Autoevals - Braintrust

Documentation Index

Fetch the complete documentation index at: /docs/llms.txt

Use this file to discover all available pages before exploring further.

It bundles together a variety of automatic evaluation methods including:

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

npm install autoevals

pnpm

pnpm add autoevals

pip

pip 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.

Python

import asyncio

from autoevals.llm import Factuality

# Create a new LLM-based evaluator
evaluator = Factuality()

# Synchronous evaluation
input = "Which country has the highest population?"
output = "People's Republic of China"
expected = "China"

# Using the synchronous API
result = evaluator(output, expected, input=input)
print(f"Factuality score (sync): {result.score}")
print(f"Factuality metadata (sync): {result.metadata['rationale']}")

# Using the asynchronous API
async def main():
    result = await evaluator.eval_async(output, expected, input=input)
    print(f"Factuality score (async): {result.score}")
    print(f"Factuality metadata (async): {result.metadata['rationale']}")

# Run the async example
asyncio.run(main())

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 gateway and the BRAINTRUST_API_KEY environment variable.

Python

# NOTE: ensure BRAINTRUST_API_KEY is set in your environment and OPENAI_API_KEY is not set
from autoevals.llm import Factuality

# Create an LLM-based evaluator using the Claude 3.5 Sonnet model from Anthropic
evaluator = Factuality(model="claude-3-5-sonnet-latest")

# Evaluate an example LLM completion
input = "Which country has the highest population?"
output = "People's Republic of China"
expected = "China"

result = evaluator(output, expected, input=input)

# The evaluator returns a score from [0,1] and includes the raw outputs from the evaluator
print(f"Factuality score: {result.score}")
print(f"Factuality metadata: {result.metadata['rationale']}")

// 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:

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

Global configuration

Set up a client that all your evaluators will use:

Python

import asyncio

import openai
from autoevals import init
from autoevals.llm import Factuality

client = init(openai.AsyncOpenAI(base_url="https://api.openai.com/v1/"))

async def main():
    evaluator = Factuality()
    result = await evaluator.eval_async(
        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.",
    )
    print(f"Factuality score: {result.score}")

asyncio.run(main())

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:

Python

import openai
from autoevals.llm import Factuality

custom_client = openai.OpenAI(base_url="https://custom-api.example.com/v1/")
evaluator = Factuality(client=custom_client)

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);
})();

Supported evaluation methods

LLM-as-a-judge evaluations

RAG evaluations

Composite evaluations

Embedding evaluations

Heuristic evaluations

Custom evaluation prompts

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

TypeScript

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);
})();

Python

from autoevals import LLMClassifier

# Define a prompt prefix for a LLMClassifier (returns just one answer)
prompt_prefix = """
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}}
"""

# Define the scoring mechanism
# 1 if the generated answer is better than the expected answer
# 0 otherwise
output_scores = {"1": 1, "2": 0}

evaluator = LLMClassifier(
    name="TitleQuality",
    prompt_template=prompt_prefix,
    choice_scores=output_scores,
    use_cot=True,
)

# Evaluate an example LLM completion
page_content = """
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"""
output = "Standardize error responses from GoTrue, Postgres, and Realtime APIs for better DX"
expected = "Standardize Error Responses across APIs"

response = evaluator(output, expected, input=page_content)

print(f"Score: {response.score}")
print(f"Metadata: {response.metadata}")

Create 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:

TypeScript

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}`);
})();

Python

from autoevals import Score

def banana_scorer(output, expected, input):
    return Score(name="banana_scorer", score=1 if "banana" in output else 0)

input = "What is 1 banana + 2 bananas?"
out = "3"
expected = "3 bananas"

result = banana_scorer(out, expected, input)

print(f"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: