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feat(typesafe): support Jev 1.14 images, box questions, and long-context headers (#46596)
* feat(typesafe): support Jev 1.14 images, box questions, and long-context headers * fix(cost): drop the typesafe/jev-1.14.0 entry main already prices * fix(typesafe): send images only with the images header and box questions only to models that answer them TypeSafe takes assets and input_image parts only when the request carries TypeSafe-Danger-Zone-Images: true, so without it they follow drop_params like any other provider that cannot take images. Box support now comes from a supports_box_questions cost-map flag set on jev-1.14.0 and jev-preview, so jev-1.13.0 and jev-latest refuse or drop a box question before the call. A malformed provider_specific_header answers 400 instead of 500. * fix(typesafe): allow null box confidence and validate asset references and base64 * fix(typesafe): gate box questions on the images header, drop unreadable assets under drop_params, forward opt-in headers on the pass-through --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
Mateo Wang
GitHub
parent
efd8274315
commit
3fec5075fa
28 changed files with 1551 additions and 78 deletions
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@ -2294,6 +2294,7 @@ if TYPE_CHECKING:
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supports_vision: Callable[..., bool]
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supports_audio_input: Callable[..., bool]
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supports_video_input: Callable[..., bool]
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supports_box_questions: Callable[..., bool]
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supports_audio_output: Callable[..., bool]
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supports_system_messages: Callable[..., bool]
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supports_reasoning: Callable[..., bool]
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@ -38,6 +38,7 @@ UTILS_NAMES: Final = (
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"supports_vision",
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"supports_audio_input",
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"supports_video_input",
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"supports_box_questions",
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"supports_audio_output",
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"supports_system_messages",
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"supports_reasoning",
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@ -488,6 +489,7 @@ _UTILS_IMPORT_MAP: Final = {
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"supports_vision": (".utils", "supports_vision"),
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"supports_audio_input": (".utils", "supports_audio_input"),
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"supports_video_input": (".utils", "supports_video_input"),
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"supports_box_questions": (".utils", "supports_box_questions"),
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"supports_audio_output": (".utils", "supports_audio_output"),
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"supports_system_messages": (".utils", "supports_system_messages"),
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"supports_reasoning": (".utils", "supports_reasoning"),
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@ -1686,6 +1686,7 @@ ALLOWED_VERTEX_AI_PASSTHROUGH_HEADERS: Final = {
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# e.g., 'x-pass-anthropic-beta: value' becomes 'anthropic-beta: value'
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# Works for all LLM pass-through endpoints (Vertex AI, Anthropic, Bedrock, etc.)
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PASS_THROUGH_HEADER_PREFIX: Final = "x-pass-"
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TYPESAFE_DANGER_ZONE_HEADER_PREFIX: Final = "typesafe-danger-zone-"
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INTERNAL_KWARG_PREFIX: Final = "_litellm_"
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CONTROL_OPTIONS_KEY: Final = f"{INTERNAL_KWARG_PREFIX}control"
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@ -2,7 +2,7 @@ from collections.abc import Mapping, Sequence
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from dataclasses import dataclass, field, replace
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from functools import partial
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from types import MappingProxyType
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from typing import Final, TypeAlias
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from typing import Final, Literal, TypeAlias
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import httpx
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from pydantic import ConfigDict, TypeAdapter, ValidationError
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@ -10,6 +10,7 @@ from typing_extensions import assert_never
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import litellm
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from litellm.litellm_core_utils.core_helpers import RESPONSE_COST_HEADER, normalize_drop_params
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from litellm.litellm_core_utils.get_provider_specific_headers import ProviderSpecificHeaderUtils
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.litellm_core_utils.prompt_templates.image_handling import (
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async_convert_urls_to_base64,
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@ -31,10 +32,17 @@ from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
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from litellm.llms.openai.decisions.transformation import ir_to_openai_response, openai_request_to_ir
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from litellm.types.decisions import (
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DecisionQuestion,
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DecisionsAsset,
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DecisionsAssetReference,
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DecisionsAssets,
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DecisionsAudio,
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DecisionsBase64Image,
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DecisionsImage,
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DecisionsIRAnswer,
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DecisionsIRBoxQuestion,
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DecisionsIRMessages,
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DecisionsIRQuestion,
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DecisionsIRRefusal,
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DecisionsIRRequest,
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DecisionsIRResponse,
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DecisionsIRState,
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@ -48,12 +56,13 @@ from litellm.types.decisions import (
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OpenAIDecisionResponse,
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UnsupportedDecisionsRequest,
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)
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from litellm.types.utils import LlmProviders
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from litellm.types.utils import LlmProviders, ProviderSpecificHeader
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from litellm.utils import ProviderConfigManager
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SystemOneQuestions: TypeAlias = Mapping[str, DecisionQuestion | Mapping[str, object]]
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OpenAIDecisionQuestions: TypeAlias = Sequence[OpenAIDecisionQuestion | Mapping[str, object]]
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DecisionsImages: TypeAlias = Sequence[str | DecisionsBase64Image | Mapping[str, object]]
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DecisionsAssetEntries: TypeAlias = Mapping[str, DecisionsAsset | Mapping[str, object]]
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DecisionsAudioClips: TypeAlias = Sequence[DecisionsAudio | Mapping[str, object]]
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DecisionsVideoClips: TypeAlias = Sequence[DecisionsVideo | Mapping[str, object]]
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DecisionsRequestFormat: TypeAlias = DecisionsRequestBody | OpenAIDecisionRequestBody
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@ -66,12 +75,22 @@ _SAFETY_IDENTIFIER_ADAPTER: Final[TypeAdapter[str | None]] = TypeAdapter(
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_IMAGES_ADAPTER: Final[TypeAdapter[tuple[DecisionsImage, ...] | None]] = TypeAdapter(
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tuple[DecisionsImage, ...] | None, config=ConfigDict(title="images")
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)
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_PROVIDER_SPECIFIC_HEADER_ADAPTER: Final[
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TypeAdapter[ProviderSpecificHeader | tuple[ProviderSpecificHeader, ...] | None]
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] = TypeAdapter(ProviderSpecificHeader | tuple[ProviderSpecificHeader, ...] | None)
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_HANDLER: Final = BaseLLMHTTPHandler()
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_BASE64_MARKER: Final = ";base64,"
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_IMAGE_ADAPTER: Final[TypeAdapter[DecisionsImage]] = TypeAdapter(DecisionsImage, config=ConfigDict(title="images"))
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_IMAGE_ENTRIES_ADAPTER: Final[TypeAdapter[tuple[object, ...]]] = TypeAdapter(tuple[object, ...])
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_ASSETS_ADAPTER: Final[TypeAdapter[DecisionsAssets | None]] = TypeAdapter(
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DecisionsAssets | None, config=ConfigDict(title="assets")
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)
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_ASSET_ENTRY_ADAPTER: Final[TypeAdapter[tuple[DecisionsAssetReference, DecisionsAsset]]] = TypeAdapter(
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tuple[DecisionsAssetReference, DecisionsAsset]
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)
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_ASSET_ENTRIES_ADAPTER: Final[TypeAdapter[Mapping[str, object]]] = TypeAdapter(Mapping[str, object])
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def _valid_image(image: object) -> DecisionsImage | None:
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@ -96,6 +115,29 @@ def _no_image(_image: DecisionsImage) -> bool:
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return False
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def _valid_asset(entry: tuple[str, object]) -> tuple[str, DecisionsAsset] | None:
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try:
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return _ASSET_ENTRY_ADAPTER.validate_python(entry)
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except ValidationError:
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return None
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def _asset_entries(assets: object) -> Mapping[str, object]:
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try:
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return _ASSET_ENTRIES_ADAPTER.validate_python(assets)
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except ValidationError:
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return MappingProxyType({})
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def _request_assets(assets: object, drops_params: bool) -> Mapping[str, DecisionsAsset] | None:
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if not drops_params:
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return _ASSETS_ADAPTER.validate_python(assets)
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valid: Final = MappingProxyType(
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dict(entry for entry in map(_valid_asset, _asset_entries(assets).items()) if entry is not None)
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)
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return valid or None
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def _request_images(images: object, drops_params: bool) -> tuple[DecisionsImage, ...]:
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if not drops_params:
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return _IMAGES_ADAPTER.validate_python(images) or ()
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@ -107,6 +149,7 @@ class SystemOneRequestFields:
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state: DecisionsJSON | None
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questions: SystemOneQuestions | None
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images: object = None
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assets: object = None
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audio: DecisionsAudioClips | None = None
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videos: DecisionsVideoClips | None = None
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@ -116,6 +159,7 @@ class SystemOneRequestFields:
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"state": self.state,
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"questions": self.questions,
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"images": _request_images(self.images, drops_params) or None,
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"assets": _request_assets(self.assets, drops_params),
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"audio": self.audio,
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"videos": self.videos,
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}
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@ -158,6 +202,7 @@ class DecisionsCall:
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headers: Mapping[str, str]
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timeout: float | httpx.Timeout | None
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litellm_params: Mapping[str, object] = field(repr=False)
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caller_questions: tuple[DecisionsIRQuestion, ...]
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def supported_decisions_providers() -> tuple[str, ...]:
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@ -229,8 +274,9 @@ class _UnsupportedParams:
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@dataclass(frozen=True, slots=True)
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class _NoInputLeftAfterDrop:
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class _NothingLeftAfterDrop:
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names: tuple[str, ...]
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missing: Literal["input to decide on", "questions to answer"]
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@dataclass(frozen=True, slots=True)
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@ -239,12 +285,22 @@ class _MalformedImages:
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def _unsupported_params(
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ir_request: DecisionsIRRequest, provider_config: BaseDecisionsConfig, model: str, custom_llm_provider: str
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ir_request: DecisionsIRRequest,
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provider_config: BaseDecisionsConfig,
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model: str,
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custom_llm_provider: str,
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headers: Mapping[str, str],
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) -> tuple[str, ...]:
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refuses_safety_identifier: Final = (
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ir_request.safety_identifier is not None and not provider_config.supports_safety_identifier
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)
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refused_image_param: Final = None if provider_config.supports_images else image_param(ir_request.input)
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refused_image_param: Final = (
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None if provider_config.supports_images_in(ir_request.input, headers) else image_param(ir_request.input)
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)
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refuses_assets: Final = bool(ir_request.assets) and not provider_config.supports_assets(headers)
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refuses_box_questions: Final = _has_box_question(ir_request.questions) and not (
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provider_config.supports_box_questions(model, custom_llm_provider, headers)
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)
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refuses_audio: Final = bool(ir_request.audio) and not provider_config.supports_audio_input(
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model, custom_llm_provider
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)
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@ -254,18 +310,42 @@ def _unsupported_params(
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return (
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*(("safety_identifier",) if refuses_safety_identifier else ()),
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*(() if refused_image_param is None else (refused_image_param,)),
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*(("assets",) if refuses_assets else ()),
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*(("box questions",) if refuses_box_questions else ()),
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*(("audio",) if refuses_audio else ()),
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*(("videos",) if refuses_videos else ()),
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)
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def _has_box_question(questions: Sequence[DecisionsIRQuestion]) -> bool:
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return any(isinstance(question, DecisionsIRBoxQuestion) for question in questions)
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def _is_not_box_question(question: DecisionsIRQuestion) -> bool:
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return not isinstance(question, DecisionsIRBoxQuestion)
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def _without_unsupported_params(
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ir_request: DecisionsIRRequest, provider_config: BaseDecisionsConfig
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ir_request: DecisionsIRRequest,
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provider_config: BaseDecisionsConfig,
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model: str,
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custom_llm_provider: str,
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headers: Mapping[str, str],
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) -> DecisionsIRRequest:
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return replace(
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ir_request,
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input=ir_request.input if provider_config.supports_images else with_images_where(ir_request.input, _no_image),
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input=(
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ir_request.input
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if provider_config.supports_images_in(ir_request.input, headers)
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else with_images_where(ir_request.input, _no_image)
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),
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safety_identifier=ir_request.safety_identifier if provider_config.supports_safety_identifier else None,
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assets=ir_request.assets if provider_config.supports_assets(headers) else MappingProxyType({}),
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questions=(
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ir_request.questions
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if provider_config.supports_box_questions(model, custom_llm_provider, headers)
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else tuple(filter(_is_not_box_question, ir_request.questions))
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),
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)
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@ -286,28 +366,46 @@ def _without_malformed_images(
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def _provider_ir_request(
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request: DecisionsRequestFormat,
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ir_request: DecisionsIRRequest,
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*,
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safety_identifier: str | None,
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provider_config: BaseDecisionsConfig,
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model: str,
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custom_llm_provider: str,
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headers: Mapping[str, str],
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kwargs: Mapping[str, object],
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) -> DecisionsIRRequest | _UnsupportedParams | _NoInputLeftAfterDrop | _MalformedImages:
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ir_request: Final = _without_malformed_images(_ir_request(request, safety_identifier), _drops_params(kwargs))
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if isinstance(ir_request, _MalformedImages):
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return ir_request
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unsupported: Final = _unsupported_params(ir_request, provider_config, model, custom_llm_provider)
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) -> DecisionsIRRequest | _UnsupportedParams | _NothingLeftAfterDrop:
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unsupported: Final = _unsupported_params(ir_request, provider_config, model, custom_llm_provider, headers)
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if not unsupported:
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return ir_request
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if not _drops_params(kwargs):
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return _UnsupportedParams(names=unsupported)
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supported: Final = _without_unsupported_params(ir_request, provider_config)
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supported: Final = _without_unsupported_params(ir_request, provider_config, model, custom_llm_provider, headers)
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if _leaves_no_input(supported.input):
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return _NoInputLeftAfterDrop(names=unsupported)
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return _NothingLeftAfterDrop(names=unsupported, missing="input to decide on")
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if not supported.questions:
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return _NothingLeftAfterDrop(names=unsupported, missing="questions to answer")
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return supported
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def _provider_specific_header(
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value: object,
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) -> ProviderSpecificHeader | tuple[ProviderSpecificHeader, ...] | None:
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try:
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return _PROVIDER_SPECIFIC_HEADER_ADAPTER.validate_python(value)
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except ValidationError:
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return None
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def _outbound_headers(
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extra_headers: Mapping[str, str] | None, custom_llm_provider: str, kwargs: Mapping[str, object]
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) -> Mapping[str, str]:
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forwarded: Final = ProviderSpecificHeaderUtils.get_provider_specific_headers(
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provider_specific_header=_provider_specific_header(kwargs.get("provider_specific_header")),
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custom_llm_provider=custom_llm_provider,
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)
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return MappingProxyType({**(extra_headers or {}), **forwarded})
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def prepare_call(
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*,
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model: str,
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@ -338,6 +436,7 @@ def prepare_call(
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except ValueError as error:
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raise litellm.BadRequestError(message=str(error), model=model, llm_provider=provider) from error
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try:
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headers: Final = _outbound_headers(extra_headers, provider, kwargs)
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request_safety_identifier: Final = _request_safety_identifier(safety_identifier, provider_config, kwargs)
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request: Final = _fields_without_unsupported_media(
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fields, provider_config, canonical_model, provider, kwargs
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@ -368,12 +467,24 @@ def prepare_call(
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llm_provider=provider,
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)
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caller_ir_request: Final = _without_malformed_images(
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_ir_request(request, request_safety_identifier), _drops_params(kwargs)
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)
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if isinstance(caller_ir_request, _MalformedImages):
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raise litellm.BadRequestError(
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message=(
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f"Invalid Decisions request: {caller_ir_request.count} input_image part(s) have an image_url that is "
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"neither a data:image/...;base64, URL nor an http(s) URL"
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),
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model=model,
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llm_provider=provider,
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)
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ir_request: Final = _provider_ir_request(
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request,
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safety_identifier=request_safety_identifier,
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caller_ir_request,
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provider_config=provider_config,
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model=canonical_model,
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custom_llm_provider=provider,
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headers=headers,
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kwargs=kwargs,
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)
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if isinstance(ir_request, _UnsupportedParams):
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@ -386,20 +497,11 @@ def prepare_call(
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model=model,
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llm_provider=provider,
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)
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if isinstance(ir_request, _MalformedImages):
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raise litellm.BadRequestError(
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message=(
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f"Invalid Decisions request: {ir_request.count} input_image part(s) have an image_url that is "
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"neither a data:image/...;base64, URL nor an http(s) URL"
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),
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model=model,
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llm_provider=provider,
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)
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if isinstance(ir_request, _NoInputLeftAfterDrop):
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if isinstance(ir_request, _NothingLeftAfterDrop):
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raise litellm.BadRequestError(
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message=(
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f"{provider} does not support parameters: {list(ir_request.names)}, for model={model}, "
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"and dropping them leaves no input to decide on"
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f"and dropping them leaves no {ir_request.missing}"
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),
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model=model,
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llm_provider=provider,
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@ -425,9 +527,10 @@ def prepare_call(
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api_base=resolved_api_base,
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api_key=resolved_api_key,
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logging_obj=logging_obj if isinstance(logging_obj, LiteLLMLoggingObj) else None,
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headers=extra_headers or {},
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headers=headers,
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timeout=timeout,
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litellm_params=kwargs,
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caller_questions=caller_ir_request.questions,
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)
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@ -561,14 +664,24 @@ def _format_response(response: DecisionsIRResponse, call: DecisionsCall) -> Deci
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return formatted
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def _caller_answers(call: DecisionsCall, answers: Sequence[DecisionsIRAnswer]) -> tuple[DecisionsIRAnswer, ...]:
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sent_answers: Final = iter(answers)
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return tuple(
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next(sent_answers) if question in call.ir_request.questions else DecisionsIRRefusal()
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for question in call.caller_questions
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)
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def _formatted_response(
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response: DecisionsIRResponse, call: DecisionsCall
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) -> DecisionsResponse | OpenAIDecisionResponse:
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caller_request: Final = replace(call.ir_request, questions=call.caller_questions)
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caller_response: Final = replace(response, answers=_caller_answers(call, response.answers))
|
||||
match call.request:
|
||||
case DecisionsRequestBody():
|
||||
return ir_to_systemone_response(response, call.ir_request)
|
||||
return ir_to_systemone_response(caller_response, caller_request)
|
||||
case OpenAIDecisionRequestBody():
|
||||
return ir_to_openai_response(response, call.ir_request, call.requested_model)
|
||||
return ir_to_openai_response(caller_response, caller_request, call.requested_model)
|
||||
case _:
|
||||
assert_never(call.request)
|
||||
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ _SYSTEMONE_QUESTIONS_ADAPTER: Final[TypeAdapter[SystemOneQuestions | None]] = Ty
|
|||
_OPENAI_QUESTIONS_ADAPTER: Final[TypeAdapter[OpenAIDecisionQuestions | None]] = TypeAdapter(
|
||||
OpenAIDecisionQuestions | None
|
||||
)
|
||||
_SYSTEMONE_FIELDS: Final = frozenset({"state", "images"})
|
||||
_SYSTEMONE_FIELDS: Final = frozenset({"state", "images", "assets"})
|
||||
|
||||
|
||||
def _request_fields(
|
||||
|
|
@ -55,6 +55,7 @@ def _request_fields(
|
|||
state=_STATE_ADAPTER.validate_python(kwargs["state"]),
|
||||
questions=_SYSTEMONE_QUESTIONS_ADAPTER.validate_python(questions),
|
||||
images=kwargs.get("images"),
|
||||
assets=kwargs.get("assets"),
|
||||
audio=audio,
|
||||
videos=videos,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from collections.abc import Sequence
|
||||
from collections.abc import Mapping, Sequence
|
||||
from typing import Final
|
||||
|
||||
from litellm.types.utils import ProviderSpecificHeader
|
||||
|
|
@ -9,7 +9,7 @@ class ProviderSpecificHeaderUtils:
|
|||
def get_provider_specific_headers(
|
||||
provider_specific_header: ProviderSpecificHeader | Sequence[ProviderSpecificHeader] | None,
|
||||
custom_llm_provider: str | None,
|
||||
) -> dict:
|
||||
) -> Mapping[str, str]:
|
||||
"""
|
||||
Get the provider specific headers for the given custom llm provider.
|
||||
|
||||
|
|
|
|||
|
|
@ -14,6 +14,8 @@ import litellm
|
|||
from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.decisions import (
|
||||
BoxAnswer,
|
||||
BoxQuestion,
|
||||
ChoiceAnswer,
|
||||
ChoiceQuestion,
|
||||
DecisionAnswer,
|
||||
|
|
@ -21,6 +23,8 @@ from litellm.types.decisions import (
|
|||
DecisionsBase64Image,
|
||||
DecisionsImage,
|
||||
DecisionsIRAnswer,
|
||||
DecisionsIRBoxAnswer,
|
||||
DecisionsIRBoxQuestion,
|
||||
DecisionsIRChoiceAnswer,
|
||||
DecisionsIRChoiceOption,
|
||||
DecisionsIRChoiceProbability,
|
||||
|
|
@ -98,6 +102,8 @@ def _ir_question(name: str, question: DecisionQuestion) -> DecisionsIRQuestion:
|
|||
),
|
||||
extra=extra,
|
||||
)
|
||||
case BoxQuestion():
|
||||
return DecisionsIRBoxQuestion(name=name, instructions=question.instructions, extra=extra)
|
||||
case _:
|
||||
assert_never(question)
|
||||
|
||||
|
|
@ -105,6 +111,7 @@ def _ir_question(name: str, question: DecisionQuestion) -> DecisionsIRQuestion:
|
|||
def systemone_request_to_ir(request: DecisionsRequestBody) -> DecisionsIRRequest:
|
||||
return DecisionsIRRequest(
|
||||
input=DecisionsIRState(state=request.state, images=tuple(request.images or ())),
|
||||
assets=MappingProxyType(dict(request.assets or {})),
|
||||
audio=request.audio,
|
||||
videos=request.videos,
|
||||
questions=tuple(_ir_question(name, question) for name, question in request.questions.items()),
|
||||
|
|
@ -282,6 +289,8 @@ def _systemone_question(question: DecisionsIRQuestion) -> Mapping[str, object]:
|
|||
"criteria": [_level_criterion(level) for level in question.levels],
|
||||
**question.extra,
|
||||
}
|
||||
case DecisionsIRBoxQuestion():
|
||||
return {"type": "box", "instructions": question.instructions, **question.extra}
|
||||
case _:
|
||||
assert_never(question)
|
||||
|
||||
|
|
@ -311,6 +320,11 @@ def ir_to_systemone_request(
|
|||
),
|
||||
"questions": {key: _systemone_question(question) for key, question in keyed_questions},
|
||||
**({"images": [_systemone_image(image) for image in images]} if images else {}),
|
||||
**(
|
||||
{"assets": {key: asset.model_dump(exclude_none=True) for key, asset in request.assets.items()}}
|
||||
if request.assets
|
||||
else {}
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -351,6 +365,10 @@ def _ir_answer(question: DecisionsIRQuestion, answer: DecisionAnswer | None) ->
|
|||
return _ir_choice_answer(question, answer)
|
||||
case DecisionsIRScoreQuestion(), ScoreAnswer():
|
||||
return _ir_score_answer(question, answer)
|
||||
case DecisionsIRBoxQuestion(), BoxAnswer():
|
||||
return DecisionsIRBoxAnswer(
|
||||
box=answer.box, confidence=answer.confidence, extra=MappingProxyType(answer.model_extra or {})
|
||||
)
|
||||
case _:
|
||||
return DecisionsIRRefusal()
|
||||
|
||||
|
|
@ -403,6 +421,10 @@ def _systemone_answer(answer: DecisionsIRAnswer) -> DecisionAnswer | None:
|
|||
"probabilities": {str(item.value): item.probability for item in answer.probabilities},
|
||||
}
|
||||
)
|
||||
case DecisionsIRBoxAnswer():
|
||||
return BoxAnswer.model_validate(
|
||||
{**answer.extra, "type": "box", "box": answer.box, "confidence": answer.confidence}
|
||||
)
|
||||
case DecisionsIRRefusal():
|
||||
return None
|
||||
case _:
|
||||
|
|
@ -507,6 +529,19 @@ class BaseDecisionsConfig(ABC):
|
|||
) -> tuple[Mapping[str, str], bytes | None]:
|
||||
return headers, None
|
||||
|
||||
def supports_images_in(
|
||||
self, decision_input: DecisionsIRState | DecisionsIRMessages, headers: Mapping[str, str]
|
||||
) -> bool:
|
||||
return self.supports_images
|
||||
|
||||
def supports_assets(self, headers: Mapping[str, str]) -> bool:
|
||||
return False
|
||||
|
||||
def supports_box_questions(self, model: str, custom_llm_provider: str, headers: Mapping[str, str]) -> bool:
|
||||
return _is_decisions_model(model, custom_llm_provider) and litellm.supports_box_questions(
|
||||
model=model, custom_llm_provider=custom_llm_provider
|
||||
)
|
||||
|
||||
def supports_audio_input(self, model: str, custom_llm_provider: str) -> bool:
|
||||
return _is_decisions_model(model, custom_llm_provider) and litellm.supports_audio_input(
|
||||
model=model, custom_llm_provider=custom_llm_provider
|
||||
|
|
|
|||
|
|
@ -10,6 +10,8 @@ from litellm.secret_managers.main import get_secret_str
|
|||
from litellm.types.decisions import (
|
||||
DecisionsImage,
|
||||
DecisionsIRAnswer,
|
||||
DecisionsIRBoxAnswer,
|
||||
DecisionsIRBoxQuestion,
|
||||
DecisionsIRChoiceAnswer,
|
||||
DecisionsIRChoiceOption,
|
||||
DecisionsIRChoiceProbability,
|
||||
|
|
@ -28,6 +30,8 @@ from litellm.types.decisions import (
|
|||
DecisionsIRState,
|
||||
DecisionsIRUsage,
|
||||
DecisionsJSON,
|
||||
OpenAIBoxAnswer,
|
||||
OpenAIBoxQuestion,
|
||||
OpenAIChoiceAnswer,
|
||||
OpenAIChoiceProbability,
|
||||
OpenAIChoiceQuestion,
|
||||
|
|
@ -85,6 +89,8 @@ def _ir_question(question: OpenAIDecisionQuestion) -> DecisionsIRQuestion:
|
|||
DecisionsIRScoreLevel(label=level.label, description=level.description) for level in question.levels
|
||||
),
|
||||
)
|
||||
case OpenAIBoxQuestion():
|
||||
return DecisionsIRBoxQuestion(name=question.name, instructions=question.instructions)
|
||||
case _:
|
||||
assert_never(question)
|
||||
|
||||
|
|
@ -145,6 +151,12 @@ def _openai_question(question: DecisionsIRQuestion) -> Mapping[str, object]:
|
|||
for level in question.levels
|
||||
],
|
||||
}
|
||||
case DecisionsIRBoxQuestion():
|
||||
return {
|
||||
"type": "box",
|
||||
**_text_field("name", question.name),
|
||||
"instructions": decisions_text(question.instructions),
|
||||
}
|
||||
case _:
|
||||
assert_never(question)
|
||||
|
||||
|
|
@ -287,6 +299,8 @@ def _openai_answer(question: DecisionsIRQuestion, answer: DecisionsIRAnswer) ->
|
|||
return _openai_choice_answer(question, answer)
|
||||
case DecisionsIRScoreQuestion(), DecisionsIRScoreAnswer():
|
||||
return _openai_score_answer(question, answer)
|
||||
case DecisionsIRBoxQuestion(), DecisionsIRBoxAnswer():
|
||||
return OpenAIBoxAnswer(name=question.name, box=answer.box, confidence=answer.confidence)
|
||||
case _:
|
||||
return OpenAIRefusalAnswer(name=question.name)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,106 @@
|
|||
from litellm.llms.base_llm.decisions.transformation import BaseDecisionsConfig
|
||||
import hashlib
|
||||
from collections.abc import Mapping
|
||||
from dataclasses import replace
|
||||
from itertools import chain
|
||||
from types import MappingProxyType
|
||||
from typing import Final, Literal
|
||||
|
||||
from litellm.constants import TYPESAFE_DANGER_ZONE_HEADER_PREFIX
|
||||
from litellm.llms.base_llm.decisions.transformation import BaseDecisionsConfig, image_media_type
|
||||
from litellm.types.decisions import (
|
||||
DecisionsAsset,
|
||||
DecisionsIRMessages,
|
||||
DecisionsIRRequest,
|
||||
DecisionsIRState,
|
||||
OpenAIDecisionInputImage,
|
||||
OpenAIDecisionInputMessage,
|
||||
OpenAIDecisionInputText,
|
||||
UnsupportedDecisionsRequest,
|
||||
)
|
||||
|
||||
_IMAGES_HEADER: Final = f"{TYPESAFE_DANGER_ZONE_HEADER_PREFIX}images"
|
||||
_ASSET_FORMATS: Final[Mapping[str, Literal["png", "jpg"]]] = MappingProxyType(
|
||||
{"image/png": "png", "image/jpeg": "jpg", "image/jpg": "jpg"}
|
||||
)
|
||||
_URL_SAFE_TO_STANDARD_BASE64: Final = str.maketrans("-_", "+/")
|
||||
|
||||
|
||||
def _image_reference(image_url: str) -> str:
|
||||
return f"image-{hashlib.sha256(image_url.encode()).hexdigest()[:32]}"
|
||||
|
||||
|
||||
def _image_asset(image_url: str) -> DecisionsAsset | None:
|
||||
asset_format: Final = _ASSET_FORMATS.get(image_media_type(image_url) or "")
|
||||
if asset_format is None:
|
||||
return None
|
||||
return DecisionsAsset(
|
||||
type="image",
|
||||
format=asset_format,
|
||||
data_base64=image_url.partition(",")[2].translate(_URL_SAFE_TO_STANDARD_BASE64),
|
||||
)
|
||||
|
||||
|
||||
def _part_text(part: OpenAIDecisionInputText | OpenAIDecisionInputImage) -> str:
|
||||
if isinstance(part, OpenAIDecisionInputText):
|
||||
return part.text
|
||||
return part.image_url if _image_asset(part.image_url) is None else _image_reference(part.image_url)
|
||||
|
||||
|
||||
def _message_texts(message: OpenAIDecisionInputMessage) -> tuple[str, ...]:
|
||||
if isinstance(message.content, str):
|
||||
return (message.content,)
|
||||
return tuple(map(_part_text, message.content))
|
||||
|
||||
|
||||
def _message_assets(message: OpenAIDecisionInputMessage) -> Mapping[str, DecisionsAsset]:
|
||||
if isinstance(message.content, str):
|
||||
return {}
|
||||
return {
|
||||
_image_reference(part.image_url): asset
|
||||
for part in message.content
|
||||
if isinstance(part, OpenAIDecisionInputImage) and (asset := _image_asset(part.image_url)) is not None
|
||||
}
|
||||
|
||||
|
||||
def _with_images_as_assets(request: DecisionsIRRequest) -> DecisionsIRRequest:
|
||||
if not isinstance(request.input, DecisionsIRMessages):
|
||||
return request
|
||||
messages: Final = request.input.messages
|
||||
return replace(
|
||||
request,
|
||||
input=DecisionsIRState(state="\n\n".join(chain.from_iterable(map(_message_texts, messages)))),
|
||||
assets=MappingProxyType(
|
||||
dict(chain(request.assets.items(), *(_message_assets(message).items() for message in messages)))
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class TypeSafeDecisionsConfig(BaseDecisionsConfig):
|
||||
api_key_env = ("TYPESAFE_API_KEY",)
|
||||
api_base_env = ("TYPESAFE_API_BASE",)
|
||||
image_media_types = frozenset(_ASSET_FORMATS)
|
||||
inlines_remote_images = True
|
||||
|
||||
def get_default_api_base(self) -> str | None:
|
||||
return "https://api.typesafe.ai"
|
||||
|
||||
def supports_images_in(
|
||||
self, decision_input: DecisionsIRState | DecisionsIRMessages, headers: Mapping[str, str]
|
||||
) -> bool:
|
||||
return isinstance(decision_input, DecisionsIRMessages) and self.supports_assets(headers)
|
||||
|
||||
def supports_assets(self, headers: Mapping[str, str]) -> bool:
|
||||
return any(
|
||||
name.lower() == _IMAGES_HEADER and value.strip().lower() == "true" for name, value in headers.items()
|
||||
)
|
||||
|
||||
def supports_box_questions(self, model: str, custom_llm_provider: str, headers: Mapping[str, str]) -> bool:
|
||||
return self.supports_assets(headers) and super().supports_box_questions(model, custom_llm_provider, headers)
|
||||
|
||||
def transform_decisions_request(
|
||||
self,
|
||||
model: str,
|
||||
request: DecisionsIRRequest,
|
||||
custom_llm_provider: str,
|
||||
) -> Mapping[str, object] | UnsupportedDecisionsRequest:
|
||||
return super().transform_decisions_request(model, _with_images_as_assets(request), custom_llm_provider)
|
||||
|
|
|
|||
|
|
@ -69875,7 +69875,8 @@
|
|||
"max_input_tokens": 65536,
|
||||
"mode": "evaluation",
|
||||
"output_cost_per_token": 0.0,
|
||||
"source": "https://docs.typesafe.ai/models"
|
||||
"source": "https://docs.typesafe.ai/models",
|
||||
"supports_box_questions": true
|
||||
},
|
||||
"typesafe/jev-latest": {
|
||||
"input_cost_per_token": 4.2e-08,
|
||||
|
|
@ -69891,7 +69892,8 @@
|
|||
"max_input_tokens": 65536,
|
||||
"mode": "evaluation",
|
||||
"output_cost_per_token": 0.0,
|
||||
"source": "https://docs.typesafe.ai/models"
|
||||
"source": "https://docs.typesafe.ai/models",
|
||||
"supports_box_questions": true
|
||||
},
|
||||
"wandb/deepseek-ai/DeepSeek-V4.1-Flash": {
|
||||
"cache_read_input_token_cost": 3e-08,
|
||||
|
|
|
|||
|
|
@ -9649,6 +9649,37 @@
|
|||
"decisions": {
|
||||
"components": {
|
||||
"schemas": {
|
||||
"BoxAnswer": {
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"box": {
|
||||
"$ref": "#/components/schemas/DecisionsBox"
|
||||
},
|
||||
"confidence": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Confidence"
|
||||
},
|
||||
"type": {
|
||||
"const": "box",
|
||||
"title": "Type",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"type",
|
||||
"box",
|
||||
"confidence"
|
||||
],
|
||||
"title": "BoxAnswer",
|
||||
"type": "object"
|
||||
},
|
||||
"ChoiceAnswer": {
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
|
|
@ -9682,6 +9713,35 @@
|
|||
"title": "ChoiceAnswer",
|
||||
"type": "object"
|
||||
},
|
||||
"DecisionsBox": {
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"bottom": {
|
||||
"title": "Bottom",
|
||||
"type": "number"
|
||||
},
|
||||
"left": {
|
||||
"title": "Left",
|
||||
"type": "number"
|
||||
},
|
||||
"right": {
|
||||
"title": "Right",
|
||||
"type": "number"
|
||||
},
|
||||
"top": {
|
||||
"title": "Top",
|
||||
"type": "number"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"top",
|
||||
"bottom",
|
||||
"left",
|
||||
"right"
|
||||
],
|
||||
"title": "DecisionsBox",
|
||||
"type": "object"
|
||||
},
|
||||
"DecisionsResponse": {
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
|
|
@ -9689,6 +9749,7 @@
|
|||
"additionalProperties": {
|
||||
"discriminator": {
|
||||
"mapping": {
|
||||
"box": "#/components/schemas/BoxAnswer",
|
||||
"choice": "#/components/schemas/ChoiceAnswer",
|
||||
"noul": "#/components/schemas/NoulAnswer",
|
||||
"score": "#/components/schemas/ScoreAnswer"
|
||||
|
|
@ -9704,6 +9765,9 @@
|
|||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/ScoreAnswer"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/BoxAnswer"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
|
@ -9775,6 +9839,48 @@
|
|||
"title": "NoulAnswer",
|
||||
"type": "object"
|
||||
},
|
||||
"OpenAIBoxAnswer": {
|
||||
"properties": {
|
||||
"box": {
|
||||
"$ref": "#/components/schemas/DecisionsBox"
|
||||
},
|
||||
"confidence": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Confidence"
|
||||
},
|
||||
"name": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Name"
|
||||
},
|
||||
"type": {
|
||||
"const": "box",
|
||||
"default": "box",
|
||||
"title": "Type",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"name",
|
||||
"box",
|
||||
"confidence"
|
||||
],
|
||||
"title": "OpenAIBoxAnswer",
|
||||
"type": "object"
|
||||
},
|
||||
"OpenAIChoiceAnswer": {
|
||||
"properties": {
|
||||
"choice": {
|
||||
|
|
@ -9885,6 +9991,7 @@
|
|||
"items": {
|
||||
"discriminator": {
|
||||
"mapping": {
|
||||
"box": "#/components/schemas/OpenAIBoxAnswer",
|
||||
"choice": "#/components/schemas/OpenAIChoiceAnswer",
|
||||
"predicate": "#/components/schemas/OpenAIPredicateAnswer",
|
||||
"refusal": "#/components/schemas/OpenAIRefusalAnswer",
|
||||
|
|
@ -9902,6 +10009,9 @@
|
|||
{
|
||||
"$ref": "#/components/schemas/OpenAIScoreAnswer"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIBoxAnswer"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIRefusalAnswer"
|
||||
}
|
||||
|
|
@ -10144,7 +10254,7 @@
|
|||
"paths": {
|
||||
"/decisions": {
|
||||
"post": {
|
||||
"description": "Judge an `input` against 1 to 128 `questions` and get the answers back in the same order.\n\nQuestion types are `predicate` (yes/no with `probability`), `choice` (one of the `choices`, with\n`probabilities`) and `score` (an index into `levels`, with `probabilities`). Requests that break these\nrules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy\nmodel list. System One decision models accept this format too, LiteLLM translates the request and the\nanswers. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"description": "Judge an `input` against 1 to 128 `questions` and get the answers back in the same order.\n\nQuestion types are `predicate` (yes/no with `probability`), `choice` (one of the `choices`, with\n`probabilities`), `score` (an index into `levels`, with `probabilities`) and `box` (the image region the\n`instructions` describe, with `confidence`). Requests that break these rules are rejected with a 400 before\nany provider is called. `model` is any decision model in the proxy\nmodel list. System One decision models accept this format too, LiteLLM translates the request and the\nanswers. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"operationId": "decisions_decisions_post",
|
||||
"requestBody": {
|
||||
"content": {
|
||||
|
|
@ -10518,6 +10628,39 @@
|
|||
],
|
||||
"title": "OpenAIScoreQuestion",
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"instructions": {
|
||||
"description": "What to find in the input's image",
|
||||
"title": "Instructions",
|
||||
"type": "string"
|
||||
},
|
||||
"name": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"description": "Echoed in the matching answer",
|
||||
"title": "Name"
|
||||
},
|
||||
"type": {
|
||||
"const": "box",
|
||||
"title": "Type",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"type",
|
||||
"instructions"
|
||||
],
|
||||
"title": "OpenAIBoxQuestion",
|
||||
"type": "object"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
|
@ -10614,7 +10757,7 @@
|
|||
},
|
||||
"/systemone": {
|
||||
"post": {
|
||||
"description": "Judge a `state` against 1 to 128 named `questions` and get one calibrated answer per question name.\n\nQuestion types are `noul` (probability the answer is yes), `choice` (one label out of `criteria`, with\n`confidence` and `probabilities`) and `score` (an index into the `criteria` levels, with `confidence`,\n`legend` and `probabilities`). Requests that break these rules are rejected with a 400 before any provider\nis called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format\ntoo, LiteLLM translates it and keeps the answers keyed by question name. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"description": "Judge a `state` against 1 to 128 named `questions` and get one calibrated answer per question name.\n\nQuestion types are `noul` (probability the answer is yes), `choice` (one label out of `criteria`, with\n`confidence` and `probabilities`), `score` (an index into the `criteria` levels, with `confidence`,\n`legend` and `probabilities`) and `box` (the image region the `instructions` describe, with `confidence`).\nTypeSafe models also take images in `assets`, each one referenced by its key in `state`. Requests that break\nthese rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format\ntoo, LiteLLM translates it and keeps the answers keyed by question name. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"operationId": "systemone_systemone_post",
|
||||
"requestBody": {
|
||||
"content": {
|
||||
|
|
@ -10649,6 +10792,59 @@
|
|||
"schema": {
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"assets": {
|
||||
"anyOf": [
|
||||
{
|
||||
"patternProperties": {
|
||||
"\\S": {
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"data_base64": {
|
||||
"description": "Base64-encoded image bytes, no data: prefix",
|
||||
"pattern": "^[A-Za-z0-9+/]+={0,2}$",
|
||||
"title": "Data Base64",
|
||||
"type": "string"
|
||||
},
|
||||
"format": {
|
||||
"description": "The image's format, in lowercase",
|
||||
"enum": [
|
||||
"png",
|
||||
"jpg",
|
||||
"jpeg"
|
||||
],
|
||||
"title": "Format",
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"anyOf": [
|
||||
{
|
||||
"const": "image",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Type"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"format",
|
||||
"data_base64"
|
||||
],
|
||||
"title": "DecisionsAsset",
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"description": "TypeSafe only. Images keyed by a reference string. Write the reference in state, instructions or criteria where the image belongs",
|
||||
"title": "Assets"
|
||||
},
|
||||
"audio": {
|
||||
"default": [],
|
||||
"description": "Clef-omni only. Up to 4 embedded audio clips, each a base64 data URL or {content_type, base64}",
|
||||
|
|
@ -10693,7 +10889,7 @@
|
|||
"items": {
|
||||
"anyOf": [
|
||||
{
|
||||
"pattern": "^(?i:data:image/[^;,\\s]+(?:;[^;,\\s]+)*;base64,.+|https?://\\S+)$",
|
||||
"pattern": "^(?i:data:image/[^;,\\s]+(?:;[^;,\\s]+)*;base64,[A-Za-z0-9+/_-]+={0,2}|https?://\\S+)$",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
|
|
@ -10930,6 +11126,39 @@
|
|||
],
|
||||
"title": "ScoreQuestion",
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"instructions": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"additionalProperties": true,
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"items": {},
|
||||
"type": "array"
|
||||
}
|
||||
],
|
||||
"description": "What to find, and in which image, such as 'the coffee mug in `photo`'",
|
||||
"title": "Instructions"
|
||||
},
|
||||
"type": {
|
||||
"const": "box",
|
||||
"title": "Type",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"type",
|
||||
"instructions"
|
||||
],
|
||||
"title": "BoxQuestion",
|
||||
"type": "object"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
|
@ -11035,7 +11264,7 @@
|
|||
},
|
||||
"/v1/decisions": {
|
||||
"post": {
|
||||
"description": "Judge an `input` against 1 to 128 `questions` and get the answers back in the same order.\n\nQuestion types are `predicate` (yes/no with `probability`), `choice` (one of the `choices`, with\n`probabilities`) and `score` (an index into `levels`, with `probabilities`). Requests that break these\nrules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy\nmodel list. System One decision models accept this format too, LiteLLM translates the request and the\nanswers. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"description": "Judge an `input` against 1 to 128 `questions` and get the answers back in the same order.\n\nQuestion types are `predicate` (yes/no with `probability`), `choice` (one of the `choices`, with\n`probabilities`), `score` (an index into `levels`, with `probabilities`) and `box` (the image region the\n`instructions` describe, with `confidence`). Requests that break these rules are rejected with a 400 before\nany provider is called. `model` is any decision model in the proxy\nmodel list. System One decision models accept this format too, LiteLLM translates the request and the\nanswers. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"operationId": "decisions_v1_decisions_post",
|
||||
"requestBody": {
|
||||
"content": {
|
||||
|
|
@ -11409,6 +11638,39 @@
|
|||
],
|
||||
"title": "OpenAIScoreQuestion",
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"instructions": {
|
||||
"description": "What to find in the input's image",
|
||||
"title": "Instructions",
|
||||
"type": "string"
|
||||
},
|
||||
"name": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"description": "Echoed in the matching answer",
|
||||
"title": "Name"
|
||||
},
|
||||
"type": {
|
||||
"const": "box",
|
||||
"title": "Type",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"type",
|
||||
"instructions"
|
||||
],
|
||||
"title": "OpenAIBoxQuestion",
|
||||
"type": "object"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
|
@ -11505,7 +11767,7 @@
|
|||
},
|
||||
"/v1/systemone": {
|
||||
"post": {
|
||||
"description": "Judge a `state` against 1 to 128 named `questions` and get one calibrated answer per question name.\n\nQuestion types are `noul` (probability the answer is yes), `choice` (one label out of `criteria`, with\n`confidence` and `probabilities`) and `score` (an index into the `criteria` levels, with `confidence`,\n`legend` and `probabilities`). Requests that break these rules are rejected with a 400 before any provider\nis called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format\ntoo, LiteLLM translates it and keeps the answers keyed by question name. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"description": "Judge a `state` against 1 to 128 named `questions` and get one calibrated answer per question name.\n\nQuestion types are `noul` (probability the answer is yes), `choice` (one label out of `criteria`, with\n`confidence` and `probabilities`), `score` (an index into the `criteria` levels, with `confidence`,\n`legend` and `probabilities`) and `box` (the image region the `instructions` describe, with `confidence`).\nTypeSafe models also take images in `assets`, each one referenced by its key in `state`. Requests that break\nthese rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format\ntoo, LiteLLM translates it and keeps the answers keyed by question name. Streaming is not supported.\n\n[Docs](https://docs.litellm.ai/docs/decisions)",
|
||||
"operationId": "systemone_v1_systemone_post",
|
||||
"requestBody": {
|
||||
"content": {
|
||||
|
|
@ -11540,6 +11802,59 @@
|
|||
"schema": {
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"assets": {
|
||||
"anyOf": [
|
||||
{
|
||||
"patternProperties": {
|
||||
"\\S": {
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"data_base64": {
|
||||
"description": "Base64-encoded image bytes, no data: prefix",
|
||||
"pattern": "^[A-Za-z0-9+/]+={0,2}$",
|
||||
"title": "Data Base64",
|
||||
"type": "string"
|
||||
},
|
||||
"format": {
|
||||
"description": "The image's format, in lowercase",
|
||||
"enum": [
|
||||
"png",
|
||||
"jpg",
|
||||
"jpeg"
|
||||
],
|
||||
"title": "Format",
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"anyOf": [
|
||||
{
|
||||
"const": "image",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Type"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"format",
|
||||
"data_base64"
|
||||
],
|
||||
"title": "DecisionsAsset",
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"description": "TypeSafe only. Images keyed by a reference string. Write the reference in state, instructions or criteria where the image belongs",
|
||||
"title": "Assets"
|
||||
},
|
||||
"audio": {
|
||||
"default": [],
|
||||
"description": "Clef-omni only. Up to 4 embedded audio clips, each a base64 data URL or {content_type, base64}",
|
||||
|
|
@ -11584,7 +11899,7 @@
|
|||
"items": {
|
||||
"anyOf": [
|
||||
{
|
||||
"pattern": "^(?i:data:image/[^;,\\s]+(?:;[^;,\\s]+)*;base64,.+|https?://\\S+)$",
|
||||
"pattern": "^(?i:data:image/[^;,\\s]+(?:;[^;,\\s]+)*;base64,[A-Za-z0-9+/_-]+={0,2}|https?://\\S+)$",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
|
|
@ -11821,6 +12136,39 @@
|
|||
],
|
||||
"title": "ScoreQuestion",
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"instructions": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"additionalProperties": true,
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"items": {},
|
||||
"type": "array"
|
||||
}
|
||||
],
|
||||
"description": "What to find, and in which image, such as 'the coffee mug in `photo`'",
|
||||
"title": "Instructions"
|
||||
},
|
||||
"type": {
|
||||
"const": "box",
|
||||
"title": "Type",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"type",
|
||||
"instructions"
|
||||
],
|
||||
"title": "BoxQuestion",
|
||||
"type": "object"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -92,7 +92,7 @@ async def _invalid_request(
|
|||
)
|
||||
|
||||
|
||||
_FIELDS_CHECKED_AFTER_ROUTING: Final = frozenset({"safety_identifier", "images", "audio", "videos"})
|
||||
_FIELDS_CHECKED_AFTER_ROUTING: Final = frozenset({"safety_identifier", "images", "assets", "audio", "videos"})
|
||||
|
||||
|
||||
def _fields_checked_before_routing(data: Mapping[str, object]) -> Mapping[str, object]:
|
||||
|
|
@ -200,9 +200,10 @@ async def systemone(
|
|||
Judge a `state` against 1 to 128 named `questions` and get one calibrated answer per question name.
|
||||
|
||||
Question types are `noul` (probability the answer is yes), `choice` (one label out of `criteria`, with
|
||||
`confidence` and `probabilities`) and `score` (an index into the `criteria` levels, with `confidence`,
|
||||
`legend` and `probabilities`). Requests that break these rules are rejected with a 400 before any provider
|
||||
is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format
|
||||
`confidence` and `probabilities`), `score` (an index into the `criteria` levels, with `confidence`,
|
||||
`legend` and `probabilities`) and `box` (the image region the `instructions` describe, with `confidence`).
|
||||
TypeSafe models also take images in `assets`, each one referenced by its key in `state`. Requests that break
|
||||
these rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format
|
||||
too, LiteLLM translates it and keeps the answers keyed by question name. Streaming is not supported.
|
||||
|
||||
[Docs](https://docs.litellm.ai/docs/decisions)
|
||||
|
|
@ -243,8 +244,9 @@ async def decisions(
|
|||
Judge an `input` against 1 to 128 `questions` and get the answers back in the same order.
|
||||
|
||||
Question types are `predicate` (yes/no with `probability`), `choice` (one of the `choices`, with
|
||||
`probabilities`) and `score` (an index into `levels`, with `probabilities`). Requests that break these
|
||||
rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy
|
||||
`probabilities`), `score` (an index into `levels`, with `probabilities`) and `box` (the image region the
|
||||
`instructions` describe, with `confidence`). Requests that break these rules are rejected with a 400 before
|
||||
any provider is called. `model` is any decision model in the proxy
|
||||
model list. System One decision models accept this format too, LiteLLM translates the request and the
|
||||
answers. Streaming is not supported.
|
||||
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ from litellm.constants import (
|
|||
SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY,
|
||||
SESSION_ID_GENERATED_METADATA_KEY,
|
||||
SESSION_ID_OMITTED_METADATA_KEY,
|
||||
TYPESAFE_DANGER_ZONE_HEADER_PREFIX,
|
||||
X_LITELLM_DISABLE_CALLBACKS,
|
||||
)
|
||||
from litellm.integrations.custom_guardrail import without_server_streaming_classification
|
||||
|
|
@ -4025,7 +4026,7 @@ _ANTHROPIC_OAUTH_CREDENTIAL_PROVIDERS: Final = ",".join(sorted(ANTHROPIC_OAUTH_F
|
|||
|
||||
def add_provider_specific_headers_to_request(
|
||||
data: dict,
|
||||
headers: dict,
|
||||
headers: Mapping[str, str],
|
||||
) -> bool:
|
||||
from litellm.llms.anthropic.common_utils import is_anthropic_oauth_key
|
||||
|
||||
|
|
@ -4035,12 +4036,18 @@ def add_provider_specific_headers_to_request(
|
|||
for header, value in headers.items()
|
||||
if header.lower() == "authorization" and is_anthropic_oauth_key(value)
|
||||
}
|
||||
typesafe_danger_zone_headers: Final = {
|
||||
header: value
|
||||
for header, value in headers.items()
|
||||
if header.lower().startswith(TYPESAFE_DANGER_ZONE_HEADER_PREFIX)
|
||||
}
|
||||
|
||||
scoped_headers: Final = [
|
||||
ProviderSpecificHeader(custom_llm_provider=providers, extra_headers=extra_headers)
|
||||
for providers, extra_headers in (
|
||||
(_ANTHROPIC_API_HEADER_PROVIDERS, anthropic_api_headers),
|
||||
(_ANTHROPIC_OAUTH_CREDENTIAL_PROVIDERS, anthropic_oauth_credential_headers),
|
||||
(LlmProviders.TYPESAFE.value, typesafe_danger_zone_headers),
|
||||
)
|
||||
if extra_headers
|
||||
]
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ from litellm.constants import (
|
|||
AZURE_SPEECH_STT_DOMAIN,
|
||||
AZURE_SPEECH_SUBSCRIPTION_KEY_HEADER,
|
||||
BEDROCK_AGENT_RUNTIME_PASS_THROUGH_ROUTES,
|
||||
TYPESAFE_DANGER_ZONE_HEADER_PREFIX,
|
||||
)
|
||||
from litellm.integrations.custom_guardrail import guardrail_request_data_with_streaming
|
||||
from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix
|
||||
|
|
@ -654,10 +655,16 @@ async def typesafe_proxy_route(
|
|||
custom_llm_provider="typesafe",
|
||||
region_name=None,
|
||||
)
|
||||
danger_zone_headers: Final = {
|
||||
name: value
|
||||
for name, value in request.headers.items()
|
||||
if name.lower().startswith(TYPESAFE_DANGER_ZONE_HEADER_PREFIX)
|
||||
}
|
||||
endpoint_func: Final = create_pass_through_route(
|
||||
endpoint=endpoint,
|
||||
target=str(updated_url),
|
||||
custom_headers={
|
||||
**danger_zone_headers,
|
||||
"Authorization": f"Bearer {typesafe_api_key}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ from typing import Final
|
|||
import httpx
|
||||
|
||||
from litellm.decisions.call import (
|
||||
DecisionsAssetEntries,
|
||||
DecisionsAudioClips,
|
||||
DecisionsCall,
|
||||
DecisionsImages,
|
||||
|
|
@ -25,6 +26,7 @@ def _prepare_systemone_call(
|
|||
state: DecisionsJSON | None,
|
||||
questions: SystemOneQuestions | None,
|
||||
images: DecisionsImages | None,
|
||||
assets: DecisionsAssetEntries | None,
|
||||
safety_identifier: str | None,
|
||||
audio: DecisionsAudioClips | None,
|
||||
videos: DecisionsVideoClips | None,
|
||||
|
|
@ -38,7 +40,9 @@ def _prepare_systemone_call(
|
|||
reject_other_format(model=model, kwargs=kwargs, field_name="input", other_function="decisions")
|
||||
return prepare_call(
|
||||
model=model,
|
||||
fields=SystemOneRequestFields(state=state, questions=questions, images=images, audio=audio, videos=videos),
|
||||
fields=SystemOneRequestFields(
|
||||
state=state, questions=questions, images=images, assets=assets, audio=audio, videos=videos
|
||||
),
|
||||
safety_identifier=safety_identifier,
|
||||
api_key=api_key,
|
||||
api_base=api_base,
|
||||
|
|
@ -68,6 +72,7 @@ async def asystemone(
|
|||
audio: DecisionsAudioClips | None = None,
|
||||
videos: DecisionsVideoClips | None = None,
|
||||
images: DecisionsImages | None = None,
|
||||
assets: DecisionsAssetEntries | None = None,
|
||||
**kwargs: object, # kwargs-ok: litellm_params forwarded by @client
|
||||
) -> DecisionsResponse:
|
||||
call: Final = _prepare_systemone_call(
|
||||
|
|
@ -75,6 +80,7 @@ async def asystemone(
|
|||
state=state,
|
||||
questions=questions,
|
||||
images=images,
|
||||
assets=assets,
|
||||
safety_identifier=safety_identifier,
|
||||
audio=audio,
|
||||
videos=videos,
|
||||
|
|
@ -102,6 +108,7 @@ def systemone(
|
|||
audio: DecisionsAudioClips | None = None,
|
||||
videos: DecisionsVideoClips | None = None,
|
||||
images: DecisionsImages | None = None,
|
||||
assets: DecisionsAssetEntries | None = None,
|
||||
**kwargs: object, # kwargs-ok: litellm_params forwarded by @client
|
||||
) -> DecisionsResponse:
|
||||
call: Final = _prepare_systemone_call(
|
||||
|
|
@ -109,6 +116,7 @@ def systemone(
|
|||
state=state,
|
||||
questions=questions,
|
||||
images=images,
|
||||
assets=assets,
|
||||
safety_identifier=safety_identifier,
|
||||
audio=audio,
|
||||
videos=videos,
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from dataclasses import dataclass, field
|
|||
from types import MappingProxyType
|
||||
from typing import Annotated, Final, Literal, TypeAlias
|
||||
|
||||
from pydantic import ConfigDict, Field, PrivateAttr, field_validator, model_validator, with_config
|
||||
from pydantic import AfterValidator, ConfigDict, Field, PrivateAttr, field_validator, model_validator, with_config
|
||||
from typing_extensions import ReadOnly, Required, TypedDict
|
||||
|
||||
from litellm.types.llms.base import LiteLLMPydanticObjectBase
|
||||
|
|
@ -11,6 +11,7 @@ from litellm.types.llms.base import LiteLLMPydanticObjectBase
|
|||
DecisionsJSON: TypeAlias = str | Mapping[str, object] | Sequence[object]
|
||||
NoulCriteria: TypeAlias = Mapping[Literal["true", "false"], DecisionsJSON | None]
|
||||
MAX_DECISION_QUESTIONS: Final = 128
|
||||
MAX_ASSET_REFERENCE_BYTES: Final = 1024
|
||||
|
||||
|
||||
class NoulQuestion(LiteLLMPydanticObjectBase):
|
||||
|
|
@ -56,8 +57,17 @@ class ScoreQuestion(LiteLLMPydanticObjectBase):
|
|||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
|
||||
class BoxQuestion(LiteLLMPydanticObjectBase):
|
||||
type: Literal["box"]
|
||||
instructions: Annotated[
|
||||
DecisionsJSON, Field(description="What to find, and in which image, such as 'the coffee mug in `photo`'")
|
||||
]
|
||||
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
|
||||
DecisionQuestion: TypeAlias = Annotated[
|
||||
NoulQuestion | ChoiceQuestion | ScoreQuestion,
|
||||
NoulQuestion | ChoiceQuestion | ScoreQuestion | BoxQuestion,
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
|
||||
|
|
@ -75,11 +85,33 @@ class DecisionsBase64Image(LiteLLMPydanticObjectBase):
|
|||
|
||||
|
||||
DecisionsImage: TypeAlias = (
|
||||
Annotated[str, Field(pattern=r"^(?i:data:image/[^;,\s]+(?:;[^;,\s]+)*;base64,.+|https?://\S+)$")]
|
||||
Annotated[
|
||||
str, Field(pattern=r"^(?i:data:image/[^;,\s]+(?:;[^;,\s]+)*;base64,[A-Za-z0-9+/_-]+={0,2}|https?://\S+)$")
|
||||
]
|
||||
| DecisionsBase64Image
|
||||
)
|
||||
|
||||
|
||||
class DecisionsAsset(LiteLLMPydanticObjectBase):
|
||||
type: Literal["image"] | None = None
|
||||
format: Annotated[Literal["png", "jpg", "jpeg"], Field(description="The image's format, in lowercase")]
|
||||
data_base64: Annotated[
|
||||
str, Field(pattern=r"^[A-Za-z0-9+/]+={0,2}$", description="Base64-encoded image bytes, no data: prefix")
|
||||
]
|
||||
|
||||
model_config = ConfigDict(extra="forbid", frozen=True)
|
||||
|
||||
|
||||
def _fits_reference_limit(reference: str) -> str:
|
||||
if len(reference.encode()) > MAX_ASSET_REFERENCE_BYTES:
|
||||
raise ValueError(f"an asset reference can be at most {MAX_ASSET_REFERENCE_BYTES} bytes of UTF-8")
|
||||
return reference
|
||||
|
||||
|
||||
DecisionsAssetReference: TypeAlias = Annotated[str, Field(pattern=r"\S"), AfterValidator(_fits_reference_limit)]
|
||||
DecisionsAssets: TypeAlias = Mapping[DecisionsAssetReference, DecisionsAsset]
|
||||
|
||||
|
||||
class DecisionsBase64Audio(LiteLLMPydanticObjectBase):
|
||||
content_type: Annotated[str, Field(pattern=r"^audio/\S+$", description="An audio/* media type, such as audio/wav")]
|
||||
base64: Annotated[str, Field(min_length=1, description="Base64-encoded audio bytes")]
|
||||
|
|
@ -135,6 +167,15 @@ class DecisionsRequestBody(DecisionsMediaBody):
|
|||
Sequence[DecisionsImage] | None,
|
||||
Field(description="Images as data URLs or content_type plus base64, for providers that look at images"),
|
||||
] = None
|
||||
assets: Annotated[
|
||||
DecisionsAssets | None,
|
||||
Field(
|
||||
description=(
|
||||
"TypeSafe only. Images keyed by a reference string. Write the reference in state, instructions "
|
||||
"or criteria where the image belongs"
|
||||
)
|
||||
),
|
||||
] = None
|
||||
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
|
|
@ -149,6 +190,7 @@ class DecisionsCallParams(TypedDict, total=False):
|
|||
state: Required[ReadOnly[DecisionsJSON]]
|
||||
questions: Required[ReadOnly[DecisionQuestionMap]]
|
||||
images: ReadOnly[Sequence[DecisionsImage] | None]
|
||||
assets: ReadOnly[DecisionsAssets | None]
|
||||
audio: ReadOnly[Sequence[DecisionsAudio]]
|
||||
videos: ReadOnly[Sequence[DecisionsVideo]]
|
||||
api_key: ReadOnly[str | None]
|
||||
|
|
@ -184,8 +226,25 @@ class ScoreAnswer(LiteLLMPydanticObjectBase):
|
|||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
|
||||
class DecisionsBox(LiteLLMPydanticObjectBase):
|
||||
top: float
|
||||
bottom: float
|
||||
left: float
|
||||
right: float
|
||||
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
|
||||
class BoxAnswer(LiteLLMPydanticObjectBase):
|
||||
type: Literal["box"]
|
||||
box: DecisionsBox
|
||||
confidence: float | None
|
||||
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
||||
|
||||
DecisionAnswer: TypeAlias = Annotated[
|
||||
NoulAnswer | ChoiceAnswer | ScoreAnswer,
|
||||
NoulAnswer | ChoiceAnswer | ScoreAnswer | BoxAnswer,
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
|
||||
|
|
@ -311,8 +370,16 @@ class OpenAIScoreQuestion(LiteLLMPydanticObjectBase):
|
|||
model_config = ConfigDict(extra="forbid", frozen=True)
|
||||
|
||||
|
||||
class OpenAIBoxQuestion(LiteLLMPydanticObjectBase):
|
||||
type: Literal["box"]
|
||||
name: Annotated[str | None, Field(description="Echoed in the matching answer")] = None
|
||||
instructions: Annotated[str, Field(description="What to find in the input's image")]
|
||||
|
||||
model_config = ConfigDict(extra="forbid", frozen=True)
|
||||
|
||||
|
||||
OpenAIDecisionQuestion: TypeAlias = Annotated[
|
||||
OpenAIPredicateQuestion | OpenAIChoiceQuestion | OpenAIScoreQuestion,
|
||||
OpenAIPredicateQuestion | OpenAIChoiceQuestion | OpenAIScoreQuestion | OpenAIBoxQuestion,
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
|
||||
|
|
@ -394,8 +461,17 @@ class OpenAIRefusalAnswer(LiteLLMPydanticObjectBase):
|
|||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
|
||||
class OpenAIBoxAnswer(LiteLLMPydanticObjectBase):
|
||||
type: Literal["box"] = "box"
|
||||
name: str | None
|
||||
box: DecisionsBox
|
||||
confidence: float | None
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
|
||||
OpenAIDecisionAnswer: TypeAlias = Annotated[
|
||||
OpenAIPredicateAnswer | OpenAIChoiceAnswer | OpenAIScoreAnswer | OpenAIRefusalAnswer,
|
||||
OpenAIPredicateAnswer | OpenAIChoiceAnswer | OpenAIScoreAnswer | OpenAIBoxAnswer | OpenAIRefusalAnswer,
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
|
||||
|
|
@ -432,6 +508,7 @@ class OpenAIDecisionResponse(_HiddenParamsResponse):
|
|||
|
||||
|
||||
_NO_EXTRA: Final[Mapping[str, object]] = MappingProxyType({})
|
||||
_NO_ASSETS: Final[Mapping[str, DecisionsAsset]] = MappingProxyType({})
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
|
|
@ -481,7 +558,16 @@ class DecisionsIRScoreQuestion:
|
|||
extra: Mapping[str, object] = field(default_factory=lambda: _NO_EXTRA)
|
||||
|
||||
|
||||
DecisionsIRQuestion: TypeAlias = DecisionsIRPredicateQuestion | DecisionsIRChoiceQuestion | DecisionsIRScoreQuestion
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class DecisionsIRBoxQuestion:
|
||||
name: str | None
|
||||
instructions: DecisionsJSON
|
||||
extra: Mapping[str, object] = field(default_factory=lambda: _NO_EXTRA)
|
||||
|
||||
|
||||
DecisionsIRQuestion: TypeAlias = (
|
||||
DecisionsIRPredicateQuestion | DecisionsIRChoiceQuestion | DecisionsIRScoreQuestion | DecisionsIRBoxQuestion
|
||||
)
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
|
|
@ -489,6 +575,7 @@ class DecisionsIRRequest:
|
|||
input: DecisionsIRState | DecisionsIRMessages
|
||||
questions: tuple[DecisionsIRQuestion, ...]
|
||||
safety_identifier: str | None = None
|
||||
assets: Mapping[str, DecisionsAsset] = field(default_factory=lambda: _NO_ASSETS)
|
||||
audio: tuple[DecisionsAudio, ...] = ()
|
||||
videos: tuple[DecisionsVideo, ...] = ()
|
||||
|
||||
|
|
@ -528,13 +615,24 @@ class DecisionsIRScoreAnswer:
|
|||
extra: Mapping[str, object] = field(default_factory=lambda: _NO_EXTRA)
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class DecisionsIRBoxAnswer:
|
||||
box: DecisionsBox
|
||||
confidence: float | None
|
||||
extra: Mapping[str, object] = field(default_factory=lambda: _NO_EXTRA)
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class DecisionsIRRefusal:
|
||||
pass
|
||||
|
||||
|
||||
DecisionsIRAnswer: TypeAlias = (
|
||||
DecisionsIRPredicateAnswer | DecisionsIRChoiceAnswer | DecisionsIRScoreAnswer | DecisionsIRRefusal
|
||||
DecisionsIRPredicateAnswer
|
||||
| DecisionsIRChoiceAnswer
|
||||
| DecisionsIRScoreAnswer
|
||||
| DecisionsIRBoxAnswer
|
||||
| DecisionsIRRefusal
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -196,6 +196,7 @@ class ProviderSpecificModelInfo(TypedDict, total=False):
|
|||
supports_computer_use: bool | None
|
||||
supports_audio_input: bool | None
|
||||
supports_video_input: ReadOnly[bool | None]
|
||||
supports_box_questions: ReadOnly[bool | None]
|
||||
supports_embedding_image_input: bool | None
|
||||
supports_audio_output: bool | None
|
||||
supports_pdf_input: bool | None
|
||||
|
|
|
|||
|
|
@ -3015,6 +3015,10 @@ def supports_video_input(model: str, custom_llm_provider: str | None = None) ->
|
|||
return supports_factory(model=model, custom_llm_provider=custom_llm_provider, key="supports_video_input")
|
||||
|
||||
|
||||
def supports_box_questions(model: str, custom_llm_provider: str | None = None) -> bool:
|
||||
return supports_factory(model=model, custom_llm_provider=custom_llm_provider, key="supports_box_questions")
|
||||
|
||||
|
||||
def supports_pdf_input(model: str, custom_llm_provider: str | None = None) -> bool:
|
||||
"""Check if a given model supports pdf input in a chat completion call"""
|
||||
return supports_factory(model=model, custom_llm_provider=custom_llm_provider, key="supports_pdf_input")
|
||||
|
|
@ -6445,6 +6449,7 @@ def get_model_info_helper(
|
|||
supports_thinking_cache_preservation=_model_info.get("supports_thinking_cache_preservation", None),
|
||||
supports_audio_input=_model_info.get("supports_audio_input", None),
|
||||
supports_video_input=_model_info.get("supports_video_input", None),
|
||||
supports_box_questions=_model_info.get("supports_box_questions", None),
|
||||
supports_audio_output=_model_info.get("supports_audio_output", None),
|
||||
supports_pdf_input=_model_info.get("supports_pdf_input", None),
|
||||
supports_embedding_image_input=_model_info.get("supports_embedding_image_input", None),
|
||||
|
|
@ -6596,6 +6601,7 @@ def get_model_info(
|
|||
supports_prompt_cache_breakpoint: Optional[bool]
|
||||
supports_audio_input: Optional[bool]
|
||||
supports_video_input: Optional[bool]
|
||||
supports_box_questions: Optional[bool]
|
||||
supports_audio_output: Optional[bool]
|
||||
supports_pdf_input: Optional[bool]
|
||||
supports_web_search: Optional[bool]
|
||||
|
|
|
|||
|
|
@ -69875,7 +69875,8 @@
|
|||
"max_input_tokens": 65536,
|
||||
"mode": "evaluation",
|
||||
"output_cost_per_token": 0.0,
|
||||
"source": "https://docs.typesafe.ai/models"
|
||||
"source": "https://docs.typesafe.ai/models",
|
||||
"supports_box_questions": true
|
||||
},
|
||||
"typesafe/jev-latest": {
|
||||
"input_cost_per_token": 4.2e-08,
|
||||
|
|
@ -69891,7 +69892,8 @@
|
|||
"max_input_tokens": 65536,
|
||||
"mode": "evaluation",
|
||||
"output_cost_per_token": 0.0,
|
||||
"source": "https://docs.typesafe.ai/models"
|
||||
"source": "https://docs.typesafe.ai/models",
|
||||
"supports_box_questions": true
|
||||
},
|
||||
"wandb/deepseek-ai/DeepSeek-V4.1-Flash": {
|
||||
"cache_read_input_token_cost": 3e-08,
|
||||
|
|
|
|||
|
|
@ -1032,6 +1032,9 @@
|
|||
"supports_bedrock_runtime_chat_completions_tools_with_reasoning": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"supports_box_questions": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"supports_computer_use": {
|
||||
"type": "boolean"
|
||||
},
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ import respx
|
|||
from botocore.auth import SigV4Auth
|
||||
from botocore.awsrequest import AWSRequest
|
||||
from botocore.credentials import Credentials
|
||||
from respx.models import Call
|
||||
|
||||
import litellm
|
||||
from litellm.constants import AWS_SIGNING_MAX_THREADS
|
||||
|
|
@ -3071,3 +3072,133 @@ async def test_fireworks_ai_bills_cached_input_tokens_at_the_cache_read_rate(
|
|||
assert recording_logger.standard_logging_object is not None
|
||||
assert recording_logger.standard_logging_object["response_cost"] == pytest.approx(uncached + cached)
|
||||
assert recording_logger.standard_logging_object["response_cost"] < 300 * float(prices["input_cost_per_token"])
|
||||
|
||||
|
||||
_BOX_QUESTION_REQUESTS: Final[tuple[tuple[str, Mapping[str, object]], ...]] = (
|
||||
(
|
||||
"decisions",
|
||||
MappingProxyType(
|
||||
{
|
||||
"input": "review",
|
||||
"questions": (
|
||||
*_PREDICATE_QUESTIONS,
|
||||
{"type": "box", "name": "crack", "instructions": "the crack on the screen"},
|
||||
),
|
||||
}
|
||||
),
|
||||
),
|
||||
(
|
||||
"systemone",
|
||||
MappingProxyType(
|
||||
{
|
||||
"state": "review",
|
||||
"questions": {
|
||||
"is_defect": {"type": "noul", "instructions": "Is this a defect?"},
|
||||
"crack": {"type": "box", "instructions": "the crack on the screen"},
|
||||
},
|
||||
}
|
||||
),
|
||||
),
|
||||
)
|
||||
_UNSUPPORTED_JEV_PARAMS: Final[tuple[tuple[str, Mapping[str, object], str], ...]] = (
|
||||
*((api, request_kwargs, "box questions") for api, request_kwargs in _BOX_QUESTION_REQUESTS),
|
||||
(
|
||||
"systemone",
|
||||
MappingProxyType(
|
||||
{
|
||||
"state": "review photo-1",
|
||||
"questions": _SYSTEMONE_PREDICATE,
|
||||
"assets": {"photo-1": {"format": "png", "data_base64": "AA=="}},
|
||||
}
|
||||
),
|
||||
"assets",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("api", "request_kwargs", "param"),
|
||||
_UNSUPPORTED_JEV_PARAMS,
|
||||
ids=("decisions_box", "systemone_box", "systemone_assets"),
|
||||
)
|
||||
def test_assets_and_box_questions_are_refused_before_http_when_the_provider_cannot_take_them(
|
||||
api: str,
|
||||
request_kwargs: Mapping[str, object],
|
||||
param: str,
|
||||
respx_mock: respx.MockRouter,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "drop_params", False)
|
||||
route: Final = respx_mock.post(_PERPLEXITY_DECISIONS_URL).respond(json=_RESPONSE)
|
||||
|
||||
with pytest.raises(litellm.UnsupportedParamsError, match=rf"\['{param}'\].*drop_params") as caught:
|
||||
getattr(litellm, api)(model="perplexity/pplx-decider-v1-27b", api_key="caller-key", **request_kwargs)
|
||||
|
||||
assert caught.value.status_code == 400
|
||||
assert not route.called
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_assets_are_dropped_from_the_wire_under_drop_params(respx_mock: respx.MockRouter) -> None:
|
||||
route: Final = respx_mock.post(_PERPLEXITY_DECISIONS_URL).respond(json=_RESPONSE)
|
||||
|
||||
await litellm.asystemone(
|
||||
model="perplexity/pplx-decider-v1-27b",
|
||||
state="review photo-1",
|
||||
questions={"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
|
||||
assets={"photo-1": {"format": "png", "data_base64": "AA=="}},
|
||||
api_key="caller-key",
|
||||
drop_params=True,
|
||||
)
|
||||
|
||||
sent: Final[Call] = route.calls.last
|
||||
assert json.loads(sent.request.content) == {
|
||||
"model": "pplx-decider-v1-27b",
|
||||
"state": "review photo-1",
|
||||
"questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
|
||||
}
|
||||
|
||||
|
||||
_DROPPED_BOX_ANSWERS: Final[Mapping[str, object]] = MappingProxyType(
|
||||
{
|
||||
"decisions": [
|
||||
{"type": "predicate", "name": "is_defect", "probability": 0.9},
|
||||
{"type": "refusal", "name": "crack"},
|
||||
],
|
||||
"systemone": {"is_defect": {"type": "noul", "noul": 0.9}},
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(("api", "request_kwargs"), _BOX_QUESTION_REQUESTS, ids=_PREDICATE_REQUEST_IDS)
|
||||
async def test_box_questions_are_dropped_under_drop_params_and_answered_as_refusals(
|
||||
api: str, request_kwargs: Mapping[str, object], respx_mock: respx.MockRouter
|
||||
) -> None:
|
||||
route: Final = respx_mock.post(_PERPLEXITY_DECISIONS_URL).respond(json=_RESPONSE)
|
||||
|
||||
response: Final = await getattr(litellm, f"a{api}")(
|
||||
model="perplexity/pplx-decider-v1-27b", api_key="caller-key", drop_params=True, **request_kwargs
|
||||
)
|
||||
|
||||
sent: Final[Call] = route.calls.last
|
||||
assert json.loads(sent.request.content)["questions"] == {
|
||||
"is_defect": {"type": "noul", "instructions": "Is this a defect?"}
|
||||
}
|
||||
assert response.model_dump(mode="json")["answers"] == _DROPPED_BOX_ANSWERS[api]
|
||||
|
||||
|
||||
def test_dropping_every_question_answers_400_before_http(respx_mock: respx.MockRouter) -> None:
|
||||
route: Final = respx_mock.post(_PERPLEXITY_DECISIONS_URL).respond(json=_RESPONSE)
|
||||
|
||||
with pytest.raises(litellm.BadRequestError, match="leaves no questions to answer") as caught:
|
||||
litellm.decisions(
|
||||
model="perplexity/pplx-decider-v1-27b",
|
||||
input="review",
|
||||
questions=[{"type": "box", "name": "crack", "instructions": "the crack on the screen"}],
|
||||
api_key="caller-key",
|
||||
drop_params=True,
|
||||
)
|
||||
|
||||
assert caught.value.status_code == 400
|
||||
assert not route.called
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ from collections.abc import Mapping
|
|||
from typing import Final
|
||||
|
||||
import pytest
|
||||
from pydantic import TypeAdapter
|
||||
from pydantic import TypeAdapter, ValidationError
|
||||
|
||||
from litellm.llms.base_llm.decisions.transformation import (
|
||||
ir_to_systemone_request,
|
||||
|
|
@ -195,7 +195,9 @@ def test_each_media_kind_is_gated_by_its_own_support_flag() -> None:
|
|||
audio: Final = ["data:audio/wav;base64,YQ=="]
|
||||
videos: Final = ["data:video/mp4;base64,YQ=="]
|
||||
audio_only: Final = systemone_request_to_ir(_SYSTEMONE_BODY.validate_python({**_SYSTEMONE_REQUEST, "audio": audio}))
|
||||
video_only: Final = systemone_request_to_ir(_SYSTEMONE_BODY.validate_python({**_SYSTEMONE_REQUEST, "videos": videos}))
|
||||
video_only: Final = systemone_request_to_ir(
|
||||
_SYSTEMONE_BODY.validate_python({**_SYSTEMONE_REQUEST, "videos": videos})
|
||||
)
|
||||
|
||||
assert ir_to_systemone_request("clef-omni", audio_only, supports_audio=True) == {
|
||||
"model": "clef-omni",
|
||||
|
|
@ -207,5 +209,58 @@ def test_each_media_kind_is_gated_by_its_own_support_flag() -> None:
|
|||
**_SYSTEMONE_REQUEST,
|
||||
"videos": videos,
|
||||
}
|
||||
assert isinstance(ir_to_systemone_request("clef-omni", audio_only, supports_video=True), UnsupportedDecisionsRequest)
|
||||
assert isinstance(ir_to_systemone_request("clef-omni", video_only, supports_audio=True), UnsupportedDecisionsRequest)
|
||||
assert isinstance(
|
||||
ir_to_systemone_request("clef-omni", audio_only, supports_video=True), UnsupportedDecisionsRequest
|
||||
)
|
||||
assert isinstance(
|
||||
ir_to_systemone_request("clef-omni", video_only, supports_audio=True), UnsupportedDecisionsRequest
|
||||
)
|
||||
|
||||
|
||||
_ASSET_REQUEST: Final[Mapping[str, object]] = {
|
||||
"state": {"photo": "img-7f3a"},
|
||||
"questions": {"has_square": {"type": "noul", "instructions": "Is there a red square in `photo`?"}},
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"assets",
|
||||
[
|
||||
{" ": {"type": "image", "format": "png", "data_base64": "iVBORw0KGgo="}},
|
||||
{"r" * 1025: {"type": "image", "format": "png", "data_base64": "iVBORw0KGgo="}},
|
||||
{"img-7f3a": {"type": "image", "format": "png", "data_base64": "not base64!"}},
|
||||
],
|
||||
ids=["blank-reference", "reference-over-1024-bytes", "bad-base64"],
|
||||
)
|
||||
def test_assets_typesafe_would_reject_are_rejected_by_validation(assets: Mapping[str, object]) -> None:
|
||||
with pytest.raises(ValidationError):
|
||||
_SYSTEMONE_BODY.validate_python({**_ASSET_REQUEST, "assets": assets})
|
||||
|
||||
|
||||
def test_an_asset_reference_of_exactly_1024_bytes_is_accepted() -> None:
|
||||
reference: Final = "r" * 1024
|
||||
body: Final = _SYSTEMONE_BODY.validate_python(
|
||||
{**_ASSET_REQUEST, "assets": {reference: {"type": "image", "format": "png", "data_base64": "iVBO"}}}
|
||||
)
|
||||
|
||||
assert tuple(body.assets or {}) == (reference,)
|
||||
|
||||
|
||||
def test_a_box_answer_without_confidence_reaches_the_caller_unchanged() -> None:
|
||||
request: Final = {**_ASSET_REQUEST, "questions": {"square": {"type": "box", "instructions": "the square"}}}
|
||||
payload: Final = {
|
||||
"model": "jev-1.14.0",
|
||||
"answers": {
|
||||
"square": {
|
||||
"type": "box",
|
||||
"box": {"top": 0.5, "bottom": 0.75, "left": 0.25, "right": 0.5},
|
||||
"confidence": None,
|
||||
}
|
||||
},
|
||||
"usage": {"input_tokens": 737, "output_tokens": 20},
|
||||
}
|
||||
ir: Final = systemone_request_to_ir(_SYSTEMONE_BODY.validate_python(request))
|
||||
|
||||
response: Final = ir_to_systemone_response(parse_systemone_response(payload, ir), ir)
|
||||
|
||||
assert response.model_dump(mode="json") == payload
|
||||
|
|
|
|||
|
|
@ -1001,6 +1001,38 @@ def test_malformed_images_are_refused_unless_the_deployment_drops_params(
|
|||
}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("deployment_drops_params", (False, True), ids=("strict", "drop_params"))
|
||||
def test_malformed_assets_for_a_deployment_without_asset_support_are_dropped_only_when_it_drops_params(
|
||||
client: TestClient,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
respx_mock: respx.MockRouter,
|
||||
deployment_drops_params: bool,
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "drop_params", False)
|
||||
_route_decider_to(
|
||||
monkeypatch,
|
||||
{"model": "perplexity/pplx-decider-v1-27b", "api_key": "test-key", "drop_params": deployment_drops_params},
|
||||
)
|
||||
upstream: Final = respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=_RESPONSE)
|
||||
|
||||
response: Final = client.post(
|
||||
"/v1/systemone",
|
||||
json={**_IMAGE_REQUEST_BODIES[1], "assets": {"photo-1": {"type": "image", "part": "file1"}}},
|
||||
)
|
||||
|
||||
if not deployment_drops_params:
|
||||
assert response.status_code == 400, response.text
|
||||
assert "assets" in response.json()["error"]["message"]
|
||||
assert not upstream.called
|
||||
return
|
||||
assert response.status_code == 200, response.text
|
||||
assert json.loads(upstream.calls[0].request.content) == {
|
||||
"model": "pplx-decider-v1-27b",
|
||||
"state": _IMAGE_TEXT,
|
||||
"questions": {"is_defect": {"type": "noul", "instructions": "Is this a defect?"}},
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("endpoint", ("/v1/systemone", "/v1/decisions"))
|
||||
@pytest.mark.parametrize("deployment_drops_params", (False, True), ids=("strict", "drop_params"))
|
||||
def test_clips_for_a_model_without_media_support_are_refused_unless_the_deployment_drops_params(
|
||||
|
|
|
|||
|
|
@ -7822,6 +7822,20 @@ class TestTypeSafePassthroughRoute:
|
|||
assert sent.headers["authorization"] == "Bearer typesafe-test-key"
|
||||
assert json.loads(sent.content or b"{}") == (body or {})
|
||||
|
||||
def test_forwards_only_the_typesafe_danger_zone_headers_upstream(self, client: TestClient) -> None:
|
||||
with respx.mock(assert_all_called=True) as upstream:
|
||||
route = upstream.post("https://typesafe.example/base/v1/systemone").mock(
|
||||
return_value=httpx.Response(200, json={"id": "upstream_123"})
|
||||
)
|
||||
client.post(
|
||||
"/typesafe/v1/systemone",
|
||||
json={"state": "x"},
|
||||
headers={"TypeSafe-Danger-Zone-Images": "true", "X-Unrelated": "1"},
|
||||
)
|
||||
|
||||
sent: Final = route.calls.last.request.headers
|
||||
assert (sent.get("typesafe-danger-zone-images"), sent.get("x-unrelated")) == ("true", None)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"provider, endpoint, is_decision_request",
|
||||
(
|
||||
|
|
|
|||
|
|
@ -7848,6 +7848,23 @@ def test_client_anthropic_api_headers_stay_off_openai_compatible_providers():
|
|||
assert forwarded == {}
|
||||
|
||||
|
||||
TYPESAFE_DANGER_ZONE_HEADERS = {
|
||||
"TypeSafe-Danger-Zone-Images": "true",
|
||||
"typesafe-danger-zone-long-context": "true",
|
||||
}
|
||||
|
||||
|
||||
def test_client_typesafe_danger_zone_headers_reach_typesafe():
|
||||
client_headers = {**TYPESAFE_DANGER_ZONE_HEADERS, "x-request-id": "abc"}
|
||||
|
||||
assert _headers_forwarded_to(client_headers, "typesafe") == TYPESAFE_DANGER_ZONE_HEADERS
|
||||
|
||||
|
||||
@pytest.mark.parametrize("custom_llm_provider", ["perplexity", "openai", "anthropic"])
|
||||
def test_client_typesafe_danger_zone_headers_stay_off_other_providers(custom_llm_provider: str):
|
||||
assert _headers_forwarded_to(TYPESAFE_DANGER_ZONE_HEADERS, custom_llm_provider) == {}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("authorization_header_name", AUTHORIZATION_HEADER_CASINGS)
|
||||
def test_add_provider_specific_headers_reports_a_forwarded_oauth_credential(authorization_header_name):
|
||||
assert add_provider_specific_headers_to_request(data={}, headers=_client_headers(authorization_header_name)) is True
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ import litellm
|
|||
from litellm.integrations.custom_logger import CustomLogger
|
||||
from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
|
||||
from litellm.types.decisions import DecisionsResponse, OpenAIDecisionResponse
|
||||
from tests.unit.conftest import AsyncOnlyImageFetch
|
||||
|
||||
_STATE: Final = "Customer asked for a refund"
|
||||
_SYSTEMONE_QUESTIONS: Final[Mapping[str, object]] = MappingProxyType(
|
||||
|
|
@ -158,3 +159,330 @@ async def test_router_asystemone_logs_the_systemone_call_type(
|
|||
assert isinstance(response, DecisionsResponse)
|
||||
assert recorder.call_types == ["asystemone"]
|
||||
assert not isinstance(response, OpenAIDecisionResponse)
|
||||
|
||||
|
||||
_BOX_RESPONSE: Final[Mapping[str, object]] = {
|
||||
"model": "jev-1.14.0",
|
||||
"answers": {
|
||||
"has_mug": {"type": "noul", "noul": 0.97},
|
||||
"mug": {"type": "box", "box": {"top": 0.1, "bottom": 0.5, "left": 0.2, "right": 0.6}, "confidence": 0.84},
|
||||
},
|
||||
"usage": {"input_tokens": 299, "output_tokens": 37},
|
||||
}
|
||||
_MUG_BOX: Final[Mapping[str, float]] = MappingProxyType({"top": 0.1, "bottom": 0.5, "left": 0.2, "right": 0.6})
|
||||
_IMAGES_HEADER: Final[Mapping[str, str]] = MappingProxyType({"TypeSafe-Danger-Zone-Images": "true"})
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_systemone_sends_assets_and_box_questions_to_typesafe(respx_mock: respx.MockRouter) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_BOX_RESPONSE)
|
||||
questions: Final[Mapping[str, object]] = {
|
||||
"has_mug": {"type": "noul", "instructions": "Is there a mug in `photo-1`?"},
|
||||
"mug": {"type": "box", "instructions": "the mug in `photo-1`"},
|
||||
}
|
||||
|
||||
response: Final = await litellm.asystemone(
|
||||
model="typesafe/jev-1.14.0",
|
||||
state={"photo": "photo-1"},
|
||||
assets={"photo-1": {"type": "image", "format": "png", "data_base64": "AA=="}},
|
||||
questions=questions,
|
||||
api_key="caller-key",
|
||||
extra_headers=_IMAGES_HEADER,
|
||||
)
|
||||
|
||||
assert json.loads(route.calls.last.request.content) == {
|
||||
"model": "jev-1.14.0",
|
||||
"state": {"photo": "photo-1"},
|
||||
"questions": questions,
|
||||
"assets": {"photo-1": {"type": "image", "format": "png", "data_base64": "AA=="}},
|
||||
}
|
||||
assert isinstance(response, DecisionsResponse)
|
||||
assert response.answers["mug"].model_dump(mode="json") == {"type": "box", "box": _MUG_BOX, "confidence": 0.84}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decisions_input_images_reach_typesafe_as_assets_referenced_in_the_state(
|
||||
respx_mock: respx.MockRouter,
|
||||
) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_BOX_RESPONSE)
|
||||
|
||||
response: Final = await litellm.adecisions(
|
||||
model="typesafe/jev-1.14.0",
|
||||
input=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "input_text", "text": "Desk photo:"},
|
||||
{"type": "input_image", "image_url": _PNG_DATA_URL},
|
||||
],
|
||||
}
|
||||
],
|
||||
questions=[
|
||||
{"type": "predicate", "name": "has_mug", "instructions": "Is there a mug on the desk?"},
|
||||
{"type": "box", "name": "mug", "instructions": "the mug on the desk"},
|
||||
],
|
||||
api_key="caller-key",
|
||||
extra_headers=_IMAGES_HEADER,
|
||||
)
|
||||
|
||||
sent: Final = json.loads(route.calls.last.request.content)
|
||||
(reference,) = sent["assets"]
|
||||
assert sent["state"] == f"Desk photo:\n\n{reference}"
|
||||
assert sent["assets"][reference] == {"type": "image", "format": "png", "data_base64": "AA=="}
|
||||
assert sent["questions"]["mug"] == {"type": "box", "instructions": "the mug on the desk"}
|
||||
assert isinstance(response, OpenAIDecisionResponse)
|
||||
assert [answer.model_dump(mode="json") for answer in response.answers] == [
|
||||
{"type": "predicate", "name": "has_mug", "probability": 0.97},
|
||||
{"type": "box", "name": "mug", "box": _MUG_BOX, "confidence": 0.84},
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decisions_remote_images_reach_typesafe_inlined_as_assets(
|
||||
respx_mock: respx.MockRouter, async_only_image_fetch: AsyncOnlyImageFetch
|
||||
) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_BOX_RESPONSE)
|
||||
|
||||
await litellm.adecisions(
|
||||
model="typesafe/jev-1.14.0",
|
||||
input=[{"role": "user", "content": [{"type": "input_image", "image_url": "https://images.example/desk.png"}]}],
|
||||
questions=[{"type": "predicate", "name": "has_mug", "instructions": "Is there a mug on the desk?"}],
|
||||
api_key="caller-key",
|
||||
extra_headers=_IMAGES_HEADER,
|
||||
)
|
||||
|
||||
sent: Final = json.loads(route.calls.last.request.content)
|
||||
assert async_only_image_fetch.fetched == ["https://images.example/desk.png"]
|
||||
assert sent["assets"] == {
|
||||
sent["state"]: {"type": "image", "format": "png", "data_base64": async_only_image_fetch.base64_png}
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
("scoped_provider", "expected_headers"),
|
||||
(("typesafe", {"typesafe-danger-zone-long-context": "true"}), ("perplexity", {})),
|
||||
ids=("typesafe", "another_provider"),
|
||||
)
|
||||
async def test_provider_specific_headers_reach_typesafe_only_when_scoped_to_it(
|
||||
scoped_provider: str, expected_headers: Mapping[str, str], respx_mock: respx.MockRouter
|
||||
) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_SYSTEMONE_RESPONSE)
|
||||
|
||||
await litellm.asystemone(
|
||||
model="typesafe/jev-1.14.0",
|
||||
state=_STATE,
|
||||
questions=_SYSTEMONE_QUESTIONS,
|
||||
api_key="caller-key",
|
||||
provider_specific_header={
|
||||
"custom_llm_provider": scoped_provider,
|
||||
"extra_headers": {"TypeSafe-Danger-Zone-Long-Context": "true"},
|
||||
},
|
||||
)
|
||||
|
||||
sent_headers: Final = route.calls.last.request.headers
|
||||
assert {
|
||||
name: value for name, value in sent_headers.items() if name.startswith("typesafe-danger-zone-")
|
||||
} == expected_headers
|
||||
|
||||
|
||||
_DESK_PHOTO_INPUT: Final[tuple[Mapping[str, object], ...]] = (
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "input_text", "text": "Desk photo:"},
|
||||
{"type": "input_image", "image_url": _PNG_DATA_URL},
|
||||
],
|
||||
},
|
||||
)
|
||||
_HAS_MUG_QUESTION: Final[tuple[Mapping[str, object], ...]] = (
|
||||
{"type": "predicate", "name": "has_mug", "instructions": "Is there a mug on the desk?"},
|
||||
)
|
||||
_WITHOUT_IMAGES_HEADER: Final[tuple[Mapping[str, str], ...]] = ({}, {"TypeSafe-Danger-Zone-Images": "false"})
|
||||
|
||||
|
||||
@pytest.mark.parametrize("extra_headers", _WITHOUT_IMAGES_HEADER, ids=("no_header", "header_false"))
|
||||
def test_typesafe_refuses_images_and_assets_before_http_without_the_images_header(
|
||||
extra_headers: Mapping[str, str], respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "drop_params", False)
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_BOX_RESPONSE)
|
||||
|
||||
with pytest.raises(litellm.UnsupportedParamsError, match=r"\['input_image'\]") as image_refusal:
|
||||
litellm.decisions(
|
||||
model="typesafe/jev-1.14.0",
|
||||
input=_DESK_PHOTO_INPUT,
|
||||
questions=_HAS_MUG_QUESTION,
|
||||
api_key="caller-key",
|
||||
extra_headers=extra_headers,
|
||||
)
|
||||
with pytest.raises(litellm.UnsupportedParamsError, match=r"\['assets'\]") as asset_refusal:
|
||||
litellm.systemone(
|
||||
model="typesafe/jev-1.14.0",
|
||||
state="review photo-1",
|
||||
assets={"photo-1": {"format": "png", "data_base64": "AA=="}},
|
||||
questions=_SYSTEMONE_QUESTIONS,
|
||||
api_key="caller-key",
|
||||
extra_headers=extra_headers,
|
||||
)
|
||||
|
||||
assert (image_refusal.value.status_code, asset_refusal.value.status_code) == (400, 400)
|
||||
assert not route.called
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_typesafe_drops_images_and_assets_without_the_images_header_under_drop_params(
|
||||
respx_mock: respx.MockRouter,
|
||||
) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_BOX_RESPONSE)
|
||||
|
||||
await litellm.adecisions(
|
||||
model="typesafe/jev-1.14.0",
|
||||
input=_DESK_PHOTO_INPUT,
|
||||
questions=_HAS_MUG_QUESTION,
|
||||
api_key="caller-key",
|
||||
drop_params=True,
|
||||
)
|
||||
decisions_body: Final = json.loads(route.calls.last.request.content)
|
||||
await litellm.asystemone(
|
||||
model="typesafe/jev-1.14.0",
|
||||
state="review photo-1",
|
||||
assets={"photo-1": {"format": "png", "data_base64": "AA=="}},
|
||||
questions=_SYSTEMONE_QUESTIONS,
|
||||
api_key="caller-key",
|
||||
drop_params=True,
|
||||
)
|
||||
systemone_body: Final = json.loads(route.calls.last.request.content)
|
||||
|
||||
assert (decisions_body["state"], "assets" in decisions_body) == ("Desk photo:", False)
|
||||
assert "assets" not in systemone_body
|
||||
|
||||
|
||||
_BOX_UNSUPPORTED: Final[tuple[tuple[str, Mapping[str, str]], ...]] = (
|
||||
("typesafe/jev-1.13.0", _IMAGES_HEADER),
|
||||
("typesafe/jev-1.14.0", MappingProxyType({})),
|
||||
)
|
||||
_BOX_UNSUPPORTED_IDS: Final = ("model_without_box_support", "without_the_images_header")
|
||||
|
||||
|
||||
@pytest.mark.parametrize(("model", "extra_headers"), _BOX_UNSUPPORTED, ids=_BOX_UNSUPPORTED_IDS)
|
||||
def test_typesafe_refuses_box_questions_before_http_when_the_model_or_the_headers_do_not_allow_them(
|
||||
model: str, extra_headers: Mapping[str, str], respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "drop_params", False)
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_SYSTEMONE_RESPONSE)
|
||||
|
||||
with pytest.raises(litellm.UnsupportedParamsError, match=r"\['box questions'\]") as caught:
|
||||
litellm.systemone(
|
||||
model=model,
|
||||
state="a mug on a desk",
|
||||
questions={"mug": {"type": "box", "instructions": "the mug"}, **_SYSTEMONE_QUESTIONS},
|
||||
api_key="caller-key",
|
||||
extra_headers=extra_headers,
|
||||
)
|
||||
|
||||
assert caught.value.status_code == 400
|
||||
assert not route.called
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(("model", "extra_headers"), _BOX_UNSUPPORTED, ids=_BOX_UNSUPPORTED_IDS)
|
||||
async def test_typesafe_answers_box_questions_it_cannot_take_as_refusals_under_drop_params(
|
||||
model: str, extra_headers: Mapping[str, str], respx_mock: respx.MockRouter
|
||||
) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_SYSTEMONE_RESPONSE)
|
||||
|
||||
response: Final = await litellm.adecisions(
|
||||
model=model,
|
||||
input="Customer asked for a refund",
|
||||
questions=(*_OPENAI_QUESTIONS, {"type": "box", "name": "mug", "instructions": "the mug"}),
|
||||
api_key="caller-key",
|
||||
extra_headers=extra_headers,
|
||||
drop_params=True,
|
||||
)
|
||||
|
||||
assert json.loads(route.calls.last.request.content)["questions"] == {
|
||||
"is_refund": {"type": "noul", "instructions": "Is this a refund request?"}
|
||||
}
|
||||
assert isinstance(response, OpenAIDecisionResponse)
|
||||
assert [answer.model_dump(mode="json") for answer in response.answers] == [
|
||||
{"type": "predicate", "name": "is_refund", "probability": 0.93},
|
||||
{"type": "refusal", "name": "mug"},
|
||||
]
|
||||
|
||||
|
||||
def test_a_malformed_provider_specific_header_is_ignored(respx_mock: respx.MockRouter) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_SYSTEMONE_RESPONSE)
|
||||
|
||||
response: Final = litellm.systemone(
|
||||
model="typesafe/jev-1.14.0",
|
||||
state=_STATE,
|
||||
questions=_SYSTEMONE_QUESTIONS,
|
||||
api_key="caller-key",
|
||||
provider_specific_header={"custom_llm_provider": "typesafe", "extra_headers": "not a header map"},
|
||||
)
|
||||
|
||||
assert isinstance(response, DecisionsResponse)
|
||||
assert not [name for name in route.calls.last.request.headers if name.startswith("typesafe-danger-zone-")]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_url_safe_base64_images_reach_typesafe_as_standard_base64_assets(respx_mock: respx.MockRouter) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_BOX_RESPONSE)
|
||||
|
||||
await litellm.adecisions(
|
||||
model="typesafe/jev-1.14.0",
|
||||
input=[{"role": "user", "content": [{"type": "input_image", "image_url": "data:image/png;base64,-_8="}]}],
|
||||
questions=_HAS_MUG_QUESTION,
|
||||
api_key="caller-key",
|
||||
extra_headers=_IMAGES_HEADER,
|
||||
)
|
||||
|
||||
assert list(json.loads(route.calls.last.request.content)["assets"].values()) == [
|
||||
{"type": "image", "format": "png", "data_base64": "+/8="}
|
||||
]
|
||||
|
||||
|
||||
def test_an_image_whose_data_url_is_not_base64_answers_400_before_typesafe_is_called(
|
||||
respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "drop_params", False)
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_BOX_RESPONSE)
|
||||
|
||||
with pytest.raises(litellm.BadRequestError, match="input_image") as caught:
|
||||
litellm.decisions(
|
||||
model="typesafe/jev-1.14.0",
|
||||
input=[
|
||||
{"role": "user", "content": [{"type": "input_image", "image_url": "data:image/png;base64,not base64!"}]}
|
||||
],
|
||||
questions=_HAS_MUG_QUESTION,
|
||||
api_key="caller-key",
|
||||
extra_headers=_IMAGES_HEADER,
|
||||
)
|
||||
|
||||
assert caught.value.status_code == 400
|
||||
assert not route.called
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_malformed_assets_are_dropped_and_well_formed_ones_kept_under_drop_params(
|
||||
respx_mock: respx.MockRouter,
|
||||
) -> None:
|
||||
route: Final = respx_mock.post(_TYPESAFE_URL).respond(json=_SYSTEMONE_RESPONSE)
|
||||
|
||||
await litellm.asystemone(
|
||||
model="typesafe/jev-1.14.0",
|
||||
state="compare photo-1 with photo-2",
|
||||
assets={
|
||||
"photo-1": {"type": "image", "format": "png", "data_base64": "AA=="},
|
||||
"photo-2": {"type": "image", "part": "file1"},
|
||||
},
|
||||
questions=_SYSTEMONE_QUESTIONS,
|
||||
api_key="caller-key",
|
||||
extra_headers=_IMAGES_HEADER,
|
||||
drop_params=True,
|
||||
)
|
||||
|
||||
assert json.loads(route.calls.last.request.content)["assets"] == {
|
||||
"photo-1": {"type": "image", "format": "png", "data_base64": "AA=="}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1135,6 +1135,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
|
|||
"supports_tool_choice": {"type": "boolean"},
|
||||
"supports_tool_search": {"type": "boolean"},
|
||||
"supports_video_input": {"type": "boolean"},
|
||||
"supports_box_questions": {"type": "boolean"},
|
||||
"supports_vision": {"type": "boolean"},
|
||||
"supports_web_search": {"type": "boolean"},
|
||||
"supports_bedrock_runtime_chat_completions_tools_with_reasoning": {"type": "boolean"},
|
||||
|
|
|
|||
171
ui/litellm-dashboard/src/lib/http/schema.d.ts
generated
vendored
171
ui/litellm-dashboard/src/lib/http/schema.d.ts
generated
vendored
|
|
@ -4713,8 +4713,9 @@ export interface paths {
|
|||
* @description Judge an `input` against 1 to 128 `questions` and get the answers back in the same order.
|
||||
*
|
||||
* Question types are `predicate` (yes/no with `probability`), `choice` (one of the `choices`, with
|
||||
* `probabilities`) and `score` (an index into `levels`, with `probabilities`). Requests that break these
|
||||
* rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy
|
||||
* `probabilities`), `score` (an index into `levels`, with `probabilities`) and `box` (the image region the
|
||||
* `instructions` describe, with `confidence`). Requests that break these rules are rejected with a 400 before
|
||||
* any provider is called. `model` is any decision model in the proxy
|
||||
* model list. System One decision models accept this format too, LiteLLM translates the request and the
|
||||
* answers. Streaming is not supported.
|
||||
*
|
||||
|
|
@ -15943,9 +15944,10 @@ export interface paths {
|
|||
* @description Judge a `state` against 1 to 128 named `questions` and get one calibrated answer per question name.
|
||||
*
|
||||
* Question types are `noul` (probability the answer is yes), `choice` (one label out of `criteria`, with
|
||||
* `confidence` and `probabilities`) and `score` (an index into the `criteria` levels, with `confidence`,
|
||||
* `legend` and `probabilities`). Requests that break these rules are rejected with a 400 before any provider
|
||||
* is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format
|
||||
* `confidence` and `probabilities`), `score` (an index into the `criteria` levels, with `confidence`,
|
||||
* `legend` and `probabilities`) and `box` (the image region the `instructions` describe, with `confidence`).
|
||||
* TypeSafe models also take images in `assets`, each one referenced by its key in `state`. Requests that break
|
||||
* these rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format
|
||||
* too, LiteLLM translates it and keeps the answers keyed by question name. Streaming is not supported.
|
||||
*
|
||||
* [Docs](https://docs.litellm.ai/docs/decisions)
|
||||
|
|
@ -19849,8 +19851,9 @@ export interface paths {
|
|||
* @description Judge an `input` against 1 to 128 `questions` and get the answers back in the same order.
|
||||
*
|
||||
* Question types are `predicate` (yes/no with `probability`), `choice` (one of the `choices`, with
|
||||
* `probabilities`) and `score` (an index into `levels`, with `probabilities`). Requests that break these
|
||||
* rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy
|
||||
* `probabilities`), `score` (an index into `levels`, with `probabilities`) and `box` (the image region the
|
||||
* `instructions` describe, with `confidence`). Requests that break these rules are rejected with a 400 before
|
||||
* any provider is called. `model` is any decision model in the proxy
|
||||
* model list. System One decision models accept this format too, LiteLLM translates the request and the
|
||||
* answers. Streaming is not supported.
|
||||
*
|
||||
|
|
@ -22225,9 +22228,10 @@ export interface paths {
|
|||
* @description Judge a `state` against 1 to 128 named `questions` and get one calibrated answer per question name.
|
||||
*
|
||||
* Question types are `noul` (probability the answer is yes), `choice` (one label out of `criteria`, with
|
||||
* `confidence` and `probabilities`) and `score` (an index into the `criteria` levels, with `confidence`,
|
||||
* `legend` and `probabilities`). Requests that break these rules are rejected with a 400 before any provider
|
||||
* is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format
|
||||
* `confidence` and `probabilities`), `score` (an index into the `criteria` levels, with `confidence`,
|
||||
* `legend` and `probabilities`) and `box` (the image region the `instructions` describe, with `confidence`).
|
||||
* TypeSafe models also take images in `assets`, each one referenced by its key in `state`. Requests that break
|
||||
* these rules are rejected with a 400 before any provider is called. `model` is any decision model in the proxy model list. OpenAI decision models accept this format
|
||||
* too, LiteLLM translates it and keeps the answers keyed by question name. Streaming is not supported.
|
||||
*
|
||||
* [Docs](https://docs.litellm.ai/docs/decisions)
|
||||
|
|
@ -27542,6 +27546,19 @@ export interface components {
|
|||
/** Input Reference */
|
||||
input_reference?: string | null;
|
||||
};
|
||||
/** BoxAnswer */
|
||||
BoxAnswer: {
|
||||
box: components["schemas"]["DecisionsBox"];
|
||||
/** Confidence */
|
||||
confidence: number | null;
|
||||
/**
|
||||
* @description discriminator enum property added by openapi-typescript
|
||||
* @enum {string}
|
||||
*/
|
||||
type: "box";
|
||||
} & {
|
||||
[key: string]: unknown;
|
||||
};
|
||||
/**
|
||||
* BreakdownMetrics
|
||||
* @description Breakdown of spend by different dimensions
|
||||
|
|
@ -31354,11 +31371,24 @@ export interface components {
|
|||
*/
|
||||
threshold: number;
|
||||
};
|
||||
/** DecisionsBox */
|
||||
DecisionsBox: {
|
||||
/** Bottom */
|
||||
bottom: number;
|
||||
/** Left */
|
||||
left: number;
|
||||
/** Right */
|
||||
right: number;
|
||||
/** Top */
|
||||
top: number;
|
||||
} & {
|
||||
[key: string]: unknown;
|
||||
};
|
||||
/** DecisionsResponse */
|
||||
DecisionsResponse: {
|
||||
/** Answers */
|
||||
answers: {
|
||||
[key: string]: components["schemas"]["NoulAnswer"] | components["schemas"]["ChoiceAnswer"] | components["schemas"]["ScoreAnswer"];
|
||||
[key: string]: components["schemas"]["NoulAnswer"] | components["schemas"]["ChoiceAnswer"] | components["schemas"]["ScoreAnswer"] | components["schemas"]["BoxAnswer"];
|
||||
};
|
||||
/** Model */
|
||||
model?: string | null;
|
||||
|
|
@ -39343,6 +39373,19 @@ export interface components {
|
|||
[key: string]: unknown;
|
||||
} | null;
|
||||
};
|
||||
/** OpenAIBoxAnswer */
|
||||
OpenAIBoxAnswer: {
|
||||
box: components["schemas"]["DecisionsBox"];
|
||||
/** Confidence */
|
||||
confidence: number | null;
|
||||
/** Name */
|
||||
name: string | null;
|
||||
/**
|
||||
* @description discriminator enum property added by openapi-typescript
|
||||
* @enum {string}
|
||||
*/
|
||||
type: "box";
|
||||
};
|
||||
/** OpenAIChoiceAnswer */
|
||||
OpenAIChoiceAnswer: {
|
||||
/** Choice */
|
||||
|
|
@ -39390,7 +39433,7 @@ export interface components {
|
|||
/** OpenAIDecisionResponse */
|
||||
OpenAIDecisionResponse: {
|
||||
/** Answers */
|
||||
answers: (components["schemas"]["OpenAIPredicateAnswer"] | components["schemas"]["OpenAIChoiceAnswer"] | components["schemas"]["OpenAIScoreAnswer"] | components["schemas"]["OpenAIRefusalAnswer"])[];
|
||||
answers: (components["schemas"]["OpenAIPredicateAnswer"] | components["schemas"]["OpenAIChoiceAnswer"] | components["schemas"]["OpenAIScoreAnswer"] | components["schemas"]["OpenAIBoxAnswer"] | components["schemas"]["OpenAIRefusalAnswer"])[];
|
||||
/** Model */
|
||||
model: string;
|
||||
usage: components["schemas"]["OpenAIDecisionUsage"];
|
||||
|
|
@ -57521,6 +57564,22 @@ export interface operations {
|
|||
* @constant
|
||||
*/
|
||||
type: "score";
|
||||
} | {
|
||||
/**
|
||||
* Instructions
|
||||
* @description What to find in the input's image
|
||||
*/
|
||||
instructions: string;
|
||||
/**
|
||||
* Name
|
||||
* @description Echoed in the matching answer
|
||||
*/
|
||||
name?: string | null;
|
||||
/**
|
||||
* Type
|
||||
* @constant
|
||||
*/
|
||||
type: "box";
|
||||
})[];
|
||||
/**
|
||||
* Safety Identifier
|
||||
|
|
@ -71788,6 +71847,27 @@ export interface operations {
|
|||
* }
|
||||
*/
|
||||
"application/json": {
|
||||
/**
|
||||
* Assets
|
||||
* @description TypeSafe only. Images keyed by a reference string. Write the reference in state, instructions or criteria where the image belongs
|
||||
*/
|
||||
assets?: {
|
||||
[key: string]: {
|
||||
/**
|
||||
* Data Base64
|
||||
* @description Base64-encoded image bytes, no data: prefix
|
||||
*/
|
||||
data_base64: string;
|
||||
/**
|
||||
* Format
|
||||
* @description The image's format, in lowercase
|
||||
* @enum {string}
|
||||
*/
|
||||
format: "png" | "jpg" | "jpeg";
|
||||
/** Type */
|
||||
type?: "image" | null;
|
||||
};
|
||||
} | null;
|
||||
/**
|
||||
* Audio
|
||||
* @description Clef-omni only. Up to 4 embedded audio clips, each a base64 data URL or {content_type, base64}
|
||||
|
|
@ -71901,6 +71981,21 @@ export interface operations {
|
|||
type: "score";
|
||||
} & {
|
||||
[key: string]: unknown;
|
||||
}) | ({
|
||||
/**
|
||||
* Instructions
|
||||
* @description What to find, and in which image, such as 'the coffee mug in `photo`'
|
||||
*/
|
||||
instructions: string | {
|
||||
[key: string]: unknown;
|
||||
} | unknown[];
|
||||
/**
|
||||
* Type
|
||||
* @constant
|
||||
*/
|
||||
type: "box";
|
||||
} & {
|
||||
[key: string]: unknown;
|
||||
});
|
||||
};
|
||||
/**
|
||||
|
|
@ -77418,6 +77513,22 @@ export interface operations {
|
|||
* @constant
|
||||
*/
|
||||
type: "score";
|
||||
} | {
|
||||
/**
|
||||
* Instructions
|
||||
* @description What to find in the input's image
|
||||
*/
|
||||
instructions: string;
|
||||
/**
|
||||
* Name
|
||||
* @description Echoed in the matching answer
|
||||
*/
|
||||
name?: string | null;
|
||||
/**
|
||||
* Type
|
||||
* @constant
|
||||
*/
|
||||
type: "box";
|
||||
})[];
|
||||
/**
|
||||
* Safety Identifier
|
||||
|
|
@ -80668,6 +80779,27 @@ export interface operations {
|
|||
* }
|
||||
*/
|
||||
"application/json": {
|
||||
/**
|
||||
* Assets
|
||||
* @description TypeSafe only. Images keyed by a reference string. Write the reference in state, instructions or criteria where the image belongs
|
||||
*/
|
||||
assets?: {
|
||||
[key: string]: {
|
||||
/**
|
||||
* Data Base64
|
||||
* @description Base64-encoded image bytes, no data: prefix
|
||||
*/
|
||||
data_base64: string;
|
||||
/**
|
||||
* Format
|
||||
* @description The image's format, in lowercase
|
||||
* @enum {string}
|
||||
*/
|
||||
format: "png" | "jpg" | "jpeg";
|
||||
/** Type */
|
||||
type?: "image" | null;
|
||||
};
|
||||
} | null;
|
||||
/**
|
||||
* Audio
|
||||
* @description Clef-omni only. Up to 4 embedded audio clips, each a base64 data URL or {content_type, base64}
|
||||
|
|
@ -80781,6 +80913,21 @@ export interface operations {
|
|||
type: "score";
|
||||
} & {
|
||||
[key: string]: unknown;
|
||||
}) | ({
|
||||
/**
|
||||
* Instructions
|
||||
* @description What to find, and in which image, such as 'the coffee mug in `photo`'
|
||||
*/
|
||||
instructions: string | {
|
||||
[key: string]: unknown;
|
||||
} | unknown[];
|
||||
/**
|
||||
* Type
|
||||
* @constant
|
||||
*/
|
||||
type: "box";
|
||||
} & {
|
||||
[key: string]: unknown;
|
||||
});
|
||||
};
|
||||
/**
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue