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

npm install autoevals

Getting started

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.

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 other AI providers

When you use Autoevals, it will look for an OPENAI_BASE_URL environment variable to use as the base for requests to an OpenAI compatible API. If OPENAI_BASE_URL is not set, it will default to the AI proxy.

If you choose to use the proxy, you'll also get:

Simplified access to many AI providers
Reduced costs with automatic request caching
Increased observability when you enable logging to Braintrust

The proxy is free to use, even if you don't have a Braintrust account.

If you have a Braintrust account, you can optionally set the BRAINTRUST_API_KEY environment variable instead of OPENAI_API_KEY to unlock additional features like logging and monitoring. You can also route requests to supported AI providers and models or custom models you have configured in Braintrust.

// NOTE: ensure BRAINTRUST_API_KEY is set in your environment and OPENAI_API_KEY is not set
import { Factuality } from "autoevals";
 
(async () => {
  const input = "Which country has the highest population?";
  const output = "People's Republic of China";
  const expected = "China";
 
  // Run an LLM-based evaluator using the Claude 3.5 Sonnet model from Anthropic
  const result = await Factuality({
    model: "claude-3-5-sonnet-latest",
    output,
    expected,
    input,
  });
 
  // The evaluator returns a score from [0,1] and includes the raw outputs from the evaluator
  console.log(`Factuality score: ${result.score}`);
  console.log(`Factuality metadata: ${result.metadata?.rationale}`);
})();

Custom client configuration

There are two ways you can configure a custom client when you need to use a different OpenAI compatible API:

Global configuration: Initialize a client that will be used by all evaluators
Instance configuration: Configure a client for a specific evaluator

Global configuration

Set up a client that all your evaluators will use:

import OpenAI from "openai";
import { init, Factuality } from "autoevals";
 
const client = new OpenAI({
  baseURL: "https://api.openai.com/v1/",
});
 
init({ client });
 
(async () => {
  const result = await Factuality({
    input: "What is the speed of light in a vacuum?",
    output: "The speed of light in a vacuum is 299,792,458 meters per second.",
    expected:
      "The speed of light in a vacuum is approximately 300,000 kilometers per second (or precisely 299,792,458 meters per second).",
  });
 
  console.log("Factuality Score:", result);
})();

Instance configuration

Configure a client for a specific evaluator instance:

import OpenAI from "openai";
import { Factuality } from "autoevals";
 
(async () => {
  const customClient = new OpenAI({
    baseURL: "https://custom-api.example.com/v1/",
  });
 
  const result = await Factuality({
    client: customClient,
    output: "Paris is the capital of France",
    expected:
      "Paris is the capital of France and has a population of over 2 million",
    input: "Tell me about Paris",
  });
  console.log(result);
})();

Using Braintrust with Autoevals (optional)

Once you grade an output using Autoevals, you can optionally use Braintrust to log and compare your evaluation results. This integration is completely optional and not required for using Autoevals.

Create a file named example.eval.js (it must take the form *.eval.[ts|tsx|js|jsx]):

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 evaluations

Battle
Closed QA
Humor
Factuality
Moderation
Security
Summarization
SQL
Translation
Fine-tuned binary classifiers

RAG evaluations

Context precision
Context relevancy
Context recall
Context entity recall
Faithfulness
Answer relevancy
Answer similarity
Answer correctness

Composite evaluations

Semantic list contains
JSON validity

Embedding evaluations

Embedding similarity

Heuristic evaluations

Levenshtein distance
Exact match
Numeric difference
JSON diff

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.