AI API Framework

This is a FastApi application that will be able to wrap different inference backends to simplify interacting with them.

The standard interface for chat is the OpenAI Chat Completion API.

Running Dev:

Keep uv up-to-date:

uv self update

First time here?

Database

You will need to have Postgres running locally (although you could probably make Sqlite work without too much trouble).

The simplest way is to just pull a docker container. We're using pgvector since we may end up storying embdeddings at some point:

   docker run --name pgvector_postgres -e POSTGRES_PASSWORD=postgres -e POSTGRES_DB=postgres -p 5433:5432 pgvector/pgvector:pg15

Add the connection string to a .env to pull it into the settings (see .env_example).

Running code

This project uses uv. You should be able to install it by following the installation documentation. The curl shell command is the recommended and fastest way to install uv.

Once installed you can sync the dependencies. This will automatically create a .venv folder, but you can generally ignore it while using uv.

uv sync

Start the server:

uv run fastapi dev

Running tests:

uv run pytest

Database Migrations

This project uses alembic to manage changes to the database scheme.

Create a migration:

uv run alembic revision --autogenerate -m "First Migration"

Update tables:

uv run alembic upgrade head

Create an API Key

You will need an API key to interact with the endpoints. There is a script create_admin_user.py with some code to create a user and generate and API key. API keys are not stored in the database, so look for the output on the terminal with the key and save it. Like previous commands you can run this with uv (or python with an active venv):

uv run create_admin_user.py -email admin@example.com --name "Testy Test"