🤖 Inquira - AI Data Assistant for VS Code

Transform your data analysis workflow with AI-powered insights and automated code generation

📋 Table of Contents

• ✨ Features
• 🚀 Installation
• ⚙️ Configuration
• 🎯 Usage
• 🔧 Commands
• 🎨 Interface
• 🛠️ Development
• 📊 Performance
• 🤝 Contributing
• 📄 License

✨ Features

🤖 AI-Powered Data Analysis

• Intelligent Query Processing: Natural language queries about your data
• Context-Aware Responses: Understands your data schema and relationships
• Smart Code Generation: Generates optimized Python code for data analysis
• Real-time AI Responses: Powered by Google Gemini 2.5 Flash

📊 Schema Management

• Automatic Schema Generation: Analyzes CSV files and creates data schemas
• Schema Validation: Ensures data integrity and type safety
• Dynamic Schema Updates: Adapts to changing data structures
• Schema Viewing: Integrated schema viewer in VS Code

💻 Code Integration

• Seamless Code Insertion: Automatically inserts generated code into your editor
• Syntax Highlighting: Proper Python syntax highlighting for generated code
• Import Management: Handles necessary library imports
• Code Optimization: Generates efficient, production-ready code

🎨 Modern UI/UX

• Full-Width Layout: Maximizes screen real estate for content
• Markdown Rendering: Rich text formatting with headers, lists, and code blocks
• Responsive Design: Adapts to different screen sizes
• Dark/Light Theme Support: Follows VS Code theme preferences
• Smooth Scrolling: Optimized scrolling performance

⚡ Performance Optimized

• Lazy Loading: Only activates when needed
• Minimal Bundle Size: Just 19KB package size
• Fast Startup: Optimized activation events
• Memory Efficient: In-memory chat storage with session persistence

🔧 Developer Experience

• TypeScript: Full type safety and IntelliSense support
• ESLint: Code quality and consistency
• Source Maps: Enhanced debugging experience
• Hot Reload: Development with live reloading

🚀 Installation

Option 1: VS Code Marketplace (Recommended)

1. Open VS Code
2. Go to Extensions (Ctrl+Shift+X / Cmd+Shift+X)
3. Search for "Inquira"
4. Click Install

Option 2: Manual Installation

1. Download the .vsix file from releases
2. In VS Code: Extensions → Install from VSIX...
3. Select the downloaded .vsix file

Option 3: From Source

git clone https://github.com/adarsh9780/inquira-extension.git
cd inquira-extension
npm install
npm run compile
code --install-extension inquira-extension-0.1.0.vsix

⚙️ Configuration

Required Setup

1. Google Gemini API Key:

   • Get your API key from Google AI Studio
   • Free tier available with generous limits

2. Data File Path:

   • Path to your CSV data file
   • Supports absolute and relative paths
   • Example: /Users/username/data/sales.csv

Configuration Steps

1. Open Inquira view in VS Code Explorer
2. Click the ⚙️ Settings button
3. Fill in:
   • API Key: Your Google Gemini API key
   • Data Path: Path to your CSV file
   • Context: Optional description of your data
   • Model: Gemini model version (default: gemini-2.5-flash)
4. Click Save Settings
5. The extension will automatically generate your data schema

Advanced Configuration

{
  "inquira.apiKey": "your-gemini-api-key",
  "inquira.dataPath": "/path/to/your/data.csv",
  "inquira.hasSchema": true,
  "inquira.modelName": "gemini-2.5-flash",
  "inquira.context": "Sales data for Q1 2024"
}

🎯 Usage

Basic Workflow

1. Setup: Configure API key and data path
2. Schema Generation: Extension analyzes your CSV automatically
3. Query: Ask questions in natural language
4. Code Generation: Get optimized Python code
5. Integration: Code inserts directly into your editor

Example Queries

"Show me the total sales by region"
"Find customers with orders over $1000"
"Calculate average order value by month"
"Create a chart of sales trends"
"Filter data for customers from California"

Generated Code Examples

import duckdb
import pandas as pd

# Load and analyze data efficiently
query = "SELECT region, SUM(sales) as total_sales FROM data GROUP BY region"
df = duckdb.query(query).to_df()

# Display results
print(df)

🔧 Commands

🎨 Interface

Main Interface

┌─────────────────┐
│ 🤖 Inquira      │ ← Header with controls
│ ⚙️ 👁️           │
├─────────────────┤
│                 │
│ Assistant:      │ ← Full-width responses
│ [Rich content   │
│  with markdown] │
│                 │
│ User message    │ ← Right-aligned input
│ [Your query]    │
│                 │
├─────────────────┤
│ Ask a question... │ ← Input area
│ [Send] ➤        │
└─────────────────┘

Key Components

• 🤖 Robot Icon: Extension branding
• ⚙️ Settings: Configuration panel
• 👁️ Schema Viewer: Data structure inspection
• Input Area: Natural language queries
• Message History: Scrollable conversation
• Code Blocks: Syntax-highlighted generated code

🛠️ Development

Prerequisites

• Node.js 18+
• VS Code 1.74+
• Google Gemini API key

Setup

# Clone repository
git clone https://github.com/adarsh9780/inquira-extension.git
cd inquira-extension

# Install dependencies
npm install

# Compile TypeScript
npm run compile

# Watch for changes
npm run watch

Project Structure

inquira-extension/
├── src/
│   ├── extension.ts              # Main extension entry point
│   ├── providers/
│   │   └── InquiraViewProvider.ts # Webview provider
│   └── services/
│       ├── LLMService.ts         # AI integration
│       ├── SchemaManager.ts      # Data schema handling
│       ├── CodeInjector.ts       # Code insertion
│       └── ChatHistoryService.ts # Chat persistence
├── media/
│   ├── main.css                  # UI styles
│   └── webview.js               # Frontend logic
├── out/                          # Compiled JavaScript
├── package.json                  # Extension manifest
├── tsconfig.json                 # TypeScript config
└── README.md                     # This file

Testing

# Run tests
npm test

# Lint code
npm run lint

# Package extension
npm run package

📊 Performance

Bundle Size

• Total Size: 19.09 KB
• Files: 13 optimized files
• Compression: 99% reduction from development build

Memory Usage

• Startup: Minimal impact (< 5MB)
• Runtime: Efficient in-memory storage
• Activation: Lazy loading only when needed

Speed Metrics

• First Load: < 100ms
• Query Response: 2-5 seconds (depends on query complexity)
• Code Generation: Instant insertion
• UI Rendering: Smooth 60fps scrolling

🤝 Contributing

Ways to Contribute

• 🐛 Bug Reports: Use GitHub Issues
• 💡 Feature Requests: Suggest new capabilities
• 🔧 Code Contributions: Submit Pull Requests
• 📖 Documentation: Improve guides and examples

Development Workflow

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes
4. Run tests: npm test
5. Submit a Pull Request

Code Standards

• TypeScript: Strict type checking enabled
• ESLint: Airbnb configuration
• Prettier: Consistent code formatting
• Conventional Commits: Structured commit messages

📄 License

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

Permissions

• ✅ Commercial use
• ✅ Modification
• ✅ Distribution
• ✅ Private use

Limitations

• ❌ Liability
• ❌ Warranty

🙏 Acknowledgments

• Google Gemini: AI model powering the analysis
• VS Code: Excellent extension platform
• DuckDB: High-performance analytical database
• TypeScript: Type-safe development experience

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@inquira.dev

Made with ❤️ for data scientists and analysts

Transform your data workflow with AI-powered insights and automated code generation! 🚀