@arizeai/phoenix-evals/docs/overview.mdx

A library for running evaluations for AI use cases

---
title: "Overview"
description: "Bundled docs for @arizeai/phoenix-evals"
---

`@arizeai/phoenix-evals` provides evaluator building blocks for TypeScript workflows. It includes LLM-based evaluators, code-based evaluators, prompt templating helpers, and compatibility points for Phoenix experiments.

## Install

`@arizeai/phoenix-evals` depends on model adapters from the AI SDK ecosystem. Install the package plus at least one provider adapter for the models you plan to use.

```bash
npm install @arizeai/phoenix-evals
```

### Common Setups

```bash
npm install @arizeai/phoenix-evals @ai-sdk/openai
```

```bash
npm install @arizeai/phoenix-evals @ai-sdk/google
```

You can also pair it with Phoenix experiments:

```bash
npm install @arizeai/phoenix-evals @arizeai/phoenix-client @ai-sdk/openai
```

### Runtime Expectations

- Node.js 22.12+ (required by AI SDK v7, which is ESM-only)
- an AI SDK v7-compatible provider package such as `@ai-sdk/openai` (v4+)
- credentials required by your chosen provider

## Minimal Example

```ts
import { openai } from "@ai-sdk/openai";
import { createFaithfulnessEvaluator } from "@arizeai/phoenix-evals";

const faithfulness = createFaithfulnessEvaluator({
  model: openai("gpt-4o-mini"),
});

const result = await faithfulness.evaluate({
  input: "What is Phoenix?",
  context: "Phoenix is an open-source AI observability platform from Arize.",
  output: "Phoenix is an open-source AI observability platform from Arize.",
});
```

## Docs And Source In `node_modules`

After install, a coding agent can inspect the installed package directly:

```text
node_modules/@arizeai/phoenix-evals/docs/
node_modules/@arizeai/phoenix-evals/src/
```

The bundled docs cover evaluator creation, LLM evaluators, classification metrics, templates, classification, and Phoenix integration.

## Where To Start

- [Create evaluator](./create-evaluator) for custom and code-based evaluator flows
- [LLM evaluators](./llm-evaluators) and [Classification](./classification) for model-backed evaluation
- [Classification metrics](./classification-metrics) for precision/recall/F-beta code evaluators
- [Templates](./templates) and [Phoenix integration](./phoenix-integration) for prompt helpers and experiment wiring

## Source Layout

- `src/index.ts` re-exports the package surface you usually import from `@arizeai/phoenix-evals`
- `src/llm/` contains classification helpers and built-in LLM evaluator factories
- `src/code/` contains deterministic classification-metric evaluators (precision, recall, F-beta)
- `src/helpers/` contains `createEvaluator` and evaluation-result helpers
- `src/template/` contains `formatTemplate` and `getTemplateVariables`
- `src/types/` contains shared evaluator and prompt types

<section className="hidden" data-agent-context="source-map" aria-label="Source map">
  <h2>Source Map</h2>
  <ul>
    <li><code>src/index.ts</code></li>
    <li><code>src/llm/</code></li>
    <li><code>src/code/</code></li>
    <li><code>src/helpers/</code></li>
    <li><code>src/template/</code></li>
    <li><code>src/core/</code></li>
    <li><code>src/types/</code></li>
  </ul>
</section>