= aws_bedrock_embeddings
:type: processor
:status: experimental
:categories: ["AI"]



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component_type_dropdown::[]


Computes vector embeddings on text, using the AWS Bedrock API.

Introduced in version 4.37.0.


[tabs]
======
Common::
+
--

```yml
# Common config fields, showing default values
label: ""
aws_bedrock_embeddings:
  model: amazon.titan-embed-text-v1 # No default (required)
  text: "" # No default (optional)
```

--
Advanced::
+
--

```yml
# All config fields, showing default values
label: ""
aws_bedrock_embeddings:
  region: ""
  endpoint: ""
  credentials:
    profile: ""
    id: ""
    secret: ""
    token: ""
    from_ec2_role: false
    role: ""
    role_external_id: ""
  model: amazon.titan-embed-text-v1 # No default (required)
  text: "" # No default (optional)
```

--
======

This processor sends text to your chosen large language model (LLM) and computes vector embeddings, using the AWS Bedrock API.
For more information, see the https://docs.aws.amazon.com/bedrock/latest/userguide[AWS Bedrock documentation^].

== Examples

[tabs]
======
Store embedding vectors in Clickhouse::
+
--

Compute embeddings for some generated data and store it within https://clickhouse.com/[Clickhouse^]

```yamlinput:
  generate:
    interval: 1s
    mapping: |
      root = {"text": fake("paragraph")}
pipeline:
  processors:
  - branch:
      request_map: |
        root = this.text
      processors:
      - aws_bedrock_embeddings:
          model: amazon.titan-embed-text-v1
      result_map: |
        root.embeddings = this
output:
  sql_insert:
    driver: clickhouse
    dsn: "clickhouse://localhost:9000"
    table: searchable_text
    columns: ["id", "text", "vector"]
    args_mapping: "root = [uuid_v4(), this.text, this.embeddings]"
```

--
======

== Fields

=== `region`

The AWS region to target.


*Type*: `string`

*Default*: `""`

=== `endpoint`

Allows you to specify a custom endpoint for the AWS API.


*Type*: `string`

*Default*: `""`

=== `credentials`

Optional manual configuration of AWS credentials to use. More information can be found in xref:guides:cloud/aws.adoc[].


*Type*: `object`


=== `credentials.profile`

A profile from `~/.aws/credentials` to use.


*Type*: `string`

*Default*: `""`

=== `credentials.id`

The ID of credentials to use.


*Type*: `string`

*Default*: `""`

=== `credentials.secret`

The secret for the credentials being used.
[CAUTION]
====
This field contains sensitive information that usually shouldn't be added to a config directly, read our xref:configuration:secrets.adoc[secrets page for more info].
====



*Type*: `string`

*Default*: `""`

=== `credentials.token`

The token for the credentials being used, required when using short term credentials.


*Type*: `string`

*Default*: `""`

=== `credentials.from_ec2_role`

Use the credentials of a host EC2 machine configured to assume https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_use_switch-role-ec2.html[an IAM role associated with the instance^].


*Type*: `bool`

*Default*: `false`
Requires version 4.2.0 or newer

=== `credentials.role`

A role ARN to assume.


*Type*: `string`

*Default*: `""`

=== `credentials.role_external_id`

An external ID to provide when assuming a role.


*Type*: `string`

*Default*: `""`

=== `model`

The model ID to use. For a full list see the https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html[AWS Bedrock documentation^].


*Type*: `string`


```yml
# Examples

model: amazon.titan-embed-text-v1

model: amazon.titan-embed-text-v2:0

model: cohere.embed-english-v3

model: cohere.embed-multilingual-v3
```

=== `text`

The prompt you want to generate a response for. By default, the processor submits the entire payload as a string.


*Type*: `string`