# 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](https://braintrust.dev/).
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](https://github.com/openai/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.
### Requirements
- Python 3.9 or higher
- Compatible with both OpenAI Python SDK v0.x and v1.x
## Installation
### TypeScript
```bash
npm install autoevals
```
### Python
```bash
pip install autoevals
```
## Getting started
Use Autoevals to model-grade an example LLM completion using the [Factuality prompt](templates/factuality.yaml).
By default, Autoevals uses your `OPENAI_API_KEY` environment variable to authenticate with OpenAI's API.
### Python
```python
from autoevals.llm import *
import asyncio
# 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())
```
### TypeScript
```typescript
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 look for a `BRAINTRUST_AI_GATEWAY_URL` environment variable and then default to the [Braintrust Gateway](https://www.braintrust.dev/docs/deploy/gateway).
When you use the Braintrust Gateway, 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 Braintrust-hosted Gateway is free to use while it is in beta.
Set the `BRAINTRUST_API_KEY` environment variable to authenticate Gateway requests. You can also route requests to supported AI providers and models or custom models you have configured in Braintrust.
### Python
```python
# NOTE: ensure BRAINTRUST_API_KEY is set in your environment
from autoevals.llm import *
# 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']}")
```
### TypeScript
```typescript
// NOTE: ensure BRAINTRUST_API_KEY is set in your environment
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
```python
import openai
import asyncio
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())
```
#### TypeScript
```typescript
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
```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)
```
#### TypeScript
```typescript
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](https://www.braintrust.dev/docs/libs/python) to log and compare your evaluation results. This integration is completely optional and not required for using Autoevals.
### TypeScript
Create a file named `example.eval.js` (it must take the form `*.eval.[ts|tsx|js|jsx]`):
```typescript
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
```bash
npx braintrust run example.eval.js
```
### Python
Create a file named `eval_example.py` (it must take the form `eval_*.py`):
```python
import braintrust
from autoevals.llm import Factuality
Eval(
"Autoevals",
data=lambda: [
dict(
input="Which country has the highest population?",
expected="China",
),
],
task=lambda *args: "People's Republic of China",
scores=[Factuality],
)
```
## 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
For detailed documentation on all scorers, including parameters, score ranges, and usage examples, see the [**Scorer Reference**](SCORERS.md).
## Custom evaluation prompts
Autoevals supports custom evaluation prompts for model-graded evaluation. To use them, simply pass in a prompt and scoring mechanism:
### Python
```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}")
```
### TypeScript
```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);
})();
```
## Score results
Every scorer returns a small `Score` result object. This is the public surface
consumers should read when they need to store, compare, or export evaluation
results:
- `name`: the scorer name
- `score`: a number between 0 and 1, or `None` / `null` when the evaluation is skipped
- `metadata`: optional scorer-specific details, such as rationale text or a
selected choice. Keys are scorer-specific; consumers should not assume
metadata keys are shared across scorer types.
- `error`: deprecated and retained for backward compatibility; some scorers may
still populate it, but callers should primarily handle thrown exceptions
Inputs, expected values, model prompts, and other runtime context are not part
of the `Score` object. Keep those separately if your application needs them.
## 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:
### Python
```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?"
output = "3"
expected = "3 bananas"
result = banana_scorer(output, expected, input)
print(f"Banana score: {result.score}")
```
### TypeScript
```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}`);
})();
```
## 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](/py/autoevals/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 [for your reference](https://www.braintrust.dev/docs/reference/autoevals).
## Contributing
We welcome contributions!
To install the development dependencies, run `make develop`, and run `source env.sh` to activate the environment. Make a `.env` file from the `.env.example` file and set the environment variables. Run `direnv allow` to load the environment variables.
To run the tests, run `pytest` from the root directory.
Send a PR and we'll review it! We'll take care of versioning and releasing.