Gen AI evaluation service API

The Gen AI evaluation service lets you evaluate your large language models (LLMs), both pointwise and pairwise, across several metrics, with your own criteria. You can provide inference-time inputs, LLM responses and additional parameters, and the Gen AI evaluation service returns metrics specific to the evaluation task.

Metrics include model-based metrics, such as PointwiseMetric and PairwiseMetric, and in-memory computed metrics, such as rouge, bleu, and tool function-call metrics. PointwiseMetric and PairwiseMetric are generic model-based metrics that you can customize with your own criteria. Because the service takes the prediction results directly from models as input, the evaluation service can perform both inference and subsequent evaluation on all models supported by Vertex AI.

For more information on evaluating a model, see Gen AI evaluation service overview.

Limitations

The following are limitations of the evaluation service:

• Model-based metrics consume gemini-1.5-pro quota. The Gen AI evaluation service leverages gemini-1.5-pro as the underlying judge model to compute model-based metrics.
• The evaluation service may have a propagation delay in your first call.

Example syntax

Syntax to send an evaluation call.

curl

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \

https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}:evaluateInstances \
-d '{
  "contents": [{
    ...
  }],
  "tools": [{
    "function_declarations": [
      {
        ...
      }
    ]
  }]
}'

Python

import json

from google import auth
from google.api_core import exceptions
from google.auth.transport import requests as google_auth_requests

creds, _ = auth.default(
    scopes=['https://www.googleapis.com/auth/cloud-platform'])

data = {
  ...
}

uri = f'https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}:evaluateInstances'
result = google_auth_requests.AuthorizedSession(creds).post(uri, json=data)

print(json.dumps(result.json(), indent=2))

Parameter list

Parameters
exact_match_input	Optional: ExactMatchInput Input to assess if the prediction matches the reference exactly.
bleu_input	Optional: BleuInput Input to compute BLEU score by comparing the prediction against the reference.
rouge_input	Optional: RougeInput Input to compute rouge scores by comparing the prediction against the reference. Different rouge scores are supported by rouge_type.
fluency_input	Optional: FluencyInput Input to assess a single response's language mastery.
coherence_input	Optional: CoherenceInput Input to assess a single response's ability to provide a coherent, easy-to-follow reply.
safety_input	Optional: SafetyInput Input to assess a single response's level of safety.
groundedness_input	Optional: GroundednessInput Input to assess a single response's ability to provide or reference information included only in the input text.
fulfillment_input	Optional: FulfillmentInput Input to assess a single response's ability to completely fulfill instructions.
summarization_quality_input	Optional: SummarizationQualityInput Input to assess a single response's overall ability to summarize text.
pairwise_summarization_quality_input	Optional: PairwiseSummarizationQualityInput Input to compare two responses' overall summarization quality.
summarization_helpfulness_input	Optional: SummarizationHelpfulnessInput Input to assess a single response's ability to provide a summarization, which contains the details necessary to substitute the original text.
summarization_verbosity_input	Optional: SummarizationVerbosityInput Input to assess a single response's ability to provide a succinct summarization.
question_answering_quality_input	Optional: QuestionAnsweringQualityInput Input to assess a single response's overall ability to answer questions, given a body of text to reference.
pairwise_question_answering_quality_input	Optional: PairwiseQuestionAnsweringQualityInput Input to compare two responses' overall ability to answer questions, given a body of text to reference.
question_answering_relevance_input	Optional: QuestionAnsweringRelevanceInput Input to assess a single response's ability to respond with relevant information when asked a question.
question_answering_helpfulness_input	Optional: QuestionAnsweringHelpfulnessInput Input to assess a single response's ability to provide key details when answering a question.
question_answering_correctness_input	Optional: QuestionAnsweringCorrectnessInput Input to assess a single response's ability to correctly answer a question.
pointwise_metric_input	Optional: PointwiseMetricInput Input for a generic pointwise evaluation.
pairwise_metric_input	Optional: PairwiseMetricInput Input for a generic pairwise evaluation.
tool_call_valid_input	Optional: ToolCallValidInput Input to assess a single response's ability to predict a valid tool call.
tool_name_match_input	Optional: ToolNameMatchInput Input to assess a single response's ability to predict a tool call with the right tool name.
tool_parameter_key_match_input	Optional: ToolParameterKeyMatchInput Input to assess a single response's ability to predict a tool call with correct parameter names.
tool_parameter_kv_match_input	Optional: ToolParameterKvMatchInput Input to assess a single response's ability to predict a tool call with correct parameter names and values

ExactMatchInput

{
  "exact_match_input": {
    "metric_spec": {},
    "instances": [
      {
        "prediction": string,
        "reference": string
      }
    ]
  }
}

Parameters
metric_spec	Optional: ExactMatchSpec. Metric spec, defining the metric's behavior.
instances	Optional: ExactMatchInstance[] Evaluation input, consisting of LLM response and reference.
instances.prediction	Optional: string LLM response.
instances.reference	Optional: string Golden LLM response for reference.

ExactMatchResults

{
  "exact_match_results": {
    "exact_match_metric_values": [
      {
        "score": float
      }
    ]
  }
}

Output
exact_match_metric_values	ExactMatchMetricValue[] Evaluation results per instance input.
exact_match_metric_values.score	float One of the following: 0: Instance was not an exact match 1: Exact match

BleuInput

{
  "bleu_input": {
    "metric_spec": {
      "use_effective_order": bool
    },
    "instances": [
      {
        "prediction": string,
        "reference": string
      }
    ]
  }
}

Parameters
metric_spec	Optional: BleuSpec Metric spec, defining the metric's behavior.
metric_spec.use_effective_order	Optional: bool Whether to take into account n-gram orders without any match.
instances	Optional: BleuInstance[] Evaluation input, consisting of LLM response and reference.
instances.prediction	Optional: string LLM response.
instances.reference	Optional: string Golden LLM response for reference.

BleuResults

{
  "bleu_results": {
    "bleu_metric_values": [
      {
        "score": float
      }
    ]
  }
}

Output
bleu_metric_values	BleuMetricValue[] Evaluation results per instance input.
bleu_metric_values.score	float: [0, 1], where higher scores mean the prediction is more like the reference.

RougeInput

{
  "rouge_input": {
    "metric_spec": {
      "rouge_type": string,
      "use_stemmer": bool,
      "split_summaries": bool
    },
    "instances": [
      {
        "prediction": string,
        "reference": string
      }
    ]
  }
}

Parameters
metric_spec	Optional: RougeSpec Metric spec, defining the metric's behavior.
metric_spec.rouge_type	Optional: string Acceptable values: rougen[1-9]: compute rouge scores based on the overlap of n-grams between the prediction and the reference. rougeL: compute rouge scores based on the Longest Common Subsequence (LCS) between the prediction and the reference. rougeLsum: first splits the prediction and the reference into sentences and then computes the LCS for each tuple. The final rougeLsum score is the average of these individual LCS scores.
metric_spec.use_stemmer	Optional: bool Whether Porter stemmer should be used to strip word suffixes to improve matching.
metric_spec.split_summaries	Optional: bool Whether to add newlines between sentences for rougeLsum.
instances	Optional: RougeInstance[] Evaluation input, consisting of LLM response and reference.
instances.prediction	Optional: string LLM response.
instances.reference	Optional: string Golden LLM response for reference.

RougeResults

{
  "rouge_results": {
    "rouge_metric_values": [
      {
        "score": float
      }
    ]
  }
}

Output
rouge_metric_values	RougeValue[] Evaluation results per instance input.
rouge_metric_values.score	float: [0, 1], where higher scores mean the prediction is more like the reference.

FluencyInput

{
  "fluency_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string
    }
  }
}

Parameters
metric_spec	Optional: FluencySpec Metric spec, defining the metric's behavior.
instance	Optional: FluencyInstance Evaluation input, consisting of LLM response.
instance.prediction	Optional: string LLM response.

FluencyResult

{
  "fluency_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: Inarticulate 2: Somewhat Inarticulate 3: Neutral 4: Somewhat fluent 5: Fluent
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

CoherenceInput

{
  "coherence_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string
    }
  }
}

Parameters
metric_spec	Optional: CoherenceSpec Metric spec, defining the metric's behavior.
instance	Optional: CoherenceInstance Evaluation input, consisting of LLM response.
instance.prediction	Optional: string LLM response.

CoherenceResult

{
  "coherence_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: Incoherent 2: Somewhat incoherent 3: Neutral 4: Somewhat coherent 5: Coherent
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

SafetyInput

{
  "safety_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string
    }
  }
}

Parameters
metric_spec	Optional: SafetySpec Metric spec, defining the metric's behavior.
instance	Optional: SafetyInstance Evaluation input, consisting of LLM response.
instance.prediction	Optional: string LLM response.

SafetyResult

{
  "safety_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 0: Unsafe 1: Safe
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

GroundednessInput

{
  "groundedness_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "context": string
    }
  }
}

Parameter	Description
metric_spec	Optional: GroundednessSpec Metric spec, defining the metric's behavior.
instance	Optional: GroundednessInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

GroundednessResult

{
  "groundedness_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 0: Ungrounded 1: Grounded
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

FulfillmentInput

{
  "fulfillment_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "instruction": string
    }
  }
}

Parameters
metric_spec	Optional: FulfillmentSpec Metric spec, defining the metric's behavior.
instance	Optional: FulfillmentInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.instruction	Optional: string Instruction used at inference time.

FulfillmentResult

{
  "fulfillment_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: No fulfillment 2: Poor fulfillment 3: Some fulfillment 4: Good fulfillment 5: Complete fulfillment
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

SummarizationQualityInput

{
  "summarization_quality_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "instruction": string,
      "context": string,
    }
  }
}

Parameters
metric_spec	Optional: SummarizationQualitySpec Metric spec, defining the metric's behavior.
instance	Optional: SummarizationQualityInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

SummarizationQualityResult

{
  "summarization_quality_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: Very bad 2: Bad 3: Ok 4: Good 5: Very good
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

PairwiseSummarizationQualityInput

{
  "pairwise_summarization_quality_input": {
    "metric_spec": {},
    "instance": {
      "baseline_prediction": string,
      "prediction": string,
      "instruction": string,
      "context": string,
    }
  }
}

Parameters
metric_spec	Optional: PairwiseSummarizationQualitySpec Metric spec, defining the metric's behavior.
instance	Optional: PairwiseSummarizationQualityInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.baseline_prediction	Optional: string Baseline model LLM response.
instance.prediction	Optional: string Candidate model LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

PairwiseSummarizationQualityResult

{
  "pairwise_summarization_quality_result": {
    "pairwise_choice": PairwiseChoice,
    "explanation": string,
    "confidence": float
  }
}

Output
pairwise_choice	PairwiseChoice: Enum with possible values as follows: BASELINE: Baseline prediction is better CANDIDATE: Candidate prediction is better TIE: Tie between Baseline and Candidate predictions.
explanation	string: Justification for pairwise_choice assignment.
confidence	float: [0, 1] Confidence score of our result.

SummarizationHelpfulnessInput

{
  "summarization_helpfulness_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "instruction": string,
      "context": string,
    }
  }
}

Parameters
metric_spec	Optional: SummarizationHelpfulnessSpec Metric spec, defining the metric's behavior.
instance	Optional: SummarizationHelpfulnessInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

SummarizationHelpfulnessResult

{
  "summarization_helpfulness_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: Unhelpful 2: Somewhat unhelpful 3: Neutral 4: Somewhat helpful 5: Helpful
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

SummarizationVerbosityInput

{
  "summarization_verbosity_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "instruction": string,
      "context": string,
    }
  }
}

Parameters
metric_spec	Optional: SummarizationVerbositySpec Metric spec, defining the metric's behavior.
instance	Optional: SummarizationVerbosityInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

SummarizationVerbosityResult

{
  "summarization_verbosity_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float. One of the following: -2: Terse -1: Somewhat terse 0: Optimal 1: Somewhat verbose 2: Verbose
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

QuestionAnsweringQualityInput

{
  "question_answering_quality_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "instruction": string,
      "context": string,
    }
  }
}

Parameters
metric_spec	Optional: QuestionAnsweringQualitySpec Metric spec, defining the metric's behavior.
instance	Optional: QuestionAnsweringQualityInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

QuestionAnsweringQualityResult

{
  "question_answering_quality_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: Very bad 2: Bad 3: Ok 4: Good 5: Very good
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

PairwiseQuestionAnsweringQualityInput

{
  "question_answering_quality_input": {
    "metric_spec": {},
    "instance": {
      "baseline_prediction": string,
      "prediction": string,
      "instruction": string,
      "context": string
    }
  }
}

Parameters
metric_spec	Optional: QuestionAnsweringQualitySpec Metric spec, defining the metric's behavior.
instance	Optional: QuestionAnsweringQualityInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.baseline_prediction	Optional: string Baseline model LLM response.
instance.prediction	Optional: string Candidate model LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

PairwiseQuestionAnsweringQualityResult

{
  "pairwise_question_answering_quality_result": {
    "pairwise_choice": PairwiseChoice,
    "explanation": string,
    "confidence": float
  }
}

Output
pairwise_choice	PairwiseChoice: Enum with possible values as follows: BASELINE: Baseline prediction is better CANDIDATE: Candidate prediction is better TIE: Tie between Baseline and Candidate predictions.
explanation	string: Justification for pairwise_choice assignment.
confidence	float: [0, 1] Confidence score of our result.

QuestionAnsweringRelevanceInput

{
  "question_answering_quality_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "instruction": string,
      "context": string
    }
  }
}

Parameters
metric_spec	Optional: QuestionAnsweringRelevanceSpec Metric spec, defining the metric's behavior.
instance	Optional: QuestionAnsweringRelevanceInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

QuestionAnsweringRelevancyResult

{
  "question_answering_relevancy_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: Irrelevant 2: Somewhat irrelevant 3: Neutral 4: Somewhat relevant 5: Relevant
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

QuestionAnsweringHelpfulnessInput

{
  "question_answering_helpfulness_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "instruction": string,
      "context": string
    }
  }
}

Parameters
metric_spec	Optional: QuestionAnsweringHelpfulnessSpec Metric spec, defining the metric's behavior.
instance	Optional: QuestionAnsweringHelpfulnessInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

QuestionAnsweringHelpfulnessResult

{
  "question_answering_helpfulness_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 1: Unhelpful 2: Somewhat unhelpful 3: Neutral 4: Somewhat helpful 5: Helpful
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

QuestionAnsweringCorrectnessInput

{
  "question_answering_correctness_input": {
    "metric_spec": {
      "use_reference": bool
    },
    "instance": {
      "prediction": string,
      "reference": string,
      "instruction": string,
      "context": string
    }
  }
}

Parameters
metric_spec	Optional: QuestionAnsweringCorrectnessSpec Metric spec, defining the metric's behavior.
metric_spec.use_reference	Optional: bool If reference is used or not in the evaluation.
instance	Optional: QuestionAnsweringCorrectnessInstance Evaluation input, consisting of inference inputs and corresponding response.
instance.prediction	Optional: string LLM response.
instance.reference	Optional: string Golden LLM response for reference.
instance.instruction	Optional: string Instruction used at inference time.
instance.context	Optional: string Inference-time text containing all information, which can be used in the LLM response.

QuestionAnsweringCorrectnessResult

{
  "question_answering_correctness_result": {
    "score": float,
    "explanation": string,
    "confidence": float
  }
}

Output
score	float: One of the following: 0: Incorrect 1: Correct
explanation	string: Justification for score assignment.
confidence	float: [0, 1] Confidence score of our result.

PointwiseMetricInput

{
  "pointwise_metric_input": {
    "metric_spec": {
      "metric_prompt_template": string
    },
    "instance": {
      "json_instance": string,
    }
  }
}

Parameters
metric_spec	Required: PointwiseMetricSpec Metric spec, defining the metric's behavior.
metric_spec.metric_prompt_template	Required: string A prompt template defining the metric. It is rendered by the key-value pairs in instance.json_instance
instance	Required: PointwiseMetricInstance Evaluation input, consisting of json_instance.
instance.json_instance	Optional: string The key-value pairs in Json format. For example, {"key_1": "value_1", "key_2": "value_2"}. It is used to render metric_spec.metric_prompt_template.

PointwiseMetricResult

{
  "pointwise_metric_result": {
    "score": float,
    "explanation": string,
  }
}

Output
score	float: A score for pointwise metric evaluation result.
explanation	string: Justification for score assignment.

PairwiseMetricInput

{
  "pairwise_metric_input": {
    "metric_spec": {
      "metric_prompt_template": string
    },
    "instance": {
      "json_instance": string,
    }
  }
}

Parameters
metric_spec	Required: PairwiseMetricSpec Metric spec, defining the metric's behavior.
metric_spec.metric_prompt_template	Required: string A prompt template defining the metric. It is rendered by the key-value pairs in instance.json_instance
instance	Required: PairwiseMetricInstance Evaluation input, consisting of json_instance.
instance.json_instance	Optional: string The key-value pairs in JSON format. For example, {"key_1": "value_1", "key_2": "value_2"}. It is used to render metric_spec.metric_prompt_template.

PairwiseMetricResult

{
  "pairwise_metric_result": {
    "score": float,
    "explanation": string,
  }
}

Output
score	float: A score for pairwise metric evaluation result.
explanation	string: Justification for score assignment.

ToolCallValidInput

{
  "tool_call_valid_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "reference": string
    }
  }
}

Parameters
metric_spec	Optional: ToolCallValidSpec Metric spec, defining the metric's behavior.
instance	Optional: ToolCallValidInstance Evaluation input, consisting of LLM response and reference.
instance.prediction	Optional: string Candidate model LLM response, which is a JSON serialized string that contains content and tool_calls keys. The content value is the text output from the model. The tool_call value is a JSON serialized string of a list of tool calls. An example is: { "content": "", "tool_calls": [ { "name": "book_tickets", "arguments": { "movie": "Mission Impossible Dead Reckoning Part 1", "theater": "Regal Edwards 14", "location": "Mountain View CA", "showtime": "7:30", "date": "2024-03-30", "num_tix": "2" } } ] }
instance.reference	Optional: string Golden model output in the same format as prediction.

ToolCallValidResults

{
  "tool_call_valid_results": {
    "tool_call_valid_metric_values": [
      {
        "score": float
      }
    ]
  }
}

Output
tool_call_valid_metric_values	repeated ToolCallValidMetricValue: Evaluation results per instance input.
tool_call_valid_metric_values.score	float: One of the following: 0: Invalid tool call 1: Valid tool call

ToolNameMatchInput

{
  "tool_name_match_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "reference": string
    }
  }
}

Parameters
metric_spec	Optional: ToolNameMatchSpec Metric spec, defining the metric's behavior.
instance	Optional: ToolNameMatchInstance Evaluation input, consisting of LLM response and reference.
instance.prediction	Optional: string Candidate model LLM response, which is a JSON serialized string that contains content and tool_calls keys. The content value is the text output from the model. The tool_call value is a JSON serialized string of a list of tool calls.
instance.reference	Optional: string Golden model output in the same format as prediction.

ToolNameMatchResults

{
  "tool_name_match_results": {
    "tool_name_match_metric_values": [
      {
        "score": float
      }
    ]
  }
}

Output
tool_name_match_metric_values	repeated ToolNameMatchMetricValue: Evaluation results per instance input.
tool_name_match_metric_values.score	float: One of the following: 0: Tool call name doesn't match the reference. 1: Tool call name matches the reference.

ToolParameterKeyMatchInput

{
  "tool_parameter_key_match_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "reference": string
    }
  }
}

Parameters
metric_spec	Optional: ToolParameterKeyMatchSpec Metric spec, defining the metric's behavior.
instance	Optional: ToolParameterKeyMatchInstance Evaluation input, consisting of LLM response and reference.
instance.prediction	Optional: string Candidate model LLM response, which is a JSON serialized string that contains content and tool_calls keys. The content value is the text output from the model. The tool_call value is a JSON serialized string of a list of tool calls.
instance.reference	Optional: string Golden model output in the same format as prediction.

ToolParameterKeyMatchResults

{
  "tool_parameter_key_match_results": {
    "tool_parameter_key_match_metric_values": [
      {
        "score": float
      }
    ]
  }
}

Output
tool_parameter_key_match_metric_values	repeated ToolParameterKeyMatchMetricValue: Evaluation results per instance input.
tool_parameter_key_match_metric_values.score	float: [0, 1], where higher scores mean more parameters match the reference parameters' names.

ToolParameterKVMatchInput

{
  "tool_parameter_kv_match_input": {
    "metric_spec": {},
    "instance": {
      "prediction": string,
      "reference": string
    }
  }
}

Parameters
metric_spec	Optional: ToolParameterKVMatchSpec Metric spec, defining the metric's behavior.
instance	Optional: ToolParameterKVMatchInstance Evaluation input, consisting of LLM response and reference.
instance.prediction	Optional: string Candidate model LLM response, which is a JSON serialized string that contains content and tool_calls keys. The content value is the text output from the model. The tool_call value is a JSON serialized string of a list of tool calls.
instance.reference	Optional: string Golden model output in the same format as prediction.

ToolParameterKVMatchResults

{
  "tool_parameter_kv_match_results": {
    "tool_parameter_kv_match_metric_values": [
      {
        "score": float
      }
    ]
  }
}

Output
tool_parameter_kv_match_metric_values	repeated ToolParameterKVMatchMetricValue: Evaluation results per instance input.
tool_parameter_kv_match_metric_values.score	float: [0, 1], where higher scores mean more parameters match the reference parameters' names and values.

Examples

Evaluate an output

The following example demonstrates how to call the Gen AI Evaluation API to evaluate the output of an LLM using a variety of evaluation metrics, including the following:

• summarization_quality
• groundedness
• fulfillment
• summarization_helpfulness

• summarization_verbosity

Python

import pandas as pd

import vertexai
from vertexai.preview.evaluation import EvalTask, MetricPromptTemplateExamples

# TODO(developer): Update and un-comment below line
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

eval_dataset = pd.DataFrame(
    {
        "instruction": [
            "Summarize the text in one sentence.",
            "Summarize the text such that a five-year-old can understand.",
        ],
        "context": [
            """As part of a comprehensive initiative to tackle urban congestion and foster
            sustainable urban living, a major city has revealed ambitious plans for an
            extensive overhaul of its public transportation system. The project aims not
            only to improve the efficiency and reliability of public transit but also to
            reduce the city\'s carbon footprint and promote eco-friendly commuting options.
            City officials anticipate that this strategic investment will enhance
            accessibility for residents and visitors alike, ushering in a new era of
            efficient, environmentally conscious urban transportation.""",
            """A team of archaeologists has unearthed ancient artifacts shedding light on a
            previously unknown civilization. The findings challenge existing historical
            narratives and provide valuable insights into human history.""",
        ],
        "response": [
            "A major city is revamping its public transportation system to fight congestion, reduce emissions, and make getting around greener and easier.",
            "Some people who dig for old things found some very special tools and objects that tell us about people who lived a long, long time ago! What they found is like a new puzzle piece that helps us understand how people used to live.",
        ],
    }
)

eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[
        MetricPromptTemplateExamples.Pointwise.SUMMARIZATION_QUALITY,
        MetricPromptTemplateExamples.Pointwise.GROUNDEDNESS,
        MetricPromptTemplateExamples.Pointwise.VERBOSITY,
        MetricPromptTemplateExamples.Pointwise.INSTRUCTION_FOLLOWING,
    ],
)

prompt_template = (
    "Instruction: {instruction}. Article: {context}. Summary: {response}"
)
result = eval_task.evaluate(prompt_template=prompt_template)

print("Summary Metrics:\n")

for key, value in result.summary_metrics.items():
    print(f"{key}: \t{value}")

print("\n\nMetrics Table:\n")
print(result.metrics_table)
# Example response:
# Summary Metrics:
# row_count:      2
# summarization_quality/mean:     3.5
# summarization_quality/std:      2.1213203435596424
# ...

Evaluate an output: pairwise summarization quality

The following example demonstrates how to call the Gen AI evaluation service API to evaluate the output of an LLM using a pairwise summarization quality comparison.

REST

Before using any of the request data, make the following replacements:

• PROJECT_ID: Your project ID.
• LOCATION: The region to process the request.
• PREDICTION: LLM response.
• BASELINE_PREDICTION: Baseline model LLM response.
• INSTRUCTION: The instruction used at inference time.
• CONTEXT: Inference-time text containing all relevant information, that can be used in the LLM response.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID-/locations/LOCATION:evaluateInstances \

Request JSON body:

{
  "pairwise_summarization_quality_input": {
    "metric_spec": {},
    "instance": {
      "prediction": "PREDICTION",
      "baseline_prediction": "BASELINE_PREDICTION",
      "instruction": "INSTRUCTION",
      "context": "CONTEXT",
    }
  }
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID-/locations/LOCATION:evaluateInstances \"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID-/locations/LOCATION:evaluateInstances \" | Select-Object -Expand Content

Python

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

import pandas as pd

import vertexai
from vertexai.generative_models import GenerativeModel
from vertexai.evaluation import (
    EvalTask,
    PairwiseMetric,
    MetricPromptTemplateExamples,
)

# TODO(developer): Update & uncomment line below
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

prompt = """
Summarize the text such that a five-year-old can understand.

# Text

As part of a comprehensive initiative to tackle urban congestion and foster
sustainable urban living, a major city has revealed ambitious plans for an
extensive overhaul of its public transportation system. The project aims not
only to improve the efficiency and reliability of public transit but also to
reduce the city\'s carbon footprint and promote eco-friendly commuting options.
City officials anticipate that this strategic investment will enhance
accessibility for residents and visitors alike, ushering in a new era of
efficient, environmentally conscious urban transportation.
"""

eval_dataset = pd.DataFrame({"prompt": [prompt]})

# Baseline model for pairwise comparison
baseline_model = GenerativeModel("gemini-1.5-pro-001")

# Candidate model for pairwise comparison
candidate_model = GenerativeModel(
    "gemini-1.5-pro-002", generation_config={"temperature": 0.4}
)

prompt_template = MetricPromptTemplateExamples.get_prompt_template(
    "pairwise_summarization_quality"
)

summarization_quality_metric = PairwiseMetric(
    metric="pairwise_summarization_quality",
    metric_prompt_template=prompt_template,
    baseline_model=baseline_model,
)

eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[summarization_quality_metric],
    experiment="pairwise-experiment",
)
result = eval_task.evaluate(model=candidate_model)

baseline_model_response = result.metrics_table["baseline_model_response"].iloc[0]
candidate_model_response = result.metrics_table["response"].iloc[0]
winner_model = result.metrics_table[
    "pairwise_summarization_quality/pairwise_choice"
].iloc[0]
explanation = result.metrics_table[
    "pairwise_summarization_quality/explanation"
].iloc[0]

print(f"Baseline's story:\n{baseline_model_response}")
print(f"Candidate's story:\n{candidate_model_response}")
print(f"Winner: {winner_model}")
print(f"Explanation: {explanation}")
# Example response:
# Baseline's story:
# A big city wants to make it easier for people to get around without using cars! They're going to make buses and trains ...
#
# Candidate's story:
# A big city wants to make it easier for people to get around without using cars! ... This will help keep the air clean ...
#
# Winner: CANDIDATE
# Explanation: Both responses adhere to the prompt's constraints, are grounded in the provided text, and ... However, Response B ...

Get ROUGE score

The following example calls the Gen AI evaluation service API to get the ROUGE score of a prediction, generated by a number of inputs. The ROUGE inputs use metric_spec, which determines the metric's behavior.

REST

Before using any of the request data, make the following replacements:

• PROJECT_ID: Your project ID.
• LOCATION: The region to process the request.
• PREDICTION: LLM response.
• REFERENCE: Golden LLM response for reference.
• ROUGE_TYPE: The calculation used to determine the rouge score. See metric_spec.rouge_type for acceptable values.
• USE_STEMMER: Determines whether the Porter stemmer is used to strip word suffixes to improve matching. For acceptable values, see metric_spec.use_stemmer.
• SPLIT_SUMMARIES: Determines if new lines are added between rougeLsum sentences. For acceptable values, see metric_spec.split_summaries .

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID-/locations/REGION:evaluateInstances \

Request JSON body:

{
  "rouge_input": {
    "instances": {
      "prediction": "PREDICTION",
      "reference": "REFERENCE.",
    },
    "metric_spec": {
      "rouge_type": "ROUGE_TYPE",
      "use_stemmer": USE_STEMMER,
      "split_summaries": SPLIT_SUMMARIES,
    }
  }
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID-/locations/REGION:evaluateInstances \"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID-/locations/REGION:evaluateInstances \" | Select-Object -Expand Content

Python

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

import pandas as pd

import vertexai
from vertexai.preview.evaluation import EvalTask

# TODO(developer): Update & uncomment line below
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

reference_summarization = """
The Great Barrier Reef, the world's largest coral reef system, is
located off the coast of Queensland, Australia. It's a vast
ecosystem spanning over 2,300 kilometers with thousands of reefs
and islands. While it harbors an incredible diversity of marine
life, including endangered species, it faces serious threats from
climate change, ocean acidification, and coral bleaching."""

# Compare pre-generated model responses against the reference (ground truth).
eval_dataset = pd.DataFrame(
    {
        "response": [
            """The Great Barrier Reef, the world's largest coral reef system located
        in Australia, is a vast and diverse ecosystem. However, it faces serious
        threats from climate change, ocean acidification, and coral bleaching,
        endangering its rich marine life.""",
            """The Great Barrier Reef, a vast coral reef system off the coast of
        Queensland, Australia, is the world's largest. It's a complex ecosystem
        supporting diverse marine life, including endangered species. However,
        climate change, ocean acidification, and coral bleaching are serious
        threats to its survival.""",
            """The Great Barrier Reef, the world's largest coral reef system off the
        coast of Australia, is a vast and diverse ecosystem with thousands of
        reefs and islands. It is home to a multitude of marine life, including
        endangered species, but faces serious threats from climate change, ocean
        acidification, and coral bleaching.""",
        ],
        "reference": [reference_summarization] * 3,
    }
)
eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[
        "rouge_1",
        "rouge_2",
        "rouge_l",
        "rouge_l_sum",
    ],
)
result = eval_task.evaluate()

print("Summary Metrics:\n")
for key, value in result.summary_metrics.items():
    print(f"{key}: \t{value}")

print("\n\nMetrics Table:\n")
print(result.metrics_table)
# Example response:
#
# Summary Metrics:
#
# row_count:      3
# rouge_1/mean:   0.7191161666666667
# rouge_1/std:    0.06765143922270488
# rouge_2/mean:   0.5441118566666666
# ...
# Metrics Table:
#
#                                        response                         reference  ...  rouge_l/score  rouge_l_sum/score
# 0  The Great Barrier Reef, the world's ...  \n    The Great Barrier Reef, the ...  ...       0.577320           0.639175
# 1  The Great Barrier Reef, a vast coral...  \n    The Great Barrier Reef, the ...  ...       0.552381           0.666667
# 2  The Great Barrier Reef, the world's ...  \n    The Great Barrier Reef, the ...  ...       0.774775           0.774775

What's next

• For detailed documentation, see Run an evaluation.