IA Ollama Installation

Local (User Space)

• sudo pacman -S podman
• systemctl --user enable podman.socket --now
• vim ~/.config/containers/storage.conf

[storage]
driver = "overlay"
graphroot = "/home/user/path/to/podman-storage"
runroot = "/tmp/podman-run-1000"

• mkdir -p /home/user/path/to/podman-storage
• mkdir -p /home/user/path/to/ollama
• vim /home/user/path/to/run-ollama.sh

#!/bin/sh

if podman container exists ollama; then
    podman start ollama
else
    podman run -d \
      --name ollama \
      --restart=always \
      --device nvidia.com/gpu=all \
      -v /home/user/path/to/ollama:/root/.ollama \
      -p 11434:11434 \
      docker.io/ollama/ollama
fi

if ! podman exec ollama ollama list | grep -q "qwen3.5:9b"; then
    podman exec -it ollama ollama pull qwen3.5:9b
fi

• Now exec the run.sh for running the ollama and model

RAG (IA with documentation to read)

• mkdir -p /home/user/path/to/rag-api
• mkdir -p /home/user/path/to/documents

cat > /home/user/path/to/rag-api/Containerfile <<EOF
FROM python:3.12

WORKDIR /app

RUN pip install fastapi uvicorn \
    llama-index \
    llama-index-llms-ollama \
    llama-index-embeddings-ollama \
    chromadb \
    pypdf

COPY app.py .

CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
EOF

cat > /home/user/path/to/rag-api/app.py <<EOF
from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def root():
    return {"status": "ok"}
EOF