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Tutorial: Running BubbleBrain in 10 Minutes

What you will build: A working local AI server with a connected RAG engine and OpenAPI Swagger UI, capable of answering product queries.

What you will learn:

  • How to initialize the Poetry environment
  • How to start necessary Docker containers
  • How to make your first API request

Prerequisites:

  • Python 3.13+ installed
  • Docker and Docker Compose installed
  • OpenAI and Pinecone API keys

Step 1: Environment Setup

First, you will configure environment variables so the application can communicate with external services.

Copy the .env.example file:

cp .env.example .env

Tip: If an error occurs, make sure the .env.example file exists in the root directory of your project.

Open the .env file and insert your keys:

OPENAI_API_KEY="sk-..."
PINECONE_API_KEY="pc-..."

Step 2: Starting Containers

To allow the application to cache data, we will start Redis.

docker-compose up -d

You will see the following output:

[+] Running 1/1
 ✔ Container redis-stack  Started

Step 3: Starting the Server

Finally, we will start the FastAPI development server.

poetry install
poetry run uvicorn app.main:app --reload

After this, you will have a working API accessible at http://localhost:8000. To see the generated Swagger UI, navigate to http://localhost:8000/docs in your browser.