4/25/2025

Setting Up Automated Tasks & Scripts Using Ollama

Automating tasks in today’s fast-paced digital world is more than just a convenience; it’s a necessity. With the rise of intuitive AI tools, like Ollama — a fantastic open-source platform that allows you to deploy large language models right on your own hardware — the process of creating automated tasks has never been more accessible.

In this blog post, we’ll dive deep into how to set up automated tasks & scripts using Ollama, ensuring you can streamline various processes in your day-to-day operations!

What is Ollama?

Ollama is an open-source machine learning model API designed to help users manage various large language models (LLMs) conveniently. It provides an easy way to install, run, and manage models like Mistral, Gemma, and others without needing technical expertise. This simplifies model management through the use of simple commands while ensuring robust performance on your local hardware.

Getting Started with Ollama

To kick things off, let’s go over the basic steps for getting Ollama running on your machine. The good news is Ollama supports multiple operating systems like macOS, Windows, Linux, or even via a Docker image, making it versatile for everyone!

Step 1: Install Ollama

First things first, you’ll need to install Ollama. Head over to this link to download the installer suitable for your operating system. The installation process is straightforward – here’s how you can do it on various platforms:

macOS: Download the macOS version and follow the installation prompts.
Windows: Use the provided executable to get set up easily.
Linux: Use the command
1curl -fsSL https://ollama.com/install.sh | sh
to install from the terminal.

After installation, you can verify that everything’s set up correctly by entering

ollama run llama3

in your terminal to start a conversational AI interaction with the installed Llama model.

Step 2: Setup Environment Variables

Before diving into creating scripts, it’s crucial to set up your environment correctly. This can include configuring

API_URL

MODEL

, or any other specific settings you need to ensure your Ollama instance communicates correctly with your automation scripts.

Step 3: Explore the Model Library

Ollama supports multiple models that can be employed for different tasks. You can access a list of available models by navigating to the Ollama Model Library. Here, you’ll find models like:

1Mistral 7B
: Great for general-purpose tasks.
1Gemma
: Excellent for generating human-like text.
1Phi 4
: Optimized for structured data and precision tasks.

Automating Tasks with Ollama

Now that Ollama is installed and configured, you can start automating tasks. Here’s where the FUN begins! We’ll look at how you can utilize Ollama to automate email responses, data entry, or even script running in your local system!

Example 1: Automating Email Responses

A practical application of Ollama is automating your email management. Say goodbye to sifting through countless emails!

Step 1: Gmail API Setup

You’ll need to set up the Gmail API to grab those emails and respond automatically. Here’s a quick way to get the API credentials:

Enable Gmail API: Head to Google Developers Console and create a new project.
Create Credentials: Once your project is up, enable the Gmail API and create OAuth 2.0 credentials.
Download credentials: Make sure to save your
1credentials.json
as this will be crucial for API access.

Step 2: Script Your Automation

Now, let’s whip up a Python script that will use Ollama’s LLM capabilities to summarize and respond to emails based on their content. ```python import requests import os from google.oauth2.credentials import Credentials from google_auth_oauthlib.flow import InstalledAppFlow from googleapiclient.discovery import build

Set up Ollama API URL and Model

API_URL = "http://localhost:11434/api/generate" MODEL = "gemma:27b"

Authenticating Gmail API

def authenticate_gmail(): creds = None

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# Load token if exists, else go through auth flow
if os.path.exists('token.json'):
    creds = Credentials.from_authorized_user_file('token.json')
if not creds or not creds.valid:
    flow = InstalledAppFlow.from_client_secrets_file('credentials.json', ['https://www.googleapis.com/auth/gmail.readonly'])
    creds = flow.run_local_server()
    with open('token.json', 'w') as token:
        token.write(creds.to_json())
return build('gmail', 'v1', credentials=creds)

Fetch emails and process them

... ```

Example 2: Command Automation

Another cool usage of Ollama is to automate command executions on your machine using simple bash scripts. Here’s how:

Step 1: Create Your Bash Script

Create a bash file, let’s say

automate_commands.sh

, that includes commands to run your desired tasks. ```bash

!/bin/bash

Start your Ollama model

ollama run gemma:27b

echo "Model is now running!" ```

Step 2: Make It Executable

Ensure your script has executable permissions:

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bash
gchmod +x automate_commands.sh

Working with n8n for Workflow Automation

If you’re looking for a visual approach to automate tasks using Ollama with various applications, consider using n8n. This is an automation tool that lets you create workflows using Ollama easily.

Integrate Ollama into n8n: You can create a new workflow that pulls data from various sources (Google Docs, Notion, or even APIs) and processes them using Ollama.
Multi-Channel Interaction: Allow your bot to interact across platforms like Slack, Discord, or even WhatsApp by creating triggers that respond to specific events!
Automate Repetitive Tasks: With n8n’s multi-step workflows, you can handle tasks like emailing reports, summarizing documents, or managing appointments automatically.

Benefits of Automation with Ollama

Time-Saving: Automation reduces the time spent on monotonous tasks, allowing users to focus on more critical tasks.
Consistency: Automated scripts ensure tasks are performed consistently without human error.
Scalability: Automation tasks can easily be scaled as projects grow without requiring more human resources.
Customization: Users can tailor their automation scripts to fit their specific needs and requirements, making Ollama a powerful tool for personalized workflows.

Conclusion

Setting up automated tasks & scripts using Ollama broadens the possibilities of usability in machine learning models. With just a little creativity, you can streamline your work processes and boost your productivity. Whether you’re summarizing emails, automating responses, or managing other interactions, Ollama empowers you to achieve efficiency in your work with minimal effort.

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