TensorZero Gateway supports multimodal inference (e.g. image and PDF inputs). See Integrations for a list of supported models.
You can also find the runnable code for this example on GitHub.

​
Setup

​
Object Storage

TensorZero uses object storage to store files (e.g. images, PDFs) used during multimodal inference. It supports any S3-compatible object storage service, including AWS S3, GCP Cloud Storage, Cloudflare R2, and many more. You can configure the object storage service in the object_storage section of the configuration file. In this example, we’ll use a local deployment of MinIO, an open-source S3-compatible object storage service. 
[object_storage]
type = "s3_compatible"
endpoint = "http://minio:9000"  # optional: defaults to AWS S3
# region = "us-east-1"  # optional: depends on your S3-compatible storage provider
bucket_name = "tensorzero"  # optional: depends on your S3-compatible storage provider
# IMPORTANT: for production environments, remove the following setting and use a secure method of authentication in
# combination with a production-grade object storage service.
allow_http = true
You can also store files in a local directory (type = "filesystem") or disable file storage (type = "disabled"). See Configuration Reference for more details. The TensorZero Gateway will attempt to retrieve credentials from the following resources in order of priority:
  1. S3_ACCESS_KEY_ID and S3_SECRET_ACCESS_KEY environment variables
  2. AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables
  3. Credentials from the AWS SDK (default profile)

​
Docker Compose

We’ll use Docker Compose to deploy the TensorZero Gateway, ClickHouse, and MinIO.

​
Inference

With the setup out of the way, you can now use the TensorZero Gateway to perform multimodal inference. The TensorZero Gateway accepts both embedded files (encoded as base64 strings) and remote files (specified by a URL). 
from tensorzero import TensorZeroGateway

with TensorZeroGateway.build_http(
    gateway_url="http://localhost:3000",
) as client:
    response = client.inference(
        model_name="openai::gpt-4o-mini",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": "Do the images share any common features?",
                        },
                        # Remote image of Ferris the crab
                        {
                            "type": "file",
                            "url": "https://raw.githubusercontent.com/tensorzero/tensorzero/eac2a230d4a4db1ea09e9c876e45bdb23a300364/tensorzero-core/tests/e2e/providers/ferris.png",
                        },
                        # One-pixel orange image encoded as a base64 string
                        {
                            "type": "file",
                            "mime_type": "image/png",
                            "data": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAAXNSR0IArs4c6QAAAA1JREFUGFdj+O/P8B8ABe0CTsv8mHgAAAAASUVORK5CYII=",
                        },
                    ],
                }
            ],
        },
    )

    print(response)