Getting Started with AWS Bedrock

This guide shows how to set up a minimal deployment to use the TensorZero Gateway with the AWS Bedrock API.

Setup

For this minimal setup, you’ll need just two files in your project directory:

Directoryconfig/
- tensorzero.toml
docker-compose.yml

For production deployments, see our Deployment Guide.

Configuration

Create a minimal configuration file that defines a model and a simple chat function:

[models.claude_3_haiku_20240307]
routing = ["aws_bedrock"]

[models.claude_3_haiku_20240307.providers.aws_bedrock]
type = "aws_bedrock"
model_id = "anthropic.claude-3-haiku-20240307-v1:0"

[functions.my_function_name]
type = "chat"

[functions.my_function_name.variants.my_variant_name]
type = "chat_completion"
model = "claude_3_haiku_20240307"

# Disable observability to keep this example minimal (not recommended in production)
[gateway]
disable_observability = true

See the list of available models on AWS Bedrock.

See the Configuration Reference for optional fields (e.g. overriding the region).

Credentials

You must make sure that the gateway has the necessary permissions to access AWS Bedrock.

The simplest way is to set the following environment variables before running the gateway:

AWS_ACCESS_KEY_ID=...
AWS_REGION=us-east-1
AWS_SECRET_ACCESS_KEY=...

Deployment (Docker Compose)

Create a minimal Docker Compose configuration:

# This is a simplified example for learning purposes. Do not use this in production.
# For production-ready deployments, see: https://www.tensorzero.com/docs/gateway/deployment

services:
  gateway:
    image: tensorzero/gateway
    volumes:
      - ./config:/app/config:ro
    environment:
      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID:?Environment variable AWS_ACCESS_KEY_ID must be set.}
      - AWS_REGION=${AWS_REGION:?Environment variable AWS_REGION must be set.}
      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY:?Environment variable AWS_SECRET_ACCESS_KEY must be set.}
    ports:
      - "3000:3000"

You can start the gateway with docker compose up.

Inference

Make an inference request to the gateway:

curl -X POST http://localhost:3000/inference \
  -H "Content-Type: application/json" \
  -d '{
    "function_name": "my_function_name",
    "input": {
      "messages": [
        {
          "role": "user",
          "content": "What is the capital of Japan?"
        }
      ]
    }
  }'