Installation#

Choose the installation method that best fits your needs and environment.

Development Installation#

Recommended for: Contributing to the project or customizing the code.

Clone the repository and set up a development environment:

git clone https://github.com/jeipollack/euclid_rag
cd euclid_rag
python3 -m venv .venv
source .venv/bin/activate
make init

This setup includes:

  • Virtual environment for isolated dependencies

  • Development dependencies for testing and building

  • Editable installation so changes are immediately available

Docker Installation#

Recommended for: Production deployment and containerized environments.

For containerized deployment with Docker Compose:

git clone https://github.com/jeipollack/euclid_rag
cd euclid_rag
docker compose up --build

Docker Features#

This setup includes:

  • Parallelized build stages for faster container building

  • Optimized image size for efficient deployment

  • Separate Ollama container managed by Docker Compose

  • Dynamic package versioning for correct version tracking

  • Isolated services with automatic orchestration

Setting up the LLM Model#

After the containers are running, you need to pull the desired model:

docker exec -it euclid_rag-ollama-1 ollama pull mistral:latest

Note

The model must be explicitly requested with the docker exec command after the containers are started.

Verification#

Test your installation by importing the package:

import euclid.rag.chatbot

print("euclid_rag installed successfully!")

Next Steps#

After installation, proceed to: