Argo API Bridge

This project provides a compatibility layer that transforms OpenAI-style API requests into Argonne National Lab's Argo API format. It supports chat completions, text completions, and embeddings endpoints.

Setup

1. Create Conda Environment

First, create a new conda environment with Python 3.12:

conda create -n argo_bridge python=3.12
conda activate argo_bridge

2. Install Requirements

Install the required Python packages using pip:

pip install -r requirements.txt

3. Run the Server

Start the server with default settings (port 7285):

python argo_bridge.py

Configuration Options

The server supports the following command-line arguments:

• --username: Set the username for API requests (default: 'APS')
• --port: Set the port number for the server (default: 7285)
• --dlog: Enable debug-level logging (when set, logging is at DEBUG level; by default it is INFO level)

Example with custom settings:

python argo_bridge.py --username custom_user --port 8000 --dlog

Endpoints

The API exposes the following endpoints:

• Chat Completions: /chat/completions (POST)
• Text (Legacy) Completions: /completions (POST)
• Embeddings: /embeddings (POST)

Supported Models

The server accepts both Argo and OpenAI model identifiers.

Chat and Completion Models

• GPT-3.5: (gpt35, gpt-3.5)
• GPT-3.5 Large: (gpt35large)
• GPT-4: (gpt4, gpt-4)
• GPT-4 Large: (gpt4large)
• GPT-4 Turbo: (gpt4turbo, gpt-4-turbo)
• GPT-4o: (gpt4o, gpt-4o, gpt-4o-mini)
• GPT-o1 Preview: (gpto1preview, o1-preview)
• GPT-o1 Mini: (gpto1mini, o1-mini, o1mini)
• GPT-o3 Mini: (gpto3mini, o3-mini, o3mini)

Embedding Models

• v3small: (text-embedding-3-small, v3small)
• v3large: (text-embedding-3-large, v3large)
• ada002: (text-embedding-ada-002, ada002)

Production Deployment

For personal use, the development server should be plenty, but if you wish to scale up gunicorn is included in the requirements.

gunicorn --workers 4 --bind localhost:7285 argo_bridge:app

Testing

Run the test suite using unittest:

python -m unittest test_server.py