Gemini API Postman Workspace

A comprehensive Postman workspace for exploring, testing, and integrating with Google's Gemini APIs. This workspace provides pre-built collections, environments, tests, documentation, and automation tools to streamline your AI development workflow.

Quick Start

1. Get Your API Key

1. Visit Google AI Studio
2. Create a new API key
3. Keep it secure - you'll need it for authentication

2. Import to Postman

1. Download this repository
2. Open Postman
3. Click Import and select the collection files from collections/
4. Import the environment files from environments/

3. Configure Environment

1. Select "Gemini API - Development" environment
2. Set your GEMINI_API_KEY variable
3. Run the "API Connectivity Test" to verify setup

4. Start Exploring

• Try text generation requests
• Explore chat and conversation flows
• Test image analysis capabilities
• Generate and review code

Workspace Structure

postman-gemini-workspace/
├── collections/           # Postman collection files
│   ├── gemini-api-collection.json
│   ├── text-generation.json
│   ├── chat-conversation.json
│   ├── vision-image-analysis.json
│   └── code-generation.json
├── environments/          # Environment configurations
│   ├── development.json
│   ├── production.json
│   └── .env.example
├── tests/                # Test scripts and validation
│   ├── api-validation-tests.js
│   └── collection-tests.json
├── mocks/                # Mock server for local development
│   └── gemini-mock-server.js
├── scripts/              # Automation and utility scripts
│   ├── validate-collections.js
│   ├── start-mock-server.js
│   ├── check-api-updates.js
│   └── update-workspace.js
├── docs/                 # Documentation
│   ├── getting-started.md
│   ├── advanced-usage.md
│   └── api-reference.md
└── .github/workflows/    # CI/CD automation
    └── update-workspace.yml

Features

Collections

Collection	Requests	Purpose
Text Generation	6	Basic text generation with various parameters
Chat & Conversation	6	Multi-turn conversations and context management
Vision & Image Analysis	7	Image understanding and visual Q&A
Code Generation	7	Programming assistance and code review

Key Capabilities

• Complete API Coverage: All major Gemini API endpoints
• Production Ready: Proper error handling and validation
• Multiple Environments: Development and production configurations
• Comprehensive Testing: Built-in test scripts and validation
• Mock Server: Local development without API costs
• Auto-Updates: GitHub Actions for maintenance
• Rich Documentation: Getting started guides and API reference

Use Cases

Content Creation

• Blog post generation
• Creative writing assistance
• Marketing copy creation
• Social media content

Code Development

• Code generation and completion
• Bug detection and fixing
• Code review and optimization
• Documentation generation

Image Analysis

• Content moderation
• Accessibility descriptions
• Visual question answering
• UI/UX analysis

Chat Applications

• Customer support bots
• Educational assistants
• Interactive help systems
• Conversational interfaces

Environment Variables

Required Variables

Variable	Description	Example
GEMINI_API_KEY	Your Google AI Studio API key	AIza...
base_url	Gemini API base URL	https://generativelanguage.googleapis.com
api_version	API version to use	v1beta

Model Configuration

Variable	Default	Purpose
model_name	gemini-1.5-pro	Default text model
vision_model_name	gemini-1.5-pro	Vision tasks model
chat_model_name	gemini-1.5-pro	Chat model
code_model_name	gemini-1.5-pro	Code generation model

Performance Settings

Variable	Development	Production
request_timeout	30000ms	60000ms
rate_limit_requests_per_minute	60	300
default_temperature	0.7	0.3
safety_threshold	BLOCK_MEDIUM_AND_ABOVE	BLOCK_LOW_AND_ABOVE

Testing

Running Tests

# Install dependencies
npm install

# Validate collections
npm run validate

# Run API tests
npm run test

# Run tests against production
npm run test:prod

Mock Server

Start the local mock server for development:

# Start mock server
npm run mock

# Or use the script directly
node scripts/start-mock-server.js start --daemon --port=3000

Set base_url to http://localhost:3000 to use the mock server.

Test Scripts

All requests include comprehensive test scripts that validate:

• Response structure and format
• Content quality and relevance
• Error handling and edge cases
• Performance metrics
• Safety ratings

Automation

GitHub Actions

The workspace includes automated maintenance via GitHub Actions:

• Weekly Updates: Checks for new models and API changes
• Security Scanning: Validates for secrets and vulnerabilities
• Performance Monitoring: Benchmarks response times
• Documentation: Auto-generates updated docs

Manual Triggers

# Check for API updates
node scripts/check-api-updates.js

# Update workspace
node scripts/update-workspace.js

# Validate all collections
node scripts/validate-collections.js

Documentation

Quick Links

• Getting Started Guide - Setup and basic usage
• Advanced Usage - Power user features
• API Reference - Complete parameter reference

Examples

Basic Text Generation

{
  "contents": [
    {
      "parts": [{"text": "Explain quantum computing"}]
    }
  ],
  "generationConfig": {
    "temperature": 0.7,
    "maxOutputTokens": 1024
  }
}

Image Analysis

{
  "contents": [
    {
      "parts": [
        {"text": "What's in this image?"},
        {
          "inline_data": {
            "mime_type": "image/jpeg",
            "data": "base64_image_data"
          }
        }
      ]
    }
  ]
}

Chat Conversation

{
  "contents": [
    {
      "role": "user",
      "parts": [{"text": "Hello, I need help with Python"}]
    },
    {
      "role": "model",
      "parts": [{"text": "I'd be happy to help! What would you like to know?"}]
    },
    {
      "role": "user",
      "parts": [{"text": "How do I read a CSV file?"}]
    }
  ]
}

Development

Prerequisites

• Node.js 18+
• Postman desktop app
• Google AI Studio API key

Setup for Development

1. Clone this repository
2. Install dependencies: npm install
3. Copy .env.example to .env and configure
4. Start mock server: npm run mock
5. Import collections into Postman
6. Start testing and developing!

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: npm test
5. Submit a pull request

Code Style

• Use 2 spaces for indentation
• Follow existing patterns in collections
• Include comprehensive test scripts
• Document new features

Security

API Key Management

• Never commit API keys to the repository
• Use environment variables for sensitive data
• Rotate keys regularly
• Use separate keys for development and production

Content Safety

• All requests include safety settings
• Configurable safety thresholds
• Content filtering validation
• Monitoring for sensitive content

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Support

Getting Help

• Check the documentation
• Review issues for common problems
• Join the Postman Community
• Visit Google AI documentation

Reporting Issues

1. Check existing issues first
2. Provide clear reproduction steps
3. Include environment details
4. Share relevant collection exports (remove API keys!)

Acknowledgments

• Google AI team for the Gemini API
• Postman team for the excellent platform
• Open source community for inspiration
• Contributors and testers

Project Status

• Core Collections: Complete
• Documentation: Complete
• Testing Suite: Complete
• Mock Server: Complete
• CI/CD: Complete
• Ongoing: API updates and maintenance

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Ready to explore the power of Gemini AI? Start with the Getting Started Guide and join thousands of developers building the future with AI!

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*This workspace is not officially affiliated with Google or Postman. It's a GCOS project to help developers work with Gemini APIs more effectively.