mirror of
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feat(decisions): add Parallel AI web-grounded decisions (#49188)
* feat(decisions): add Parallel AI web-grounded decisions * fix(decisions): name the Parallel model decision * fix(parallel_ai): restrict environment credentials to trusted decision endpoints * fix(decisions): keep Parallel evidence fields off other providers and handle missing confidence Only fields a provider names reach the OpenAI response, so existing providers keep their base shape. A malformed api_base answers 400 on both credential paths. The complexity router classifier and the Playground handle a provider that omits confidence. Regenerates the lazy OpenAPI snapshot and the dashboard API types * chore(cost-map): declare the Decisions routes on parallel_ai/decision The provider endpoint files are generated from the cost map, so the row must name both routes for the generated System One flag to hold * fix(decisions): report no classifier confidence when a provider omits it A choice answer with no confidence is a valid classifier answer, so the complexity router picks its tier and leaves classifier_confidence and the confidence signal out instead of reporting a different metric in its place. Also show the Parallel AI logo in the provider picker and add a Parallel translation fixture captured from the live API for both Decisions routes. * fix(decisions): keep a token-priced row on token billing when it also sets a query price A plain input_cost_per_query bills flat only on a row with no token price, and input_cost_per_query_decisions always bills flat, so a custom Decisions row that already bills tokens costs the same as before this PR * fix(decisions): bill a deployment's query price and accept Parallel bases that end in a route A proxy request logs through an object built before the router picks the deployment, so a query price on litellm_params never reached Decisions billing. Parallel Search accepts a base ending in /v1/search and shares PARALLEL_AI_API_BASE with Decisions, so Decisions now accepts that base and one ending in /v1/decisions. * fix(decisions): keep per-query billing and optional confidence on Parallel only A query price on any other Decisions provider moved that provider off token billing, and a choice or score answer with no confidence became a 200 with a null for every provider. Both now apply to Parallel alone, so other providers bill and fail as they do on main. A Parallel deployment with explicit zero token rates and a query price read as free, so every budget check was skipped while it billed per call. It now counts as priced. The dashboard key for Parallel is now PARALLEL_AI so a served bundle that predates the provider still sends parallel_ai as the provider slug, and the README table lists the provider. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
This commit is contained in:
Devin Gupta
GitHub
parent
08b951a814
commit
216041e243
40 changed files with 1242 additions and 55 deletions
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@ -444,6 +444,7 @@ Set `LITELLM_PROXY_API_BASE` and `LITELLM_PROXY_API_KEY` and every model call th
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| [OpenAI-like (`openai_like`)](https://docs.litellm.ai/docs/providers/openai_compatible) | | | | ✅ | | | | | | |
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| [OpenRouter (`openrouter`)](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | | | | | | | |
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| [OVHCloud AI Endpoints (`ovhcloud`)](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | ✅ | | | | | | | |
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| [Parallel AI (`parallel_ai`)](https://docs.litellm.ai/docs/search/parallel_ai) | | | | | | | | | | |
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| [Perplexity AI (`perplexity`)](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | | | | | | | |
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| [Petals (`petals`)](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | | | | | | | |
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| [Pinstripes (`pinstripes`)](https://docs.litellm.ai/docs/providers/pinstripes) | ✅ | ✅ | ✅ | | | | | | | |
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@ -1754,6 +1754,9 @@ if TYPE_CHECKING:
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from .llms.typesafe.decisions.transformation import (
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TypeSafeDecisionsConfig as TypeSafeDecisionsConfig,
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)
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from .llms.parallel_ai.decisions.transformation import (
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ParallelAIDecisionsConfig as ParallelAIDecisionsConfig,
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)
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from .llms.openrouter.decisions.transformation import (
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OpenRouterDecisionsConfig as OpenRouterDecisionsConfig,
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)
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@ -163,6 +163,7 @@ LLM_CONFIG_NAMES: Final = (
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"HostedVLLMRerankConfig",
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"PerplexityDecisionsConfig",
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"TypeSafeDecisionsConfig",
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"ParallelAIDecisionsConfig",
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"NaceDecisionsConfig",
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"OpenRouterDecisionsConfig",
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"FireworksAIDecisionsConfig",
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@ -740,6 +741,7 @@ _LLM_CONFIGS_IMPORT_MAP: Final = {
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"PerplexityDecisionsConfig": (".llms.perplexity.decisions.transformation", "PerplexityDecisionsConfig"),
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"NaceDecisionsConfig": (".llms.nace.decisions.transformation", "NaceDecisionsConfig"),
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"TypeSafeDecisionsConfig": (".llms.typesafe.decisions.transformation", "TypeSafeDecisionsConfig"),
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"ParallelAIDecisionsConfig": (".llms.parallel_ai.decisions.transformation", "ParallelAIDecisionsConfig"),
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"OpenRouterDecisionsConfig": (".llms.openrouter.decisions.transformation", "OpenRouterDecisionsConfig"),
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"FireworksAIDecisionsConfig": (".llms.fireworks_ai.decisions.transformation", "FireworksAIDecisionsConfig"),
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"CloudflareDecisionsConfig": (".llms.cloudflare.decisions.transformation", "CloudflareDecisionsConfig"),
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@ -25,7 +25,11 @@ from litellm.constants import (
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)
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from litellm.litellm_core_utils.core_helpers import as_str_mapping, billed_usage_cost
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from litellm.litellm_core_utils.cost_discount import resolve_cost_discount
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from litellm.litellm_core_utils.decisions_pricing import decisions_pricing_fields, is_decisions_call_type
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from litellm.litellm_core_utils.decisions_pricing import (
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decisions_pricing_fields,
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decisions_request_price,
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is_decisions_call_type,
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)
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from litellm.litellm_core_utils.hidden_params import HIDDEN_PARAMS_ATTR
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from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import (
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StandardBuiltInToolCostTracking,
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@ -605,6 +609,9 @@ def cost_per_token(
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decisions_overrides: Final = decisions_pricing_fields(
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litellm.model_cost.get(decisions_model_info["key"], {}) # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType] # model cost map is dynamically loaded
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)
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request_price: Final = decisions_request_price(decisions_model_info, decisions_overrides)
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if request_price is not None:
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return request_price, 0.0
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if decisions_overrides:
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return generic_cost_per_token(
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model=model,
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@ -13,6 +13,7 @@ from typing_extensions import assert_never
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import litellm
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from litellm.cost_calculator import cost_map_key_prices_anything
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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.decisions_pricing import QUERY_PRICED_DECISIONS_PROVIDERS
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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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@ -555,7 +556,7 @@ def prepare_call(
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resolved_api_base, static_api_key = provider_config.resolve_connection(
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dynamic_api_base or call_api_base, dynamic_api_key or api_key
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)
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except ValueError as error:
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except (ValueError, httpx.InvalidURL) as error:
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raise litellm.BadRequestError(message=str(error), model=model, llm_provider=provider) from error
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if resolved_api_base is None:
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raise litellm.BadRequestError(
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@ -613,6 +614,9 @@ def prepare_call(
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litellm_params={
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"litellm_call_id": kwargs.get("litellm_call_id"),
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"api_base": provider_config.get_complete_url(resolved_api_base, canonical_model),
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"input_cost_per_query": (
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kwargs.get("input_cost_per_query") if provider in QUERY_PRICED_DECISIONS_PROVIDERS else None
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),
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},
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custom_llm_provider=provider,
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)
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@ -787,7 +791,9 @@ def _formatted_response(
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case DecisionsRequestBody():
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return ir_to_systemone_response(caller_response, caller_request)
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case OpenAIDecisionRequestBody():
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return ir_to_openai_response(caller_response, caller_request, call.requested_model)
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return ir_to_openai_response(
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caller_response, caller_request, call.requested_model, call.provider_config.openai_evidence_fields
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)
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case _:
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assert_never(call.request)
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@ -12,6 +12,7 @@ from typing import Final
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DECISIONS_PRICING_SUFFIX: Final = "_decisions"
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DECISIONS_CALL_TYPES: Final = frozenset({"decisions", "adecisions", "systemone", "asystemone"})
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QUERY_PRICED_DECISIONS_PROVIDERS: Final = frozenset({"parallel_ai"})
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def is_decisions_call_type(call_type: str) -> bool:
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@ -27,6 +28,17 @@ def decisions_pricing_fields(row: Mapping[str, object]) -> Mapping[str, object]:
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}
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def decisions_request_price(model_info: Mapping[str, object], overrides: Mapping[str, object]) -> float | None:
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if model_info.get("litellm_provider") not in QUERY_PRICED_DECISIONS_PROVIDERS:
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return None
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rates: Final = {**model_info, **overrides}
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request_price: Final = rates.get("input_cost_per_query")
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if not isinstance(request_price, (int, float)):
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return None
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bills_tokens: Final = any(rates.get(field) for field in ("input_cost_per_token", "output_cost_per_token"))
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return None if bills_tokens and "input_cost_per_query" not in overrides else float(request_price)
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def apply_decisions_pricing(row: Mapping[str, object]) -> Mapping[str, object]:
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"""Return ``row`` priced for a Decisions call: ``_decisions`` values replace their plain fields."""
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plain: Final = {key: value for key, value in row.items() if not key.endswith(DECISIONS_PRICING_SUFFIX)}
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@ -431,6 +431,10 @@ class DecisionsResponseValidationError(BaseLLMException):
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pass
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def _lacks_confidence(answer: DecisionsIRAnswer) -> bool:
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return isinstance(answer, (DecisionsIRChoiceAnswer, DecisionsIRScoreAnswer)) and answer.confidence is None
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def parse_systemone_response(payload: object, request: DecisionsIRRequest) -> DecisionsIRResponse:
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return systemone_response_to_ir(
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_SYSTEMONE_RESPONSE_ADAPTER.validate_python(_normalize_systemone_response(payload)), request
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@ -534,6 +538,8 @@ class BaseDecisionsConfig(ABC):
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max_total_image_bytes: int | None = None
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image_media_types: frozenset[str] | None = None
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inlines_remote_images: bool = False
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openai_evidence_fields: frozenset[str] = frozenset()
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confidence_optional: bool = False
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health_check_questions: Mapping[str, Mapping[str, object]] = MappingProxyType(
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{"reachable": MappingProxyType({"type": "noul", "instructions": "Is the service reachable?"})}
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)
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@ -682,13 +688,20 @@ class BaseDecisionsConfig(ABC):
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) -> DecisionsIRResponse:
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try:
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payload: Final[object] = _PAYLOAD_ADAPTER.validate_json(raw_response.content)
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return self.parse_response(payload, request)
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response: Final = self.parse_response(payload, request)
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except ValidationError as error:
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raise DecisionsResponseValidationError(
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status_code=500,
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message=f"Decisions provider '{custom_llm_provider}' returned an unexpected response: "
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f"{error.title} {_validation_summary(error)}",
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) from error
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if not self.confidence_optional and any(_lacks_confidence(answer) for answer in response.answers):
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raise DecisionsResponseValidationError(
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status_code=500,
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message=f"Decisions provider '{custom_llm_provider}' returned an unexpected response: "
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"a choice or score answer has no confidence",
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)
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return response
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def get_error_class(
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self,
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@ -307,10 +307,13 @@ def _openai_answer(question: DecisionsIRQuestion, answer: DecisionsIRAnswer) ->
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def ir_to_openai_response(
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response: DecisionsIRResponse, request: DecisionsIRRequest, requested_model: str
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response: DecisionsIRResponse,
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request: DecisionsIRRequest,
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requested_model: str,
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evidence_fields: frozenset[str] = frozenset(),
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) -> OpenAIDecisionResponse:
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usage: Final = response.usage or DecisionsIRUsage()
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return OpenAIDecisionResponse(
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formatted: Final = OpenAIDecisionResponse(
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model=response.model or requested_model,
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answers=tuple(
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_openai_answer(question, answer)
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@ -327,6 +330,7 @@ def ir_to_openai_response(
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total_tokens=usage.input_tokens + usage.output_tokens,
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),
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)
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return formatted.model_copy(update={name: response.extra[name] for name in evidence_fields & response.extra.keys()})
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class OpenAIDecisionsConfig(BaseDecisionsConfig):
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0
litellm/llms/parallel_ai/decisions/__init__.py
Normal file
0
litellm/llms/parallel_ai/decisions/__init__.py
Normal file
89
litellm/llms/parallel_ai/decisions/transformation.py
Normal file
89
litellm/llms/parallel_ai/decisions/transformation.py
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@ -0,0 +1,89 @@
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from collections.abc import Mapping
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from typing import Final
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import httpx
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from pydantic import BaseModel, ConfigDict
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from litellm.llms.base_llm.decisions.transformation import BaseDecisionsConfig, ir_to_systemone_request
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from litellm.types.decisions import (
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DecisionsIRRequest,
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DecisionsIRResponse,
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UnsupportedDecisionsRequest,
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)
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class _ParallelResponse(BaseModel):
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model_config = ConfigDict(extra="allow", frozen=True)
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mode: str
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answers: Mapping[str, Mapping[str, object]]
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class ParallelAIDecisionsConfig(BaseDecisionsConfig):
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path = "/v1/decisions"
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api_key_env = ("PARALLEL_AI_API_KEY", "PARALLEL_API_KEY")
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api_base_env = ("PARALLEL_AI_API_BASE",)
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confidence_optional = True
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openai_evidence_fields = frozenset({"citations", "decision_id", "mode"})
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def get_default_api_base(self) -> str:
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return "https://api.parallel.ai"
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def resolve_connection(self, api_base: str | None, api_key: str | None) -> tuple[str | None, str | None]:
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configured_base: Final = self.resolve_api_base(None)
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resolved_base: Final = api_base or configured_base
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destination: Final = self._decisions_url(resolved_base)
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if api_key:
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return resolved_base, api_key
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if destination not in {self._decisions_url(None), self._decisions_url(configured_base)}:
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raise ValueError(
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"Parallel Decisions requires an explicit api_key for a custom api_base unless "
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"the destination is configured in PARALLEL_AI_API_BASE"
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)
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return resolved_base, self.resolve_api_key(None)
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def get_complete_url(self, api_base: str, model: str) -> str:
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return super().get_complete_url(api_base.rstrip("/").removesuffix("/v1/search").removesuffix(self.path), model)
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def _decisions_url(self, api_base: str | None) -> httpx.URL:
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return httpx.URL(self.get_complete_url(api_base or self.get_default_api_base(), "decision"))
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def validate_environment(
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self,
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headers: Mapping[str, str],
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model: str,
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api_key: str | None,
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litellm_params: Mapping[str, object],
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) -> Mapping[str, str]:
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return {
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**{
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name: value
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for name, value in headers.items()
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if name.lower() not in {"authorization", "x-api-key", "content-type"}
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},
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**({"x-api-key": api_key} if api_key is not None else {}),
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"Content-Type": "application/json",
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}
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def transform_decisions_request(
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self, model: str, request: DecisionsIRRequest, custom_llm_provider: str
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) -> Mapping[str, object] | UnsupportedDecisionsRequest:
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if model != "decision":
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return UnsupportedDecisionsRequest(reason="Parallel Decisions supports the decision model")
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if len(request.questions) > 100:
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return UnsupportedDecisionsRequest(reason="Parallel Decisions supports at most 100 questions")
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body: Final = ir_to_systemone_request(model, request)
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if isinstance(body, UnsupportedDecisionsRequest):
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return body
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return {"mode": "fast", **{key: value for key, value in body.items() if key != "model"}}
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def parse_response(self, payload: object, request: DecisionsIRRequest) -> DecisionsIRResponse:
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response: Final = _ParallelResponse.model_validate(payload)
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return super().parse_response(
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{
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**response.model_dump(),
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"model": "decision",
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"answers": {key: answer for key, answer in response.answers.items() if answer.get("type") != "refusal"},
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},
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request,
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)
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@ -46412,6 +46412,19 @@
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"supports_response_schema": true,
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"supports_tool_choice": false
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},
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"parallel_ai/decision": {
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"input_cost_per_query": 0.001,
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"input_cost_per_token": 0.0,
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"output_cost_per_token": 0.0,
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"litellm_provider": "parallel_ai",
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"max_input_tokens": 32000,
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"mode": "evaluation",
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"source": "https://docs.parallel.ai/getting-started/pricing#decisions-api",
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"supported_endpoints": [
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"/v1/decisions",
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"/v1/systemone"
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]
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},
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"parallel_ai/search": {
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"input_cost_per_query": 0.005,
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"litellm_provider": "parallel_ai",
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@ -3158,7 +3158,7 @@
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"batches": false,
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"messages": false,
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"interactions": false,
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"systemone": false,
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"systemone": true,
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"a2a": false,
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"generateContent": false
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}
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@ -10237,8 +10237,15 @@
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"type": "string"
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},
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"confidence": {
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"title": "Confidence",
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"type": "number"
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"anyOf": [
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{
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"title": "Confidence"
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},
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"probabilities": {
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"additionalProperties": {
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@ -10256,7 +10263,6 @@
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"required": [
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"type",
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"choice",
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"confidence",
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"probabilities"
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],
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"title": "ChoiceAnswer",
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@ -10687,8 +10693,15 @@
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"title": "Choice"
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},
|
||||
"confidence": {
|
||||
"title": "Confidence",
|
||||
"type": "number"
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Confidence"
|
||||
},
|
||||
"name": {
|
||||
"anyOf": [
|
||||
|
|
@ -10718,8 +10731,7 @@
|
|||
"required": [
|
||||
"name",
|
||||
"choice",
|
||||
"probabilities",
|
||||
"confidence"
|
||||
"probabilities"
|
||||
],
|
||||
"title": "OpenAIChoiceAnswer",
|
||||
"type": "object"
|
||||
|
|
@ -10924,8 +10936,15 @@
|
|||
"OpenAIScoreAnswer": {
|
||||
"properties": {
|
||||
"confidence": {
|
||||
"title": "Confidence",
|
||||
"type": "number"
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Confidence"
|
||||
},
|
||||
"name": {
|
||||
"anyOf": [
|
||||
|
|
@ -10959,8 +10978,7 @@
|
|||
"required": [
|
||||
"name",
|
||||
"score",
|
||||
"probabilities",
|
||||
"confidence"
|
||||
"probabilities"
|
||||
],
|
||||
"title": "OpenAIScoreAnswer",
|
||||
"type": "object"
|
||||
|
|
@ -10992,8 +11010,15 @@
|
|||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"confidence": {
|
||||
"title": "Confidence",
|
||||
"type": "number"
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Confidence"
|
||||
},
|
||||
"legend": {
|
||||
"additionalProperties": {
|
||||
|
|
@ -11034,7 +11059,6 @@
|
|||
"required": [
|
||||
"type",
|
||||
"score",
|
||||
"confidence",
|
||||
"legend",
|
||||
"probabilities"
|
||||
],
|
||||
|
|
@ -23542,8 +23566,15 @@
|
|||
"type": "string"
|
||||
},
|
||||
"confidence": {
|
||||
"title": "Confidence",
|
||||
"type": "number"
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Confidence"
|
||||
},
|
||||
"probabilities": {
|
||||
"additionalProperties": {
|
||||
|
|
@ -23561,7 +23592,6 @@
|
|||
"required": [
|
||||
"type",
|
||||
"choice",
|
||||
"confidence",
|
||||
"probabilities"
|
||||
],
|
||||
"title": "ChoiceAnswer",
|
||||
|
|
@ -29400,8 +29430,15 @@
|
|||
"additionalProperties": true,
|
||||
"properties": {
|
||||
"confidence": {
|
||||
"title": "Confidence",
|
||||
"type": "number"
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Confidence"
|
||||
},
|
||||
"legend": {
|
||||
"additionalProperties": {
|
||||
|
|
@ -29442,7 +29479,6 @@
|
|||
"required": [
|
||||
"type",
|
||||
"score",
|
||||
"confidence",
|
||||
"legend",
|
||||
"probabilities"
|
||||
],
|
||||
|
|
|
|||
|
|
@ -43,7 +43,9 @@ from litellm.constants import (
|
|||
REGISTRY_ERROR_NEGATIVE_CACHE_TTL,
|
||||
TAG_REGISTRY_MAX_SIZE,
|
||||
)
|
||||
from litellm.litellm_core_utils.core_helpers import as_str_mapping
|
||||
from litellm.litellm_core_utils.dd_tracing import tracer
|
||||
from litellm.litellm_core_utils.decisions_pricing import decisions_pricing_fields, decisions_request_price
|
||||
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
|
||||
from litellm.litellm_core_utils.safe_json_loads import safe_json_loads
|
||||
from litellm.litellm_core_utils.tool_fields import DropPolicy
|
||||
|
|
@ -730,7 +732,7 @@ def _is_cost_explicitly_configured(deployments: Sequence[DeploymentTypedDict]) -
|
|||
|
||||
|
||||
def _has_explicit_positive_cost(deployments: Sequence[DeploymentTypedDict]) -> bool:
|
||||
"""Whether any of the deployments carries an explicit positive per-token price.
|
||||
"""Whether any of the deployments carries an explicit positive per-token or per-query price.
|
||||
|
||||
The group's price is read from the deployments named after the routed group and from
|
||||
that group's own alias target, while an alias chain is served from the deployments named
|
||||
|
|
@ -741,14 +743,21 @@ def _has_explicit_positive_cost(deployments: Sequence[DeploymentTypedDict]) -> b
|
|||
model_id = (deployment.get("model_info") or _EMPTY_COST_ENTRY).get("id")
|
||||
if model_id is None:
|
||||
continue
|
||||
raw_entry = litellm.model_cost.get(model_id, _EMPTY_COST_ENTRY)
|
||||
if _is_positive_cost(raw_entry.get("input_cost_per_token")) or _is_positive_cost(
|
||||
raw_entry.get("output_cost_per_token")
|
||||
raw_entry = as_str_mapping(litellm.model_cost.get(model_id)) or _EMPTY_COST_ENTRY
|
||||
if (
|
||||
_is_positive_cost(raw_entry.get("input_cost_per_token"))
|
||||
or _is_positive_cost(raw_entry.get("output_cost_per_token"))
|
||||
or _is_positive_cost(_deployment_query_price(deployment["litellm_params"], raw_entry))
|
||||
):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _deployment_query_price(litellm_params: Mapping[str, object], cost_entry: Mapping[str, object]) -> float | None:
|
||||
provider: Final = litellm_params.get("custom_llm_provider") or str(litellm_params.get("model")).partition("/")[0]
|
||||
return decisions_request_price({**cost_entry, "litellm_provider": provider}, decisions_pricing_fields(cost_entry))
|
||||
|
||||
|
||||
_EMPTY_COST_ENTRY: Final[Mapping[str, object]] = MappingProxyType({})
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -2641,6 +2641,34 @@
|
|||
],
|
||||
"default_model_placeholder": "nace/drex-latest"
|
||||
},
|
||||
{
|
||||
"provider": "PARALLEL_AI",
|
||||
"provider_display_name": "Parallel AI",
|
||||
"litellm_provider": "parallel_ai",
|
||||
"credential_fields": [
|
||||
{
|
||||
"key": "api_base",
|
||||
"label": "API Base",
|
||||
"placeholder": "https://api.parallel.ai",
|
||||
"tooltip": null,
|
||||
"required": false,
|
||||
"field_type": "text",
|
||||
"options": null,
|
||||
"default_value": null
|
||||
},
|
||||
{
|
||||
"key": "api_key",
|
||||
"label": "API Key",
|
||||
"placeholder": null,
|
||||
"tooltip": null,
|
||||
"required": true,
|
||||
"field_type": "password",
|
||||
"options": null,
|
||||
"default_value": null
|
||||
}
|
||||
],
|
||||
"default_model_placeholder": "parallel_ai/decision"
|
||||
},
|
||||
{
|
||||
"provider": "NEBIUS",
|
||||
"provider_display_name": "Nebius",
|
||||
|
|
|
|||
|
|
@ -1219,9 +1219,11 @@ def _with_classifier_forecast(
|
|||
forecasted_decision: Final[StandardLoggingRoutingDecision] = {
|
||||
**with_input,
|
||||
"classifier_probabilities": outcome.jev_verdict.probabilities,
|
||||
"classifier_confidence": outcome.jev_verdict.confidence,
|
||||
}
|
||||
return forecasted_decision
|
||||
confidence: Final = outcome.jev_verdict.confidence
|
||||
return (
|
||||
forecasted_decision if confidence is None else {**forecasted_decision, "classifier_confidence": confidence}
|
||||
)
|
||||
if outcome.llm_v2_forecast is not None:
|
||||
return _with_llm_v2_forecast(with_input, outcome.llm_v2_forecast)
|
||||
forecast: Final = outcome.capability_forecast
|
||||
|
|
@ -2468,7 +2470,7 @@ class ComplexityRouter(CustomLogger):
|
|||
score=None,
|
||||
signals=(
|
||||
f"{config.provider}-classifier:{tier_name}",
|
||||
f"{config.provider}-confidence={answer.confidence:.6f}",
|
||||
*(() if answer.confidence is None else (f"{config.provider}-confidence={answer.confidence:.6f}",)),
|
||||
*(
|
||||
f"tier-probability:{label}={probability:.6f}"
|
||||
for label, probability in answer.probabilities.items()
|
||||
|
|
|
|||
|
|
@ -210,7 +210,7 @@ class JevChoiceAnswer(LiteLLMBaseModel):
|
|||
type: Literal["choice"]
|
||||
choice: str
|
||||
probabilities: Mapping[str, JevProbability]
|
||||
confidence: JevProbability
|
||||
confidence: JevProbability | None = None
|
||||
|
||||
|
||||
class JevUsage(LiteLLMBaseModel):
|
||||
|
|
@ -425,7 +425,7 @@ class HttpJevClassifierClient:
|
|||
class JevVerdict(NamedTuple):
|
||||
label: str
|
||||
probabilities: Mapping[str, float]
|
||||
confidence: float
|
||||
confidence: float | None
|
||||
model: str
|
||||
cost: float | None
|
||||
provider: str = "typesafe"
|
||||
|
|
|
|||
|
|
@ -210,7 +210,7 @@ class NoulAnswer(LiteLLMPydanticObjectBase):
|
|||
class ChoiceAnswer(LiteLLMPydanticObjectBase):
|
||||
type: Literal["choice"]
|
||||
choice: str
|
||||
confidence: float
|
||||
confidence: float | None = None
|
||||
probabilities: Mapping[str, float]
|
||||
|
||||
model_config = ConfigDict(extra="allow", frozen=True)
|
||||
|
|
@ -219,7 +219,7 @@ class ChoiceAnswer(LiteLLMPydanticObjectBase):
|
|||
class ScoreAnswer(LiteLLMPydanticObjectBase):
|
||||
type: Literal["score"]
|
||||
score: float
|
||||
confidence: float
|
||||
confidence: float | None = None
|
||||
legend: Mapping[str, DecisionsJSON]
|
||||
probabilities: Mapping[str, float]
|
||||
|
||||
|
|
@ -431,7 +431,7 @@ class OpenAIChoiceAnswer(LiteLLMPydanticObjectBase):
|
|||
name: str | None
|
||||
choice: str | bool
|
||||
probabilities: tuple[OpenAIChoiceProbability, ...]
|
||||
confidence: float
|
||||
confidence: float | None = None
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
|
|
@ -449,7 +449,7 @@ class OpenAIScoreAnswer(LiteLLMPydanticObjectBase):
|
|||
name: str | None
|
||||
score: float
|
||||
probabilities: tuple[OpenAIScoreProbability, ...]
|
||||
confidence: float
|
||||
confidence: float | None = None
|
||||
|
||||
model_config = ConfigDict(frozen=True)
|
||||
|
||||
|
|
@ -595,7 +595,7 @@ class DecisionsIRChoiceProbability:
|
|||
@dataclass(frozen=True, slots=True)
|
||||
class DecisionsIRChoiceAnswer:
|
||||
choice: str | bool
|
||||
confidence: float
|
||||
confidence: float | None
|
||||
probabilities: tuple[DecisionsIRChoiceProbability, ...]
|
||||
extra: Mapping[str, object] = field(default_factory=lambda: _NO_EXTRA)
|
||||
|
||||
|
|
@ -610,7 +610,7 @@ class DecisionsIRScoreProbability:
|
|||
@dataclass(frozen=True, slots=True)
|
||||
class DecisionsIRScoreAnswer:
|
||||
score: float
|
||||
confidence: float
|
||||
confidence: float | None
|
||||
probabilities: tuple[DecisionsIRScoreProbability, ...]
|
||||
extra: Mapping[str, object] = field(default_factory=lambda: _NO_EXTRA)
|
||||
|
||||
|
|
|
|||
|
|
@ -4580,6 +4580,7 @@ class LlmProviders(str, Enum):
|
|||
CLF_AI_GATEWAY = "clf_ai_gateway"
|
||||
PERPLEXITY = "perplexity"
|
||||
TYPESAFE = "typesafe"
|
||||
PARALLEL_AI = "parallel_ai"
|
||||
NACE = "nace"
|
||||
STRANDS_DECIDER = "strands_decider"
|
||||
LAYA = "laya"
|
||||
|
|
|
|||
|
|
@ -10105,12 +10105,19 @@ class ProviderConfigManager:
|
|||
return litellm.LayaDecisionsConfig()
|
||||
if provider == LlmProviders.BESPOKE:
|
||||
return litellm.BespokeDecisionsConfig()
|
||||
if provider in (LlmProviders.OPENAI, LlmProviders.AZURE, LlmProviders.AZURE_AI, LlmProviders.EU_JEV):
|
||||
if provider in (
|
||||
LlmProviders.OPENAI,
|
||||
LlmProviders.AZURE,
|
||||
LlmProviders.AZURE_AI,
|
||||
LlmProviders.EU_JEV,
|
||||
LlmProviders.PARALLEL_AI,
|
||||
):
|
||||
configs: Final = {
|
||||
LlmProviders.OPENAI: litellm.OpenAIDecisionsConfig,
|
||||
LlmProviders.AZURE: litellm.AzureDecisionsConfig,
|
||||
LlmProviders.AZURE_AI: litellm.AzureAIDecisionsConfig,
|
||||
LlmProviders.EU_JEV: litellm.EuJevDecisionsConfig,
|
||||
LlmProviders.PARALLEL_AI: litellm.ParallelAIDecisionsConfig,
|
||||
}
|
||||
return configs[provider]()
|
||||
return None
|
||||
|
|
|
|||
|
|
@ -46412,6 +46412,19 @@
|
|||
"supports_response_schema": true,
|
||||
"supports_tool_choice": false
|
||||
},
|
||||
"parallel_ai/decision": {
|
||||
"input_cost_per_query": 0.001,
|
||||
"input_cost_per_token": 0.0,
|
||||
"output_cost_per_token": 0.0,
|
||||
"litellm_provider": "parallel_ai",
|
||||
"max_input_tokens": 32000,
|
||||
"mode": "evaluation",
|
||||
"source": "https://docs.parallel.ai/getting-started/pricing#decisions-api",
|
||||
"supported_endpoints": [
|
||||
"/v1/decisions",
|
||||
"/v1/systemone"
|
||||
]
|
||||
},
|
||||
"parallel_ai/search": {
|
||||
"input_cost_per_query": 0.005,
|
||||
"litellm_provider": "parallel_ai",
|
||||
|
|
|
|||
|
|
@ -3158,7 +3158,7 @@
|
|||
"batches": false,
|
||||
"messages": false,
|
||||
"interactions": false,
|
||||
"systemone": false,
|
||||
"systemone": true,
|
||||
"a2a": false,
|
||||
"generateContent": false
|
||||
}
|
||||
|
|
|
|||
|
|
@ -587,6 +587,11 @@ model_list:
|
|||
model: perplexity/pplx-decider-v1-27b
|
||||
api_base: http://127.0.0.1:8191
|
||||
api_key: synthetic-perplexity-key
|
||||
- model_name: parallel_ai/decision
|
||||
litellm_params:
|
||||
model: parallel_ai/decision
|
||||
api_base: http://127.0.0.1:8191
|
||||
api_key: synthetic-parallel-key
|
||||
- model_name: typesafe/jev-1.13.0
|
||||
litellm_params:
|
||||
model: typesafe/jev-1.13.0
|
||||
|
|
|
|||
257
tests/integration/translation/decisions/bases/parallel_ai.py
Normal file
257
tests/integration/translation/decisions/bases/parallel_ai.py
Normal file
|
|
@ -0,0 +1,257 @@
|
|||
from typing import Final
|
||||
|
||||
from integration.translation.case import TranslationTestCase
|
||||
from pydantic import JsonValue
|
||||
|
||||
"""Provider request and reply shape from https://docs.parallel.ai/api-reference/decisions/make-web-grounded-typed-decisions. Mock reply captured live on 2026-10-31.
|
||||
"""
|
||||
_CITATIONS: Final[list[JsonValue]] = [
|
||||
{
|
||||
"url": "https://access.redhat.com/solutions/7108129",
|
||||
"title": 'NFS client hangs and "nfs4_handle_delegation_recall_error" found in dmesg. - Red Hat Customer Portal',
|
||||
"excerpts": [
|
||||
'NFS client hangs and "nfs4_handle_delegation_recall_error" found in dmesg.\n\nSolution Verified - Updated 2026-06-18T01:07:40+00:00 -\nEnglish\n* No translations currently exist.\n\nIssue\n\n* NFS client hangs while returning delegations.\n* Below delegation recall errors may be reported in dmesg:\nNFS: nfs4_handle_delegation_recall_error: unhandled error -512\nNFS: nfs4_handle_delegation_recall_error: unhandled error -10024\n* We have a hanging process, it\'s using data ontop of a mounted NFS export. The fcntl() system call issued by a user process has not returned, The user process processing does not finish - usually it finishes within 2 minutes.\n\nEnvironment\n\n* Red Hat Enterprise Linux (RHEL) 8, all minor releases\n* kernel:\n* rhel8.4: kernels before 4.18.0-305.194.1.el8_4 affected\n* rhel8.10: kernels before 4.18.0-553.37.1.el8_10 affected\n* NFSv4'
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://github.com/moby/buildkit/issues/2804",
|
||||
"title": "GitHub cache exporter (`type=gha`) hangs reletively low number of cache reads/writes, satirating api limits/triggering throttling. · Issue #2804 · moby/buildkit · GitHub",
|
||||
"excerpts": [
|
||||
'Copy link\nOpen\nOpen\nGitHub cache exporter ( type=gha ) hangs reletively low number of cache reads/writes, satirating api limits/triggering throttling. #2804\nCopy link\n\nDescription\n\n@bendavies\nbendavies\nopened on Apr 13, 2022\nIssue body actions\n\nBehaviour\n\nCache operations using type=gha will start hanging after a relavatively low number of cache reads/writes.\nWe are a small team (5 devs) and are hitting these limits constantly on our main repository. Not exactly high throughput.\n\n...\n\nCache reads and writes go towards saturating the limit, and both are affected by this throttling.\n@dhadka mentions there is a limit of about "150 cache requests in 5 minutes", and seems about right: #2276 (comment)\n\nExpected behaviour\n\nI don\'t know, but the current behaviour of hanging is not great. there is no feedback as to what is hanging resulting in the user just being confused.'
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://github.com/huginn/huginn/issues/2963",
|
||||
"title": "WebsiteAgent hangs on a specific url, never finishes job or times out · Issue #2963 · huginn/huginn · GitHub",
|
||||
"excerpts": [
|
||||
'WebsiteAgent hangs on a specific url, never finishes job or times out\n\n* Page: GitHub issue\n* URL: https://github.com/huginn/huginn/issues/2963\n* State: open\n* Author: BoKKeR\n* Created: 2021-03-14T16:42:01Z\n* Updated: 2021-03-16T19:09:24Z\n* Repository: huginn/huginn\n* Number: #2963\n* Comments: 1\nI know it might be controversial but I am trying to get a new GPU for a while. Wanted to setup a monitor for when the stock on amd.com changes. But the site just won\'t load. The WebsiteAgent gets stuck in\nFetching https://www.amd.com/de/direct-buy/se\nThe only way to stop it is by stopping the container. As I am running the service in a container I cant find any of the debug log locations. Can anyone else try if they are encountering the same error?\n{\n "debug": "true",\n "expected_update_period_in_days": "2",\n "url": "https://www.amd.com/de/direct-buy/se",\n "type": "html",'
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://github.com/mozilla-firefox/firefox-crb/blob/main/docs/bug-mgmt/guides/severity.md",
|
||||
"title": "firefox-crb/docs/bug-mgmt/guides/severity.md at main · mozilla-firefox/firefox-crb · GitHub",
|
||||
"excerpts": [
|
||||
"We use the severity field in Bugzilla to indicate the scope of a bug's effect on Firefox.\nThe field is display alongside the bug's priority.\n\nValues\n\nSeverity levels and their definitions are enumerated at https://wiki.mozilla.org/BMO/UserGuide/BugFields#bug_severity.\nBy default, new bugs have a severity of --.\n\nExamples of S1 bugs\n\n* WebExtensions disabled for all users\n* Web search not working from URL bar\n* Crashes with data loss\n\nExamples of S2 bugs\n\nBugs that could reasonably be expected to cause a Firefox user to switch browsers, either because the severity is bad enough, or the frequency of occurrence is high enough.\n* Bug 1640441 - Slack hangs indefinitely in a onResize loop"
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://github.com/n0-computer/iroh/issues/4604",
|
||||
"title": "Endpoint::close hangs forever if the tokio runtime gets dropped while the connection is open · Issue #4604 · n0-computer/iroh · GitHub",
|
||||
"excerpts": [
|
||||
"@alpergundogdu\nalpergundogdu\nopened on Oct 14, 2026\nIssue body actions\nDescribe the bug\nAfter opening a connection from inside a short-lived tokio runtime, and dropping the runtime while the connection is still open, Endpoint::close() never returns.\nIn my repro it hangs reliably; when I keep the runtime open, it closes instantly.\nLocal repro/test (has the control tests too): https://github.com/decdn/iroh-tests/blob/main/tests/bug_endpoint_close_after_runtime_drop.rs\n\n...\n\nIf spawning on the caller's runtime is intended, a note on Endpoint::connect saying the driver lives there would have saved us a lot of time. (Maybe it's an expectation/documentation issue?)\nIroh\nVersion: iroh 1.3.0, noq 1.3.0\nEndpoint configuration: bound to 127.0.0.1, no relays.\nPlatform(s)\nDesktop:\n\n...\n\nSorry @flub , that was unclear: the endpoint's runtime stays alive, and close() is called from it. Only a separate short-lived runtime that runs connect() gets dropped."
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://bugzilla.mozilla.org/show_bug.cgi?id=1640441",
|
||||
"title": "1640441 - Slack hangs indefinitely in a onResize loop",
|
||||
"excerpts": [
|
||||
"Closed Bug 1640441 Opened 6 years ago Closed 6 years ago\n\nSlack hangs indefinitely in a on Resize loop\n\nCategories\n\n(Web Compatibility :: Site Reports, defect, P1)\n\nProduct:\nWeb Compatibility ▾\nComponent:\nSite Reports ▾\nType:\ndefect\nPriority:\nP1 Severity:\nS2\n\nTracking\n\n(Not tracked)\n\nStatus:\nRESOLVED FIXED\n\nPeople\n\n(Reporter: Harald, Assigned: emilio)\n\nReferences\n\n(Depends on 1 open bug)\n\nDetails\n\n(Keywords: enterprise, perf, webcompat:site-wait)\n\nAttachments\n\n(1 file, 1 obsolete file)\n\n|relevant unminified code.\n6 years ago Emilio Cobos Álvarez [:emilio]\n* 13.50 KB,\n\n...\n\n* Hover over the link\n* ... tab immediately becomes unresponsive ...\nThis may well be a bug in slack, but IMO it's still on us to try and fix what's going on. When it happens, it affects not only Slack but my entire Firefox session. This is a serious usability issue -- I'm now at the point of restarting Firefox several times a day because of this bug, if"
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://www.ibm.com/support/pages/cognos-business-intelligence-1022-fix-lists",
|
||||
"title": "Cognos Business Intelligence 10.2.2 Fix Lists",
|
||||
"excerpts": [
|
||||
'IBM Support \n\nCognos Business Intelligence 10.2.2 Fix Lists\n\nRelease Notes\n\nAbstract\n\nA comprehensive list of defect corrections for major releases, refresh packs and fix packs of Cognos Business Intelligence 10.2.2.\nDetails of the APARs listed below can be accessed by clicking the link for the APAR number. If you have questions about a particular defect, please contact Customer Support.\n\n...\n\n* PI05143 | MULTIPLE VALUES IN CONTEXT FILTERS CAUSE A CROSSTAB REPORT TO SHOW DASH "-" INSTEAD OF CORRECT VALUES\n* PI05166 | QE-DEF-0359 The query contains a reference to at least one object that does not exist\n* PI05233 | Export deployments from 8.4.1 with export folder or package tasks in Content Administration contain unknown content steps\n\n...\n\n* PI08523 | Progressive Chart - Incorrect Colours when "Reverse Catagory Order" = True\n* PI08585 | Cognos hangs when starting against a Content Store from previous version (upgrade) and using SSL gateway UR'
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://josm.eu/ticket/8175",
|
||||
"title": "\n #8175 ([PATCH] Cadastre grab query sometimes hangs completely)\n – JOSM\n\n ",
|
||||
"excerpts": [
|
||||
'Opened 14 years ago\nClosed 14 years ago\nLast modified 14 years ago\n\n#8175 closed defect ( fixed )\n\n[PATCH] Cadastre grab query sometimes hangs completely\n\n* Reported by: | Don-vip | Owned by: | pieren\n* Priority: | normal | Milestone: | \n* Component: | Plugin cadastre-fr | Version: | \n* Keywords: | | Cc: | \n\nDescription\n\nDuring cadastre grab, the HTTP connection with the WMS server sometimes completely hangs (the progress dialog is frozen and cancel button does not work).\nThe only solution is to restart JOSM to have a clean state and grab the cadastre again. We had a similar problem with history request ( #7640 ) when dealing with the API and solved it in [r5324](https://josm.eu/changeset/5324/josm "fix #7640 - History query will not cancel or timeout\n\n...\n\nby Don-vip , 14 years ago\n\n* Attachment: | 8175.patch \u200b added\n\ncomment:1 by Don-vip , 14 years ago'
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://bugzilla.mozilla.org/show_bug.cgi?id=1749908",
|
||||
"title": "1749908 - Infinite loop in HTTP3 hangs socket thread - Bugzilla",
|
||||
"excerpts": [
|
||||
"Closed Bug 1749908 Opened 4 years ago Closed 4 years ago\n\nInfinite loop in HTTP3 hangs socket thread\n\nCategories\n\n(Core :: Networking, defect)\n\nProduct:\nCore ▾\nComponent:\nNetworking ▾\nPlatform:\nDesktop\nAll\nType:\ndefect\nPriority:\nNot set Severity:\n--\n\nTracking\n\n( )\n\nStatus:\nVERIFIED DUPLICATE of bug 1749910\n\nPeople\n\n(Reporter: heftig, Unassigned)\n\nDetails\n\nBottom ↓\nTags ▾\nTimeline ▾\n| |Jan Alexander Steffens [:heftig]\n* Reporter | \n* ### Description\n•\n* 4 years ago\n* \nWhile browsing, pages suddenly stopped loading.\n\n...\n\nIt's not related to a specific version, we're getting reports ESR is even affected. Suspicion is around some long existing HTTP3 bug that's being triggered by an external service updating.\n\n...\n\nI agree it is not the root cause but it certainly caused a massive problem for many users of Firefox, the majority of whom are opted-in by default. If they had not been, they would not have been affected."
|
||||
],
|
||||
},
|
||||
{
|
||||
"url": "https://bugzilla.mozilla.org/show_bug.cgi?id=1748414",
|
||||
"title": "1748414 - Firefox 94 sometimes hangs on TLSv1.3 requests",
|
||||
"excerpts": [
|
||||
"Closed Bug 1748414 Opened 4 years ago Closed 4 years ago\n\nFirefox 94 sometimes hangs on TLSv1 .3 requests\n\nCategories\n\n(Core :: Networking: HTTP, defect)\n\nProduct:\nCore ▾\nComponent:\nNetworking: HTTP ▾\nVersion:\nFirefox 94\nType:\ndefect\nPriority:\nNot set Severity:\n--\n\nTracking\n\n( )\n\nStatus:\nRESOLVED DUPLICATE of bug 1740856\n\nPeople\n\n(Reporter: code, Unassigned)\n\nDetails\n\nBottom ↓\n\n...\n\nSteps to reproduce:\nIn the beginning of November, colleagues started to experience hangs of 1 to 1.5 minutes when working with our own web app. We use latest official releases for Windows, Ubuntu and FreeBSD. The problems exist for all plattforms and very likely started with Firefox 94.\nActual results:\nSometimes while interacting normally with our web app, a request hangs for a long time (up to 1.5 minutes; but usually succeeds). This typically happens on POST requests."
|
||||
],
|
||||
},
|
||||
]
|
||||
DECISION_TEST_CASE: Final = TranslationTestCase(
|
||||
scenario="basic",
|
||||
litellm_endpoint="/v1/decisions",
|
||||
litellm_request={
|
||||
"model": "parallel_ai/decision",
|
||||
"input": "Ticket (billing): The export job hangs at 99% and never finishes",
|
||||
"questions": [
|
||||
{"type": "predicate", "name": "defect", "instructions": "Is this a defect?"},
|
||||
{
|
||||
"type": "choice",
|
||||
"name": "severity",
|
||||
"instructions": "How severe is it?",
|
||||
"choices": [
|
||||
{"value": "low", "description": "cosmetic"},
|
||||
{"value": "high", "description": "blocks users"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"type": "score",
|
||||
"name": "confidence",
|
||||
"instructions": "How sure are you?",
|
||||
"levels": [{"label": "unsure"}, {"label": "sure"}],
|
||||
},
|
||||
],
|
||||
"cache": {"no-cache": True},
|
||||
},
|
||||
expected_provider_endpoint="/v1/decisions",
|
||||
expected_provider_headers={"x-api-key": "synthetic-parallel-key", "content-type": "application/json"},
|
||||
expected_provider_request={
|
||||
"mode": "fast",
|
||||
"state": "Ticket (billing): The export job hangs at 99% and never finishes",
|
||||
"questions": {
|
||||
"defect": {"type": "noul", "instructions": "Is this a defect?"},
|
||||
"severity": {
|
||||
"type": "choice",
|
||||
"instructions": "How severe is it?",
|
||||
"criteria": {"low": "cosmetic", "high": "blocks users"},
|
||||
},
|
||||
"confidence": {"type": "score", "instructions": "How sure are you?", "criteria": ["unsure", "sure"]},
|
||||
},
|
||||
},
|
||||
mock_provider_response={
|
||||
"mode": "fast",
|
||||
"answers": {
|
||||
"defect": {"type": "noul", "noul": 0.6442559258537284},
|
||||
"severity": {
|
||||
"type": "choice",
|
||||
"choice": "high",
|
||||
"probabilities": {"low": 0.1616964510265985, "high": 0.8383035489734014},
|
||||
"confidence": 0.6766070979468028,
|
||||
},
|
||||
"confidence": {
|
||||
"type": "score",
|
||||
"score": 0.25195637565890455,
|
||||
"legend": {"0": "unsure", "1": "sure"},
|
||||
"probabilities": {"0": 0.7480436243410955, "1": 0.25195637565890455},
|
||||
"confidence": 0.4960872486821909,
|
||||
},
|
||||
},
|
||||
"usage": {"input_tokens": 3166, "output_tokens": 0},
|
||||
"citations": _CITATIONS,
|
||||
"decision_id": "decisions_9fd99def328ed16256c11df297dea899",
|
||||
},
|
||||
expected_litellm_response={
|
||||
"model": "decision",
|
||||
"answers": [
|
||||
{"type": "predicate", "name": "defect", "probability": 0.6442559258537284},
|
||||
{
|
||||
"type": "choice",
|
||||
"name": "severity",
|
||||
"choice": "high",
|
||||
"probabilities": [
|
||||
{"value": "low", "probability": 0.1616964510265985},
|
||||
{"value": "high", "probability": 0.8383035489734014},
|
||||
],
|
||||
"confidence": 0.6766070979468028,
|
||||
},
|
||||
{
|
||||
"type": "score",
|
||||
"name": "confidence",
|
||||
"score": 0.25195637565890455,
|
||||
"probabilities": [
|
||||
{"value": 0, "label": "unsure", "probability": 0.7480436243410955},
|
||||
{"value": 1, "label": "sure", "probability": 0.25195637565890455},
|
||||
],
|
||||
"confidence": 0.4960872486821909,
|
||||
},
|
||||
],
|
||||
"usage": {
|
||||
"input_tokens": 3166,
|
||||
"input_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0},
|
||||
"output_tokens": 0,
|
||||
"output_tokens_details": {"reasoning_tokens": 0},
|
||||
"total_tokens": 3166,
|
||||
},
|
||||
"mode": "fast",
|
||||
"citations": _CITATIONS,
|
||||
"decision_id": "decisions_9fd99def328ed16256c11df297dea899",
|
||||
},
|
||||
)
|
||||
DECISION_SYSTEMONE_TEST_CASE: Final = TranslationTestCase(
|
||||
scenario="systemone",
|
||||
litellm_endpoint="/v1/systemone",
|
||||
litellm_request={
|
||||
"model": "parallel_ai/decision",
|
||||
"state": "Ticket (billing): The export job hangs at 99% and never finishes",
|
||||
"questions": {
|
||||
"defect": {"type": "noul", "instructions": "Is this a defect?"},
|
||||
"severity": {
|
||||
"type": "choice",
|
||||
"instructions": "How severe is it?",
|
||||
"criteria": {"low": "cosmetic", "high": "blocks users"},
|
||||
},
|
||||
"confidence": {"type": "score", "instructions": "How sure are you?", "criteria": ["unsure", "sure"]},
|
||||
},
|
||||
"cache": {"no-cache": True},
|
||||
},
|
||||
expected_provider_endpoint="/v1/decisions",
|
||||
expected_provider_headers={"x-api-key": "synthetic-parallel-key", "content-type": "application/json"},
|
||||
expected_provider_request={
|
||||
"mode": "fast",
|
||||
"state": "Ticket (billing): The export job hangs at 99% and never finishes",
|
||||
"questions": {
|
||||
"defect": {"type": "noul", "instructions": "Is this a defect?"},
|
||||
"severity": {
|
||||
"type": "choice",
|
||||
"instructions": "How severe is it?",
|
||||
"criteria": {"low": "cosmetic", "high": "blocks users"},
|
||||
},
|
||||
"confidence": {"type": "score", "instructions": "How sure are you?", "criteria": ["unsure", "sure"]},
|
||||
},
|
||||
},
|
||||
mock_provider_response={
|
||||
"mode": "fast",
|
||||
"answers": {
|
||||
"defect": {"type": "noul", "noul": 0.6442559258537284},
|
||||
"severity": {
|
||||
"type": "choice",
|
||||
"choice": "high",
|
||||
"probabilities": {"low": 0.1616964510265985, "high": 0.8383035489734014},
|
||||
"confidence": 0.6766070979468028,
|
||||
},
|
||||
"confidence": {
|
||||
"type": "score",
|
||||
"score": 0.25195637565890455,
|
||||
"legend": {"0": "unsure", "1": "sure"},
|
||||
"probabilities": {"0": 0.7480436243410955, "1": 0.25195637565890455},
|
||||
"confidence": 0.4960872486821909,
|
||||
},
|
||||
},
|
||||
"usage": {"input_tokens": 3166, "output_tokens": 0},
|
||||
"citations": _CITATIONS,
|
||||
"decision_id": "decisions_9fd99def328ed16256c11df297dea899",
|
||||
},
|
||||
expected_litellm_response={
|
||||
"model": "decision",
|
||||
"answers": {
|
||||
"defect": {"type": "noul", "noul": 0.6442559258537284},
|
||||
"severity": {
|
||||
"type": "choice",
|
||||
"choice": "high",
|
||||
"probabilities": {"low": 0.1616964510265985, "high": 0.8383035489734014},
|
||||
"confidence": 0.6766070979468028,
|
||||
},
|
||||
"confidence": {
|
||||
"type": "score",
|
||||
"score": 0.25195637565890455,
|
||||
"legend": {"0": "unsure", "1": "sure"},
|
||||
"probabilities": {"0": 0.7480436243410955, "1": 0.25195637565890455},
|
||||
"confidence": 0.4960872486821909,
|
||||
},
|
||||
},
|
||||
"usage": {"input_tokens": 3166, "output_tokens": 0},
|
||||
"mode": "fast",
|
||||
"citations": _CITATIONS,
|
||||
"decision_id": "decisions_9fd99def328ed16256c11df297dea899",
|
||||
},
|
||||
)
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
import pytest
|
||||
from integration._support.client import Gateway
|
||||
from integration._support.provider import SharedProvider
|
||||
from integration.translation.case import TranslationTestCase
|
||||
from integration.translation.decisions.bases.parallel_ai import DECISION_SYSTEMONE_TEST_CASE, DECISION_TEST_CASE
|
||||
from integration.translation.runner import assert_translation
|
||||
|
||||
|
||||
@pytest.mark.parametrize("case", [DECISION_TEST_CASE, DECISION_SYSTEMONE_TEST_CASE], ids=lambda case: case.id)
|
||||
def test_decisions_basic_parallel_ai(case: TranslationTestCase, gateway: Gateway, provider: SharedProvider) -> None:
|
||||
assert_translation(case, gateway, provider)
|
||||
|
|
@ -62,6 +62,66 @@ def test_apply_decisions_pricing_leaves_rows_without_decisions_fields_unchanged(
|
|||
assert apply_decisions_pricing(plain_row) == plain_row
|
||||
|
||||
|
||||
@pytest.mark.parametrize("rate", [0.0, 0.002])
|
||||
def test_flat_decisions_rate_overrides_catalog_without_changing_chat(
|
||||
monkeypatch: pytest.MonkeyPatch, rate: float
|
||||
) -> None:
|
||||
model: Final = "parallel_ai/decision"
|
||||
monkeypatch.setitem(
|
||||
litellm.model_cost,
|
||||
model,
|
||||
{**_ROW, "litellm_provider": "parallel_ai", "input_cost_per_query_decisions": rate},
|
||||
)
|
||||
assert cost_calculator.cost_per_token(model=model, prompt_tokens=100, call_type="decisions") == (rate, 0.0)
|
||||
assert cost_calculator.cost_per_token(model=model, prompt_tokens=100, call_type="completion") == pytest.approx(
|
||||
(100 * 1e-07, 0.0)
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("query_field", ["input_cost_per_query", "input_cost_per_query_decisions"])
|
||||
def test_query_price_on_a_provider_that_bills_tokens_stays_unbilled(
|
||||
monkeypatch: pytest.MonkeyPatch, query_field: str
|
||||
) -> None:
|
||||
model: Final = "fireworks_ai/accounts/example/models/query-priced"
|
||||
monkeypatch.setitem(
|
||||
litellm.model_cost,
|
||||
model,
|
||||
{query_field: 0.5, "input_cost_per_token": 0.0, "output_cost_per_token": 0.0}
|
||||
| {"litellm_provider": "fireworks_ai", "mode": "evaluation"},
|
||||
)
|
||||
assert cost_calculator.cost_per_token(model=model, prompt_tokens=100, call_type="decisions") == (0.0, 0.0)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("call_type", ["adecisions", "asystemone"])
|
||||
@pytest.mark.parametrize(
|
||||
("token_rates", "expected_costs"),
|
||||
[
|
||||
({"input_cost_per_token": 1e-05, "output_cost_per_token": 0.0}, (100 * 1e-05, 0.0)),
|
||||
({"input_cost_per_token": 0.0, "output_cost_per_token": 1e-05}, (0.0, 10 * 1e-05)),
|
||||
],
|
||||
)
|
||||
def test_plain_query_price_leaves_a_token_priced_row_billing_tokens(
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
call_type: str,
|
||||
token_rates: dict[str, float],
|
||||
expected_costs: tuple[float, float],
|
||||
) -> None:
|
||||
model: Final = "fireworks_ai/accounts/example/models/priced-both"
|
||||
monkeypatch.setitem(
|
||||
litellm.model_cost,
|
||||
model,
|
||||
{"input_cost_per_query": 0.5, **token_rates, "litellm_provider": "fireworks_ai", "mode": "evaluation"},
|
||||
)
|
||||
|
||||
assert cost_calculator.cost_per_token(
|
||||
model=model,
|
||||
custom_llm_provider="fireworks_ai",
|
||||
prompt_tokens=100,
|
||||
completion_tokens=10,
|
||||
call_type=cast(CallTypesLiteral, call_type),
|
||||
) == pytest.approx(expected_costs)
|
||||
|
||||
|
||||
_LUNA_USAGE: Final = Usage(
|
||||
prompt_tokens=100,
|
||||
completion_tokens=10,
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
|||
from litellm.llms.base_llm.decisions.transformation import (
|
||||
BaseDecisionsConfig,
|
||||
DecisionsInputLimits,
|
||||
DecisionsResponseValidationError,
|
||||
ir_to_systemone_request,
|
||||
ir_to_systemone_response,
|
||||
parse_systemone_response,
|
||||
|
|
@ -21,6 +22,7 @@ from litellm.llms.openai.decisions.transformation import openai_request_to_ir
|
|||
from litellm.llms.typesafe.decisions.transformation import TypeSafeDecisionsConfig
|
||||
from litellm.types.decisions import (
|
||||
MAX_DECISION_QUESTIONS,
|
||||
DecisionsIRChoiceAnswer,
|
||||
DecisionsIRRefusal,
|
||||
DecisionsIRRequest,
|
||||
DecisionsIRScoreAnswer,
|
||||
|
|
@ -347,6 +349,49 @@ def test_a_box_answer_without_confidence_reaches_the_caller_unchanged() -> None:
|
|||
assert response.model_dump(mode="json") == payload
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("question", "answer"),
|
||||
[
|
||||
(
|
||||
{"type": "choice", "criteria": {"yes": "Yes", "no": "No"}},
|
||||
{"type": "choice", "choice": "yes", "probabilities": {"yes": 0.9, "no": 0.1}},
|
||||
),
|
||||
(
|
||||
{"type": "score", "criteria": ["low", "high"]},
|
||||
{"type": "score", "score": 0.5, "legend": {"0": "low", "1": "high"}, "probabilities": {"0": 0.5, "1": 0.5}},
|
||||
),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("confidence", ({}, {"confidence": None}))
|
||||
@pytest.mark.parametrize(
|
||||
("provider", "optional"), (("nace", False), ("fireworks_ai", False), ("parallel_ai", True))
|
||||
)
|
||||
def test_only_a_provider_without_confidence_may_omit_it(
|
||||
question: Mapping[str, object],
|
||||
answer: Mapping[str, object],
|
||||
confidence: Mapping[str, object],
|
||||
provider: str,
|
||||
optional: bool,
|
||||
) -> None:
|
||||
config: Final = ProviderConfigManager.get_provider_decisions_config("decision", LlmProviders(provider))
|
||||
assert config is not None
|
||||
request: Final = systemone_request_to_ir(
|
||||
DecisionsRequestBody.model_validate({"state": {"text": "Example"}, "questions": {"asked": question}})
|
||||
)
|
||||
upstream: Final = httpx.Response(
|
||||
200, json={"mode": "fast", "model": "decision", "answers": {"asked": {**answer, **confidence}}}
|
||||
)
|
||||
|
||||
if optional:
|
||||
parsed: Final = config.transform_decisions_response("decision", provider, upstream, request)
|
||||
assert isinstance(parsed.answers[0], (DecisionsIRChoiceAnswer, DecisionsIRScoreAnswer))
|
||||
assert parsed.answers[0].confidence is None
|
||||
return
|
||||
with pytest.raises(DecisionsResponseValidationError) as raised:
|
||||
config.transform_decisions_response("decision", provider, upstream, request)
|
||||
assert raised.value.status_code == 500
|
||||
|
||||
|
||||
@pytest.mark.parametrize("provider", ("nace", "openrouter", "fireworks_ai", "typesafe"))
|
||||
@pytest.mark.parametrize("legend", (["low", "high"], {"0": "low", "1": "high"}))
|
||||
def test_score_legends_preserve_labels_probabilities_and_metadata(provider: str, legend: object) -> None:
|
||||
|
|
|
|||
|
|
@ -1,10 +1,16 @@
|
|||
from collections.abc import Mapping
|
||||
from dataclasses import replace
|
||||
from typing import Final
|
||||
|
||||
import httpx
|
||||
import pytest
|
||||
from pydantic import TypeAdapter
|
||||
|
||||
from litellm.llms.base_llm.decisions.transformation import ir_to_systemone_response, systemone_request_to_ir
|
||||
from litellm.llms.base_llm.decisions.transformation import (
|
||||
DecisionsResponseValidationError,
|
||||
ir_to_systemone_response,
|
||||
systemone_request_to_ir,
|
||||
)
|
||||
from litellm.llms.openai.decisions.transformation import (
|
||||
OpenAIDecisionsConfig,
|
||||
ir_to_openai_request,
|
||||
|
|
@ -106,6 +112,35 @@ def test_an_openai_response_reaches_the_caller_unchanged() -> None:
|
|||
assert ir_to_openai_response(parsed, ir, "requested").model_dump(mode="json") == _OPENAI_RESPONSE
|
||||
|
||||
|
||||
@pytest.mark.parametrize("answer_type", ["choice", "score"])
|
||||
def test_a_choice_or_score_answer_without_confidence_stays_a_provider_error(answer_type: str) -> None:
|
||||
answers: Final = [
|
||||
{key: value for key, value in answer.items() if answer["type"] != answer_type or key != "confidence"}
|
||||
for answer in _OPENAI_RESPONSE["answers"]
|
||||
]
|
||||
with pytest.raises(DecisionsResponseValidationError) as raised:
|
||||
OpenAIDecisionsConfig().transform_decisions_response(
|
||||
"gpt-6-luna",
|
||||
"openai",
|
||||
httpx.Response(200, json={**_OPENAI_RESPONSE, "answers": answers}),
|
||||
_openai_ir(_OPENAI_REQUEST),
|
||||
)
|
||||
assert raised.value.status_code == 500
|
||||
|
||||
|
||||
def test_provider_extras_stay_off_the_openai_response_unless_the_provider_names_them() -> None:
|
||||
ir: Final = _openai_ir(_OPENAI_REQUEST)
|
||||
parsed: Final = replace(
|
||||
OpenAIDecisionsConfig().parse_response(_OPENAI_RESPONSE, ir), extra={"provider": "upstream", "citations": []}
|
||||
)
|
||||
|
||||
assert ir_to_openai_response(parsed, ir, "requested").model_dump(mode="json") == _OPENAI_RESPONSE
|
||||
assert ir_to_openai_response(parsed, ir, "requested", frozenset({"citations"})).model_dump(mode="json") == {
|
||||
**_OPENAI_RESPONSE,
|
||||
"citations": [],
|
||||
}
|
||||
|
||||
|
||||
def test_answers_openai_did_not_return_are_refusals() -> None:
|
||||
ir: Final = _openai_ir(_OPENAI_REQUEST)
|
||||
payload: Final = {**_OPENAI_RESPONSE, "answers": [_PREDICATE_ANSWER]}
|
||||
|
|
|
|||
0
tests/unit/llms/parallel_ai/decisions/__init__.py
Normal file
0
tests/unit/llms/parallel_ai/decisions/__init__.py
Normal file
423
tests/unit/llms/parallel_ai/decisions/test_transformation.py
Normal file
423
tests/unit/llms/parallel_ai/decisions/test_transformation.py
Normal file
|
|
@ -0,0 +1,423 @@
|
|||
import json
|
||||
from collections.abc import Mapping
|
||||
from dataclasses import replace
|
||||
from datetime import datetime
|
||||
from typing import Final
|
||||
|
||||
import httpx
|
||||
import pytest
|
||||
import respx
|
||||
|
||||
import litellm
|
||||
from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
|
||||
from litellm.llms.base_llm.decisions.transformation import (
|
||||
DecisionsResponseValidationError,
|
||||
ir_to_systemone_response,
|
||||
systemone_request_to_ir,
|
||||
)
|
||||
from litellm.llms.openai.decisions.transformation import ir_to_openai_response, openai_request_to_ir
|
||||
from litellm.llms.parallel_ai.decisions.transformation import ParallelAIDecisionsConfig
|
||||
from litellm.types.decisions import (
|
||||
DecisionsIRRefusal,
|
||||
DecisionsRequestBody,
|
||||
OpenAIDecisionRequestBody,
|
||||
UnsupportedDecisionsRequest,
|
||||
)
|
||||
from litellm.types.utils import LlmProviders
|
||||
from litellm.utils import ProviderConfigManager, Rules, function_setup
|
||||
|
||||
QUESTIONS: Final = {
|
||||
"available": {"type": "noul", "instructions": "Is this product available?"},
|
||||
"tier": {"type": "choice", "instructions": "Which tier is offered?", "criteria": {"free": "Free", "paid": "Paid"}},
|
||||
"fit": {"type": "score", "instructions": "How well does it fit?", "criteria": ["Poor", "Good", "Excellent"]},
|
||||
"unknown": {"type": "noul", "instructions": "Is there enough evidence?"},
|
||||
}
|
||||
REQUEST: Final = systemone_request_to_ir(
|
||||
DecisionsRequestBody.model_validate({"state": {"product": "Example"}, "questions": QUESTIONS})
|
||||
)
|
||||
PAYLOAD: Final = {
|
||||
"mode": "fast",
|
||||
"answers": {
|
||||
"available": {"type": "noul", "noul": 0.9},
|
||||
"tier": {"type": "choice", "choice": "paid", "probabilities": {"free": 0.2, "paid": 0.8}},
|
||||
"fit": {
|
||||
"type": "score",
|
||||
"score": 1.7,
|
||||
"legend": {"0": "Poor", "1": "Good", "2": "Excellent"},
|
||||
"probabilities": {"0": 0.1, "1": 0.1, "2": 0.8},
|
||||
},
|
||||
"unknown": {"type": "refusal"},
|
||||
},
|
||||
"usage": {"input_tokens": 4212, "output_tokens": 0},
|
||||
"citations": [{"url": "https://example.com/product", "title": "Product", "excerpts": ["Available now"]}],
|
||||
"decision_id": "decisions_example",
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("use_async", [False, True])
|
||||
@pytest.mark.parametrize("surface", ["decisions", "systemone"])
|
||||
async def test_sdk_sends_parallel_wire_format_and_preserves_evidence(
|
||||
use_async: bool, surface: str, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
|
||||
litellm.in_memory_llm_clients_cache.flush_cache()
|
||||
route: Final = respx_mock.post("https://api.parallel.ai/v1/decisions").respond(json=PAYLOAD)
|
||||
connection: Final = {"model": "parallel_ai/decision", "api_key": "test-key", "timeout": 5, "num_retries": 0}
|
||||
fields: Final = (
|
||||
{"state": "Example", "questions": QUESTIONS}
|
||||
if surface == "systemone"
|
||||
else {
|
||||
"input": "Example",
|
||||
"questions": [{"type": "predicate", "name": "available", "instructions": "Available?"}],
|
||||
}
|
||||
)
|
||||
sync_call: Final = litellm.systemone if surface == "systemone" else litellm.decisions
|
||||
async_call: Final = litellm.asystemone if surface == "systemone" else litellm.adecisions
|
||||
response: Final = await async_call(**connection, **fields) if use_async else sync_call(**connection, **fields)
|
||||
sent: Final = route.calls.last.request
|
||||
body: Final = json.loads(sent.content)
|
||||
assert body["mode"] == "fast"
|
||||
assert "model" not in body
|
||||
assert body["state"] == "Example"
|
||||
assert sent.headers["x-api-key"] == "test-key"
|
||||
assert "authorization" not in sent.headers
|
||||
assert response.model == "decision"
|
||||
assert response.model_dump()["citations"] == PAYLOAD["citations"]
|
||||
assert response.model_dump()["decision_id"] == PAYLOAD["decision_id"]
|
||||
await GLOBAL_LOGGING_WORKER.clear_queue()
|
||||
|
||||
|
||||
def test_provider_routes_systemone_questions_to_parallel_without_a_model_field() -> None:
|
||||
config: Final = ProviderConfigManager.get_provider_decisions_config("decision", LlmProviders.PARALLEL_AI)
|
||||
assert config is not None
|
||||
assert config.transform_decisions_request("decision", REQUEST, "parallel_ai") == {
|
||||
"mode": "fast",
|
||||
"state": {"product": "Example"},
|
||||
"questions": QUESTIONS,
|
||||
}
|
||||
assert config.get_complete_url("https://gateway.example/v1/", "decision") == "https://gateway.example/v1/decisions"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("suffix", ["", "/", "/v1", "/v1/", "/v1/decisions", "/v1/decisions/", "/v1/search", "/v1/search/"])
|
||||
def test_base_carrying_a_parallel_route_reaches_the_decisions_endpoint(suffix: str) -> None:
|
||||
complete_url: Final = ParallelAIDecisionsConfig().get_complete_url(f"https://api.parallel.ai{suffix}", "decision")
|
||||
assert complete_url == "https://api.parallel.ai/v1/decisions"
|
||||
|
||||
|
||||
def test_auth_replaces_conflicting_headers_without_forwarding_bearer_credentials() -> None:
|
||||
headers: Final = ParallelAIDecisionsConfig().validate_environment(
|
||||
{"Authorization": "Bearer old", "X-API-Key": "old", "CONTENT-TYPE": "text/plain", "X-Trace": "test"},
|
||||
"decision",
|
||||
"test-key",
|
||||
{},
|
||||
)
|
||||
assert headers == {"x-api-key": "test-key", "Content-Type": "application/json", "X-Trace": "test"}
|
||||
|
||||
|
||||
def test_openai_predicate_is_translated_to_a_named_noul_question() -> None:
|
||||
request: Final = openai_request_to_ir(
|
||||
OpenAIDecisionRequestBody.model_validate(
|
||||
{
|
||||
"input": "Example product",
|
||||
"questions": [{"type": "predicate", "name": "available", "instructions": "Available?"}],
|
||||
}
|
||||
)
|
||||
)
|
||||
assert ParallelAIDecisionsConfig().transform_decisions_request("decision", request, "parallel_ai") == {
|
||||
"mode": "fast",
|
||||
"state": "Example product",
|
||||
"questions": {"available": {"type": "noul", "instructions": "Available?"}},
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("surface", ["systemone", "decisions"])
|
||||
def test_all_answers_usage_and_evidence_survive_response_translation(surface: str) -> None:
|
||||
response: Final = ParallelAIDecisionsConfig().parse_response(PAYLOAD, REQUEST)
|
||||
assert isinstance(response.answers[3], DecisionsIRRefusal)
|
||||
formatted: Final = (
|
||||
ir_to_systemone_response(response, REQUEST)
|
||||
if surface == "systemone"
|
||||
else ir_to_openai_response(
|
||||
response, REQUEST, "parallel_ai/decision", ParallelAIDecisionsConfig.openai_evidence_fields
|
||||
)
|
||||
)
|
||||
result: Final = formatted.model_dump(mode="json")
|
||||
assert result["citations"] == PAYLOAD["citations"]
|
||||
assert result["decision_id"] == PAYLOAD["decision_id"]
|
||||
assert result["mode"] == "fast"
|
||||
assert result["usage"]["input_tokens"] == 4212
|
||||
assert result["usage"]["output_tokens"] == 0
|
||||
if surface == "systemone":
|
||||
assert result["answers"]["available"]["noul"] == 0.9
|
||||
assert result["answers"]["tier"]["choice"] == "paid"
|
||||
assert result["answers"]["fit"]["score"] == 1.7
|
||||
assert "unknown" not in result["answers"]
|
||||
else:
|
||||
assert result["answers"][3]["type"] == "refusal"
|
||||
|
||||
|
||||
def test_rejects_more_than_one_hundred_questions() -> None:
|
||||
request: Final = systemone_request_to_ir(
|
||||
DecisionsRequestBody.model_validate(
|
||||
{
|
||||
"state": "Example",
|
||||
"questions": {str(index): QUESTIONS["available"] for index in range(101)},
|
||||
}
|
||||
)
|
||||
)
|
||||
assert isinstance(
|
||||
ParallelAIDecisionsConfig().transform_decisions_request("decision", request, "parallel_ai"),
|
||||
UnsupportedDecisionsRequest,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model", ["fast", "unknown"])
|
||||
def test_rejects_unregistered_models_instead_of_forwarding_them_as_modes(model: str) -> None:
|
||||
assert isinstance(
|
||||
ParallelAIDecisionsConfig().transform_decisions_request(model, REQUEST, "parallel_ai"),
|
||||
UnsupportedDecisionsRequest,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("confidence", [None, 0.8])
|
||||
@pytest.mark.parametrize("surface", ["systemone", "decisions"])
|
||||
def test_optional_confidence_is_preserved_without_inventing_a_value(confidence: float | None, surface: str) -> None:
|
||||
payload: Final = {
|
||||
**PAYLOAD,
|
||||
"answers": {name: {**answer, "confidence": confidence} for name, answer in PAYLOAD["answers"].items()},
|
||||
}
|
||||
response: Final = ParallelAIDecisionsConfig().parse_response(payload, REQUEST)
|
||||
formatted: Final = (
|
||||
ir_to_systemone_response(response, REQUEST)
|
||||
if surface == "systemone"
|
||||
else ir_to_openai_response(
|
||||
response, REQUEST, "parallel_ai/decision", ParallelAIDecisionsConfig.openai_evidence_fields
|
||||
)
|
||||
)
|
||||
answers: Final = formatted.model_dump(mode="json")["answers"]
|
||||
values: Final = answers.values() if isinstance(answers, dict) else answers
|
||||
assert [answer["confidence"] for answer in values if answer["type"] in {"choice", "score"}] == [confidence] * 2
|
||||
|
||||
|
||||
def test_provider_extras_cannot_replace_normalized_response_fields() -> None:
|
||||
response: Final = ParallelAIDecisionsConfig().parse_response(PAYLOAD, REQUEST)
|
||||
with_collision: Final = replace(response, extra={**response.extra, "model": "wrong", "answers": [], "usage": {}})
|
||||
formatted: Final = ir_to_openai_response(
|
||||
with_collision, REQUEST, "parallel_ai/decision", ParallelAIDecisionsConfig.openai_evidence_fields
|
||||
)
|
||||
assert formatted.model == "decision"
|
||||
assert len(formatted.answers) == 4
|
||||
assert formatted.usage.input_tokens == 4212
|
||||
assert formatted.model_dump()["citations"] == PAYLOAD["citations"]
|
||||
|
||||
|
||||
def test_undocumented_parallel_fields_stay_off_the_openai_response() -> None:
|
||||
response: Final = ParallelAIDecisionsConfig().parse_response({**PAYLOAD, "internal_trace_id": "trace"}, REQUEST)
|
||||
formatted: Final = ir_to_openai_response(
|
||||
response, REQUEST, "parallel_ai/decision", ParallelAIDecisionsConfig.openai_evidence_fields
|
||||
)
|
||||
assert "internal_trace_id" not in formatted.model_dump()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("tokens", [0, 100, 32000])
|
||||
@pytest.mark.parametrize("call_type", ["decisions", "adecisions", "systemone", "asystemone"])
|
||||
def test_request_pricing_is_independent_of_token_count(tokens: int, call_type: str) -> None:
|
||||
rate: Final = litellm.model_cost["parallel_ai/decision"]["input_cost_per_query"]
|
||||
cost: Final = litellm.cost_per_token(
|
||||
model="parallel_ai/decision",
|
||||
prompt_tokens=tokens,
|
||||
completion_tokens=0,
|
||||
call_type=call_type,
|
||||
)
|
||||
assert cost == (rate, 0.0)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_deployment_query_price_replaces_the_catalog_price(
|
||||
respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
|
||||
litellm.in_memory_llm_clients_cache.flush_cache()
|
||||
respx_mock.post("https://api.parallel.ai/v1/decisions").respond(json=PAYLOAD)
|
||||
price: Final = 25 * litellm.model_cost["parallel_ai/decision"]["input_cost_per_query"]
|
||||
router: Final = litellm.Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "webjev",
|
||||
"litellm_params": {
|
||||
"model": "parallel_ai/decision",
|
||||
"api_key": "test-key",
|
||||
"input_cost_per_query": price,
|
||||
},
|
||||
}
|
||||
]
|
||||
)
|
||||
fields: Final = {
|
||||
"model": "webjev",
|
||||
"input": "Example",
|
||||
"questions": [{"type": "predicate", "name": "available", "instructions": "Available?"}],
|
||||
"litellm_call_id": "query-price-call",
|
||||
}
|
||||
logging_obj, data = function_setup("adecisions", Rules(), datetime.now(), **fields)
|
||||
response: Final = await router.adecisions(**data, litellm_logging_obj=logging_obj)
|
||||
assert response._hidden_params["response_cost"] == pytest.approx(price)
|
||||
await GLOBAL_LOGGING_WORKER.clear_queue()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_price_on_another_provider_leaves_its_token_billing_alone(
|
||||
respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
|
||||
) -> None:
|
||||
monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
|
||||
litellm.in_memory_llm_clients_cache.flush_cache()
|
||||
input_rate: Final = 1e-06
|
||||
tokens: Final = 1000
|
||||
model: Final = "fireworks_ai/accounts/example/models/token-billed"
|
||||
monkeypatch.setitem(
|
||||
litellm.model_cost,
|
||||
model,
|
||||
{"input_cost_per_token": input_rate, "output_cost_per_token": 0.0}
|
||||
| {"litellm_provider": "fireworks_ai", "mode": "evaluation"},
|
||||
)
|
||||
respx_mock.post("https://api.fireworks.ai/inference/v1/systemone").respond(
|
||||
json={
|
||||
"model": "token-billed",
|
||||
"answers": {"available": {"type": "noul", "noul": 0.9}},
|
||||
"usage": {"input_tokens": tokens, "output_tokens": 0},
|
||||
}
|
||||
)
|
||||
router: Final = litellm.Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "token-billed",
|
||||
"litellm_params": {"model": model, "api_key": "test-key", "input_cost_per_query": 0.025},
|
||||
}
|
||||
]
|
||||
)
|
||||
fields: Final = {
|
||||
"model": "token-billed",
|
||||
"input": "Example",
|
||||
"questions": [{"type": "predicate", "name": "available", "instructions": "Available?"}],
|
||||
"litellm_call_id": "token-billed-call",
|
||||
}
|
||||
logging_obj, data = function_setup("adecisions", Rules(), datetime.now(), **fields)
|
||||
response: Final = await router.adecisions(**data, litellm_logging_obj=logging_obj)
|
||||
assert response._hidden_params["response_cost"] == pytest.approx(tokens * input_rate)
|
||||
await GLOBAL_LOGGING_WORKER.clear_queue()
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"payload", [{"answers": {}}, {"mode": "fast", "answers": {"available": {"type": "noul", "noul": "invalid"}}}]
|
||||
)
|
||||
def test_malformed_provider_responses_raise_a_provider_error(payload: Mapping[str, object]) -> None:
|
||||
with pytest.raises(DecisionsResponseValidationError):
|
||||
ParallelAIDecisionsConfig().transform_decisions_response(
|
||||
"decision", "parallel_ai", httpx.Response(200, json=payload), REQUEST
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("use_async", [False, True])
|
||||
@pytest.mark.parametrize("surface", ["decisions", "systemone"])
|
||||
@pytest.mark.parametrize("key_env", ["PARALLEL_AI_API_KEY", "PARALLEL_API_KEY"])
|
||||
async def test_untrusted_base_cannot_receive_environment_key(
|
||||
use_async: bool, surface: str, key_env: str, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
|
||||
) -> None:
|
||||
monkeypatch.delenv("PARALLEL_AI_API_BASE", raising=False)
|
||||
monkeypatch.delenv("PARALLEL_AI_API_KEY", raising=False)
|
||||
monkeypatch.delenv("PARALLEL_API_KEY", raising=False)
|
||||
monkeypatch.setenv(key_env, "server-secret")
|
||||
monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
|
||||
route: Final = respx_mock.post("https://attacker.example/v1/decisions").respond(json=PAYLOAD)
|
||||
connection: Final = {"model": "parallel_ai/decision", "api_base": "https://attacker.example", "num_retries": 0}
|
||||
fields: Final = (
|
||||
{"state": "Example", "questions": QUESTIONS}
|
||||
if surface == "systemone"
|
||||
else {
|
||||
"input": "Example",
|
||||
"questions": [{"type": "predicate", "name": "available", "instructions": "Available?"}],
|
||||
}
|
||||
)
|
||||
sync_call: Final = litellm.systemone if surface == "systemone" else litellm.decisions
|
||||
async_call: Final = litellm.asystemone if surface == "systemone" else litellm.adecisions
|
||||
|
||||
async def call() -> object:
|
||||
return await async_call(**connection, **fields) if use_async else sync_call(**connection, **fields)
|
||||
|
||||
with pytest.raises(litellm.BadRequestError, match="explicit api_key"):
|
||||
await call()
|
||||
assert not route.called
|
||||
await GLOBAL_LOGGING_WORKER.clear_queue()
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"api_base",
|
||||
[
|
||||
"https://api.parallel.ai.attacker.example",
|
||||
"https://api.parallel.ai@attacker.example",
|
||||
"http://api.parallel.ai",
|
||||
"https://api.parallel.ai:8443",
|
||||
"https://attacker.example/api.parallel.ai",
|
||||
"https://api.parallel.ai/other-tenant",
|
||||
"https://api.parallel.ai/other-tenant/v1/search",
|
||||
"https://attacker.example/v1/search",
|
||||
"https://attacker.example/v1/decisions",
|
||||
],
|
||||
)
|
||||
def test_environment_key_rejects_untrusted_destination_variants(api_base: str, monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.delenv("PARALLEL_AI_API_BASE", raising=False)
|
||||
monkeypatch.setenv("PARALLEL_AI_API_KEY", "server-secret")
|
||||
with pytest.raises(ValueError, match="explicit api_key"):
|
||||
ParallelAIDecisionsConfig().resolve_connection(api_base, None)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"api_base",
|
||||
[
|
||||
None,
|
||||
"https://api.parallel.ai",
|
||||
"https://API.PARALLEL.AI:443/v1/",
|
||||
"https://api.parallel.ai/v1/decisions",
|
||||
"https://api.parallel.ai/v1/search",
|
||||
],
|
||||
)
|
||||
def test_default_destination_retains_environment_key(api_base: str | None, monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.delenv("PARALLEL_AI_API_BASE", raising=False)
|
||||
monkeypatch.setenv("PARALLEL_AI_API_KEY", "server-secret")
|
||||
resolved_base, key = ParallelAIDecisionsConfig().resolve_connection(api_base, None)
|
||||
assert resolved_base == (api_base or "https://api.parallel.ai")
|
||||
assert key == "server-secret"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"api_base", [None, "https://gateway.example/parallel/v1/", "https://gateway.example/parallel/v1/search"]
|
||||
)
|
||||
def test_operator_base_retains_environment_key(api_base: str | None, monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.setenv("PARALLEL_AI_API_BASE", "https://gateway.example/parallel")
|
||||
monkeypatch.setenv("PARALLEL_AI_API_KEY", "server-secret")
|
||||
resolved_base, key = ParallelAIDecisionsConfig().resolve_connection(api_base, None)
|
||||
assert resolved_base == (api_base or "https://gateway.example/parallel")
|
||||
assert key == "server-secret"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("api_key", [None, "caller-key"])
|
||||
@pytest.mark.parametrize("api_base", ["https://api.parallel.ai\t", "https://api.parallel.ai\r\nHost: attacker.example"])
|
||||
def test_malformed_destination_is_a_bad_request(api_base: str, api_key: str | None) -> None:
|
||||
with pytest.raises(litellm.BadRequestError):
|
||||
litellm.decisions(
|
||||
model="parallel_ai/decision",
|
||||
api_base=api_base,
|
||||
api_key=api_key,
|
||||
input="Example",
|
||||
questions=[{"type": "predicate", "name": "available", "instructions": "Available?"}],
|
||||
num_retries=0,
|
||||
)
|
||||
|
||||
|
||||
def test_custom_destination_uses_explicit_key_instead_of_environment(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.setenv("PARALLEL_AI_API_KEY", "server-secret")
|
||||
assert ParallelAIDecisionsConfig().resolve_connection("https://gateway.example", "caller-key") == (
|
||||
"https://gateway.example",
|
||||
"caller-key",
|
||||
)
|
||||
|
|
@ -536,6 +536,7 @@ def _decisions_provider_entry(provider: str) -> ProviderCreateInfo:
|
|||
(
|
||||
("TypeSafe", "TypeSafe", LlmProviders.TYPESAFE),
|
||||
("Nace", "Nace", LlmProviders.NACE),
|
||||
("PARALLEL_AI", "Parallel AI", LlmProviders.PARALLEL_AI),
|
||||
("EU_JEV", "eu/jev", LlmProviders.EU_JEV),
|
||||
),
|
||||
)
|
||||
|
|
|
|||
|
|
@ -32,6 +32,29 @@ from litellm.router import Router
|
|||
from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("model", "expected_free"),
|
||||
[("parallel_ai/decision", False), ("fireworks_ai/accounts/fireworks/routers/rune", True)],
|
||||
)
|
||||
def test_query_price_keeps_budgets_only_where_it_is_billed(model: str, expected_free: bool) -> None:
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "query-priced",
|
||||
"litellm_params": {
|
||||
"model": model,
|
||||
"api_key": "test-key",
|
||||
"input_cost_per_token": 0.0,
|
||||
"output_cost_per_token": 0.0,
|
||||
"input_cost_per_query": 0.025,
|
||||
},
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
assert is_model_cost_zero("query-priced", router) is expected_free
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_router_with_zero_cost_model():
|
||||
"""Create a mock router with a zero-cost model."""
|
||||
|
|
|
|||
|
|
@ -359,6 +359,52 @@ class TestComplexityRouterInit:
|
|||
"tier-probability:MEDIUM=0.900000",
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_jev_choice_without_confidence_routes_and_reports_no_confidence(self, mock_router_instance):
|
||||
client = _StaticJevClient(
|
||||
JevSystemOneResponse(
|
||||
answers={
|
||||
"tier": JevChoiceAnswer(
|
||||
type="choice",
|
||||
choice="MEDIUM",
|
||||
probabilities={"SIMPLE": 0.1, "MEDIUM": 0.9},
|
||||
)
|
||||
},
|
||||
)
|
||||
)
|
||||
router = ComplexityRouter(
|
||||
"test-router",
|
||||
mock_router_instance,
|
||||
{
|
||||
"classifier_type": "jev",
|
||||
"jev_classifier_config": {"api_key": "test", "timeout_ms": 100},
|
||||
"tiers": {"SIMPLE": "cheap", "MEDIUM": "mid", "COMPLEX": "strong", "REASONING": "top"},
|
||||
},
|
||||
derive_savings_baseline=False,
|
||||
jev_client=client,
|
||||
)
|
||||
|
||||
outcome = await router.aclassify("Explain this")
|
||||
result = await router.async_pre_routing_hook(
|
||||
model="test-router",
|
||||
request_kwargs={},
|
||||
messages=[{"role": "user", "content": "Explain this"}],
|
||||
)
|
||||
|
||||
assert outcome.tier == ComplexityTier.MEDIUM
|
||||
assert outcome.cause == "jev_classifier"
|
||||
assert outcome.jev_verdict is not None
|
||||
assert outcome.jev_verdict.confidence is None
|
||||
assert outcome.signals == (
|
||||
"jev-classifier:MEDIUM",
|
||||
"tier-probability:SIMPLE=0.100000",
|
||||
"tier-probability:MEDIUM=0.900000",
|
||||
)
|
||||
assert result is not None
|
||||
assert result.routing_decision is not None
|
||||
assert result.routing_decision["classifier_probabilities"] == {"SIMPLE": 0.1, "MEDIUM": 0.9}
|
||||
assert "classifier_confidence" not in result.routing_decision
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_jev_pre_routing_hook_exposes_routing_decision_provenance(
|
||||
self, mock_router_instance, monkeypatch: pytest.MonkeyPatch
|
||||
|
|
|
|||
|
|
@ -109,6 +109,20 @@ describe("ResponseView integration", () => {
|
|||
expect(screen.getByText("378 in / 63 out")).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it("leaves out the confidence badge when the provider reports no confidence", () => {
|
||||
const withoutConfidence: SystemOneResponse = {
|
||||
...sampleResponse,
|
||||
answers: {
|
||||
dept: { type: "choice", choice: "technical", confidence: null, probabilities: { technical: 1 } },
|
||||
frustration: { type: "score", score: 2, confidence: null, probabilities: { "2": 1 } },
|
||||
},
|
||||
};
|
||||
|
||||
render(<ResponseView response={withoutConfidence} isLoading={false} latencyMs={120} />);
|
||||
|
||||
expect(screen.queryByText(/% confidence/)).not.toBeInTheDocument();
|
||||
});
|
||||
|
||||
it("selects the score level nearest a fractional calibrated score", () => {
|
||||
const fractional: SystemOneResponse = {
|
||||
answers: {
|
||||
|
|
|
|||
|
|
@ -71,7 +71,7 @@ function AnswerDetails({ answer }: { answer: SystemOneAnswer }) {
|
|||
<div className="flex flex-wrap items-center gap-2">
|
||||
<span className="text-xs text-muted-foreground">Selected choice</span>
|
||||
<Badge>{answer.choice}</Badge>
|
||||
{answer.confidence !== undefined && (
|
||||
{typeof answer.confidence === "number" && (
|
||||
<Badge variant="outline">{Math.round(answer.confidence * 100)}% confidence</Badge>
|
||||
)}
|
||||
</div>
|
||||
|
|
@ -90,7 +90,7 @@ function AnswerDetails({ answer }: { answer: SystemOneAnswer }) {
|
|||
<div className="flex flex-wrap items-center gap-2">
|
||||
<span className="text-xs text-muted-foreground">Score</span>
|
||||
<Badge>{SCORE_FORMAT.format(answer.score)}</Badge>
|
||||
{answer.confidence !== undefined && (
|
||||
{typeof answer.confidence === "number" && (
|
||||
<Badge variant="outline">{Math.round(answer.confidence * 100)}% confidence</Badge>
|
||||
)}
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -62,14 +62,14 @@ const choiceAnswerFields = {
|
|||
...answerFields,
|
||||
type: z.literal("choice"),
|
||||
choice: choiceValue,
|
||||
confidence: probability,
|
||||
confidence: probability.nullish(),
|
||||
probabilities: z.array(z.looseObject({ value: choiceValue, probability })),
|
||||
};
|
||||
const scoreAnswerFields = {
|
||||
...answerFields,
|
||||
type: z.literal("score"),
|
||||
score: z.number().finite(),
|
||||
confidence: probability,
|
||||
confidence: probability.nullish(),
|
||||
probabilities: z.array(z.looseObject({ value: z.number().int(), label: z.string(), probability })),
|
||||
};
|
||||
const usageFields = {
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ const noulAnswerSchema = z.looseObject({ type: z.literal("noul"), noul: probabil
|
|||
const choiceAnswerShape = {
|
||||
type: z.literal("choice"),
|
||||
choice: z.string(),
|
||||
confidence: probability.optional(),
|
||||
confidence: probability.nullish(),
|
||||
probabilities,
|
||||
};
|
||||
const choiceAnswerSchema = z.looseObject(choiceAnswerShape);
|
||||
|
|
@ -133,7 +133,7 @@ const choiceAnswerSchema = z.looseObject(choiceAnswerShape);
|
|||
const scoreAnswerShape = {
|
||||
type: z.literal("score"),
|
||||
score: z.number().finite(),
|
||||
confidence: probability.optional(),
|
||||
confidence: probability.nullish(),
|
||||
legend: z.record(z.string(), decisionsJson).optional(),
|
||||
probabilities,
|
||||
};
|
||||
|
|
|
|||
|
|
@ -73,6 +73,12 @@ describe("provider_info_helpers", () => {
|
|||
});
|
||||
|
||||
describe("getProviderLogoAndName", () => {
|
||||
it("shows the Parallel AI logo for the parallel_ai slug", () => {
|
||||
const { logo, displayName } = getProviderLogoAndName("parallel_ai");
|
||||
expect(displayName).toBe(Providers.PARALLEL_AI);
|
||||
expect(logo).toContain("parallel_ai");
|
||||
});
|
||||
|
||||
it("should return empty logo and dash display name when providerValue is empty", () => {
|
||||
const result = getProviderLogoAndName("");
|
||||
expect(result).toEqual({ logo: "", displayName: "-" });
|
||||
|
|
@ -577,6 +583,16 @@ describe("provider_info_helpers", () => {
|
|||
expect(getProviderModels("Sail" as Providers, modelMap)).toEqual(["sail/openai/gpt-oss-120b"]);
|
||||
});
|
||||
|
||||
it("lists Parallel decision models with their provider and model placeholder", () => {
|
||||
const modelMap = {
|
||||
"parallel_ai/decision": { litellm_provider: "parallel_ai", mode: "evaluation" },
|
||||
};
|
||||
|
||||
expect(getProviderModels("PARALLEL_AI", modelMap)).toEqual(["parallel_ai/decision"]);
|
||||
expect(getProviderLogoAndName("parallel_ai").displayName).toBe("Parallel AI");
|
||||
expect(getPlaceholder("PARALLEL_AI")).toBe("parallel_ai/decision");
|
||||
});
|
||||
|
||||
it("lists Nace decision models and resolves the provider name and model placeholder", () => {
|
||||
const modelMap = {
|
||||
"nace/drex-latest": { litellm_provider: "nace", mode: "evaluation" },
|
||||
|
|
|
|||
|
|
@ -50,6 +50,7 @@ import ollamaLogo from "../../public/assets/logos/ollama.svg";
|
|||
import openaiSmallLogo from "../../public/assets/logos/openai_small.svg";
|
||||
import openrouterLogo from "../../public/assets/logos/openrouter.svg";
|
||||
import oracleLogo from "../../public/assets/logos/oracle.svg";
|
||||
import parallelAiLogo from "../../public/assets/logos/parallel_ai.png";
|
||||
import perplexityAiLogo from "../../public/assets/logos/perplexity-ai.svg";
|
||||
import qwenLogo from "../../public/assets/logos/qwen.png";
|
||||
import recraftLogo from "../../public/assets/logos/recraft.svg";
|
||||
|
|
@ -191,6 +192,7 @@ export enum Providers {
|
|||
Triton = "Triton",
|
||||
TypeSafe = "TypeSafe",
|
||||
Nace = "Nace",
|
||||
PARALLEL_AI = "Parallel AI",
|
||||
V0 = "V0",
|
||||
VERCEL_AI_GATEWAY = "Vercel Ai Gateway",
|
||||
Vertex_AI = "Vertex AI (Anthropic, Gemini, etc.)",
|
||||
|
|
@ -325,6 +327,7 @@ export const provider_map: Record<string, string> = {
|
|||
Triton: "triton",
|
||||
TypeSafe: "typesafe",
|
||||
Nace: "nace",
|
||||
PARALLEL_AI: "parallel_ai",
|
||||
V0: "v0",
|
||||
VERCEL_AI_GATEWAY: "vercel_ai_gateway",
|
||||
Vertex_AI: "vertex_ai",
|
||||
|
|
@ -419,6 +422,7 @@ export const providerLogoMap: Partial<Record<Providers, string>> = {
|
|||
[Providers.OpenAI_Compatible]: openaiSmallLogo.src,
|
||||
[Providers.Openrouter]: openrouterLogo.src,
|
||||
[Providers.Oracle]: oracleLogo.src,
|
||||
[Providers.PARALLEL_AI]: parallelAiLogo.src,
|
||||
[Providers.Perplexity]: perplexityAiLogo.src,
|
||||
[Providers.Qwen_AI_Platform]: qwenLogo.src,
|
||||
[Providers.QwenCloud]: qwenLogo.src,
|
||||
|
|
@ -514,6 +518,7 @@ const providerPlaceholderMap: Partial<Record<Providers, string>> = {
|
|||
[Providers.Tencent]: "tencent/deepseek-v4-pro",
|
||||
[Providers.TypeSafe]: "typesafe/jev-latest",
|
||||
[Providers.Nace]: "nace/drex-latest",
|
||||
[Providers.PARALLEL_AI]: "parallel_ai/decision",
|
||||
[Providers.StreamLake]: "streamlake/<model-or-endpoint>",
|
||||
[Providers.Vertex_AI]: "gemini-pro",
|
||||
[Providers.VolcEngine]: "volcengine/<any-model-on-volcengine>",
|
||||
|
|
|
|||
8
ui/litellm-dashboard/src/lib/http/schema.d.ts
generated
vendored
8
ui/litellm-dashboard/src/lib/http/schema.d.ts
generated
vendored
|
|
@ -29404,7 +29404,7 @@ export interface components {
|
|||
/** Choice */
|
||||
choice: string;
|
||||
/** Confidence */
|
||||
confidence: number;
|
||||
confidence?: number | null;
|
||||
/** Probabilities */
|
||||
probabilities: {
|
||||
[key: string]: number;
|
||||
|
|
@ -40226,7 +40226,7 @@ export interface components {
|
|||
/** Choice */
|
||||
choice: string | boolean;
|
||||
/** Confidence */
|
||||
confidence: number;
|
||||
confidence?: number | null;
|
||||
/** Name */
|
||||
name: string | null;
|
||||
/** Probabilities */
|
||||
|
|
@ -40322,7 +40322,7 @@ export interface components {
|
|||
/** OpenAIScoreAnswer */
|
||||
OpenAIScoreAnswer: {
|
||||
/** Confidence */
|
||||
confidence: number;
|
||||
confidence?: number | null;
|
||||
/** Name */
|
||||
name: string | null;
|
||||
/** Probabilities */
|
||||
|
|
@ -45564,7 +45564,7 @@ export interface components {
|
|||
/** ScoreAnswer */
|
||||
ScoreAnswer: {
|
||||
/** Confidence */
|
||||
confidence: number;
|
||||
confidence?: number | null;
|
||||
/** Legend */
|
||||
legend: {
|
||||
[key: string]: string | {
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue