Building Agents AI has become popular efforts between engineers, and to use Ruby's book It makes it accessible and enjoyable. This new friendly guide will travel through the process of building agents Agents Ai with ruby, making it easier to start or are new to the development of AI.
What are agents ai?
A Ai agent Is the plan designed to perform the jobs that require a person's intelligence. These agents can make decisions, learn from the data, and adapt to new situations. Some common types of AI agents include:
- Negotiations Contacting users.
- Recommendation Products, services, or content.
- Processor of data That is analyzing and transforms information.
Agents Ai are used in all many industries, including customer service, health services, and funding. Understanding AI Eagers of AI is important in finding the development process and outstanding points why Ruby's book a large construction election.
Learn more about what Ai Anchents and its types
Why did you use Ruby Development AI?
Ruby is a beautiful AI, especially for those who already get used to it. Its simple syntax allows a quick prototyping. Although it is not smooth like Python of Great Tasks, Ruby's libraries like Ruby-ML makes AI development easier and efficient for small projects.
All you need
Before we start building your AI agent, make sure you have the following:
- Computer (Windows, Macos, or Linux).
- Access to the Internet.
- Basic curiosity – Nothing Codes Codes Required!
Choose your type of your Age Agent
The first step in creating Ai agent determines what kind of agent you want to create. Here are some examples:
- Negotiations: Automatically conversations with users.
- Recommendation: To propose products, services, or content.
- Processor of data: Analyzing and transforming information.
Having a clear goal in mind will guide your development process.
Step Guide by Step: Creating a simple AI ERBU
If you have not yet removed your Ruby's development environment, watch this video to learn how to set Ruby in vscode and ready for all ready to go.
Step 1: Apply the required values
To create an Ai Eruby agent, we need to put Ruby-ML Gem. It provides the necessary equipment for machine learning algorithms as decisions. Start this command to install gem:
Step 2: Create the main text of ruby
Create a new Ruby File Called by name ai_agent.rb and open it to be organized. This will be when we write the code for our AI.
Step 3: Import the required libraries
Next, we introduce the ruby-ml Gem and set up the stomach of the classifier tree.
Step 4: Prepare sample data
We will use a small dataset to train our AI. The data will classify that person will be out based on the weather conditions (Outlook and heat).
# Sample data: [Outlook, Temperature] => Go Outside?
data = [
['Sunny', 'Hot', 'No'],
['Sunny', 'Hot', 'No'],
['Overcast', 'Hot', 'Yes'],
['Rainy', 'Mild', 'Yes'],
['Rainy', 'Cool', 'Yes'],
['Rainy', 'Cool', 'No'],
['Overcast', 'Cool', 'Yes'],
['Sunny', 'Mild', 'No'],
['Sunny', 'Cool', 'Yes'],
['Rainy', 'Mild', 'Yes'],
['Sunny', 'Mild', 'Yes'],
['Overcast', 'Mild', 'Yes'],
['Overcast', 'Hot', 'Yes'],
['Rainy', 'Hot', 'No']
]
Step 5: Describe Features and Labels
This page Features It is installation data (Outlook and heat), and labels It is the data output (does one go outside or not).
# Define the features and labels
features = data.map { |row| row[0..1] } # [Outlook, Temperature]
labels = data.map { |row| row[2] } # [Go Outside?]
Step 6: Get started and Train the Decision tree
Now we will start a Taking Takes model and train it with our sample data.
# Initialize the DecisionTree
tree = RubyML::Classification::DecisionTree.new
# Train the model
tree.train(features, labels)
Step 7: Check the model
After training the model, we can test the new data (eg, Overcast and cool) to see if the agent foretells that the person will go out.
# Test the agent with new data
test_data = [['Overcast', 'Cool']] # New data to predict
# Predict if the person will go outside
prediction = tree.predict(test_data)
puts "Prediction for #{test_data}: #{prediction}"
A fully code for AI agent
Here is the perfect code for AI:
require 'ruby-ml'
# Sample data: [Outlook, Temperature] => Go Outside?
data = [
['Sunny', 'Hot', 'No'],
['Sunny', 'Hot', 'No'],
['Overcast', 'Hot', 'Yes'],
['Rainy', 'Mild', 'Yes'],
['Rainy', 'Cool', 'Yes'],
['Rainy', 'Cool', 'No'],
['Overcast', 'Cool', 'Yes'],
['Sunny', 'Mild', 'No'],
['Sunny', 'Cool', 'Yes'],
['Rainy', 'Mild', 'Yes'],
['Sunny', 'Mild', 'Yes'],
['Overcast', 'Mild', 'Yes'],
['Overcast', 'Hot', 'Yes'],
['Rainy', 'Hot', 'No']
]
# Define the features and labels
features = data.map { |row| row[0..1] } # [Outlook, Temperature]
labels = data.map { |row| row[2] } # [Go Outside?]
# Initialize the DecisionTree
tree = RubyML::Classification::DecisionTree.new
# Train the model
tree.train(features, labels)
# Test the agent with new data
test_data = [['Overcast', 'Cool']] # New data to predict
# Predict if the person will go outside
prediction = tree.predict(test_data)
puts "Prediction for #{test_data}: #{prediction}"
Step 8: Run the text
Startup text and see how to remove, just make the following command in your own advance:
Expected Issue
You should see the predicts such as:
Prediction for [["Overcast", "Cool"]]: ["Yes"]
This means that AI's AI is forecasting that one will come out when the “full” vision and temperatures are cool. “
Use your AGENT AGE
When your agent works, you can send it to different places:
- For web apps: Combine an agent in Ruby in a rail web app.
- In the Tools of Command command: A package as a standalone ruby script.
- Apis: Create a service using Sinico either Rail API mode.
Consider handling your agent on the cloud platforms like Heroku either Games.
Apart from this, agents AI is used in the formulation of technology such as the Big Data Analytics, Development of Mechanical Learning Models, and the Antakes.
Prepare and maintain your AGENT AI
Ai agents appears later. Your system should make a workshop and collect customer feedback that leads to logic program reviews and training datasets. Benefits from default cars Cron works planning to restore restorative updates.
The last thoughts
Ruby provides a solution to upgrade agents AI Although the first process may not be challenging if you have the necessary tools.
Programs starting with basic structures will appear into very complex programs when users read the process better.
Ruby stands in its powerful community structure associated with the Code of flexible code while already in the full place to build AI programs.
Even if you build chatbot, a recommendation program, or data processor, Ruby's book It provides tools you need to bring your ideas into life.
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Frequently Asked Questions
1. Why did you use Ruby Development AI?
Ruby is easy to read, with a clean syntax, making it good to create agents AI immediately. It is also a good decision when you are free in language.
2. What kind of Ai agents do I create with ruby?
You can create Chatbots, complimentary systems, or data processing agents, depending on how you want to exchange them.
3. Is Ruby ready to study?
While Ruby is famous as a typewriter, they have libraries such as the ruby-ML that makes it more in small projects and reading bases.
4. Can I add my AI agency built in Ruby?
Yes, you can use it as a web app with ruby in RAIL, a command line, or API uses in sinchra or rail mode.
