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AWS Bedrock

Introduction​

Mistral AI's open and commercial models can be deployed on the AWS Bedrock cloud platform as fully managed endpoints. AWS Bedrock is a serverless service so you don't have to manage any infrastructure.

As of today, the following models are available:

  • Mistral 7B
  • Mixtral 8x7B
  • Mistral Small
  • Mistral Large

For more details, visit the models page.

Getting started​

The following sections outline the steps to deploy and query a Mistral model on the AWS Bedrock platform.

The following items are required:

  • Access to an AWS account within a region that supports the AWS Bedrock service and offers access to your model of choice: see the AWS documentation for model availability per region.
  • An AWS IAM principal (user, role) with sufficient permissions, see the AWS documentation for more details.
  • A local code environment set up with the relevant AWS SDK components, namely:

Requesting access to the model​

Follow the instructions on the AWS documentation to unlock access to the Mistral model of your choice.

Querying the model​

AWS Bedrock models are accessible through the Converse API.

Before running the examples below, make sure to sure to :

  • Properly configure the authentication credentials for your development environment. The AWS documentation provides an in-depth explanation on the required steps.
  • Create a Python virtual environment with the boto3 package (version >= 1.34.131).
  • Set the following environment variables:
    • AWS_REGION: The region where the model is deployed (e.g. us-west-2),
    • AWS_BEDROCK_MODEL_ID: The model ID (e.g. mistral.mistral-large-2407-v1:0).
import boto3
import os

region = os.environ.get("AWS_REGION")
model_id = os.environ.get("AWS_BEDROCK_MODEL_ID")

bedrock_client = boto3.client(service_name='bedrock-runtime', region_name=region)

user_msg = "Who is the best French painter? Answer in one short sentence."
messages = [{"role": "user", "content": [{"text": user_msg}]}]
temperature = 0.0
max_tokens = 1024

params = {"modelId": model_id,
          "messages": messages,
          "inferenceConfig": {"temperature": temperature,
                              "maxTokens": max_tokens}}

resp = bedrock_client.converse(**params)

print(resp["output"]["message"]["content"][0]["text"])

Going further​

For more details and examples, refer to the following resources: