# 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:**
**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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