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This guide shows how to set up a minimal deployment to use the TensorZero Gateway with GCP Vertex AI Anthropic.

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Setup

For this minimal setup, you’ll need just two files in your project directory: 
- config/
  - tensorzero.toml
- docker-compose.yml
You can also find the complete code for this example on GitHub.
For production deployments, see our Deployment Guide.

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Configuration

Create a minimal configuration file that defines a model and a simple chat function:
config/tensorzero.toml
[models.claude_3_haiku_20240307]
routing = ["gcp_vertex_anthropic"]

[models.claude_3_haiku_20240307.providers.gcp_vertex_anthropic]
type = "gcp_vertex_anthropic"
model_id = "claude-3-haiku@20240307"  # or endpoint_id = "..." for fine-tuned models and custom endpoints
location = "us-central1"
project_id = "your-project-id"  # change this

[functions.my_function_name]
type = "chat"

[functions.my_function_name.variants.my_variant_name]
type = "chat_completion"
model = "claude_3_haiku_20240307"
See the list of models available on GCP Vertex AI Anthropic. Alternatively, you can use the short-hand gcp_vertex_anthropic::model_name to use a GCP Vertex AI Anthropic model with TensorZero if you don’t need advanced features like fallbacks or custom credentials:
  • gcp_vertex_anthropic::projects/<PROJECT_ID>/locations/<REGION>/publishers/google/models/<MODEL_ID>
  • gcp_vertex_anthropic::projects/<PROJECT_ID>/locations/<REGION>/endpoints/<ENDPOINT_ID>

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Credentials

By default, TensorZero reads the path to your GCP service account JSON file from the GCP_VERTEX_CREDENTIALS_PATH environment variable (using path_from_env::GCP_VERTEX_CREDENTIALS_PATH). You must generate a GCP service account key in JSON format as described here. You can customize the credential location using:
  • sdk: use the Google Cloud SDK to auto-discover credentials
  • path::/path/to/credentials.json: use a specific file path
  • path_from_env::YOUR_ENVIRONMENT_VARIABLE: read file path from an environment variable (default behavior)
  • dynamic::ARGUMENT_NAME: provide credentials dynamically at inference time
  • { default = ..., fallback = ... }: configure credential fallbacks
See the Credential Management guide and Configuration Reference for more information.

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Deployment (Docker Compose)

Create a minimal Docker Compose configuration:
docker-compose.yml
# 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
      - ${GCP_VERTEX_CREDENTIALS_PATH:-/dev/null}:/app/gcp-credentials.json:ro
    command: --config-file /app/config/tensorzero.toml
    environment:
      - GCP_VERTEX_CREDENTIALS_PATH=${GCP_VERTEX_CREDENTIALS_PATH:+/app/gcp-credentials.json}
    ports:
      - "3000:3000"
    extra_hosts:
      - "host.docker.internal:host-gateway"
You can start the gateway with docker compose up.

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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?"
        }
      ]
    }
  }'