# Google GenAI Plugin
This Genkit plugin provides a unified interface for Google AI (Gemini) and Vertex AI models, embedding, and other services.
## Setup environment
```bash
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](https://aistudio.google.com/) and set it in your environment:
```bash
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:
```bash
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:
```python
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:
```python
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()`:
```python
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/provider-google-genai-hello/README.md`](../../samples/provider-google-genai-hello/README.md) - Basic Gemini usage
- [`py/samples/provider-vertex-ai-rerank-eval/README.md`](../../samples/provider-vertex-ai-rerank-eval/README.md) - Rerankers and evaluators
