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Model Registration: Gemini 2.0 Flash

This guide covers registering the Gemini 2.0 Flash model on the platform.

Gemini 2.0 Flash is Google's language model for classification and structured output tasks.

Registration Steps

Step 1. Navigate to Model Catalog

Go to GenAI Studio → Model Catalog and click the Create button.

Step 2. Fill in Basic Information

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Basic Information fields help organize and identify your model:

  • Name: Human-readable identifier for the model (e.g., "Gemini 2.0 Flash")
  • Description: Brief explanation of the model's purpose and capabilities
  • Group: Category for organizing similar models together (e.g., "Foundation LLMs")
  • Permissible Purpose: Approved use cases and business scenarios for this model
  • Ownership Type: License type - Proprietary, Open Source, or Internal
  • Model Type: Classification of the model (e.g., "LLM" for language models)

Step 3. Configure Inferencing Logic

Choose Input Type

Input Type: You have two options:

  • API Based - Use this when working with models through API providers (OpenAI, Anthropic, Google Vertex AI, etc.)

  • Python Function - Use this for custom Python implementations or local models

For this guide, we'll use API Based.

Select Model Provider

Model Provider: Select Google Vertex AI from the dropdown

Once you select a provider, additional fields will appear to configure how the model is called:

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  • Alias: Variable name to reference this model in pipeline code (e.g., gemini_2_0_flash)
  • Output Type: Data type returned by the model (e.g., dict[str, str])
  • Input Type: Choose between API-based (for external providers) or Python Function (for custom code)
  • Model Provider: Select the API provider hosting the model (Google Vertex AI)
  • Model: Specific model version from the provider's catalog (Gemini 2.0 Flash)

Define Arguments

The inputs to the model - messages, system instruction, temperature, etc.

Click + Add Argument to add each argument:

Alias Type Is Optional Default Value
text String ☐ -
temperature Numerical ☑ 0
system_instruction String ☑ None

Argument Descriptions:

  • text: The input prompt to send to the model

  • temperature: Controls randomness (0 = deterministic, 1 = creative)

  • system_instruction: Optional system-level instructions for the model

You can add additional arguments based on your model's requirements.

Write Scoring Logic

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Provide logic to initialize and score the model:

import os
from google import genai
from google.genai import types

client = genai.Client(api_key=os.getenv("GOOGLE_API_TOKEN"))

config = types.GenerateContentConfig(
    temperature=temperature, 
    seed=2025, 
    system_instruction=system_instruction
)

response = client.models.generate_content(
    model="gemini-2.0-flash", 
    contents=text, 
    config=config
)

return {
    "response": response.text,
}

What This Code Does:

  • Authenticates using the GOOGLE_API_TOKEN environment variable (configured in Platform Integrations)
  • Sets up generation config with temperature and system instruction
  • Calls the Gemini 2.0 Flash model with the input text
  • Returns the generated response

Step 4. Save the Model

Add any notes or additional information in the Additional Information section, then click Create to complete registration.

Step 5. Quick Example Run

Click Test Code to run a sample query.

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Use the platform's test interface to verify:

  • Verify API authentication is working
  • Test with sample inputs before using in production
  • Debug any configuration issues
  • Validate the output format matches expectations

Usage in Pipelines

Once registered, the model appears in your Resources library and can be selected for any downstream usages.

Reference in pipeline code: 

# Call the registered model
response = gemini_2_0_flash(
    text=user_prompt,
    temperature=0.7,
    system_instruction="You are a helpful assistant."
)

# Access the response
output_text = response["response"]