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

# Compute Quality Score • AI Agent SDK

> compute_quality_score: Combine multiple sub-metrics into one final score, as an example.

# compute\_quality\_score

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  <Badge color="purple">Method</Badge>
</div>

> This is a method of the [**Memory**](../classes/Memory) class in the [**memory**](../modules/memory) module.

Combine multiple sub-metrics into one final score, as an example.

## Signature

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
def compute_quality_score(completeness: float, relevance: float, clarity: float, accuracy: float, weights: Dict[str, float]) -> float
```

## Parameters

<ParamField query="completeness" type="float" required={true}>
  0-1
</ParamField>

<ParamField query="relevance" type="float" required={true}>
  0-1
</ParamField>

<ParamField query="clarity" type="float" required={true}>
  0-1
</ParamField>

<ParamField query="accuracy" type="float" required={true}>
  0-1
</ParamField>

<ParamField query="weights" type="Dict" required={false}>
  optional weighting like \{"completeness": 0.25, "relevance": 0.3, ...}
</ParamField>

### Returns

<ResponseField name="Returns" type="float">
  Weighted average 0-1
</ResponseField>

## Source

<Card title="View on GitHub" icon="github" href="https://github.com/MervinPraison/PraisonAI/blob/main/src/praisonai-agents/praisonaiagents/memory/memory.py#L501">
  `praisonaiagents/memory/memory.py` at line 501
</Card>