Google Gemini

Google Gemini is an AI platform that provides intelligent, context-aware responses to user queries.

Google Gemini is a powerful AI-driven platform designed for advanced text generation and natural language processing. The Google Gemini Connector allows Swimlane Turbine users to seamlessly integrate AI-generated responses into their security workflows. By leveraging this integration, users can enhance their playbooks with dynamic, AI-driven insights, improving decision-making and response times in security operations. This connector empowers users to automate complex text generation tasks, providing a significant boost to the efficiency and effectiveness of security processes.

Prerequisites

Before you can use the Google Gemini connector for Turbine, you'll need access to the Google Gemini API. This requires the following:

an API key authentication using the following parameters:

API Key: A unique key provided by Google to authenticate requests.

Model: The specific AI model to be used for generating responses.

Getting an API Key

Go to Google AI Studio

Navigate to the API Keys page from the Dashboard

Click Create API Key and select your project

Copy the generated API key for use in the connector

Capabilities

This Connector provides the following capabilities:

Text Generation: Generate human-like text responses to prompts

Question Answering: Ask questions and receive comprehensive answers

Content Analysis: Analyze and summarize text content

Creative Writing: Generate creative content, stories, and explanations

Code Generation: Generate and explain code snippets

Multi-model Support: Access to multiple Gemini model variants with different capabilities

Limitations

Rate Limits: Subject to Google's API rate limits and quotas

Model Availability: Some models may have regional availability restrictions

Parameter Support: Not all models support all generation parameters (e.g., top_k)

Token Limits: Responses are limited by the max_tokens parameter (default: 1024, max: 8192)

Content Safety: All responses are subject to Google's safety filters

Asset Setup

Required Configuration

Base URL: https://generativelanguage.googleapis.com (default)

API Key: Your Google Gemini API key from Google AI Studio

Model: Select from supported Gemini models:

gemini-2.5-pro - Most capable model for complex tasks

gemini-2.5-flash - Fast and efficient for most use cases

gemini-2.5-flash-lite - Lightweight version with reduced capabilities

gemini-2.0-flash - Previous generation fast model

gemini-2.0-flash-lite - Previous generation lightweight model

Optional Configuration

Max Tokens: Maximum response length (1-8192, default: 1024)

Temperature: Response creativity (0.0-1.0, default: 0.7)

Top P: Nucleus sampling (0.0-1.0, default: 0.95)

Top K: Top-k sampling (1-100, default: 64, not supported by all models)

Thinking Budget: Thinking budget for Gemini 2.5 models (0-1000, default: 0, 0 disables thinking)

Model Parameter Support

Model	Max Tokens	Temperature	Top P	Top K	Thinking
Gemini 2.5 Pro	✅	✅	✅	✅	✅
Gemini 2.5 Flash	✅	✅	✅	✅	✅
Gemini 2.5 Flash-Lite	✅	✅	✅	❌	✅
Gemini 2.0 Flash	✅	✅	✅	✅	❌
Gemini 2.0 Flash-Lite	✅	✅	✅	❌	❌

Tasks Setup

Ask Gemini Action

The connector provides a single action called "Ask Gemini" that accepts:

Input:

prompt (string, required): The question or prompt to send to Gemini

Output:

response (string): The AI-generated response

model_used (string): The model that generated the response

tokens_used (integer): Number of tokens consumed

finish_reason (string): Reason for generation completion

safety_ratings (array): Content safety assessments

Example Usage

## Example playbook action

action: ask_gemini
parameters:
  prompt: "Explain the concept of machine learning in simple terms"

Notes

API Key Security: Keep your API key secure and never expose it in client-side code

Cost Management: Monitor your usage through Google AI Studio to manage costs

Error Handling: The connector includes comprehensive error handling for common API issues

Model Selection: Choose the appropriate model based on your needs - Pro for complex tasks, Flash for speed

Parameter Tuning: Experiment with temperature and other parameters to achieve desired response characteristics

Reference URLs

Google Gemini API Documentation

Generation Configuration Reference

API Key Management

Google AI Studio

Additional Documentation

Google Gemini Connector Documentation

Google Gemini API Documentation

Configurations

Google Gemini AI Configuration

Configuration for Google Gemini AI API with model selection and generation parameters

Configuration Parameters

Parameter	Description	Type	Required
apiKey	API key for Google Gemini authentication	string	Required
model	Gemini model to use for text generation	string	Required
max_tokens	Maximum number of tokens in the response (default- 1024)	integer	Optional
temperature	Controls randomness in responses (0.0-2.0) (default- 0.7)	number	Optional
top_p	Nucleus sampling parameter (0.0-1.0) (default- 0.95)	number	Optional
top_k	Top-k sampling parameter (default- 64) - Not supported by Flash-Lite models	number	Optional

Actions

Ask Gemini

Send a prompt to Google Gemini and receive an AI-generated response. This action requires a specific prompt parameter.

Endpoint

Method: GET

Input

Argument Name	Type	Required	Description
prompt	string	Required	The prompt or question to send to Gemini

Input Example

{"prompt":"string"}

Output

Parameter	Type	Description
response	string	The AI-generated response from Gemini
model_used	string	The model that was used to generate the response
tokens_used	integer	Number of tokens used in the response
finish_reason	string	Reason why the generation finished

Output Example

{"response":"string","model_used":"string","tokens_used":123,"finish_reason":"string"}

Response Headers

Header	Description	Example
Content-Type	The media type of the resource	application/json
Date	The date and time at which the message was originated	Thu, 01 Jan 2024 00:00:00 GMT