docs: Add result_parsing_function field to LLMMetric SDK types

agentplatform/_genai/types/common.py

@@ -1874,6 +1874,11 @@ class Metric(_common.BaseModel):
         description="""The resource name of the metric definition. Example: projects/{project}/locations/{location}/evaluationMetrics/{evaluation_metric_id}""",
     )
 
+    result_parsing_function: Optional[str] = Field(
+        default=None,
+        description="""Optional. A Python function string used to parse the raw output of the LLM judge model. The function must be named `parse_results` and accept a list of model response strings. It should return a dictionary with `score` (float) and `explanation` (str) keys. When provided, this is sent to the evaluation service as `result_parser_config.custom_code_parser_config.parsing_function`.""",
+    )
+
     # Allow extra fields to support metric-specific config fields.
     model_config = ConfigDict(extra="allow")
 
@@ -1959,6 +1964,11 @@ class LLMMetric(Metric):
         description="""Optional. The name of the column in the EvaluationDataset containing the list of rubrics to use for this metric.""",
     )
 
+    result_parsing_function: Optional[str] = Field(
+        default=None,
+        description="""Optional. A Python function string used to parse the raw output of the LLM judge model. The function must be named `parse_results` and accept a list of model response strings. It should return a dictionary with `score` (float) and `explanation` (str) keys. When provided, this is sent to the evaluation service as `result_parser_config.custom_code_parser_config.parsing_function`.""",
+    )
+
     @field_validator("prompt_template", mode="before")
     @classmethod
     def validate_prompt_template(cls, value: Union[str, "MetricPromptBuilder"]) -> str:
@@ -2100,6 +2110,9 @@ class MetricDict(TypedDict, total=False):
     metric_resource_name: Optional[str]
     """The resource name of the metric definition. Example: projects/{project}/locations/{location}/evaluationMetrics/{evaluation_metric_id}"""
 
+    result_parsing_function: Optional[str]
+    """Optional. A Python function string used to parse the raw output of the LLM judge model. The function must be named `parse_results` and accept a list of model response strings. It should return a dictionary with `score` (float) and `explanation` (str) keys. When provided, this is sent to the evaluation service as `result_parser_config.custom_code_parser_config.parsing_function`."""
+
 
 MetricOrDict = Union[Metric, MetricDict]
 

vertexai/_genai/types/common.py

@@ -1866,6 +1866,11 @@ class LLMMetric(Metric):
         description="""Optional. The name of the column in the EvaluationDataset containing the list of rubrics to use for this metric.""",
     )
 
+    result_parsing_function: Optional[str] = Field(
+        default=None,
+        description="""Optional. A Python function string used to parse the raw output of the LLM judge model. The function must be named `parse_results` and accept a list of model response strings. It should return a dictionary with `score` (float) and `explanation` (str) keys.""",
+    )
+
     @field_validator("prompt_template", mode="before")
     @classmethod
     def validate_prompt_template(cls, value: Union[str, "MetricPromptBuilder"]) -> str:
@@ -2007,6 +2012,9 @@ class MetricDict(TypedDict, total=False):
     metric_resource_name: Optional[str]
     """The resource name of the metric definition. Example: projects/{project}/locations/{location}/evaluationMetrics/{evaluation_metric_id}"""
 
+    result_parsing_function: Optional[str]
+    """Optional. A Python function string used to parse the raw output of the LLM judge model. The function must be named `parse_results` and accept a list of model response strings. It should return a dictionary with `score` (float) and `explanation` (str) keys."""
+
 
 MetricOrDict = Union[Metric, MetricDict]