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

# LogSpanEmbeddingsContent

# LogSpanEmbeddingsContent

Content type for embedding generation spans.

## Overview

`LogSpanEmbeddingsContent` captures embedding API calls. It is wrapped in a [LogSpanContent](/docs/reference/sdk/v2/python/types/LogSpanContent) union via the `actual_instance` pattern.

```python theme={null}
from adaline_api.models.log_span_embeddings_content import LogSpanEmbeddingsContent
```

***

## Fields

<ParamField body="type" type="str" required>
  Must be `"Embeddings"`.
</ParamField>

<ParamField body="input" type="str" required>
  The input payload as a JSON string. Must be valid, parseable JSON (the result of `json.dumps()`).
</ParamField>

<ParamField body="output" type="str" required>
  The output payload as a JSON string. Must be valid, parseable JSON (the result of `json.dumps()`).
</ParamField>

***

## Construction Pattern

All span content is wrapped in [LogSpanContent](/docs/reference/sdk/v2/python/types/LogSpanContent) using the `actual_instance` parameter:

```python theme={null}
from adaline_api.models.log_span_content import LogSpanContent
from adaline_api.models.log_span_embeddings_content import LogSpanEmbeddingsContent

content = LogSpanContent(
    actual_instance=LogSpanEmbeddingsContent(
        type="Embeddings",
        input=json.dumps(request_body),
        output=json.dumps(response_body),
    )
)
```

***

## Example

```python theme={null}
import json
from adaline_api.models.log_span_content import LogSpanContent
from adaline_api.models.log_span_embeddings_content import LogSpanEmbeddingsContent

embed_input = {"model": "text-embedding-3-large", "input": ["search query"]}
embed_output = {"data": [{"embedding": [0.012, -0.003], "index": 0}], "model": "text-embedding-3-large"}

span.update({
    "status": "success",
    "content": LogSpanContent(
        actual_instance=LogSpanEmbeddingsContent(
            type="Embeddings",
            input=json.dumps(embed_input),
            output=json.dumps(embed_output),
        )
    ),
})
```