> ## 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.

# Gemini Evals

> Configure and run Gemini for evals

### Google Gemini via Vertex AI

Use the `LLM` wrapper with the `"vertex"` provider to run evals with Gemini models. Authenticate by setting your project and location, or by passing credentials directly.

```bash
pip install "arize-phoenix-evals>=3" google-cloud-aiplatform
```

```python
import os
from phoenix.evals import LLM

os.environ["CLOUD_ML_PROJECT_ID"] = "my-project-id"
os.environ["CLOUD_ML_REGION"] = "us-central1"

llm = LLM(provider="vertex", model="gemini-2.5-flash")
llm.generate_text(prompt="Hello there, how are you?")
# Output: "Hello! I'm doing well, thank you for asking!"
```

### Google Gemini via Google AI Studio

Alternatively, use the `"google"` provider with a Google AI Studio API key:

```bash
pip install "arize-phoenix-evals>=3" google-genai
```

```python
import os
from phoenix.evals import LLM

os.environ["GOOGLE_API_KEY"] = "<your-api-key>"

llm = LLM(provider="google", model="gemini-2.5-flash")
llm.generate_text(prompt="Hello there, how are you?")
# Output: "Hello! I'm doing well, thank you for asking!"
```