feat(agent): classifier tool with typesafe and openai providers

packages/uipath-platform/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "uipath-platform"
-version = "0.2.35"
+version = "0.2.36"
 description = "HTTP client library for programmatic access to UiPath Platform"
 readme = { file = "README.md", content-type = "text/markdown" }
 requires-python = ">=3.11"

packages/uipath-platform/src/uipath/platform/common/_endpoints_manager.py

@@ -20,6 +20,9 @@ class UiPathEndpoints(Enum):
     AH_VENDOR_COMPLETION_ENDPOINT = (
         "agenthub_/llm/raw/vendor/{vendor}/model/{model}/completions"
     )
+    AH_VENDOR_DECISION_ENDPOINT = (
+        "agenthub_/llm/raw/vendor/{vendor}/model/{model}/decisions"
+    )
     AH_CAPABILITIES_ENDPOINT = "agenthub_/llm/api/capabilities"
     AH_DISCOVERY_ENDPOINT = "agenthub_/llm/api/discovery"
 
@@ -29,6 +32,9 @@ class UiPathEndpoints(Enum):
     OR_VENDOR_COMPLETION_ENDPOINT = (
         "orchestrator_/llm/raw/vendor/{vendor}/model/{model}/completions"
     )
+    OR_VENDOR_DECISION_ENDPOINT = (
+        "orchestrator_/llm/raw/vendor/{vendor}/model/{model}/decisions"
+    )
     OR_CAPABILITIES_ENDPOINT = "orchestrator_/llm/api/capabilities"
     OR_DISCOVERY_ENDPOINT = "orchestrator_/llm/api/discovery"
 
@@ -61,6 +67,7 @@ class EndpointManager:
         get_normalized_endpoint(): Returns the appropriate normalized completion endpoint.
         get_embeddings_endpoint(): Returns the appropriate embeddings endpoint.
         get_vendor_endpoint(): Returns the appropriate vendor completion endpoint.
+        get_vendor_decisions_endpoint(): Returns the appropriate vendor decisions endpoint.
     All endpoint methods automatically select the best available endpoint using the fallback order,
     unless overridden by the UIPATH_LLM_SERVICE environment variable.
     """  # noqa: D205
@@ -210,3 +217,11 @@ def get_vendor_endpoint(cls) -> str:
             UiPathEndpoints.AH_VENDOR_COMPLETION_ENDPOINT,
             UiPathEndpoints.OR_VENDOR_COMPLETION_ENDPOINT,
         )
+
+    @classmethod
+    def get_vendor_decisions_endpoint(cls) -> str:
+        """Get the vendor decisions endpoint (typed classification decisions)."""
+        return cls._select_endpoint(
+            UiPathEndpoints.AH_VENDOR_DECISION_ENDPOINT,
+            UiPathEndpoints.OR_VENDOR_DECISION_ENDPOINT,
+        )

packages/uipath-platform/tests/common/test_endpoints_manager.py

@@ -0,0 +1,32 @@
+import pytest
+
+from uipath.platform.common._endpoints_manager import EndpointManager
+
+
+@pytest.mark.parametrize(
+    ("service", "expected"),
+    [
+        ("agenthub", "agenthub_/llm/raw/vendor/{vendor}/model/{model}/decisions"),
+        (
+            "orchestrator",
+            "orchestrator_/llm/raw/vendor/{vendor}/model/{model}/decisions",
+        ),
+    ],
+)
+def test_vendor_decisions_endpoint_follows_the_llm_service(
+    monkeypatch: pytest.MonkeyPatch, service: str, expected: str
+) -> None:
+    monkeypatch.setenv("UIPATH_LLM_SERVICE", service)
+
+    assert EndpointManager.get_vendor_decisions_endpoint() == expected
+
+
+def test_vendor_completion_endpoint_is_unchanged(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setenv("UIPATH_LLM_SERVICE", "agenthub")
+
+    assert (
+        EndpointManager.get_vendor_endpoint()
+        == "agenthub_/llm/raw/vendor/{vendor}/model/{model}/completions"
+    )

packages/uipath/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "uipath"
-version = "2.14.36"
+version = "2.14.37"
 description = "Python SDK and CLI for UiPath Platform, enabling programmatic interaction with automation services, process management, and deployment tools."
 readme = { file = "README.md", content-type = "text/markdown" }
 requires-python = ">=3.11"

packages/uipath/src/uipath/agent/models/agent.py

@@ -130,7 +130,7 @@ class AgentInternalToolType(str, CaseInsensitiveEnum):
     BATCH_TRANSFORM = "batch-transform"
     HTTP_REQUEST = "http-request"
     CREATE_FILE = "create-file"
-    JEV_CLASSIFIER = "jev-classifier"
+    CLASSIFIER = "classifier"
 
 
 class AgentEscalationRecipientType(str, CaseInsensitiveEnum):
@@ -1092,13 +1092,13 @@ class AgentInternalCreateFileToolProperties(BaseResourceProperties):
     )
 
 
-class AgentInternalJevClassifierToolProperties(BaseResourceProperties):
-    """Agent internal Jev classifier tool properties model."""
+class AgentInternalClassifierToolProperties(BaseResourceProperties):
+    """Agent internal classifier tool properties model."""
 
-    tool_type: Literal[AgentInternalToolType.JEV_CLASSIFIER] = Field(
-        alias="toolType", default=AgentInternalToolType.JEV_CLASSIFIER, frozen=True
+    tool_type: Literal[AgentInternalToolType.CLASSIFIER] = Field(
+        alias="toolType", default=AgentInternalToolType.CLASSIFIER, frozen=True
     )
-    settings: AgentInternalJevClassifierSettings = Field(..., alias="settings")
+    settings: AgentInternalClassifierSettings = Field(..., alias="settings")
 
 
 AgentInternalToolProperties = Annotated[
@@ -1108,7 +1108,7 @@ class AgentInternalJevClassifierToolProperties(BaseResourceProperties):
         AgentInternalBatchTransformToolProperties,
         AgentInternalHttpRequestToolProperties,
         AgentInternalCreateFileToolProperties,
-        AgentInternalJevClassifierToolProperties,
+        AgentInternalClassifierToolProperties,
     ],
     Field(discriminator="tool_type"),
     _case_insensitive_enum_validator("tool_type", AgentInternalToolType, "toolType"),
@@ -1144,38 +1144,31 @@ class AgentInternalBatchTransformSettings(BaseCfg):
     )
 
 
-JEV_NAME_PATTERN = r"^[A-Za-z_][A-Za-z0-9_]{0,63}$"
+# Question names are the keys of the answers in the tool output, for every provider.
+CLASSIFIER_QUESTION_NAME_PATTERN = r"^[A-Za-z_][A-Za-z0-9_]{0,63}$"
 
 
-class JevQuestionType(str, CaseInsensitiveEnum):
-    """Jev question type enumeration (matches TypeSafe's question types)."""
+class ClassifierProvider(str, CaseInsensitiveEnum):
+    """The classification API a classifier tool calls, implied by its model."""
 
-    CHOICE = "choice"
-    SCORE = "score"
-    NOUL = "noul"
+    TYPESAFE = "typesafe"  # TypeSafe AI's Jev models
+    OPENAI = "openai"  # OpenAI's Decisions API
 
 
-class JevChoiceOption(BaseCfg):
-    """An option of a Jev choice question."""
+class BaseClassifierQuestion(BaseCfg):
+    """Common fields of a classifier question.
 
-    name: str = Field(..., min_length=1)
-    description: Optional[str] = Field(None)
-
-
-class BaseJevQuestion(BaseCfg):
-    """Common fields of a Jev question.
-
-    A question carries only the fields of its type: ``options`` belong to choice
-    questions, ``levels`` to score questions and ``criteria`` to noul questions.
+    A question carries only the fields of its provider and type: ``foreign_fields``
+    lists the fields of the other types (and of the other provider) it rejects.
     """
 
     foreign_fields: ClassVar[tuple[str, ...]] = ()
 
-    name: str = Field(..., pattern=JEV_NAME_PATTERN)
+    name: str = Field(..., pattern=CLASSIFIER_QUESTION_NAME_PATTERN)
     instructions: str = Field(..., min_length=1)
 
     @model_validator(mode="after")
-    def _only_own_fields(self) -> "BaseJevQuestion":
+    def _only_own_fields(self) -> "BaseClassifierQuestion":
         extra = self.model_extra or {}
         present = [f for f in self.foreign_fields if extra.get(f) is not None]
         if present:
@@ -1185,10 +1178,28 @@ def _only_own_fields(self) -> "BaseJevQuestion":
         return self
 
 
-class JevChoiceQuestion(BaseJevQuestion):
+# --- Jev (TypeSafe) questions ---
+
+
+class JevQuestionType(str, CaseInsensitiveEnum):
+    """Jev question type enumeration (matches TypeSafe's question types)."""
+
+    CHOICE = "choice"
+    SCORE = "score"
+    NOUL = "noul"
+
+
+class JevChoiceOption(BaseCfg):
+    """An option of a Jev choice question."""
+
+    name: str = Field(..., min_length=1)
+    description: Optional[str] = Field(None)
+
+
+class JevChoiceQuestion(BaseClassifierQuestion):
     """Jev question selecting one of a set of options."""
 
-    foreign_fields: ClassVar[tuple[str, ...]] = ("levels", "criteria")
+    foreign_fields: ClassVar[tuple[str, ...]] = ("levels", "criteria", "choices")
 
     type: Literal[JevQuestionType.CHOICE] = JevQuestionType.CHOICE
     options: List[JevChoiceOption] = Field(..., min_length=2, max_length=255)
@@ -1201,10 +1212,10 @@ def _unique_option_names(self) -> "JevChoiceQuestion":
         return self
 
 
-class JevScoreQuestion(BaseJevQuestion):
+class JevScoreQuestion(BaseClassifierQuestion):
     """Jev question placing the input on an ordered scale of levels."""
 
-    foreign_fields: ClassVar[tuple[str, ...]] = ("options", "criteria")
+    foreign_fields: ClassVar[tuple[str, ...]] = ("options", "criteria", "choices")
 
     type: Literal[JevQuestionType.SCORE] = JevQuestionType.SCORE
     levels: List[Annotated[str, Field(min_length=1)]] = Field(
@@ -1221,10 +1232,10 @@ class JevNoulCriteria(BaseModel):
     false: Optional[str] = Field(None)
 
 
-class JevNoulQuestion(BaseJevQuestion):
+class JevNoulQuestion(BaseClassifierQuestion):
     """Jev yes/no question answered with a probability."""
 
-    foreign_fields: ClassVar[tuple[str, ...]] = ("options", "levels")
+    foreign_fields: ClassVar[tuple[str, ...]] = ("options", "levels", "choices")
 
     type: Literal[JevQuestionType.NOUL] = JevQuestionType.NOUL
     criteria: Optional[JevNoulCriteria] = Field(None)
@@ -1237,15 +1248,88 @@ class JevNoulQuestion(BaseJevQuestion):
 ]
 
 
-class AgentInternalJevClassifierSettings(BaseCfg):
-    """Agent internal Jev classifier tool settings model.
+# --- OpenAI Decisions questions (matches the Decisions API request) ---
+
+
+class DecisionsQuestionType(str, CaseInsensitiveEnum):
+    """OpenAI Decisions question type enumeration."""
+
+    CHOICE = "choice"
+    SCORE = "score"
+    PREDICATE = "predicate"
+
+
+class DecisionsChoice(BaseCfg):
+    """A choice of a Decisions choice question."""
+
+    value: str = Field(..., min_length=1)
+    description: Optional[str] = Field(None)
+
+
+class DecisionsScoreLevel(BaseCfg):
+    """A level of a Decisions score question."""
+
+    label: str = Field(..., min_length=1)
+    description: Optional[str] = Field(None)
+
+
+class DecisionsChoiceQuestion(BaseClassifierQuestion):
+    """Decisions question selecting one of a set of choices."""
+
+    foreign_fields: ClassVar[tuple[str, ...]] = ("levels", "options", "criteria")
+
+    type: Literal[DecisionsQuestionType.CHOICE] = DecisionsQuestionType.CHOICE
+    choices: List[DecisionsChoice] = Field(..., min_length=2)
+
+    @model_validator(mode="after")
+    def _unique_choice_values(self) -> "DecisionsChoiceQuestion":
+        values = [choice.value for choice in self.choices]
+        if len(values) != len(set(values)):
+            raise ValueError(f"Question '{self.name}' has duplicate choice values")
+        return self
+
+
+class DecisionsScoreQuestion(BaseClassifierQuestion):
+    """Decisions question placing the input on levels ordered lowest to highest."""
+
+    foreign_fields: ClassVar[tuple[str, ...]] = ("choices", "options", "criteria")
+
+    type: Literal[DecisionsQuestionType.SCORE] = DecisionsQuestionType.SCORE
+    levels: List[DecisionsScoreLevel] = Field(..., min_length=2)
+
+
+class DecisionsPredicateQuestion(BaseClassifierQuestion):
+    """Decisions question estimating the probability that a condition is true."""
+
+    foreign_fields: ClassVar[tuple[str, ...]] = (
+        "choices",
+        "levels",
+        "options",
+        "criteria",
+    )
+
+    type: Literal[DecisionsQuestionType.PREDICATE] = DecisionsQuestionType.PREDICATE
+
+
+DecisionsQuestion = Annotated[
+    Union[DecisionsChoiceQuestion, DecisionsScoreQuestion, DecisionsPredicateQuestion],
+    Field(discriminator="type"),
+    _case_insensitive_enum_validator("type", DecisionsQuestionType),
+]
+
+ClassifierQuestion = Union[JevQuestion, DecisionsQuestion]
+
+
+class AgentInternalClassifierSettings(BaseCfg):
+    """Agent internal classifier tool settings model.
 
-    Only the configuration that is never a call argument: the ``state`` and
-    ``questions`` the tool sends to Jev are declared in the resource's
-    ``inputSchema`` and supplied through its ``argumentProperties``. The model
-    is required: there is no default Jev model.
+    Only the configuration that is never a call argument: the input and the
+    ``questions`` the tool sends are declared in the resource's ``inputSchema``
+    and supplied through its ``argumentProperties``, in the shapes of the
+    provider. The provider and the model are required: there is no default.
     """
 
+    provider: ClassifierProvider = Field(...)
     model: str = Field(..., min_length=1)