Quickstart: Gen AI evaluation service workflow

This page shows you how to perform a model-based evaluation with Gen AI evaluation service using the Vertex AI SDK for Python.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Make sure that billing is enabled for your Google Cloud project.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Make sure that billing is enabled for your Google Cloud project.
Install the Vertex AI SDK for Python with Gen AI evaluation service dependency:
```
!pip install google-cloud-aiplatform[evaluation]
```
Set up your credentials. If you are running this quickstart in Colaboratory, run the following:
```
from google.colab import auth
auth.authenticate_user()
```
For other environments, refer to Authenticate to Vertex AI.

Import libraries

Import your libraries and set up your project and location.


import pandas as pd

import vertexai
from vertexai.evaluation import EvalTask, PointwiseMetric, PointwiseMetricPromptTemplate

PROJECT_ID = "PROJECT_ID"
LOCATION = "LOCATION"
EXPERIMENT_NAME = "EXPERIMENT_NAME"

vertexai.init(
    project=PROJECT_ID,
    location=LOCATION,
)

Note that EXPERIMENT_NAME can only contain lowercase alphanumeric characters and hyphens, up to a maximum of 127 characters.

Set up evaluation metrics based on your criteria

The following metric definition evaluates the text quality generated from a large language model based on two criteria: Fluency and Entertaining. The code defines a metric called custom_text_quality using those two criteria:

custom_text_quality = PointwiseMetric(
    metric="custom_text_quality",
    metric_prompt_template=PointwiseMetricPromptTemplate(
        criteria={
            "fluency": (
                "Sentences flow smoothly and are easy to read, avoiding awkward"
                " phrasing or run-on sentences. Ideas and sentences connect"
                " logically, using transitions effectively where needed."
            ),
            "entertaining": (
                "Short, amusing text that incorporates emojis, exclamations and"
                " questions to convey quick and spontaneous communication and"
                " diversion."
            ),
        },
        rating_rubric={
            "1": "The response performs well on both criteria.",
            "0": "The response is somewhat aligned with both criteria",
            "-1": "The response falls short on both criteria",
        },
    ),
)

Prepare your dataset

Add the following code to prepare your dataset:

responses = [
    # An example of good custom_text_quality
    "Life is a rollercoaster, full of ups and downs, but it's the thrill that keeps us coming back for more!",
    # An example of medium custom_text_quality
    "The weather is nice today, not too hot, not too cold.",
    # An example of poor custom_text_quality
    "The weather is, you know, whatever.",
]

eval_dataset = pd.DataFrame({
    "response" : responses,
})

Run evaluation with your dataset

Run the evaluation:

eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[custom_text_quality],
    experiment=EXPERIMENT_NAME
)

pointwise_result = eval_task.evaluate()

View the evaluation results for each response in the metrics_table Pandas DataFrame:

pointwise_result.metrics_table

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

Delete the ExperimentRun created by the evaluation:

vertexai.ExperimentRun(
    run_name=pointwise_result.metadata["experiment_run"],
    experiment=pointwise_result.metadata["experiment"],
).delete()