Ollama Course – Build AI Apps Locally

Introduction

In this tutorial, you'll learn how to set up and use Ollama to build AI applications locally. This guide covers everything from the initial setup to creating real-world applications such as a Grocery List Organizer and an AI Recruiter Agency. Whether you're a developer or an AI enthusiast, this guide will help you leverage local large language models (LLMs) effectively.

Step 1: Course Prerequisites

Before diving into building AI apps, ensure you have the following:

• Basic knowledge of programming, particularly in Python.
• Familiarity with command-line interfaces.
• A machine capable of running AI models (check for adequate RAM and CPU).

Step 2: Setting Up the Development Environment

1. Install Required Software:

   • Install Python (version 3.7 or higher).
   • Install Git for version control.
   • Ensure you have a code editor like Visual Studio Code or PyCharm.

2. Create a Virtual Environment:

   • Open your terminal.
   • Run the command:

     python -m venv ollama-env
     

   • Activate the virtual environment:
     • For Windows:

       .\ollama-env\Scripts\activate
       

     • For macOS/Linux:

       source ollama-env/bin/activate
       

Step 3: Downloading and Setting Up Ollama

1. Download Ollama:

   • Visit the Ollama website and follow the instructions to download and install the software.

2. Verify Installation:

   • In your terminal, type:

     ollama --version
     

   • This should display the installed version of Ollama.

Step 4: Pulling and Customizing Models

1. Pulling Models:

   • Use the following command to pull a specific model:

     ollama pull <model-name>
     

   • Replace <model-name> with the desired model (e.g., llama3).

2. Customizing Models:

   • Create a modelfile to customize model parameters.
   • Here’s an example:

     {
       "name": "my-custom-model",
       "parameters": {
         "temperature": 0.7,
         "max_tokens": 150
       }
     }
     

Step 5: Interacting with Ollama Models

1. Using Basic CLI Commands:

   • To test your model, use:

     ollama run <model-name>
     

   • For example, to run the llava model:

     ollama run llava
     

2. Summarizing Text and Sentiment Analysis:

   • You can submit a request for summarization:

     ollama request --model <model-name> --input "Your text here"
     

   • For sentiment analysis, the command structure is similar.

Step 6: Integrating with REST APIs

1. Setting Up the REST API:

   • Start the API server using:

     ollama serve
     

   • This will allow you to interact with your models via HTTP requests.

2. Making API Requests:

   • Use JSON format for requests. A sample request to analyze text:

     {
       "model": "<model-name>",
       "input": "Analyze this text"
     }
     

Step 7: Building Real-World Applications

1. Grocery List Organizer:

   • Create a simple application using Python to manage grocery lists.
   • Example structure:
     • Input items via a command line.
     • Store items in a local database or a file.

2. AI Recruiter Agency:

   • Develop an application that matches candidates with job descriptions.
   • Use Ollama's capabilities to analyze resumes and job listings.

Step 8: Bonus Project

• Explore the BONUS project provided at the end of the course. This project integrates multiple concepts learned throughout the tutorial and offers a practical application of your skills.

Conclusion

You now have a comprehensive understanding of how to set up and use Ollama to build AI applications locally. By following these steps, you can start creating your projects, experimenting with various models, and exploring the power of local LLMs. For further development, consider contributing to open-source projects or exploring more complex integrations with other tools and libraries. Happy coding!