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6.0

Ollama

Ollama is a framework that allows you to run various LLMs (Large Language Models) on your own hardware.

Ollama vs vLLM

This guides showcases a very basic Ollama deployment meant for trying out a self-hosted LLM deployed on the same server as SeaTable itself. You should take a look at vLLM in case you plan on deploying a production-ready LLM inference/serving engine. Compared to Ollama, vLLM provides much better performance when handling concurrent requests.

Instructions

Create ollama.yml

Create /opt/seatable-compose/ollama.yml with the following contents:

services:
  ollama:
    image: ollama/ollama:0.11.10
    restart: unless-stopped
    container_name: ollama
    # Comment out the following line if you don't have a GPU
    gpus: all
    networks:
      - backend-seatable-net
    volumes:
      - /opt/ollama:/root/.ollama

Afterwards, you should add ollama.yml to the COMPOSE_FILE variable inside your .env file:

sed -i "s/COMPOSE_FILE='\(.*\)'/COMPOSE_FILE='\1,ollama.yml'/" /opt/seatable-compose/.env

Configuration

In order to use Ollama to execute AI-based automation steps inside SeaTable, you must add the following configuration settings to your .env file:

SEATABLE_AI_LLM_TYPE='ollama_chat'
SEATABLE_AI_LLM_URL='http://ollama:11434'
# Choose a model: https://ollama.com/library
SEATABLE_AI_LLM_MODEL='gemma3:12b'

Start Ollama

You can now start Ollama by running docker compose up -d inside /opt/seatable-compose.

In addition, it is necessary to manually download the chosen AI model by executing the following command once:

docker exec -it ollama ollama pull $MODEL

You are now able to run AI-based automations steps inside SeaTable via your local Ollama deployment!