> For AI agents: the complete documentation index is available at https://viteval.dev/llms.txt, the full documentation bundle is available at https://viteval.dev/llms-full.txt.

# Configure models

Configure scorer models in `viteval.config.ts`. Choose your task model separately
inside the task function. No worker setup module is required.

## Prerequisites

- Complete [Getting started](https://viteval.dev/getting-started.md) and keep a passing evaluation.
- Choose a model provider and have its API credentials available.
- This walkthrough uses OpenAI through the AI SDK.

## Steps


1

### Install a model provider

This example uses OpenAI through the AI SDK:
```bash
pnpm add ai @ai-sdk/openai
```
Provide `OPENAI_API_KEY` in the process environment before running evaluations.
Installing the SDK does not load credentials from an env file automatically.

2

### Register scorer models

Add raw AI SDK providers and default model references to `viteval.config.ts`:
```ts
import { openai } from '@ai-sdk/openai';
import { defineConfig } from 'viteval/config';

export default defineConfig({
  models: {
    providers: [openai],
    defaults: {
      language: 'openai:gpt-4.1-mini',
      embedding: 'openai:text-embedding-3-small',
    },
  },
});
```
Use `provider:model-id` references for defaults. Deterministic scorers need no
model configuration. Registration does not make network requests.

3

### Choose the task model

The default language model is the judge, not the task model. Call the client
you want to evaluate inside the task. Replace `src/viteval.eval.ts` with:
```ts
import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';
import { evaluate, scorers } from 'viteval';

evaluate('Support policy answers', {
  data: [
    {
      input: 'Can I return an unopened item after 14 days?',
      expected: 'Unopened items can be returned within 30 days of delivery.',
    },
  ],
  task: async ({ input }) => {
    const result = await generateText({
      model: openai('gpt-4.1-mini'),
      system: 'Unopened items can be returned within 30 days of delivery.',
      prompt: input,
    });
    return result.text;
  },
  scorers: [scorers.factual(), scorers.answerSimilarity()],
  threshold: 0.8,
});
```

4

### Override a judge when needed

Pass `model` to an LLM judge factory to use a different judge for that scorer.
Embedding factories do not support per-scorer model overrides.
```ts
const factual = scorers.factual({ model: openai('gpt-4.1') });
```
Use `factual` instead of `scorers.factual()` in the evaluation's `scorers` array.
Viteval resolves defaults in each worker from the selected project config.

## Verification

```bash
pnpm exec viteval run
```

Confirm the task produces an answer and each scorer returns a numeric score.
Model-based scores vary and the example is not guaranteed to pass its threshold.
Use [saved reports](https://viteval.dev/guide/results.md) to inspect score metadata.

## Troubleshooting

### Missing model or authentication errors

Confirm `models.providers` and `models.defaults` are configured and `OPENAI_API_KEY` is
available to the evaluation process. A null score with error metadata indicates
a failed measurement, not a low-quality answer.

### Use a different judge for one scorer

Use a factory's `model` override to change
one judge. Embedding factories do not accept per-scorer model overrides.

## References

- [Scoring and thresholds](https://viteval.dev/concepts/scoring.md)
- [Configuration](https://viteval.dev/api/configuration.md)
- [Built-in scorers](https://viteval.dev/api/scoring/scorers.md)