Greetings Robot Makers! If you’re eager about creating, developing, and programming robots and you love exploring these matters in an enjoyable and hands-on manner, then you’ve landed at the right place.

Today, we’re heading into an adventure of establishing your private GPT server, operating independently and providing you with impressive data security via Raspberry Pi 5, or possibly, a Raspberry Pi 4. If this piques your interest, buckle up and let’s get straight into it!

What is Olama?

Olama is an offline AI that performs similarly to ChatGPT. Olama is designed to function entirely offline and locally, which implies you can engage in text-based conversations, share codes, photos or documents and receive an interactive responses without a grain of your data being compromised.

With the use of Raspberry Pi 5 operating through Docker, we’ll be guiding you through the process of installing and setting up Olama along with its web user interface, which bears a striking resemblance to Chat GPT. Rest assured, though it might seem complicated at first, the process is easy to navigate.

Ollama with WebUI Screenshot

Advantages of Olama: Why Choose It?

It’s natural to question the benefits of using Olama. Here, we’ll shed light on the distinctive attributes it brings to the table:

Setting Up Olama: A Step-by-Step Guide

Before proceeding with the setup, it’s pivotal to note that a Raspberry Pi 5 with 8GB RAM is the ideal machine for maximizing the potential of running Olama. Though, Olama can still function on a Raspberry Pi 4 albeit at a (much) slower pace.

Preliminary Set-Up

Our vital first step is creating two separate commands: one dedicated to Olama and another for its web user interface to enhance smooth operation. It’s imperative to have your Raspberry Pi’s operating system and Docker updated to evade any potential issues and enhance the overall performance.

1. Install Docker (see this free course if you’ve never used Docker before):
2. Download the latest snapshot of ollama-webui:

     git clone https://github.com/ollama-webui/ollama-webui webui
   

3. Create a docker-compose file (note you will need to update the volumne path to match your local setup; you may not be using the pi username):

     version: "3.9"
     services:
    ollama:
      container_name: ollama
      image: ollama/ollama:latest
      restart: always
      volumes:
        - /home/pi/ollama:/root/.ollama
    ollama-webiu:
      build:
        context: ./webui/
        args:
          OLLAMA_API_BASE_URL: '/ollama/api'
        dockerfile: Dockerfile
      image: ghcr.io/ollama/ollama-webui:main
      container_name: ollama-webui
      volumes:
        - ollama-webui:/app/backend/data
      depends_on:
        - ollama
      ports:
        - ${OLLAMA_WEBUI_PORT-3000}:8080
      environment:
        - 'OLLAMA_API_BASE_URL=http://ollama:11434/api'
      extra_hosts:
        - host.docker.internal:host-gateway
      restart: unless-stopped
     volumes:
    ollama-webui: {}
    ollama: {}
   

4. Bring the container up:

     docker-compose up -d
   

5. Access the webui at http://localhost:3000
6. Create a free account for the first login

7. Download the model you want to use (see below), by clicking on the little Cog icon, then selecting Models

   Pull a Model for use with Ollama

   

8. For a list of Models see the ollama models list on the Ollama GitHub page

Running Olama on Raspberry Pi

With the setup finalized, operating Olama is easy sailing. Be it on Raspberry Pi 4 or 5, expect outstanding performance, though keep in mind that the response time tends to be slower on the Raspberry Pi 4.

What’s remarkable is Olama’s autonomous operation, working efficiently without reliance on internet connectivity or connection to an external server.

Interacting with Language Models

With Olama, you can ask the system to tell a joke or engage in more complex tasks. The response speed depends on the complexity of the model being used. The impressive bit is that even without an active internet connection, it operates flawlessly, establishing Olama as a powerful offline AI.

How to use Python with Ollama and LangChain

LangChain is a framework, set of tools and libraries for working with language models. It is designed to be used with Ollama, but can be used with any language model.

Here is a simple example of how to use LangChain with Ollama:

from langchain_community.llms import Ollama

model = "llama2"

llm = Ollama(model=model)

question = "tell me a joke"

response = llm.invoke(question)

print(f"response is {response}")

Note: you will need to install the langchain-community package first, using pip install langchain-community

Conclusion: Exploring the Wonders of Olama and Raspberry Pi

By harnessing Olama’s potential to operate positive and secured language models, we can take our robot-building and programming abilities to an all-time high. While the complexity might seem overwhelming, it’s surprising how approachable, practical, and fruitful this venture can turn out to be.

So, gear up and set foot into the exciting domain of independent local programming with Olama and Raspberry Pi. Let your creativity flow as you build your private chat GPT server and uncover myriads of possibilities that lay within the realm of AI. Happy programming!