Quickstart

In this example we will use Adala as a standalone library directly inside Python notebook. Click here to see an extended quickstart example.

import pandas as pd

  from adala.agents import Agent
  from adala.environments import StaticEnvironment
  from adala.skills import ClassificationSkill
  from adala.runtimes import OpenAIChatRuntime
  from rich import print
  
  # Train dataset
  train_df = pd.DataFrame([
      ["It was the negative first impressions, and then it started working.", "Positive"],
      ["Not loud enough and doesn't turn on like it should.", "Negative"],
      ["I don't know what to say.", "Neutral"],
      ["Manager was rude, but the most important that mic shows very flat frequency response.", "Positive"],
      ["The phone doesn't seem to accept anything except CBR mp3s.", "Negative"],
      ["I tried it before, I bought this device for my son.", "Neutral"],
  ], columns=["text", "sentiment"])
  
  # Test dataset
  test_df = pd.DataFrame([
      "All three broke within two months of use.",
      "The device worked for a long time, can't say anything bad.",
      "Just a random line of text."
  ], columns=["text"])
  
  agent = Agent(
      # connect to a dataset
      environment=StaticEnvironment(df=train_df),
  
      # define a skill
      skills=ClassificationSkill(
          name='sentiment',
          instructions="Label text as positive, negative or neutral.",
          labels={'sentiment': ["Positive", "Negative", "Neutral"]},
          input_template="Text: {text}",
          output_template="Sentiment: {sentiment}"
      ),
  
      # define all the different runtimes your skills may use
      runtimes = {
          # You can specify your OPENAI API KEY here via `OpenAIRuntime(..., api_key='your-api-key')`
          'openai': OpenAIChatRuntime(model='gpt-3.5-turbo'),
      },
      default_runtime='openai',
      
      # NOTE! If you have access to GPT-4, you can uncomment the lines bellow for better results
  #     default_teacher_runtime='openai-gpt4',
  #     teacher_runtimes = {
  #       'openai-gpt4': OpenAIRuntime(model='gpt-4')
  #     }
  )
  
  print(agent)
  print(agent.skills)
  
  agent.learn(learning_iterations=3, accuracy_threshold=0.95)
  
  print('\n=> Run tests ...')
  predictions = agent.run(test_df)
  print('\n => Test results:')
  print(predictions)

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