Setup

Requirements

You need two things before making any requests:

A connection to the UB campus network or the UB VPN
An API key (see below)

Getting an API key

Email Chris Proctor with a brief description of your project. Once issued, your key is a long alphanumeric string. Treat it like a password:

Store it in a password manager or your OS keychain
Never hard-code it in a script or commit it to a repository

If a key is lost it cannot be recovered — request a new one.

Setting the environment variable

The most convenient way to supply your key is the GSEAI_API_TOKEN environment variable. Add it to your shell profile (~/.zshrc, ~/.bashrc, etc.):

export GSEAI_API_TOKEN=your-api-key

The gseai CLI and the GSEAIServer class both read this variable automatically, so you won’t need to pass the key on every call.

HTTPS (recommended for sensitive data)

The server is accessible over plain HTTP or HTTPS on port 11434. If your work involves sensitive or confidential data, use HTTPS:

https://gseai.gse.buffalo.edu:11434

Step 1 — Download the CA certificate:

curl -O http://gseai.gse.buffalo.edu:11434/gseai.pem

Step 2 — Install it system-wide:

macOS:

sudo security add-trusted-cert -d -r trustRoot \
    -k /Library/Keychains/System.keychain gseai.pem

Linux (Debian/Ubuntu):

sudo cp gseai.pem /usr/local/share/ca-certificates/gseai.crt
sudo update-ca-certificates

Windows: Double-click gseai.pem, choose Install Certificate, select Local Machine, and place it in the Trusted Root Certification Authorities store.

Alternative — Python only (no system install):

If you prefer not to touch system certificates, point the standard REQUESTS_CA_BUNDLE variable at the file; both httpx and requests honour it:

export REQUESTS_CA_BUNDLE=/path/to/gseai.pem

Then pass https://gseai.gse.buffalo.edu:11434 as the host when constructing GSEAIServer:

server = GSEAIServer("your-token", host="https://gseai.gse.buffalo.edu")

OpenAI SDK compatibility

The server implements the OpenAI API, so any code written for OpenAI works with GSEAI by changing the base URL and API key:

from openai import OpenAI

client = OpenAI(
    api_key="your-gseai-token",
    base_url="http://gseai.gse.buffalo.edu:11434/v1",
)

The gseai package wraps this API directly and does not require the OpenAI SDK.

Infrastructure and performance

The server runs on CPU-only hardware with 700 GB of RAM in the Computational Literacies Lab (Foster Hall). This makes it well suited to research and batch processing but not to latency-sensitive applications.

Rough performance expectations:

Chat / text generation — seconds to a minute depending on model and response length; use --stream / stream=True for incremental output
Speech transcription — roughly real-time for short clips
Image generation — 1–5 minutes per image depending on size and prompt complexity

All processing happens within the UB network. No request data is transmitted to external services or cloud providers, and the server does not maintain persistent logs of request content.

IRB considerations

If your project involves human-subjects research, the following points are relevant for IRB applications:

The server is operated on hardware under direct research-team control in Foster Hall
All traffic between your workstation and the server is encrypted with TLS 1.2 or 1.3
Access is controlled by individual API keys, revocable at any time
No participant data is transmitted to external services, cloud providers, or third-party AI platforms
The server does not retain persistent logs of request content

Researchers are responsible for documenting how they store and manage the outputs produced by the server. For technical clarification regarding IRB language, contact Chris Proctor.