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Gemini Model

This section explains how to connect Google Gemini as a Gen AI Model data source in DataDios. Gemini powers AI Studio text-to-SQL, chat, insights, and metadata discovery.

Unlike Claude, Gemini has a first-class embeddings API, so a Gemini data source can also be selected as your RAG embedding source.

Prerequisites​

Get a Gemini API key from Google AI Studio. This is the key for the Gemini Developer API (the "Direct Gemini" provider) — not a Google Cloud service account.

Steps to Create and Test a Gemini Data Source​

Step 1: Create a Data Source​

  1. Navigate to the Data Sources tab in DataDios
  2. Click + CREATE SOURCE
  3. In the data source type dropdown, expand Gen AI Models and select Gemini Model

Step 2: Fill Connection Details​

Provide the following parameters (toggle to Json for the raw form):

  • project_name: (Optional) A label to group your models under
  • provider_type: Direct Gemini (the only provider available today)
  • model_type: The Gemini chat model to use. Available options:
    • gemini-2.5-pro (default)
    • gemini-2.5-flash
    • gemini-2.5-flash-lite
    • gemini-2.0-flash
    • gemini-2.0-flash-lite
    • gemini-3.5-flash
  • embedding_model: The embedding model for vector/RAG operations — models/gemini-embedding-001
  • API_KEY: Your Gemini API key (required, stored encrypted)
  • schedule_sync: (Optional) 15 Minutes, Hourly, or Daily for metadata synchronization

JSON Configuration​

{
   "project_name": "gemini_prod",
   "provider_type": "Direct Gemini",
   "model_type": "gemini-2.5-pro",
   "embedding_model": "models/gemini-embedding-001",
   "API_KEY": "AIza...",
   "schedule_sync": ""
}

Step 3: Test Connection​

  1. Click TEST CONNECTION
  2. A valid key returns Status: OK. The key is never echoed back in any response or log.

Step 4: Save Data Source​

  1. Click CREATE to save the data source
  2. It appears on the Data Sources listing page under Gen AI Models

Using Gemini with AI Studio and RAG​

Select this data source in AI Studio for natural-language queries, SQL generation, chat, and insights (see the AI Studio documentation). Because Gemini provides embeddings, you can also pick it as the embedding source when configuring RAG / hybrid search.

To make Gemini available to every user in your tenant, set it as the Global AI Data Source under Settings → Configuration → Global Configuration.