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- streaming_iterator.py: adopted main's more defensive version of the
tool-arg queueing check (.get() instead of [], isinstance guard) —
same logic, same behavior, lower crash surface
- model_prices_and_context_window.json + backup: combined staging's
search_context_cost_per_query fields (PR #24372) with main's new
supports_service_tier field — both are independent additions to the
same Gemini model entries
- test_streaming_handler.py: kept Azure streaming regression test
(PR #24354) and added main's two new Gemini legacy vertex
finish_reason normalization tests
- test_gemini_batch_embeddings.py: kept staging's unsupported-params
filtering tests (PR #24370) and added main's index/order test
Resolved conflicts:
- streaming_handler.py: combined role check (PR #24354, Azure streaming)
with reasoning_items check (new in main) — both are independent OR
conditions in is_chunk_non_empty()
- CI/CD: accepted main's versions throughout
- Redis tests migrated to CircleCI (PR #25354): removed enable-redis
from GH Actions workflows
- E2E UI tests restructured (PR #25365): simplified CircleCI job
- Coverage via Codecov added to all GH Actions unit test workflows
- Deleted test-litellm-matrix.yml and test-proxy-e2e-azure-batches.yml
(removed in main)
Gemini API keys embedded in URLs as ?key= query parameters leak through
httpx error tracebacks, which are then captured by traceback.format_exc()
and forwarded to logging callbacks, Slack/Teams alerts, and HTTP client
responses.
Short-term: all httpx.HTTPStatusError handlers now raise
MaskedHTTPStatusError(...) from None, which masks the URL and breaks
exception chaining so the original error never appears in tracebacks.
Long-term: moved all Gemini/Vertex URL constructions from ?key={api_key}
to x-goog-api-key header (Google's documented auth method), so the key
is never in the URL at all. WebSocket realtime is the only exception
since WS clients cannot use custom headers.
Additionally hardened all outbound credential paths:
- WebSocket close reasons now pass through _redact_string()
- Callback pipeline (failure_handler) redacts traceback_exception and
error_str before forwarding to integrations (Langfuse, Datadog, etc.)
- Slack/Teams alert messages redacted in send_llm_exception_alert,
ProxyLogging.failure_handler, and post_call_failure_hook
- HTTP error responses in proxy SSE and health endpoints redacted
- Exception messages in exception_mapping_utils redacted
- print_verbose() stdout output redacted when set_verbose=True
- HTTPHandler.put() now has MaskedHTTPStatusError (was missing)
Replace _is_gemini_3_model() substring check with a
web_search_billing_unit field in model_prices JSON:
- "per_query": each search query billed individually (Gemini 3.x)
- "per_prompt" (default): flat fee per grounded API call (Gemini 2.x)
Add web_search_billing_unit to 23 Gemini 3.x model entries.
Update docs and tests accordingly.
- Gemini 2.x charges per grounded prompt (flat $0.035), clamped to 1
regardless of internal query count
- Gemini 3.x charges per search query ($0.014 each)
- Extract web_search_requests from groundingMetadata in non-streaming
responses (parity with streaming path)
- Add search_context_cost_per_query to vertex_ai and base Gemini entries
- Move tests to tests/test_litellm/ (CI directory)
1. Add missing __init__.py files in tests/test_litellm/llms/gemini/ and
subdirectories (realtime/, image_edit/) to fix ModuleNotFoundError
with pytest-xdist parallel workers.
2. Update test_transform_request_uses_dynamic_max_tokens to use
claude-3-7-sonnet-20250219 (max_output_tokens=64000) since
claude-3-5-sonnet-20241022 was removed from model_prices JSON
during deprecated model cleanup. The test assertion was outdated.
3. Update context caching TTL tests to use gemini-2.5-pro instead of
gemini-1.5-pro. The old model was removed from model_prices JSON,
causing supports_system_messages to return False, which prevented
system_instruction from appearing in the transformation output.
Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com>
Lint fixes (check_code_and_doc_quality job):
- Remove unused variable reasoning_effort in gpt_5_transformation.py (F841)
- Remove unused timezone imports in mcp_server rest_endpoints.py and server.py (F401)
- Remove unused ProxyBaseLLMRequestProcessing import in realtime endpoints.py (F401)
- Add BaseRealtimeHTTPConfig to TYPE_CHECKING block in utils.py (F821)
- Add PLR0915 per-file-ignore for mcp_server/rest_endpoints.py in ruff.toml
Test fixes (litellm_mapped_tests_llms job):
- Gemini video cost tests: pass explicit model_info to video_generation_cost()
instead of relying on gemini/veo-3.0-generate-preview being in model_prices JSON
- Anthropic max_tokens tests: mock get_max_tokens() to return expected values
instead of depending on claude-3-5-sonnet-20241022 being in model_prices JSON
- Vertex AI pydantic obj test: update from removed gemini-1.5-pro to gemini-2.5-flash,
update expected request body to use response_json_schema format
- Vertex AI/Bedrock file_content integration tests: update mocks to target
base_llm_http_handler.retrieve_file_content (the new code path via
ProviderConfigManager) instead of the old vertex_ai_files_instance/
bedrock_files_instance paths
Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com>
* 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
Moved speechConfig from RequestBody to GenerationConfig TypedDict so that
TTS configuration survives the filtering in _transform_request_body().
This fixes the 400 INVALID_ARGUMENT error when using Gemini TTS models
(gemini-2.5-flash-tts, gemini-2.5-flash-preview-tts, etc.) with both
vertex_ai and gemini providers.
Fixes: speechConfig was being created correctly in map_openai_params()
but then filtered out because GenerationConfig.__annotations__.keys()
didn't include it.
Tested with both preview and non-preview TTS model names and both
vertex_ai and gemini providers.
* Fix: Add support for GOOGLE_API_KEY environment variables for Gemini API authentication
* added test cases
* incoperated feedback to make it more maintainable
* fix failed linting CI
* fix(proxy_server.py): handle empty config yaml
Fixes https://github.com/BerriAI/litellm/issues/12163
* fix(gemini/common_utils.py): replace models/ as expected, instead of using 'strip'
Fixes https://github.com/BerriAI/litellm/issues/12160
* fix(anthropic/experimental_pass_through/messages/transformation.py): check for env var when selecting api key
* docs(config_settings.md): add api key to docs
* feat: Add Gemini TTS audio parameter support
- Add is_model_gemini_audio_model() method to detect TTS models
- Include 'audio' parameter in supported params for TTS models
- Map OpenAI audio parameter to Gemini speechConfig format
- Add _extract_audio_response_from_parts() method to transform audio
output to openai format
* updated unit-test to use pcm16
* - created typedict for speechconfig
- simplified gemini tts model detection
- moved gemini_tts test to test_litellm
* simplified is_model_gemini_audio_model more
