> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/information-extraction/chunk-relevance.md).

# Chunk Relevance

**Objective:** 

Chunk Relevance evaluates the relevance of distinct text chunks extracted from documents in response to a query. It splits the source text into meaningful units (chunks) and assesses whether the extracted segments are contextually relevant to the given prompt or query. A high score suggests that the model is efficient in isolating and extracting coherent, meaningful portions of text, ensuring relevance and cohesion.

**Required Columns in Dataset:**

`Prompt`, `Expected Chunks`, `Retrieved Chunks`

**Interpretation:**

* **High Chunk Relevance**: Indicates that the model is efficiently selecting text segments that are contextually aligned and relevant to the query.
* **Low Chunk Relevance**: Means the model often extracts irrelevant or incoherent text chunks, leading to low-quality or disjointed responses.

**Execution via UI:**

<figure><img src="/files/kyDrl5u3uP8h24JifoXa" alt=""><figcaption></figcaption></figure>

**Execution via SDK:**

```python
metrics=[
    {"name": "Chunk Relevance", "config": {"model": "gpt-4o-mini", "provider": "openai"}, "column_name": "your-text", "schema_mapping": schema_mapping}
]
```


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