AWS Bedrock provider for BAML

The aws-bedrock provider supports all text-output models available via the Converse API.

Example:

BAML
1client<llm> MyClient {
2 provider aws-bedrock
3 options {
4 inference_configuration {
5 max_tokens 100
6 }
7 // model_id "mistral.mistral-7b-instruct-v0:2"
8 // model "anthropic.claude-3-5-sonnet-20240620-v1:0"
9 // model_id "anthropic.claude-3-haiku-20240307-v1:0"
10 model "meta.llama3-8b-instruct-v1:0"
11 }
12}

Authorization

We use the AWS SDK under the hood, which will respect all authentication mechanisms supported by the SDK, including but not limited to:

  • loading the specified AWS_PROFILE from ~/.aws/config
  • built-in authn for services running in EC2, ECS, Lambda, etc.
  • You can also specify the access key ID, secret access key, and region directly (see below)

Playground setup

Add these three environment variables to your extension variables to use the AWS Bedrock provider in the playground.

  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY
  • AWS_REGION - like us-east-1

Non-forwarded options

default_role
string

The default role for any prompts that don’t specify a role. Default: system

We don’t have any checks for this field, you can pass any string you wish.

default_role
string

The role to use if the role is not in the allowed_roles. Default: "user" usually, but some models like OpenAI’s gpt-4o will use "system"

Picked the first role in allowed_roles if not “user”, otherwise “user”.

allowed_roles
string[]

Which roles should we forward to the API? Default: ["system", "user", "assistant"] usually, but some models like OpenAI’s o1-mini will use ["user", "assistant"]

When building prompts, any role not in this list will be set to the default_role.

allowed_role_metadata
string[]

Which role metadata should we forward to the API? Default: []

For example you can set this to ["foo", "bar"] to forward the cache policy to the API.

If you do not set allowed_role_metadata, we will not forward any role metadata to the API even if it is set in the prompt.

Then in your prompt you can use something like:

1client<llm> Foo {
2 provider openai
3 options {
4 allowed_role_metadata: ["foo", "bar"]
5 }
6}
7
8client<llm> FooWithout {
9 provider openai
10 options {
11 }
12}
13template_string Foo() #"
14 {{ _.role('user', foo={"type": "ephemeral"}, bar="1", cat=True) }}
15 This will be have foo and bar, but not cat metadata. But only for Foo, not FooWithout.
16 {{ _.role('user') }}
17 This will have none of the role metadata for Foo or FooWithout.
18"#

You can use the playground to see the raw curl request to see what is being sent to the API.

supports_streaming
boolean

Whether the internal LLM client should use the streaming API. Default: true

Then in your prompt you can use something like:

1client<llm> MyClientWithoutStreaming {
2 provider anthropic
3 options {
4 model claude-3-haiku-20240307
5 api_key env.ANTHROPIC_API_KEY
6 max_tokens 1000
7 supports_streaming false
8 }
9}
10
11function MyFunction() -> string {
12 client MyClientWithoutStreaming
13 prompt #"Write a short story"#
14}
1# This will be streamed from your python code perspective,
2# but under the hood it will call the non-streaming HTTP API
3# and then return a streamable response with a single event
4b.stream.MyFunction()
5
6# This will work exactly the same as before
7b.MyFunction()
region
string

The AWS region to use. Default: AWS_REGION environment variable

We don’t have any checks for this field, you can pass any string you wish.

access_key_id
string

The AWS access key ID to use. Default: AWS_ACCESS_KEY_ID environment variable

secret_access_key
string

The AWS secret access key to use. Default: AWS_SECRET_ACCESS_KEY environment variable

Forwarded options

messages
DO NOT USE

BAML will auto construct this field for you from the prompt

model (or model_id)
string

The model to use.

ModelDescription
anthropic.claude-3-5-sonnet-20240620-v1:0Smartest
anthropic.claude-3-haiku-20240307-v1:0Fastest + Cheapest
meta.llama3-8b-instruct-v1:0
meta.llama3-70b-instruct-v1:0
mistral.mistral-7b-instruct-v0:2
mistral.mixtral-8x7b-instruct-v0:1

Run aws bedrock list-foundation-models | jq '.modelSummaries.[].modelId to get a list of available foundation models; you can also use any custom models you’ve deployed.

Note that to use any of these models you’ll need to request model access.

inference_configuration
object

Additional inference configuration to send with the request; see AWS Bedrock documentation.

Example:

BAML
1client<llm> MyClient {
2 provider aws-bedrock
3 options {
4 inference_configuration {
5 max_tokens 1000
6 temperature 1.0
7 top_p 0.8
8 }
9 }
10}