Transform your Ruby application with powerful, production-ready GenAI features
ActiveGenie is a Ruby gem that provides a polished, production-ready interface for working with Generative AI (GenAI) models. Just like ActiveStorage simplifies file handling in Rails, ActiveGenie makes it effortless to integrate GenAI capabilities into your Ruby applications.
- 🎯 Data Extraction: Extract structured data from unstructured text with type validation
- 📊 Smart Scoring: Multi-reviewer evaluation system with automatic expert selection
- 💭 Sentiment Analysis: Advanced sentiment analysis with customizable rules
- 🔒 Safe & Secure: Built-in validation and sanitization
- 🛠️ Configurable: Supports multiple GenAI providers and models
- Add to your Gemfile:
gem 'active_genie'- Install the gem:
bundle install- Generate the configuration:
rails g active_genie:install- [Optional] Configure your credentials in
config/active_genie.yml:
GPT-4o-mini:
api_key: <%= ENV['OPENAI_API_KEY'] %>
provider: "openai"
claude-3-5-sonnet:
api_key: <%= ENV['ANTHROPIC_API_KEY'] %>
provider: "anthropic"The first key will be used as default in all modules, in this example
GPT-4o-mini
Extract structured data from text using AI-powered analysis, handling informal language and complex expressions.
text = "Nike Air Max 90 - Size 42 - $199.99"
schema = {
brand: {
type: 'string',
enum: ["Nike", "Adidas", "Puma"]
},
price: {
type: 'number',
minimum: 0
},
size: {
type: 'integer',
minimum: 35,
maximum: 46
}
}
result = ActiveGenie::DataExtractor.call(text, schema)
# => {
# brand: "Nike",
# brand_explanation: "Brand name found at start of text",
# price: 199.99,
# price_explanation: "Price found in USD format at end",
# size: 42,
# size_explanation: "Size explicitly stated in the middle"
# }Features:
- Structured data extraction with type validation
- Schema-based extraction with custom constraints
- Informal text analysis (litotes, hedging)
- Detailed explanations for extracted values
See the Data Extractor README for informal text processing, advanced schemas, and detailed interface documentation.
Text evaluation system that provides detailed scoring and feedback using multiple expert reviewers. Get balanced scoring through AI-powered expert reviewers that automatically adapt to your content.
text = "The code implements a binary search algorithm with O(log n) complexity"
criteria = "Evaluate technical accuracy and clarity"
result = ActiveGenie::Scoring::Basic.call(text, criteria)
# => {
# algorithm_expert_score: 95,
# algorithm_expert_reasoning: "Accurately describes binary search and its complexity",
# technical_writer_score: 90,
# technical_writer_reasoning: "Clear and concise explanation of the algorithm",
# final_score: 92.5
# }Features:
- Multi-reviewer evaluation with automatic expert selection
- Detailed feedback with scoring reasoning
- Customizable reviewer weights
- Flexible evaluation criteria
See the Scoring README for advanced usage, custom reviewers, and detailed interface documentation.
The summarizer is a tool that can be used to summarize a given text. It uses a set of rules to summarize the text out of the box. Uses the best practices of prompt engineering and engineering to make the summarization as accurate as possible.
require 'active_genie'
text = "Example text to be summarized. The fox jumps over the dog"
summarized_text = ActiveGenie::Summarizer.call(text)
puts summarized_text # => "The fox jumps over the dog"The language detector is a tool that can be used to detect the language of a given text. It uses a set of rules to detect the language of the text out of the box. Uses the best practices of prompt engineering and engineering to make the language detection as accurate as possible.
require 'active_genie'
text = "Example text to be detected"
language = ActiveGenie::LanguageDetector.call(text)
puts language # => "en"The translator is a tool that can be used to translate a given text. It uses a set of rules to translate the text out of the box. Uses the best practices of prompt engineering and engineering to make the translation as accurate as possible.
require 'active_genie'
text = "Example text to be translated"
translated_text = ActiveGenie::Translator.call(text, from: 'en', to: 'pt')
puts translated_text # => "Exemplo de texto a ser traduzido"The sentiment analyzer is a tool that can be used to analyze the sentiment of a given text. It uses a set of rules to analyze the sentiment of the text out of the box. Uses the best practices of prompt engineering and engineering to make the sentiment analysis as accurate as possible.
require 'active_genie'
text = "Example text to be analyzed"
sentiment = ActiveGenie::SentimentAnalyzer.call(text)
puts sentiment # => "positive"The Elo ranking is a tool that can be used to rank a set of items. It uses a set of rules to rank the items out of the box. Uses the best practices of prompt engineering and engineering to make the ranking as accurate as possible.
require 'active_genie'
items = ['Square', 'Circle', 'Triangle']
criterias = 'items that look rounded'
ranked_items = ActiveGenie::EloRanking.call(items, criterias, rounds: 10)
puts ranked_items # => [{ name: "Circle", score: 1500 }, { name: "Square", score: 800 }, { name: "Triangle", score: 800 }]| Option | Description | Default |
|---|---|---|
provider |
LLM provider (openai, anthropic, etc) | nil |
model |
Model to use | nil |
api_key |
Provider API key | nil |
timeout |
Request timeout in seconds | 5 |
max_retries |
Maximum retry attempts | 3 |
Note: Each module can append its own set of configuration options, see the individual module documentation for details.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
