mem0/docs/api-reference/pipeline/evaluate.mdx

---
title: '📝 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
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?"])
```