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

<AgentInstructions>

## Submitting Feedback

If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback:

POST https://docs.firebolt.io/feedback

```json
{
  "path": "/reference-sql/functions-reference/ai/ai-embed-text",
  "feedback": "Description of the issue"
}
```

Only submit feedback when you have something specific and actionable to report.

</AgentInstructions>

> Generate a text embedding with an Amazon Bedrock model and return a numeric vector.

# AI_EMBED_TEXT

Generates a vector embedding for the provided input text using an embedding model and returns it as an `ARRAY(DOUBLE)`. Provide the model identifier, the plain-text input in `INPUT_TEXT`, and a `LOCATION` that holds AWS Bedrock credentials. Optionally specify the embedding `DIMENSION` and whether to return `NULL` on errors.

For setup guidance and end-to-end examples, see [Getting started with AI](/guides/ai/getting-started-with-ai).

<Note>
  Initially, this function uses Amazon Bedrock as its backend. The `LOCATION` must reference a Bedrock `LOCATION`. For now, the only supported model is Amazon Titan Embeddings v2 for text: the `MODEL` must be `'amazon.titan-embed-text-v2:0'`.
</Note>

## Syntax

```sql theme={"theme":{"light":"github-light","dark":"github-dark"}}
AI_EMBED_TEXT(
  MODEL => <model_name>,
  INPUT_TEXT => <input_text>,
  [ DIMENSION => <dimension> ],
  LOCATION => <location>,
  [ NULL_ON_ERROR => <bool> ]
)
```

## Parameters

| Parameter       | Description                                                                                                                                                                                 | Supported input types |
| :-------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :-------------------- |
| `MODEL`         | The embedding model to invoke. For now, must be `'amazon.titan-embed-text-v2:0'`. The value is forwarded to Bedrock without further validation.                                             | `TEXT`                |
| `INPUT_TEXT`    | The plain-text content to embed.                                                                                                                                                            | `TEXT`                |
| `DIMENSION`     | Optional. The dimension of the generated embedding. For `'amazon.titan-embed-text-v2:0'`, must be one of `1024`, `512`, or `256`. Defaults to the model’s default dimension if omitted.     | `INTEGER`             |
| `LOCATION`      | The name of the `LOCATION` to use for AWS credentials. Must be a literal constant. See [CREATE LOCATION (Amazon Bedrock)](/reference-sql/commands/data-definition/create-location-bedrock). | `TEXT`                |
| `NULL_ON_ERROR` | Optional. Whether to return `NULL` instead of raising an error when a Bedrock invocation error occurs. Default `FALSE`. Must be a literal constant.                                         | `BOOL`                |

## Return type

`ARRAY(DOUBLE)`

* Returns the model’s generated embedding vector.
* If `INPUT_TEXT` is `NULL`, the function returns `NULL`.

## LLM token budget

Queries executed with `AI_EMBED_TEXT` count towards your account’s daily LLM token budget. If your account exceeds its allotted token budget, invocations of `AI_EMBED_TEXT` will fail until the budget is increased or the daily limit resets. For details on setting and monitoring your token budget, see [Set your LLM token budget](/guides/ai/getting-started-with-ai#set-your-llm-token-budget) and [Check your LLM token quota usage](/guides/ai/getting-started-with-ai#check-your-llm-token-quota-usage).

<Note>
  LLM token budget accounting is not available in Firebolt Core.
</Note>

## Example

```sql theme={"theme":{"light":"github-light","dark":"github-dark"}}
SELECT AI_EMBED_TEXT(
    MODEL => 'amazon.titan-embed-text-v2:0',
    INPUT_TEXT => 'lightning fast analytics',
    DIMENSION => 256,
    LOCATION => 'my_bedrock_location'
);
```

**Returns (example):**

```text theme={"theme":{"light":"github-light","dark":"github-dark"}}
[0.12341234, 0.754376, 0.98763459, …]
```