality (text, image, audio)
- Remove incorrect global cached_tokens subtraction
- Add tests for explicit caching and implicit/no caching scenarios
## Testing
- Added test_gemini_cache_tokens_details_no_negative_values
- Added test_gemini_without_cache_tokens_details
- All existing Gemini caching tests pass
Fixes#18750
* feat: add cache_read_input_tokens to Usage object
Addresses reviewer feedback to include cached tokens at the top level
of the Usage object. This aligns with how Anthropic provider handles
cached tokens and ensures they are visible in the final usage response.
* fix: add cacheTokensDetails field to UsageMetadata TypedDict
Fixes mypy error where cacheTokensDetails was being accessed but not defined
in the UsageMetadata TypedDict type definition.
