Google Gen AI Evals

class GoogleGenAIModel:
    model: str = "gemini-2.5-flash"
    """The model name to use."""
    vertexai: Optional[bool] = None
    """Whether to use VertexAI instead of the Developer API."""
    api_key: Optional[str] = None
    """Your Google API key. If not provided, will be read from environment variables."""
    credentials: Optional["Credentials"] = None
    """Google Cloud credentials for VertexAI access."""
    project: Optional[str] = None
    """Google Cloud project ID for VertexAI."""
    location: Optional[str] = None
    """Google Cloud location for VertexAI."""
    initial_rate_limit: int = 5
    """Initial rate limit for API calls per second."""

export GOOGLE_API_KEY=your_api_key_here

from phoenix.evals import GoogleGenAIModel

# API key will be read from environment
model = GoogleGenAIModel()

model = GoogleGenAIModel(
    vertexai=True,
    project="your-project-id",
    location="us-central1"
)

from phoenix.evals import GoogleGenAIModel

# Initialize with default settings
model = GoogleGenAIModel(model="gemini-2.5-flash")

# Simple text generation
response = model("What is the capital of France?")
print(response)  # "The capital of France is Paris."

import base64
from phoenix.evals.templates import MultimodalPrompt, PromptPart, PromptPartContentType

# Load and encode an image
with open("image.jpg", "rb") as f:
    image_bytes = f.read()
image_base64 = base64.b64encode(image_bytes).decode("utf-8")

# Create multimodal prompt
prompt = MultimodalPrompt(
    parts=[
        PromptPart(content_type=PromptPartContentType.TEXT, content="What's in this image?"),
        PromptPart(content_type=PromptPartContentType.IMAGE, content=image_base64)
    ]
)

response = model._generate(prompt=prompt)
print(response)

# Load and encode audio
with open("audio.wav", "rb") as f:
    audio_bytes = f.read()
audio_base64 = base64.b64encode(audio_bytes).decode("utf-8")

prompt = MultimodalPrompt(
    parts=[
        PromptPart(content_type=PromptPartContentType.AUDIO, content=audio_base64)
    ]
)

response = model._generate(prompt=prompt)
print(response)