# Just ask questions
chat = RubyLLM.chat
chat.ask "What's the best way to learn Ruby?"

# Analyze any file type
chat.ask "What's in this image?", with: "ruby_conf.jpg"
chat.ask "Describe this meeting", with: "meeting.wav"
chat.ask "Summarize this document", with: "contract.pdf"
chat.ask "Explain this code", with: "app.rb"

# Multiple files at once
chat.ask "Analyze these files", with: ["diagram.png", "report.pdf", "notes.txt"]

# Stream responses
chat.ask "Tell me a story about Ruby" do |chunk|
  print chunk.content
end

# Generate images
RubyLLM.paint "a sunset over mountains in watercolor style"

# Create embeddings
RubyLLM.embed "Ruby is elegant and expressive"

# Let AI use your code
class Weather < RubyLLM::Tool
  description "Get current weather"
  param :latitude
  param :longitude

  def execute(latitude:, longitude:)
    url = "https://api.open-meteo.com/v1/forecast?latitude=#{latitude}&longitude=#{longitude}&current=temperature_2m,wind_speed_10m"
    JSON.parse(Faraday.get(url).body)
  end
end

chat.with_tool(Weather).ask "What's the weather in Berlin?"

# Get structured output
class ProductSchema < RubyLLM::Schema
  string :name
  number :price
  array :features do
    string
  end
end

response = chat.with_schema(ProductSchema).ask "Analyze this product", with: "product.txt"

gem 'ruby_llm'

# config/initializers/ruby_llm.rb
RubyLLM.configure do |config|
  config.openai_api_key = ENV['OPENAI_API_KEY']
end

class Chat < ApplicationRecord
  acts_as_chat
end

chat = Chat.create! model_id: "claude-sonnet-4"
chat.ask "What's in this file?", with: "report.pdf"