> ## 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.

# Grounded Evals

[Github ](https://github.com/athina-ai/athina-evals/blob/main/athina/evals/grounded/wrapper.py)

### ❊ Info[](#-info)

Grounded evaluators are designed to assess the relevance of a response or context based on specific similarity algorithm.

**How does it work**

Grounded evaluators compare a given response to a reference or context, using various similarity measures to determine the degree of relevance or similarity.

**Required Args**

Your dataset must contain these fields:

* `response`: The LLM generated response.
* `expected_response`: The reference content to compare the response against in case of AnswerSimilarity.
* `context`: The reference content to compare the response against in case of ContextSimilarity.

**Metrics**

* `SimilarityScore`: A numeric value representing the degree of similarity or relevance.

***

### ▷ Run the AnswerSimilarity evaluator on a single datapoint[](#-run-the-answersimilarity-evaluator-on-a-single-datapoint)

***

### ▷ Run the function eval on a dataset[](#-run-the-function-eval-on-a-dataset)

```python
from athina.evals import AnswerSimilarity
from athina.evals.grounded.similarity import CosineSimilarity

# Checks the similarity between the response and the reference answer
response = "The capital of France is Paris."
expected_response = "Paris is the capital of France."
AnswerSimilarity(comparator=CosineSimilarity()).run(response=response, expected_response=expected_response).to_df()
```

1. Load your data with the `Loader`

```python
from athina.loaders import Loader
raw_data = [
    {
        "response": "Thomas Edison invented the light bulb.",
        "expected_response": "The light bulb was invented by Thomas Edison."
    },
    {
        "response":  "The capital of France is Paris.",
        "expected_response": "Paris is the capital of France."
    }
]
# Load the data from JSON, Athina or Dictionary
dataset = Loader().load_dict(raw_data)
```

2. Run the evaluator on your dataset

```python
from athina.evals import AnswerSimilarity
from athina.evals.grounded.similarity import CosineSimilarity

# Evaluates the similarity of the response to the expected response
AnswerSimilarity(comparator=CosineSimilarity()).run_batch(data=dataset).to_df()
```

***

### Following are examples of the various Grounded evaluators we support[](#following-are-examples-of-the-various-grounded-evaluators-we-support)

#### AnswerSimilarity[](#answersimilarity)

**Description:** Evaluates the similarity between the generated response and a given expected response.

**Arguments:**

* `comparator`: `Comparator` The similarity measurement function (e.g., CosineSimilarity).
* `failure_threshold`: `float` The threshold value for determining pass/fail.

**Sample Code:**

```python
from athina.evals import AnswerSimilarity
from athina.evals.grounded.similarity import CosineSimilarity

AnswerSimilarity(comparator=CosineSimilarity(), failure_threshold=0.75).run_batch(data=dataset).to_df()
```

#### ContextSimilarity[](#contextsimilarity)

**Description:** Evaluates the similarity between the generated response and the context.

**Arguments:**

* `comparator`: `Comparator` The similarity measurement function (e.g., CosineSimilarity).
* `failure_threshold`: `float` The threshold value for determining pass/fail.

**Sample Code:**

```python
from athina.evals import ContextSimilarity
from athina.evals.grounded.similarity import CosineSimilarity

ContextSimilarity(comparator=CosineSimilarity(), failure_threshold=0.75).run_batch(data=dataset).to_df()
```