genkit-plugin-google-genai 0.6.0rc1

Genkit Google GenAI Plugin

Google GenAI Plugin

This Genkit plugin provides a unified interface for Google AI (Gemini) and Vertex AI models, embedding, and other services.

Setup environment

uv venv
source .venv/bin/activate
pip install genkit-plugins-google-genai

Configuration

Google AI (AI Studio)

To use Google AI models, obtain an API key from Google AI Studio and set it in your environment:

export GEMINI_API_KEY='<your-api-key>'

Vertex AI (Google Cloud)

To use Vertex AI models, ensure you have a Google Cloud project and Application Default Credentials (ADC) set up:

gcloud auth application-default login

Features

Dynamic Models

The plugin automatically discovers available models from the API upon initialization. You can use any model name supported by the API (e.g., googleai/gemini-2.0-flash-exp, vertexai/gemini-1.5-pro).

Dynamic Configuration

New or experimental parameters can be passed flexibly using model_validate to bypass strict schema checks:

from genkit.plugins.google_genai import GeminiConfigSchema

config = GeminiConfigSchema.model_validate({
    'temperature': 1.0,
    'response_modalities': ['TEXT', 'IMAGE'],
})

Vertex AI Rerankers

The VertexAI plugin provides semantic rerankers for improving RAG quality by re-scoring documents based on relevance:

from genkit import Genkit
from genkit.plugins.google_genai import VertexAI

ai = Genkit(plugins=[VertexAI(project='my-project')])

# Rerank documents after retrieval
ranked_docs = await ai.rerank(
    reranker='vertexai/semantic-ranker-default@latest',
    query='What is machine learning?',
    documents=retrieved_docs,
    options={'top_n': 5},
)

Supported Models:

Model	Description
semantic-ranker-default@latest	Latest default semantic ranker
semantic-ranker-default-004	Semantic ranker version 004
semantic-ranker-fast-004	Fast variant (lower latency)

Vertex AI Evaluators

Built-in evaluators for assessing model output quality. Evaluators are automatically registered when using the VertexAI plugin and are accessed via ai.evaluate():

from genkit import Genkit
from genkit._core.typing import BaseDataPoint
from genkit.plugins.google_genai import VertexAI

ai = Genkit(plugins=[VertexAI(project='my-project')])

# Prepare test dataset
dataset = [
    BaseDataPoint(
        input='Write about AI.',
        output='AI is transforming industries through intelligent automation.',
    ),
]

# Evaluate fluency (scores 1-5)
results = await ai.evaluate(
    evaluator='vertexai/fluency',
    dataset=dataset,
)

for result in results.root:
    print(f'Score: {result.evaluation.score}')

Supported Metrics:

Metric	Description
BLEU	Translation quality (compare to reference)
ROUGE	Summarization quality
FLUENCY	Language mastery and readability
SAFETY	Harmful/inappropriate content detection
GROUNDEDNESS	Hallucination detection
SUMMARIZATION_QUALITY	Overall summarization ability

Examples

For comprehensive usage examples, see:

• py/samples/google-genai-media/README.md - Speech, image, and video generation
• py/samples/gemini-code-execution/README.md - Gemini code execution
• py/samples/gemini-context-caching/README.md - Context caching for large prompts
• py/samples/vertexai-imagen/README.md - Vertex AI Imagen generation