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

# 📝 evaluate

`evaluate()` method is used to evaluate the performance of a RAG app. You can find the signature below:

### Parameters

<ParamField path="question" type="Union[str, list[str]]">
  A question or a list of questions to evaluate your app on.
</ParamField>

<ParamField path="metrics" type="Optional[list[Union[BaseMetric, str]]]" optional>
  The metrics to evaluate your app on. Defaults to all metrics: `["context_relevancy", "answer_relevancy", "groundedness"]`
</ParamField>

<ParamField path="num_workers" type="int" optional>
  Specify the number of threads to use for parallel processing.
</ParamField>

### Returns

<ResponseField name="metrics" type="dict">
  Returns the metrics you have chosen to evaluate your app on as a dictionary.
</ResponseField>

## Usage

```python theme={null}
from embedchain import App

app = App()

# add data source
app.add("https://www.forbes.com/profile/elon-musk")

# run evaluation
app.evaluate("what is the net worth of Elon Musk?")
# {'answer_relevancy': 0.958019958036268, 'context_relevancy': 0.12903225806451613}

# or
# app.evaluate(["what is the net worth of Elon Musk?", "which companies does Elon Musk own?"])
```