Autoevals

Autoevals is a tool to quickly and easily evaluate AI model outputs.

It bundles together a variety of automatic evaluation methods including:

• LLM-as-a-Judge
• Heuristic (e.g. Levenshtein distance)
• Statistical (e.g. BLEU)

Autoevals is developed by the team at Braintrust.

Autoevals uses model-graded evaluation for a variety of subjective tasks including fact checking, safety, and more. Many of these evaluations are adapted from OpenAI's excellent evals project but are implemented so you can flexibly run them on individual examples, tweak the prompts, and debug their outputs.

You can also create your own model-graded evaluations with Autoevals. It's easy to add custom prompts, parse outputs, and manage exceptions.

Installation

Autoevals is distributed as a Python library on PyPI and Node.js library on NPM.

npm install autoevals

Example

Use Autoevals to model-grade an example LLM completion using the factuality prompt. By default, Autoevals uses your OPENAI_API_KEY environment variable to authenticate with OpenAI's API.

Custom Client

If you need to use a custom OpenAI client, you can initialize the library with a custom client.

import openai
from autoevals import init
from autoevals.oai import LLMClient

openai_client = openai.OpenAI(base_url="https://api.openai.com/v1/")

class CustomClient(LLMClient):
    openai=openai_client  # you can also pass in openai module and we will instantiate it for you
    embed = openai.embeddings.create
    moderation = openai.moderations.create
    RateLimitError = openai.RateLimitError

    def complete(self, **kwargs):
        # make adjustments as needed
        return self.openai.chat.completions.create(**kwargs)

# Autoevals will now use your custom client
client = init(client=CustomClient)

If you only need to use a custom client for a specific evaluator, you can pass in the client to the evaluator.

evaluator = Factuality(client=CustomClient)

import { Factuality } from "autoevals";

(async () => {
  const input = "Which country has the highest population?";
  const output = "People's Republic of China";
  const expected = "China";

  const result = await Factuality({ output, expected, input });
  console.log(`Factuality score: ${result.score}`);
  console.log(`Factuality metadata: ${result.metadata.rationale}`);
})();

Using Braintrust with Autoevals

Once you grade an output using Autoevals, it's convenient to use Braintrust to log and compare your evaluation results.

Create a file named example.eval.js (it must end with .eval.js or .eval.js):

import { Eval } from "braintrust";
import { Factuality } from "autoevals";

Eval("Autoevals", {
  data: () => [
    {
      input: "Which country has the highest population?",
      expected: "China",
    },
  ],
  task: () => "People's Republic of China",
  scores: [Factuality],
});

Then, run

npx braintrust run example.eval.js

Supported Evaluation Methods

LLM-as-a-Judge

• Battle
• ClosedQA
• Humor
• Factuality
• Moderation
• Security
• Summarization
• SQL
• Translation
• Fine-tuned binary classifiers

RAG

• Context precision
• Context relevancy
• Context recall
• Context entities recall
• Faithfullness
• Answer relevance
• Answer semantic similarity
• Answer correctness
• Aspect critique

Composite

• Semantic list contains
• JSON validity

Embeddings

• Embedding similarity
• BERTScore

Heuristic

• Levenshtein distance
• Exact match
• Numeric difference
• JSON diff
• Jaccard distance

Statistical

• BLEU
• ROUGE
• METEOR

Custom Evaluation Prompts

Autoevals supports custom evaluation prompts for model-graded evaluation. To use them, simply pass in a prompt and scoring mechanism:

import { LLMClassifierFromTemplate } from "autoevals";

(async () => {
  const promptTemplate = `You are a technical project manager who helps software engineers generate better titles for their GitHub issues.
You will look at the issue description, and pick which of two titles better describes it.

I'm going to provide you with the issue description, and two possible titles.

Issue Description: {{input}}

1: {{output}}
2: {{expected}}`;

  const choiceScores = { 1: 1, 2: 0 };

  const evaluator =
    LLMClassifierFromTemplate <
    { input: string } >
    {
      name: "TitleQuality",
      promptTemplate,
      choiceScores,
      useCoT: true,
    };

  const input = `As suggested by Nicolo, we should standardize the error responses coming from GoTrue, postgres, and realtime (and any other/future APIs) so that it's better DX when writing a client,
We can make this change on the servers themselves, but since postgrest and gotrue are fully/partially external may be harder to change, it might be an option to transform the errors within the client libraries/supabase-js, could be messy?
Nicolo also dropped this as a reference: http://spec.openapis.org/oas/v3.0.3#openapi-specification`;
  const output = `Standardize error responses from GoTrue, Postgres, and Realtime APIs for better DX`;
  const expected = `Standardize Error Responses across APIs`;

  const response = await evaluator({ input, output, expected });

  console.log("Score", response.score);
  console.log("Metadata", response.metadata);
})();

Creating custom scorers

You can also create your own scoring functions that do not use LLMs. For example, to test whether the word 'banana' is in the output, you can use the following:

import { Score } from "autoevals";

const bananaScorer = ({
  output,
  expected,
  input,
}: {
  output: string;
  expected: string;
  input: string;
}): Score => {
  return { name: "banana_scorer", score: output.includes("banana") ? 1 : 0 };
};

(async () => {
  const input = "What is 1 banana + 2 bananas?";
  const output = "3";
  const expected = "3 bananas";

  const result = bananaScorer({ output, expected, input });
  console.log(`Banana score: ${result.score}`);
})();

Why does this library exist?

There is nothing particularly novel about the evaluation methods in this library. They are all well-known and well-documented. However, there are a few things that are particularly difficult when evaluating in practice:

• Normalizing metrics between 0 and 1 is tough. For example, check out the calculation in number.py to see how it's done for numeric differences.
• Parsing the outputs on model-graded evaluations is also challenging. There are frameworks that do this, but it's hard to debug one output at a time, propagate errors, and tweak the prompts. Autoevals makes these tasks easy.
• Collecting metrics behind a uniform interface makes it easy to swap out evaluation methods and compare them. Prior to Autoevals, we couldn't find an open source library where you can simply pass in input, output, and expected values through a bunch of different evaluation methods.

Documentation

The full docs are available here.