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



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component_type_dropdown::[]


Generates responses to messages in a chat conversation, using the AWS Bedrock API.

Introduced in version 4.34.0.


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

```yml
# Common config fields, showing default values
label: ""
aws_bedrock_chat:
  model: amazon.titan-text-express-v1 # No default (required)
  prompt: "" # No default (optional)
  system_prompt: "" # No default (optional)
  max_tokens: 0 # No default (optional)
  stop: 0 # No default (optional)
```

--
Advanced::
+
--

```yml
# All config fields, showing default values
label: ""
aws_bedrock_chat:
  region: ""
  endpoint: ""
  credentials:
    profile: ""
    id: ""
    secret: ""
    token: ""
    from_ec2_role: false
    role: ""
    role_external_id: ""
  model: amazon.titan-text-express-v1 # No default (required)
  prompt: "" # No default (optional)
  system_prompt: "" # No default (optional)
  max_tokens: 0 # No default (optional)
  stop: 0 # No default (optional)
  temperature: [] # No default (optional)
  top_p: 0 # No default (optional)
```

--
======

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

== 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-text-express-v1

model: anthropic.claude-3-5-sonnet-20240620-v1:0

model: cohere.command-text-v14

model: meta.llama3-1-70b-instruct-v1:0

model: mistral.mistral-large-2402-v1:0
```

=== `prompt`

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


*Type*: `string`


=== `system_prompt`

The system prompt to submit to the AWS Bedrock LLM.


*Type*: `string`


=== `max_tokens`

The maximum number of tokens to allow in the generated response.


*Type*: `int`


=== `stop`

The likelihood of the model selecting higher-probability options while generating a response. A lower value makes the model omre likely to choose higher-probability options, while a higher value makes the model more likely to choose lower-probability options.


*Type*: `float`


=== `temperature`

A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.


*Type*: `array`


=== `top_p`

The percentage of most-likely candidates that the model considers for the next token. For example, if you choose a value of 0.8, the model selects from the top 80% of the probability distribution of tokens that could be next in the sequence.


*Type*: `float`