> ## 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.

# API Call

If you want to use your own evaluation functions, you can do so by using the `ApiCall` evaluator.

**Description:**\
Performs an API call to a specified endpoint and picks up the evaluation result from the response.

This evaluator is useful if you:

* Want to run some complex or custom logic
* Need to run evaluations on your own servers
* Want to use an evaluation metric that we don't support currently *(Send us an email at [hello@athina.ai](mailto:hello@athina.ai) to request a new metric - we can integrate pretty fast).*

**Info:**

* Input: `response`, `query`, `context`, `response`
* Type: `boolean`
* Metrics: `passed` (0 or 1)

**Arguments:**

* `url`: string - API endpoint to call. Note that this API should accept POST request.
* `headers`: dict - Headers to include in the API call.
* `payload`: dict - Body to send with the API call. This payload will have the Response added to it.

**Sample Code:**

```python
from athina.evals import ApiCall
from athina.loaders import ResponseLoader
 
# Raw data must contain response and optionally the query, context and expected_response to be passed to the API
raw_data = [
    {
        "response": "Response to be sent to the your own API based evaluator",
        "query": "Query to be sent to the your own API based evaluator"
    }
]
dataset = ResponseLoader().load_dict(raw_data)
 
ApiCall(
    url="https://8e714940905f4022b43267e348b8a713.api.mockbin.io/",
    payload={"evaluator": "custom_api_based_evaluator"},
    headers={"Authorization": "Bearer token"}
).run_batch(data=dataset).to_df()
```

The dataset should contain the `response` and optionally the `query`, `context` and `expected_response` to be passed to the API.

<Tip>
  Endpoint Result Format

  We expect the API response to be in JSON format with two keys namely `result` and `reason`.

  * `result` (boolean): should contain the evaluation result (true / false)
  *
  * `reason` (string): should contain an explanation for the evaluation result
</Tip>