> ## 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.

# Embedding Result • AI Agent SDK

> EmbeddingResult: Result from embedding generation.

# EmbeddingResult

> Defined in the [**result**](../modules/result) module.

<Badge color="blue">AI Agent</Badge>

Result from embedding generation.

Attributes:
embeddings: List of embedding vectors (each is a list of floats)
model: The model used for embedding (optional)
usage: Token usage information (optional)
metadata: Additional metadata (optional)

## Properties

<ResponseField name="embeddings" type="List">
  No description available.
</ResponseField>

<ResponseField name="model" type="Optional">
  No description available.
</ResponseField>

<ResponseField name="usage" type="Optional">
  No description available.
</ResponseField>

<ResponseField name="metadata" type="Dict">
  No description available.
</ResponseField>

## Usage

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
>>> result = EmbeddingResult(
    ...     embeddings=[[0.1, 0.2, 0.3]],
    ...     model="text-embedding-3-small",
    ...     usage={"prompt_tokens": 5, "total_tokens": 5}
    ... )
    >>> print(len(result.embeddings[0]))
    3
```

## Source

<Card title="View on GitHub" icon="github" href="https://github.com/MervinPraison/PraisonAI/blob/main/src/praisonai-agents/praisonaiagents/embedding/result.py#L13">
  `praisonaiagents/embedding/result.py` at line 13
</Card>