Getting Started with DeepSeek
This guide shows how to set up a minimal deployment to use the TensorZero Gateway with DeepSeek.
Simple Setup
You can use the short-hand deepseek::model_name
to use a DeepSeek model with TensorZero, unless you need advanced features like fallbacks or custom credentials.
You can use DeepSeek models in your TensorZero variants by setting the model
field to deepseek::model_name
.
For example:
[functions.my_function_name.variants.my_variant_name]type = "chat_completion"model = "deepseek::deepseek-chat"
Additionally, you can set model_name
in the inference request to use a specific DeepSeek model, without having to configure a function and variant in TensorZero.
curl -X POST http://localhost:3000/inference \ -H "Content-Type: application/json" \ -d '{ "model_name": "deepseek::deepseek-chat", "input": { "messages": [ { "role": "user", "content": "What is the capital of Japan?" } ] } }'
Advanced Setup
In more complex scenarios (e.g. fallbacks, custom credentials), you can configure your own model and DeepSeek provider in TensorZero.
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.deepseek_chat]routing = ["deepseek"]
[models.deepseek_chat.providers.deepseek]type = "deepseek"model_name = "deepseek-chat"
[functions.my_function_name]type = "chat"
[functions.my_function_name.variants.my_variant_name]type = "chat_completion"model = "deepseek_chat"
We have tested our integration with deepseek-chat
(DeepSeek-v3
) and deepseek-reasoner
(R1
).
DeepSeek only supports JSON mode for deepseek-chat
and neither model supports tool use yet.
Credentials
You must set the DEEPSEEK_API_KEY
environment variable before running the gateway.
You can customize the credential location by setting the api_key_location
to env::YOUR_ENVIRONMENT_VARIABLE
or dynamic::ARGUMENT_NAME
.
See the Credential Management guide and Configuration Reference for more information.
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: - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:?Environment variable DEEPSEEK_API_KEY must be set.} ports: - "3000:3000" extra_hosts: - "host.docker.internal:host-gateway"
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?" } ] } }'