Tutorial: Melakukan evaluasi menggunakan Klien GenAI di Vertex AI SDK

Halaman ini menunjukkan cara mengevaluasi model dan aplikasi AI generatif Anda di berbagai kasus penggunaan menggunakan Klien GenAI di Vertex AI SDK.

Sebelum memulai

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.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
- Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
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

Verify 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.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
- Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
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

Verify that billing is enabled for your Google Cloud project.

Instal Vertex AI SDK untuk Python:

!pip install google-cloud-aiplatform[evaluation]

Siapkan kredensial Anda. Jika Anda menjalankan tutorial ini di Colaboratory, jalankan perintah berikut:
```
from google.colab import auth
auth.authenticate_user()
```
Untuk lingkungan lain, lihat Mengautentikasi ke Vertex AI.

Membuat respons

Buat respons model untuk set data Anda menggunakan run_inference():

Siapkan set data sebagai DataFrame Pandas:

import pandas as pd

eval_df = pd.DataFrame({
  "prompt": [
      "Explain software 'technical debt' using a concise analogy of planting a garden.",
      "Write a Python function to find the nth Fibonacci number using recursion with memoization, but without using any imports.",
      "Write a four-line poem about a lonely robot, where every line must be a question and the word 'and' cannot be used.",
      "A drawer has 10 red socks and 10 blue socks. In complete darkness, what is the minimum number of socks you must pull out to guarantee you have a matching pair?",
      "An AI discovers a cure for a major disease, but the cure is based on private data it analyzed without consent. Should the cure be released? Justify your answer."
  ]
})

Membuat respons model menggunakan run_inference():

eval_dataset = client.evals.run_inference(
  model="gemini-2.5-flash",
  src=eval_df,
)

Visualisasikan hasil inferensi Anda dengan memanggil .show() pada objek EvaluationDataset untuk memeriksa output model bersama dengan perintah dan referensi asli Anda:
```
eval_dataset.show()
```

Gambar berikut menampilkan set data evaluasi dengan perintah dan respons yang dihasilkan yang sesuai:

Tabel yang menampilkan set data evaluasi dengan kolom untuk perintah dan respons.

Menjalankan evaluasi

Jalankan evaluate() untuk mengevaluasi respons model:

Evaluasi respons model menggunakan metrik berbasis rubrik adaptif GENERAL_QUALITY default:
```
eval_result = client.evals.evaluate(dataset=eval_dataset)
```
Visualisasikan hasil evaluasi Anda dengan memanggil .show() pada objek EvaluationResult untuk menampilkan metrik ringkasan dan hasil mendetail:
```
eval_result.show()
```

Gambar berikut menampilkan laporan evaluasi, yang menunjukkan metrik ringkasan dan hasil mendetail untuk setiap pasangan perintah-respons.

Laporan evaluasi yang menampilkan metrik ringkasan beserta hasil mendetail untuk setiap pasangan perintah-respons.

Pembersihan

Tidak ada resource Vertex AI yang dibuat selama tutorial ini.

Langkah berikutnya

Tentukan metrik evaluasi Anda.