> ## Documentation index
> Fetch the complete documentation index at: https://arize.com/llms.txt
> Ask it with GET https://arize.com/ask?q=<keywords or question>. Short keywords rank best.
> Use these to discover all available pages before exploring further.

# VertexAI Evals

> Configure and run VertexAI for evals

### Using Google Vertex AI with Phoenix Evals

<Info>
Requires `google-genai`

```sh
pip install "arize-phoenix-evals>=3" google-genai
```
</Info>

Create an `LLM` instance with the Google provider:

```python
from phoenix.evals import LLM

llm = LLM(provider="google", model="gemini-2.0-flash")
```

The `LLM` wrapper reads your API key from the `GOOGLE_API_KEY` environment variable, or you can authenticate via Application Default Credentials for Vertex AI.

### Using with evaluators

```python
from phoenix.evals import LLM, evaluate_dataframe
from phoenix.evals.metrics import FaithfulnessEvaluator

llm = LLM(provider="google", model="gemini-2.0-flash")
evaluator = FaithfulnessEvaluator(llm=llm)

results_df = evaluate_dataframe(dataframe=df, evaluators=[evaluator])
```