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7 commits
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ryan-crabbe-berri
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b76def0e5d
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test: require a match= on broad pytest.raises, and drop duplicate parametrize cases (#37769)
`pytest.raises(Exception)` with no `match=` passes on any error that broad. A TypeError from a refactor, a botched fixture, an import that moved: all of them read as the rejection the test claims to police, so the test goes green for the wrong reason and stays green after the behaviour it guards is gone. PT011 closes that gap for the 317 sites B017 could not reach, because B017 only fires on a single-statement body with no `as e` binding. Each pattern here is the message the code actually raised, recorded by running the sites under a plugin that logged the concrete type and text per call site, so the assertions describe observed behaviour rather than a guess. Where a site raises more than one message across its parametrize cases, the pattern is an alternation of what was seen; where the exception carries an empty `str()` and puts the text on `.message`, the site keeps a narrow `noqa` with the reason. PT014 removes four parametrize cases that were listed twice. The duplicate re-runs an assertion that already passed, and it usually marks a case someone meant to vary and forgot to edit. |
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Sameer Kankute
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c7ab9adde5
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Litellm oss staging 030626 (#29578)
* Fix incorrect agent API request example payload structure (#29556) * fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs (#29427) * fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs On /v1/messages and other LITELLM_METADATA_ROUTES, the parent OTel span is stored in litellm_params['litellm_metadata'] instead of litellm_params['metadata']. When the request body contains a native 'metadata' field (e.g. Anthropic's {"user_id": "..."}), litellm_params['metadata'] gets overwritten and the parent span is lost, producing orphan root spans with a different trace_id. Add fallback checks to litellm_metadata in: - _get_span_context(): so child spans find the correct parent - _end_proxy_span_from_kwargs(): so the proxy span gets closed Fixes: https://github.com/BerriAI/litellm/issues/27934 * test(otel): tighten assertions per Greptile review - test_span_context_metadata_takes_priority: assert litellm_metadata span is never accessed, proving metadata takes priority - test_span_context_no_parent_when_neither_has_span: assert both ctx and detected_span are None --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: remove premature end-user budget check from get_end_user_object (#29420) * fix(proxy): remove premature end-user budget check from get_end_user_object Problem: - `_check_end_user_budget()` was called inside `get_end_user_object()` - This caused budget checks to run BEFORE `skip_budget_checks` could be evaluated - Zero-cost models (e.g., local vLLM) were incorrectly blocked when end-users exceeded their budget, even though they should bypass budget checks Solution: - Remove `_check_end_user_budget()` calls from `get_end_user_object()` - Budget enforcement now happens exclusively in `common_checks()` where `skip_budget_checks` context is available - `get_end_user_object()` keeps `route` as optional in function parameter for backwards compatibility and future implementation. * refactor(tests): update budget enforcement tests to reflect changes in get_end_user_object - test_get_end_user_object() verifies data fetching - test_check_end_user_budget() verifies enforcement - test_budget_enforcement_blocks_over_budget_users() integrates _check_end_user_budget() - test_resolve_end_user_reraises_budget_exceeded() is now test_resolve_end_user since no budget exceeded is thrown in get_end_user_object() * Gemini /images/generate and /images/edits billing fixes + add support for size and aspect ratio params (#29534) * Fix Gemini image config mapping * Address Gemini image config review * Format Gemini image generation transform * Fix Gemini image token usage logging * Share Gemini image request helpers * Fix Gemini Imagen model routing * Fixes as per self code review * Fixes per internal code review * Stop gating Imagen imageSize forwarding * Document Gemini image size mapping source * chore: retrigger lint * Clarify Gemini candidate count precedence * Add Inception provider (#29522) * add inception as provider (chat, fim) * linting * seperate test suite for chat and fim * fix test coverage * fix: model hub custom pricing model info (#29293) * Opik user auth key metadata extractors (#28397) * fix: enhance Opik metadata extraction to include user API key auth context fixed after refactoring to extractor logic * test: add unit tests for OPik metadata extraction logic * fix: enhance extract_opik_metadata function to prioritize metadata sources for improved accuracy * fix(ci): clarified comments and edited unit tests * test: add unit tests for OPik metadata extraction with auth and requester overrides * fix(ui): replace fixed favicon.ico with current api get /get_favicon (#29532) Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar> * fix(vertex/gemini): keep tool_call reference when a text-only assistant message follows (#29561) `_gemini_convert_messages_with_history` tracks `last_message_with_tool_calls` so a following tool result can be matched back to its tool call. The assignment was inside a branch guarded by `assistant_msg.get("tool_calls", []) is not None`, which is also True for a text-only assistant message (an empty list is not None). As a result, an assistant message with no tool calls that appears between a tool call and its tool result overwrote the reference, and conversion failed with: Exception: Missing corresponding tool call for tool response message. This shape is common: a model emits a short narration/assistant message after a tool call before the tool result is appended. Only update `last_message_with_tool_calls` when the assistant message actually carries tool_calls (or a function_call). Adds a regression test. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> * Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models (#28572) * fix(thinking): handle None thinking param in is_thinking_enabled (#28598) Squash-merged by litellm-agent from Terrajlz's PR. * feat(helm): support tpl rendering in podAnnotations (#28609) Squash-merged by litellm-agent from devauxbr's PR. * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575) * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) When a Chat Completions request to a GPT-5.4+ model contains both `tools` and `reasoning_effort`, `completion()` auto-routes through `responses_api_bridge`. The bridge handler called `litellm.responses()` / `litellm.aresponses()` without forwarding the already-resolved `custom_llm_provider`, so the downstream call re-invoked `get_llm_provider()` with `custom_llm_provider=None` and stripped a second provider prefix from a `provider/provider/model` deployment string. For a deployment configured as `openai/openai/openai/gpt-5.5`, the bridge flow sent `openai/gpt-5.5` to the upstream API instead of the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce model-name allow-lists rejected this as `key_model_access_denied`. Fix: pass the locally-resolved `custom_llm_provider` into both the sync `responses()` and async `aresponses()` calls so the downstream `_resolve_model_provider_for_responses` sees an explicit provider and skips the second prefix-strip. New regression test `tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py` pins both call sites: each must forward `custom_llm_provider`. * fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg Greptile flagged that the previous patch passed custom_llm_provider as an explicit kwarg to responses()/aresponses() while request_data already carried it via the spread of sanitized_litellm_params, which would raise TypeError: got multiple values for keyword argument on every real bridge call. Switches to assigning request_data['custom_llm_provider'] before the call so the resolved provider wins over whatever sanitized_litellm_params spread in, without duplicating the kwarg. Updates the regression test to seed request_data with a sentinel custom_llm_provider so it actually exercises the overwrite path (the previous test mocked transform_request with a minimal dict and never hit the conflict). * chore: trigger shin-agent re-eval on retargeted staging base * chore: trigger shin-agent re-eval against updated Greptile state * Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models The 1-hour prompt-cache write tier (`cache_creation_input_token_cost_above_1hr`) was added to the us./global. variants of the Claude 4.5/4.6/4.7 family on Bedrock, but the eu./au./jp. cross-region inference profiles were left without it. AWS Bedrock pricing applies the same +10% regional premium across all geo profiles, so eu./au./jp. should carry the same 1-hour rates as us. (1.6x the 5-minute regional rate). Without these fields, cost tracking on EU/AU/JP Bedrock 1-hour-TTL prompt caching falls back to the 5-minute write rate and undercounts spend by ~60% for European, Australian, and Japanese tenants. Adds the 1-hour tier (and Sonnet 4.5's long-context >200K tier where AWS publishes one) to 14 regional Bedrock entries in both `model_prices_and_context_window.json` and the bundled `model_prices_and_context_window_backup.json`: - eu./au. Opus 4.6 ($11.00 / MTok) - eu./au. Opus 4.7 ($11.00 / MTok) - eu./au./jp. Sonnet 4.6 ($6.60 / MTok) - eu./au./jp. Sonnet 4.5 ($6.60 / MTok regular, $13.20 / MTok LC) - eu./au./jp. Haiku 4.5 ($2.20 / MTok) Also extends `tests/test_litellm/test_bedrock_anthropic_1hr_cache_pricing.py` with a `REGIONAL_EXPECTED` parametrized block covering all 13 new entries plus the existing 1.6x ratio invariant. Note: `eu.anthropic.claude-opus-4-5-20251101-v1:0` carries the wrong 5m rate today (base 6.25e-06 instead of regional 6.875e-06), which would break the 1.6x ratio check. It is intentionally left out of this PR so the scope stays "1-hour cache tier addition" — a separate follow-up should correct the EU 5m rates for Opus 4.5. --------- Co-authored-by: Terrajlz <info@jouleselectrictech.com> Co-authored-by: Bruno Devaux <devaux.br@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * Add 1-hour cache write pricing tier for Vertex AI Anthropic models (#28569) * fix(thinking): handle None thinking param in is_thinking_enabled (#28598) Squash-merged by litellm-agent from Terrajlz's PR. * feat(helm): support tpl rendering in podAnnotations (#28609) Squash-merged by litellm-agent from devauxbr's PR. * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575) * Forward custom_llm_provider through the Responses API bridge (Fixes #28505) When a Chat Completions request to a GPT-5.4+ model contains both `tools` and `reasoning_effort`, `completion()` auto-routes through `responses_api_bridge`. The bridge handler called `litellm.responses()` / `litellm.aresponses()` without forwarding the already-resolved `custom_llm_provider`, so the downstream call re-invoked `get_llm_provider()` with `custom_llm_provider=None` and stripped a second provider prefix from a `provider/provider/model` deployment string. For a deployment configured as `openai/openai/openai/gpt-5.5`, the bridge flow sent `openai/gpt-5.5` to the upstream API instead of the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce model-name allow-lists rejected this as `key_model_access_denied`. Fix: pass the locally-resolved `custom_llm_provider` into both the sync `responses()` and async `aresponses()` calls so the downstream `_resolve_model_provider_for_responses` sees an explicit provider and skips the second prefix-strip. New regression test `tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py` pins both call sites: each must forward `custom_llm_provider`. * fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg Greptile flagged that the previous patch passed custom_llm_provider as an explicit kwarg to responses()/aresponses() while request_data already carried it via the spread of sanitized_litellm_params, which would raise TypeError: got multiple values for keyword argument on every real bridge call. Switches to assigning request_data['custom_llm_provider'] before the call so the resolved provider wins over whatever sanitized_litellm_params spread in, without duplicating the kwarg. Updates the regression test to seed request_data with a sentinel custom_llm_provider so it actually exercises the overwrite path (the previous test mocked transform_request with a minimal dict and never hit the conflict). * chore: trigger shin-agent re-eval on retargeted staging base * chore: trigger shin-agent re-eval against updated Greptile state * Add 1-hour cache write pricing tier for Vertex AI Anthropic models GCP Vertex AI publishes a separate 1-hour cache write column for the Claude family (1.6x the 5-minute write rate, matching the documented Bedrock ratio). LiteLLM's Vertex AI Anthropic entries only carry the 5-minute tier, so any request that uses `cache_control: {"ttl": "1h"}` on Vertex AI Claude is undercounted in cost tracking by ~60%. The runtime side already supports the 1-hour tier — `VertexAIAnthropicConfig` extends `AnthropicConfig`, populating `ephemeral_1h_input_tokens`, and `_calculate_cache_creation_cost` reads `cache_creation_input_token_cost_above_1hr`. Only the price registry was missing data. Adds the field to 19 vertex_ai/claude-* entries across both `model_prices_and_context_window.json` and the bundled `model_prices_and_context_window_backup.json`: - Haiku 4.5 ($1.25 -> $2.00 / MTok) - Sonnet 3.7 / 4 / 4.5 / 4.6 ($3.75 -> $6.00 / MTok) - Opus 4.5 / 4.6 / 4.7 ($6.25 -> $10.00 / MTok) - Opus 4 / 4.1 ($18.75 -> $30.00 / MTok) Adds `tests/test_litellm/test_vertex_anthropic_1hr_cache_pricing.py` mirroring the Bedrock equivalent — pins each (5m, 1h) pair per model and asserts the 1.6x ratio across the family. Fixes #27781. --------- Co-authored-by: Terrajlz <info@jouleselectrictech.com> Co-authored-by: Bruno Devaux <devaux.br@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * Fix Gemini multimodal function responses (#29325) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * address greptile review: add _transform_image_usage method and model-map supports_image_size flag - Add _transform_image_usage instance method to GoogleImageGenConfig that delegates to transform_gemini_image_usage, fixing the regression test - Replace hardcoded "2.5-flash" string check in supports_gemini_image_size with a get_model_info lookup on supports_image_size (default true) - Add supports_image_size: false to all gemini-2.5-flash model entries in model_prices_and_context_window.json so capability is controlled via the model map rather than embedded in code * fix test failures: schema validation, mypy type, model info plumbing, pricing test - Add supports_image_size to ModelInfoBase TypedDict so get_model_info surfaces it - Pass supports_image_size through _get_model_info_helper constructor call - Fix supports_gemini_image_size to use value is not False (None means unset, defaults to True) - Add supports_image_size to JSON schema in test_aaamodel_prices_and_context_window_json_is_valid - Correct gemini-3.1-flash-lite pricing assertions in test to match JSON values * Add Azure AI Kimi K2.6 metadata (#27052) * Add Azure AI Kimi K2.6 metadata * Scope Kimi metadata test cost map setup * fall back to substring check for models not in model_prices_and_context_window.json Models like gemini-2.5-flash-image-preview are not in the pricing JSON, so get_model_info raises. Fall back to "2.5-flash" not in model when the JSON has no explicit supports_image_size entry for the model. * fix(inception): don't forward global litellm.api_key to Inception FIM Match the Inception chat config: resolve only an Inception-specific key (param, litellm.inception_key, or INCEPTION_API_KEY) for the text-completion FIM path. The global litellm.api_key (often an OpenAI key) was both leaking to api.inceptionlabs.ai and taking precedence over the configured Inception key when set. * fix(auth): enforce end-user budget on custom-auth path that skips common_checks get_end_user_object() no longer raises BudgetExceededError, so custom-auth deployments with custom_auth_run_common_checks unset (which skip the centralized common_checks gate) stopped enforcing the end-user budget, letting an over-budget end user keep making requests. Re-enforce the budget in _run_post_custom_auth_checks on that path. --------- Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar> Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com> Co-authored-by: aneeshsangvikar <aneeshsangvikar@fiddler.ai> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com> Co-authored-by: Suleiman Elkhoury <108065141+suleimanelkhoury@users.noreply.github.com> Co-authored-by: Dmitriy Alergant <93501479+DmitriyAlergant@users.noreply.github.com> Co-authored-by: Yanis Miraoui <yanis.miraoui19@imperial.ac.uk> Co-authored-by: Lovro Seder <vrovro@gmail.com> Co-authored-by: Thomas Mildner <12685945+Thomas-Mildner@users.noreply.github.com> Co-authored-by: José Luis Di Biase <josx@interorganic.com.ar> Co-authored-by: Lai Quang Huy <64073540+1qh@users.noreply.github.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Terrajlz <info@jouleselectrictech.com> Co-authored-by: Bruno Devaux <devaux.br@gmail.com> Co-authored-by: ZHONG Ziwen <67355585+zzw-math@users.noreply.github.com> Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Ishaan Jaffer
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e8461b5b97
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style: run black formatter on files from main merge | ||
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Cursor Agent
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9a356644bf
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fix(tests): stabilize 3 failing CI tests
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> |
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Sameer Kankute
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3c451e945a | fix aspectRatio mapping | ||
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Cesar Garcia
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999ffabc39
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fix(gemini): use JSON instead of form-data for image edit requests (#18012)
* 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 |
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Sameer Kankute
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018bd2e039
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Add Gemini image edit support (#16430)
* Add gemini image edit support * fix lint errors * fix lint errors * fix lint errors * Add docs |