Leva - Flexible Evaluation Framework for Language Models

Leva is a Ruby on Rails framework for evaluating Language Models (LLMs) using ActiveRecord datasets on production models. It provides a flexible structure for creating experiments, managing datasets, and implementing various evaluation logic on production data with security in mind.

Installation

Add this line to your application's Gemfile:

gem 'leva'

And then execute:

bundle install

Add the migrations to your database:

rails leva:install:migrations
rails db:migrate

Usage

1. Setting up Datasets

First, create a dataset and add any ActiveRecord records you want to evaluate against. To make your models compatible with Leva, include the Leva::Recordable concern in your model:

class TextContent < ApplicationRecord
  include Leva::Recordable

  # @return [String] The ground truth label for the record
  def ground_truth
    expected_label
  end

  # @return [Hash] A hash of attributes to be displayed in the dataset records index
  def index_attributes
    {
      text: text,
      expected_label: expected_label,
      created_at: created_at.strftime('%Y-%m-%d %H:%M:%S')
    }
  end

  # @return [Hash] A hash of attributes to be displayed in the dataset record show view
  def show_attributes
    {
      text: text,
      expected_label: expected_label,
      created_at: created_at.strftime('%Y-%m-%d %H:%M:%S')
    }
  end

  # @return [Hash] A hash of attributes to be displayed in the dataset record show view
  def to_llm_context
    {
      text: text,
      expected_label: expected_label,
      created_at: created_at.strftime('%Y-%m-%d %H:%M:%S')
    }
  end
end

dataset = Leva::Dataset.create(name: "Sentiment Analysis Dataset") dataset.add_record TextContent.create(text: "I love this product!", expected_label: "Positive") dataset.add_record TextContent.create(text: "Terrible experience", expected_label: "Negative") dataset.add_record TextContent.create(text: "It's ok", expected_label: "Neutral")

### 2. Implementing Runs

Create a run class to handle the execution of your inference logic:

```bash
rails generate leva:runner sentiment

class SentimentRun < Leva::BaseRun
  def execute(record)
    # Your model execution logic here
    # This could involve calling an API, running a local model, etc.
    # Return the model's output
  end
end

3. Implementing Evals

Create one or more eval classes to evaluate the model's output:

rails generate leva:eval sentiment_accuracy

class SentimentAccuracyEval < Leva::BaseEval
  def evaluate(prediction, record)
    score = prediction == record.expected_label ? 1.0 : 0.0
    [score, record.expected_label]
  end
end

class SentimentF1Eval < Leva::BaseEval
  def evaluate(prediction, record)
    # Calculate F1 score
    # ...
    [f1_score, record.f1_score]
  end
end

4. Running Experiments

You can run experiments with different runs and evals:

experiment = Leva::Experiment.create!(name: "Sentiment Analysis", dataset: dataset)

run = SentimentRun.new
evals = [SentimentAccuracyEval.new, SentimentF1Eval.new]

Leva.run_evaluation(experiment: experiment, run: run, evals: evals)

5. Using Prompts

You can also use prompts with your runs:

prompt = Leva::Prompt.create!(
  name: "Sentiment Analysis",
  version: 1,
  system_prompt: "You are an expert at analyzing text and returning the sentiment.",
  user_prompt: "Please analyze the following text and return the sentiment as Positive, Negative, or Neutral.\n\n{{TEXT}}",
  metadata: { model: "gpt-4", temperature: 0.5 }
)

experiment = Leva::Experiment.create!(
  name: "Sentiment Analysis with LLM",
  dataset: dataset,
  prompt: prompt
)

run = SentimentRun.new
evals = [SentimentAccuracyEval.new, SentimentF1Eval.new]

Leva.run_evaluation(experiment: experiment, run: run, evals: evals)

6. Analyzing Results

After the experiments are complete, analyze the results:

experiment.evaluation_results.group_by(&:evaluator_class).each do |evaluator_class, results|
  average_score = results.average(&:score)
  puts "#{evaluator_class.capitalize} Average Score: #{average_score}"
end

Configuration

Ensure you set up any required API keys or other configurations in your Rails credentials or environment variables.

Leva's Components

Classes

• Leva: Handles the process of running experiments.
• Leva::BaseRun: Base class for run implementations.
• Leva::BaseEval: Base class for eval implementations.

Models

• Leva::Dataset: Represents a collection of data to be evaluated.
• Leva::DatasetRecord: Represents individual records within a dataset.
• Leva::Experiment: Represents a single run of an evaluation on a dataset.
• Leva::RunnerResult: Stores the results of each run execution.
• Leva::EvaluationResult: Stores the results of each evaluation.
• Leva::Prompt: Represents a prompt for an LLM.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/kieranklaassen/leva.

License

The gem is available as open source under the terms of the MIT License.

Roadmap

• Parallelize evaluation