Vertex AI v1beta1 API - Class EvaluationParserConfig.Types.CustomCodeParserConfig (1.0.0-beta77) 

public sealed class EvaluationParserConfig.Types.CustomCodeParserConfig : IMessage<EvaluationParserConfig.Types.CustomCodeParserConfig>, IEquatable<EvaluationParserConfig.Types.CustomCodeParserConfig>, IDeepCloneable<EvaluationParserConfig.Types.CustomCodeParserConfig>, IBufferMessage, IMessage

Reference documentation and code samples for the Vertex AI v1beta1 API class EvaluationParserConfig.Types.CustomCodeParserConfig.

Configuration for parsing the LLM response using custom code.

Inheritance

object > EvaluationParserConfig.Types.CustomCodeParserConfig

Namespace

Google.Cloud.AIPlatform.V1Beta1

Assembly

Google.Cloud.AIPlatform.V1Beta1.dll

Constructors

CustomCodeParserConfig()

public CustomCodeParserConfig()

CustomCodeParserConfig(CustomCodeParserConfig)

public CustomCodeParserConfig(EvaluationParserConfig.Types.CustomCodeParserConfig other)
Parameter
Name Description
other EvaluationParserConfigTypesCustomCodeParserConfig

Properties

HasParsingFunction

public bool HasParsingFunction { get; }

Gets whether the "parsing_function" field is set

Property Value
Type Description
bool

ParsingFunction

public string ParsingFunction { get; set; }

Required. Python function for parsing results. The function should be defined within this string.

The function takes a list of strings (LLM responses) and should return either a list of dictionaries (for rubrics) or a single dictionary (for a metric result).

Example function signature: def parse(responses: list[str]) -> list[dict[str, Any]] | dict[str, Any]:

When parsing rubrics, return a list of dictionaries, where each dictionary represents a Rubric. Example for rubrics: [ { "content": {"property": {"description": "The response is factual."}}, "type": "FACTUALITY", "importance": "HIGH" }, { "content": {"property": {"description": "The response is fluent."}}, "type": "FLUENCY", "importance": "MEDIUM" } ]

When parsing critique results, return a dictionary representing a MetricResult. Example for a metric result: { "score": 0.8, "explanation": "The model followed most instructions.", "rubric_verdicts": [...] }

... code for result extraction and aggregation

Property Value
Type Description
string