> ## Documentation Index
> Fetch the complete documentation index at: https://docs.athina.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Running a suite of evals

> Here is a sample of all the code you need to run a suite of evals.

## Demo Video

<div
  style={{
position: "relative",
paddingBottom: "60.40268456375839%",
height: "0",
}}
>
  <iframe
    src="https://www.loom.com/embed/c20aeb829bc3487f9a282222c9d1de97"
    frameBorder="0"
    webkitAllowFullScreen
    mozAllowFullScreen
    allowFullScreen
    style={{
  position: "absolute",
  top: "0",
  left: "0",
  width: "100%",
  height: "100%",
}}
  />
</div>

## Example Code

```python
import os
from athina.evals import DoesResponseAnswerQuery, ContextContainsEnoughInformation, Faithfulness
from athina.loaders import Loader
from athina.keys import AthinaApiKey, OpenAiApiKey
from athina.runner.run import EvalRunner
from athina.datasets import yc_query_mini
import pandas as pd

from dotenv import load_dotenv
load_dotenv()

# Configure an API key.
OpenAiApiKey.set_key(os.getenv('OPENAI_API_KEY'))

# Load the dataset
dataset = [
    {
    "query": "query_string",
    "context": ["chunk_1", "chunk_2"],
    "response": "llm_generated_response_string",
    "expected_response": "ground truth (optional)",
    },
    { ... },
    { ... },
    { ... },
]

# Evaluate a dataset across a suite of eval criteria

EvalRunner.run_suite(
    evals=[
        RagasAnswerCorrectness(),
        RagasContextPrecision(),
        RagasContextRelevancy(),
        RagasContextRecall(),
        RagasFaithfulness(),
        ResponseFaithfulness(),
        Groundedness(),
        ContextSufficiency(),
    ],
    data=dataset,
    max_parallel_evals=10
)
```

### Response Format

![Eval Suite Response Format](https://mintlify.s3.us-west-1.amazonaws.com/athinaai/images/eval-suite-df.png)