Bewertung der Qualität der paarweisen Zusammenfassung

In diesem Beispiel wird gezeigt, wie Sie zwei Modelle für generative KI anhand von paarweisen Vergleichen auf ihre Qualität bei der Zusammenfassung hin bewerten. Bei der Bewertung wird ein Messwert verwendet, mit dem beurteilt wird, wie gut die einzelnen Modelle einen bestimmten Text zusammenfassen.

Weitere Informationen

Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:

Codebeispiel

Go

Bevor Sie dieses Beispiel anwenden, folgen Sie den Go-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Go API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten. 

import (
	context_pkg "context"
	"fmt"
	"io"

	aiplatform "cloud.google.com/go/aiplatform/apiv1beta1"
	aiplatformpb "cloud.google.com/go/aiplatform/apiv1beta1/aiplatformpb"
	"google.golang.org/api/option"
)

// pairwiseEvaluation lets the judge model to compare the responses of two models and pick the better one
func pairwiseEvaluation(w io.Writer, projectID, location string) error {
	// location = "us-central1"
	ctx := context_pkg.Background()
	apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	client, err := aiplatform.NewEvaluationClient(ctx, option.WithEndpoint(apiEndpoint))

	if err != nil {
		return fmt.Errorf("unable to create aiplatform client: %w", err)
	}
	defer client.Close()

	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.
`
	instruction := "Summarize the text such that a five-year-old can understand."
	baselineResponse := `
The city wants to make it easier for people to get around without using cars.
They're going to make the buses and trains better and faster, so people will want to
use them more. This will help the air be cleaner and make the city a better place to live.
`
	candidateResponse := `
The city is making big changes to how people get around. They want to make the buses and
trains work better and be easier for everyone to use. This will also help the environment
by getting people to use less gas. The city thinks these changes will make it easier for
everyone to get where they need to go.
`

	req := aiplatformpb.EvaluateInstancesRequest{
		Location: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		MetricInputs: &aiplatformpb.EvaluateInstancesRequest_PairwiseSummarizationQualityInput{
			PairwiseSummarizationQualityInput: &aiplatformpb.PairwiseSummarizationQualityInput{
				MetricSpec: &aiplatformpb.PairwiseSummarizationQualitySpec{},
				Instance: &aiplatformpb.PairwiseSummarizationQualityInstance{
					Context:            &context,
					Instruction:        &instruction,
					Prediction:         &candidateResponse,
					BaselinePrediction: &baselineResponse,
				},
			},
		},
	}

	resp, err := client.EvaluateInstances(ctx, &req)
	if err != nil {
		return fmt.Errorf("evaluateInstances failed: %v", err)
	}

	results := resp.GetPairwiseSummarizationQualityResult()
	fmt.Fprintf(w, "choice: %s\n", results.GetPairwiseChoice())
	fmt.Fprintf(w, "confidence: %.2f\n", results.GetConfidence())
	fmt.Fprintf(w, "explanation:\n%s\n", results.GetExplanation())
	// Example response:
	// choice: BASELINE
	// confidence: 0.50
	// explanation:
	// BASELINE response is easier to understand. For example, the phrase "..." is easier to understand than "...". Thus, BASELINE response is ...

	return nil
}

Python

Bevor Sie dieses Beispiel anwenden, folgen Sie den Python-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Python API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten. 

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-2.0-flash-lite-001")

# Candidate model for pairwise comparison
candidate_model = GenerativeModel(
    "gemini-2.0-flash-001", 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 ...

Weitere Informationen

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