refactor: rename mention node to nested_node for generic sub-graph support

api/core/llm_generator/llm_generator.py

@@ -813,7 +813,19 @@ Parameter: {parameter_info.get("name")} ({param_type}) - {parameter_info.get("de
             if isinstance(v, dict)
         ]
 
-        outputs = content.get("outputs", {"result": {"type": parameter_type}})
+        # Convert outputs from array format [{name, type}] to dict format {name: {type}}
+        # Array format is required for OpenAI/Azure strict JSON schema compatibility
+        raw_outputs = content.get("outputs", [])
+        if isinstance(raw_outputs, list):
+            outputs = {
+                item.get("name", "result"): {"type": item.get("type", parameter_type)}
+                for item in raw_outputs
+                if isinstance(item, dict) and item.get("name")
+            }
+            if not outputs:
+                outputs = {"result": {"type": parameter_type}}
+        else:
+            outputs = raw_outputs or {"result": {"type": parameter_type}}
 
         return {
             "variables": variables,

api/core/llm_generator/output_models.py

@@ -3,32 +3,65 @@ from __future__ import annotations
 from pydantic import BaseModel, ConfigDict, Field
 
 from core.variables.types import SegmentType
-from core.workflow.nodes.base.entities import VariableSelector
 
 
 class SuggestedQuestionsOutput(BaseModel):
+    """Output model for suggested questions generation."""
+
     model_config = ConfigDict(extra="forbid")
 
-    questions: list[str] = Field(min_length=3, max_length=3)
+    questions: list[str] = Field(
+        min_length=3,
+        max_length=3,
+        description="Exactly 3 suggested follow-up questions for the user",
+    )
 
 
-class CodeNodeOutput(BaseModel):
+class VariableSelectorOutput(BaseModel):
+    """Variable selector mapping code variable to upstream node output.
+
+    Note: Separate from VariableSelector to ensure 'additionalProperties: false'
+    in JSON schema for OpenAI/Azure strict mode.
+    """
+
     model_config = ConfigDict(extra="forbid")
 
-    type: SegmentType
+    variable: str = Field(description="Variable name used in the generated code")
+    value_selector: list[str] = Field(description="Path to upstream node output, format: [node_id, output_name]")
+
+
+class CodeNodeOutputItem(BaseModel):
+    """Single output variable definition.
+
+    Note: OpenAI/Azure strict mode requires 'additionalProperties: false' and
+    does not support dynamic object keys, so outputs use array format.
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    name: str = Field(description="Output variable name returned by the main function")
+    type: SegmentType = Field(description="Data type of the output variable")
 
 
 class CodeNodeStructuredOutput(BaseModel):
+    """Structured output for code node generation."""
+
     model_config = ConfigDict(extra="forbid")
 
-    variables: list[VariableSelector]
-    code: str
-    outputs: dict[str, CodeNodeOutput]
-    explanation: str
+    variables: list[VariableSelectorOutput] = Field(
+        description="Input variables mapping code variables to upstream node outputs"
+    )
+    code: str = Field(description="Generated code with a main function that processes inputs and returns outputs")
+    outputs: list[CodeNodeOutputItem] = Field(
+        description="Output variable definitions specifying name and type for each return value"
+    )
+    explanation: str = Field(description="Brief explanation of what the generated code does")
 
 
 class InstructionModifyOutput(BaseModel):
+    """Output model for instruction-based prompt modification."""
+
     model_config = ConfigDict(extra="forbid")
 
-    modified: str
-    message: str
+    modified: str = Field(description="The modified prompt content after applying the instruction")
+    message: str = Field(description="Brief explanation of what changes were made")