[PLT-1684] Vb/step reasoning ontology plt 1684

docs/labelbox/index.rst

@@ -46,6 +46,7 @@ Labelbox Python SDK Documentation
     search-filters
     send-to-annotate-params
     slice
+    step_reasoning_tool
     task
     task-queue
     user

docs/labelbox/step_reasoning_tool.rst

@@ -0,0 +1,6 @@
+Step Reasoning Tool
+===============================================================================================
+
+.. automodule:: labelbox.schema.tool_building.step_reasoning_tool
+   :members:
+   :show-inheritance:

libs/labelbox/src/labelbox/client.py

@@ -56,7 +56,7 @@
     FeatureSchema,
     Ontology,
     PromptResponseClassification,
-    Tool,
+    tool_type_cls_from_type,
 )
 from labelbox.schema.ontology_kind import (
     EditorTaskType,
@@ -1098,7 +1098,8 @@ def create_ontology_from_feature_schemas(
             if "tool" in feature_schema.normalized:
                 tool = feature_schema.normalized["tool"]
                 try:
-                    Tool.Type(tool)
+                    tool_type_cls = tool_type_cls_from_type(tool)
+                    tool_type_cls(tool)
                     tools.append(feature_schema.normalized)
                 except ValueError:
                     raise ValueError(

libs/labelbox/src/labelbox/schema/ontology.py

@@ -12,6 +12,8 @@
 
 from labelbox.orm.db_object import DbObject
 from labelbox.orm.model import Field, Relationship
+from labelbox.schema.tool_building.step_reasoning_tool import StepReasoningTool
+from labelbox.schema.tool_building.tool_type import ToolType
 
 FeatureSchemaId: Type[str] = Annotated[
     str, StringConstraints(min_length=25, max_length=25)
@@ -187,7 +189,7 @@ def __post_init__(self):
     @classmethod
     def from_dict(cls, dictionary: Dict[str, Any]) -> Dict[str, Any]:
         return cls(
-            class_type=cls.Type(dictionary["type"]),
+            class_type=Classification.Type(dictionary["type"]),
             name=dictionary["name"],
             instructions=dictionary["instructions"],
             required=dictionary.get("required", False),
@@ -351,7 +353,7 @@ class Type(Enum):
     @classmethod
     def from_dict(cls, dictionary: Dict[str, Any]) -> Dict[str, Any]:
         return cls(
-            class_type=cls.Type(dictionary["type"]),
+            class_type=PromptResponseClassification.Type(dictionary["type"]),
             name=dictionary["name"],
             instructions=dictionary["instructions"],
             required=True,  # always required
@@ -458,7 +460,7 @@ def from_dict(cls, dictionary: Dict[str, Any]) -> Dict[str, Any]:
             schema_id=dictionary.get("schemaNodeId", None),
             feature_schema_id=dictionary.get("featureSchemaId", None),
             required=dictionary.get("required", False),
-            tool=cls.Type(dictionary["tool"]),
+            tool=Tool.Type(dictionary["tool"]),
             classifications=[
                 Classification.from_dict(c)
                 for c in dictionary["classifications"]
@@ -488,6 +490,18 @@ def add_classification(self, classification: Classification) -> None:
         self.classifications.append(classification)
 
 
+def tool_cls_from_type(tool_type: str):
+    if tool_type.lower() == ToolType.STEP_REASONING.value:
+        return StepReasoningTool
+    return Tool
+
+
+def tool_type_cls_from_type(tool_type: str):
+    if tool_type.lower() == ToolType.STEP_REASONING.value:
+        return ToolType
+    return Tool.Type
+
+
 class Ontology(DbObject):
     """An ontology specifies which tools and classifications are available
     to a project. This is read only for now.
@@ -525,7 +539,8 @@ def tools(self) -> List[Tool]:
         """Get list of tools (AKA objects) in an Ontology."""
         if self._tools is None:
             self._tools = [
-                Tool.from_dict(tool) for tool in self.normalized["tools"]
+                tool_cls_from_type(tool["tool"]).from_dict(tool)
+                for tool in self.normalized["tools"]
             ]
         return self._tools
 
@@ -581,7 +596,7 @@ class OntologyBuilder:
 
     """
 
-    tools: List[Tool] = field(default_factory=list)
+    tools: List[Union[Tool, StepReasoningTool]] = field(default_factory=list)
     classifications: List[
         Union[Classification, PromptResponseClassification]
     ] = field(default_factory=list)

libs/labelbox/src/labelbox/schema/tool_building/__init__.py

@@ -0,0 +1,2 @@
+import labelbox.schema.tool_building.tool_type
+import labelbox.schema.tool_building.step_reasoning_tool

libs/labelbox/src/labelbox/schema/tool_building/step_reasoning_tool.py

@@ -0,0 +1,194 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+from labelbox.schema.tool_building.tool_type import ToolType
+
+
+@dataclass
+class StepReasoningVariant:
+    id: int
+    name: str
+
+    def asdict(self) -> Dict[str, Any]:
+        return {"id": self.id, "name": self.name}
+
+
+@dataclass
+class IncorrectStepReasoningVariant:
+    id: int
+    name: str
+    regenerate_conversations_after_incorrect_step: Optional[bool] = True
+    rate_alternative_responses: Optional[bool] = False
+
+    def asdict(self) -> Dict[str, Any]:
+        actions = []
+        if self.regenerate_conversations_after_incorrect_step:
+            actions.append("regenerateSteps")
+        if self.rate_alternative_responses:
+            actions.append("generateAndRateAlternativeSteps")
+        return {"id": self.id, "name": self.name, "actions": actions}
+
+    @classmethod
+    def from_dict(
+        cls, dictionary: Dict[str, Any]
+    ) -> "IncorrectStepReasoningVariant":
+        return cls(
+            id=dictionary["id"],
+            name=dictionary["name"],
+            regenerate_conversations_after_incorrect_step="regenerateSteps"
+            in dictionary.get("actions", []),
+            rate_alternative_responses="generateAndRateAlternativeSteps"
+            in dictionary.get("actions", []),
+        )
+
+
+def _create_correct_step() -> StepReasoningVariant:
+    return StepReasoningVariant(
+        id=StepReasoningVariants.CORRECT_STEP_ID, name="Correct"
+    )
+
+
+def _create_neutral_step() -> StepReasoningVariant:
+    return StepReasoningVariant(
+        id=StepReasoningVariants.NEUTRAL_STEP_ID, name="Neutral"
+    )
+
+
+def _create_incorrect_step() -> IncorrectStepReasoningVariant:
+    return IncorrectStepReasoningVariant(
+        id=StepReasoningVariants.INCORRECT_STEP_ID, name="Incorrect"
+    )
+
+
+@dataclass
+class StepReasoningVariants:
+    """
+    This class is used to define the possible options for evaluating a step
+    Currently the options are correct, neutral, and incorrect
+    """
+
+    CORRECT_STEP_ID = 0
+    NEUTRAL_STEP_ID = 1
+    INCORRECT_STEP_ID = 2
+
+    correct_step: StepReasoningVariant = field(
+        default_factory=_create_correct_step
+    )
+    neutral_step: StepReasoningVariant = field(
+        default_factory=_create_neutral_step
+    )
+    incorrect_step: IncorrectStepReasoningVariant = field(
+        default_factory=_create_incorrect_step
+    )
+
+    def asdict(self):
+        return [
+            self.correct_step.asdict(),
+            self.neutral_step.asdict(),
+            self.incorrect_step.asdict(),
+        ]
+
+    @classmethod
+    def from_dict(cls, dictionary: List[Dict[str, Any]]):
+        correct_step = None
+        neutral_step = None
+        incorrect_step = None
+
+        for variant in dictionary:
+            if variant["id"] == cls.CORRECT_STEP_ID:
+                correct_step = StepReasoningVariant(**variant)
+            elif variant["id"] == cls.NEUTRAL_STEP_ID:
+                neutral_step = StepReasoningVariant(**variant)
+            elif variant["id"] == cls.INCORRECT_STEP_ID:
+                incorrect_step = IncorrectStepReasoningVariant.from_dict(
+                    variant
+                )
+
+        if not all([correct_step, neutral_step, incorrect_step]):
+            raise ValueError("Invalid step reasoning variants")
+
+        return cls(
+            correct_step=correct_step,  # type: ignore
+            neutral_step=neutral_step,  # type: ignore
+            incorrect_step=incorrect_step,  # type: ignore
+        )
+
+
+@dataclass
+class StepReasoningDefinition:
+    variants: StepReasoningVariants = field(
+        default_factory=StepReasoningVariants
+    )
+    version: int = field(default=1)
+    title: Optional[str] = None
+    value: Optional[str] = None
+
+    def asdict(self) -> Dict[str, Any]:
+        result = {"variants": self.variants.asdict(), "version": self.version}
+        if self.title is not None:
+            result["title"] = self.title
+        if self.value is not None:
+            result["value"] = self.value
+        return result
+
+    @classmethod
+    def from_dict(cls, dictionary: Dict[str, Any]) -> "StepReasoningDefinition":
+        variants = StepReasoningVariants.from_dict(dictionary["variants"])
+        title = dictionary.get("title", None)
+        value = dictionary.get("value", None)
+        return cls(variants=variants, title=title, value=value)
+
+
+@dataclass
+class StepReasoningTool:
+    """
+    Use this class in OntologyBuilder to create a tool for step reasoning
+    The definition field lists the possible options to evaulate a step
+    """
+
+    name: str
+    type: ToolType = field(default=ToolType.STEP_REASONING, init=False)
+    required: bool = False
+    schema_id: Optional[str] = None
+    feature_schema_id: Optional[str] = None
+    color: Optional[str] = None
+    definition: StepReasoningDefinition = field(
+        default_factory=StepReasoningDefinition
+    )
+
+    def reset_regenerate_conversations_after_incorrect_step(self):
+        """
+        For live models, the default acation will invoke the model to generate alternatives if a step is marked as incorrect
+        This method will reset the action to not regenerate the conversation
+        """
+        self.definition.variants.incorrect_step.regenerate_conversations_after_incorrect_step = False
+
+    def set_rate_alternative_responses(self):
+        """
+        For live models, will require labelers to rate the alternatives generated by the model
+        """
+        self.definition.variants.incorrect_step.rate_alternative_responses = (
+            True
+        )
+
+    def asdict(self) -> Dict[str, Any]:
+        return {
+            "tool": self.type.value,
+            "name": self.name,
+            "required": self.required,
+            "schemaNodeId": self.schema_id,
+            "featureSchemaId": self.feature_schema_id,
+            "definition": self.definition.asdict(),
+        }
+
+    @classmethod
+    def from_dict(cls, dictionary: Dict[str, Any]) -> "StepReasoningTool":
+        return cls(
+            name=dictionary["name"],
+            schema_id=dictionary.get("schemaNodeId", None),
+            feature_schema_id=dictionary.get("featureSchemaId", None),
+            required=dictionary.get("required", False),
+            definition=StepReasoningDefinition.from_dict(
+                dictionary["definition"]
+            ),
+        )

libs/labelbox/src/labelbox/schema/tool_building/tool_type.py

@@ -0,0 +1,5 @@
+from enum import Enum
+
+
+class ToolType(Enum):
+    STEP_REASONING = "step-reasoning"

libs/labelbox/tests/integration/conftest.py

@@ -21,6 +21,8 @@
 )
 from labelbox.schema.data_row import DataRowMetadataField
 from labelbox.schema.ontology_kind import OntologyKind
+from labelbox.schema.tool_building.step_reasoning_tool import StepReasoningTool
+from labelbox.schema.tool_building.tool_type import ToolType
 from labelbox.schema.user import User
 
 
@@ -562,6 +564,7 @@ def feature_schema(client, point):
 @pytest.fixture
 def chat_evaluation_ontology(client, rand_gen):
     ontology_name = f"test-chat-evaluation-ontology-{rand_gen(str)}"
+
     ontology_builder = OntologyBuilder(
         tools=[
             Tool(
@@ -576,6 +579,7 @@ def chat_evaluation_ontology(client, rand_gen):
                 tool=Tool.Type.MESSAGE_RANKING,
                 name="model output multi ranking",
             ),
+            StepReasoningTool(name="step reasoning"),
         ],
         classifications=[
             Classification(
@@ -626,14 +630,12 @@ def chat_evaluation_ontology(client, rand_gen):
             ),
         ],
     )
-
     ontology = client.create_ontology(
         ontology_name,
         ontology_builder.asdict(),
         media_type=MediaType.Conversational,
         ontology_kind=OntologyKind.ModelEvaluation,
     )
-
     yield ontology
 
     try: