8/27/2024

Building a Chatbot using Ollama and Slack

Creating a chatbot has never been easier! In this blog post, we’ll explore how to build a complex chatbot using the Ollama framework and integrate it seamlessly into Slack. With a focus on practical applications and customization, you'll find everything you need to know right here!

What is Ollama?

Ollama is an open-source framework that allows you to RUN various large language models locally. It supports models like Llama 3.1, Mistral, and Gemma 2, providing developers with versatility and power for their chatbot applications. If you're a developer looking to harness the power of AI without cloud dependencies, Ollama is the way to go. You can find out more about it on the Ollama GitHub page.

Benefits of Using Ollama

Local Deployment: Run models on your own hardware, ensuring privacy & security.
Variety of Models: Flexibility to choose from multiple language models to suit your application’s needs.
Customizability: Tailor model behaviors with custom prompts and instructions to obtain desired outputs.

Setting Up Your Development Environment

Before we dive into building the chatbot, you need to set a few things up:

Prerequisites

Ollama Installed: You can install Ollama on your system using this command:
1 2bash curl -fsSL https://ollama.com/install.sh | sh
Python 3.8 or later: Make sure you've got Python set up. Use a virtual environment to avoid dependencies mess.
Slack Account: Sign up for a Slack account if you haven't already.

Creating the Slack App

Go to the Slack API website and click on Create a new app.
Choose From scratch, name your app, and select the workspace you want to install it into.
Under the Bot Users section, add a bot user which will be responsible for sending messages.
Set relevant permissions in OAuth & Permissions, ensuring to include
1chat:write
,
1chat:write.customize
, and other necessary scopes.
Install the app to your workspace and copy the Bot User OAuth Access Token.

Set Up Your Python Project

Now that you've configured your Slack app, let's set up a Python project. Create a directory for your chatbot, and inside it, create a

requirements.txt

file with the following content: ``` requests slack-bolt

For handling Ollama requests

ollama-python ```

Install the required libraries using:

1
2

bash
pip install -r requirements.txt

Connecting Ollama to the Slack Bot

With Ollama, you can integrate natural language processing capabilities right into your Slack bot. Let's see how that works:

Writing the Backend Code

Create a new Python file,

app.py

, and add the following code: ```python import os from slack_bolt import App from slack_bolt.adapter.socket_mode import SocketModeHandler import requests

Initialize Slack app with bot token

app = App(token=os.environ['SLACK_BOT_TOKEN'])

def get_ollama_response(prompt): url = 'http://localhost:11434/api/generate' response = requests.post(url, json={'model': 'llama3.1', 'prompt': prompt}) return response.json()['response']

@app.event('app_mention') def mention_handler(event, say): user_message = event.get('text') response = get_ollama_response(user_message) say(response)

if name == 'main': handler = SocketModeHandler(app, os.environ['SLACK_APP_TOKEN']) handler.start() ```

Breaking it Down

Ollama API Call: The function
1get_ollama_response
handles requests to the Ollama server. Remember to have the Ollama server running!
Event Listener: Whenever the bot gets mentioned, the
1mention_handler
function gets triggered, captures the user message, and fetches a response from Ollama.

Running the App

Start your Ollama server by running:
1 2bash ollama serve
Now run your Slack bot with:
1 2bash python app.py

Testing the Bot

To test if everything is working fine:

Head over to your Slack workspace where the bot was added.
Mention your bot (e.g.,
1@YourBotName How’s the weather?
).
You should receive a response generated by the Ollama model!

Enhancing Your Bot with Advanced Features

Once you have your basic bot up and running, let’s think about adding some advanced functionalities.

Handling Multiple Commands

You can expand your bot’s capabilities by listening for multiple commands by elegant use of decorators provided by the Slack Bolt framework. For instance, matching commands using a

@app.command

decorator.

Integrating Other Tools

You may want to extend your chatbot’s utility by allowing it to use external tools or APIs available through the LangChain framework. Integrating functionalities like web scraping, database access, or performing calculations can really boost your bot's usefulness!

Customizing Responses

The Ollama models can be customized through specific system prompts tailored to changing contexts or user expectations, which you can specify in the prompt by changing:

python
system_prompt = f"""
You are a helpful assistant that responds to questions and provides information accurately.
"""

Deploying Your Slack Bot

Once you’re comfortable with your bot running locally, you might want to deploy it so it can run continuously.

Using a Cloud Provider

To deploy your bot:

Consider using platforms like Heroku, AWS or Digital Ocean. You can package your application with a
1Dockerfile
and deploy it, or you can push it directly to a service like Railway.
Ensure you set your environment variables using the platform you're deploying it to, so your bot can communicate with both Slack and Ollama.

Conclusion

Congrats! You’ve successfully built a chatbot using Ollama and Slack. Now enjoy watching your chatbot engage users & assist with casual inquiries.

A Tool to Further Your Journey

If you’re interested in DYNAMIZING your chatbot experience, consider using Arsturn for even more capabilities! Arsturn’s platform allows you to instantly create custom chatbots, supercharging your engagement efforts. No credit card is required to start, so why not dive in?

Join thousands already utilizing Conversational AI to build meaningful connections across their digital channels. Enhance brand engagement with the power of AI! Check it out now at Arsturn.com.

Next Steps

Experiment further with Ollama!
Customize your chatbots' personalities!
Explore advanced functionalities and integrations!

With endless possibilities ahead, continue innovating & happy coding!