This project provides a Docker image that runs the Ollama service with basic authentication using the Caddy server. The image is designed to be easy to use, with a simple command to get started. The basic authentication credentials can be set using environment variables when running the Docker container.

To use this Docker image, you can run the following command:

mkdir ./data;
docker run \
 -p 80:80 \
 -p 443:443 \
 -v ./data:/data \
 -e DOMAIN=my-domain.com \
 -e [email protected] \
 -e TOKEN=my-token-here \
 ghcr.io/iosifnicolae2/ollama-openai-auth:latest 

This will start the Ollama service with basic authentication using the Caddy server. The service will be available at https://my-domain.com on the host machine. The OpenAI token authentication credentials will be set to my-token-here. Also, there will be provisioned an https certificate for the domain my-domain.com using Let's Encrypt.

To build the Docker image yourself, you can use the following command:

docker build -t ollama-openai-auth .

This will build the Docker image using the Dockerfile in the current directory, and tag it with the name ollama-openai-auth.

The Ollama service is started automatically when the Docker container is launched. It will be available at https://localhost on the host machine.

You can access the OpenAI compatible API like below:

curl https://my-domain.com/v1/chat/completions   -H "Content-Type: application/json"   -H "Authorization: Bearer my-token-here"   -d '{
    "model": "llama2",
    "messages": [
      {
        "role": "system",
        "content": "You are a poetic assistant, skilled in explaining complex programming concepts with creative flair."
      },
      {
        "role": "user",
        "content": "Compose a poem that explains the concept of recursion in programming."
      }
    ]
  }'

or using OpenAI Python library:

from openai import OpenAI

client = OpenAI(
    base_url = 'https://my-domain.com/v1',
    api_key='my-token-here',
)

response = client.chat.completions.create(
  model="llama2",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"},
    {"role": "assistant", "content": "The LA Dodgers won in 2020."},
    {"role": "user", "content": "Where was it played?"}
  ]
)
print(response.choices[0].message.content)