Introduction
Creating a chatbot that truly feels personal and responsive might sound like a complecated task. But with the right tools, it’s easier and can be more rewarding than you might think. In this article, I’ll guide you through building a chatbot using Flowise, an intuitive low-code platform, and Ollama, a robust system for running AI models locally. Together, they offer a powerful combination for crafting chatbots that are efficient, private, and fully customizable.
Why Choose Flowise and Ollama?
What Is Flowise?
Flowise is an open-source, low-code tool for creating custom AI workflows and LLM (Large Language Model) agents. It simplifies development with a drag-and-drop interface, letting you test, iterate, and deploy your ideas quickly. See the Flowise website for more information.
What Is Ollama?
Ollama enables you to run open-source AI models locally, ensuring your data stays private while delivering lightning-fast performance. With Ollama, you get total control over your chatbot’s brain, making it an ideal partner for Flowise. See my blog on Ollama for installation instructions.
Getting Started with Flowise
Before we get into building the chatbot, let’s set up Flowise.
Step 1: Install Flowise
To use Flowise, ensure Node.js is installed on your machine. Flowise works best with:
Node.js v18.15.0
Node.js v20 or higher
Option A: Install Locally Using NPM
Install Flowise globally:
npm install -g flowiseStart Flowise:
npx flowise startOpen Flowise by visiting: http://localhost:3000.
Option B: Deploy with Docker (You will need Docker installed)
Navigate to the docker folder of the Flowise project.
Copy .env.example and rename it .env.
Start Flowise with Docker Compose:
docker compose up -dVisit Flowise at http://localhost:3000.
Stop the containers when needed:
docker compose stopAlternatively, you can use a Docker image:
Build the Docker image:
docker build --no-cache -t flowise .Run the image:
docker run -d --name flowise -p 3000:3000 flowiseStop the image when necessary:
docker stop flowiseStep 2: Designing Your Chatbot
Now that Flowise is ready, it’s time to design your chatbot. When you go to your Flowise URL you will be see this Chaflows page.
Click the add new buutton atr the top right. You will then have a blank canvas.
Click on the plus icon to begin adding components.
Step 3: Building the Chatflow in Flowise
Flowise’s visual interface makes creating a chatbot simple. Here’s how to structure your chatbot:
1. Add a Memory Node (Buffer Memory)
This node gives your chatbot a short-term memory, allowing it to reference earlier messages in the conversation. For instance:
User: “Call me Sarah.”
Bot: “Got it, Sarah! How can I assist you today?”
2. Add a Conversation Chain
Link your nodes to a Conversation Chain, which processes user input and generates context-aware responses.
Once you have the componets on the canvas you can join them up by draging the conection points.
Step 4: Integrating Ollama for LLM Power
With your chatbot structure in place, let’s add intelligence using Ollama. Click on the plus icon and find ChatOllama.
Set Up the ChatOllama Node
Base URL: Enter the URL for your local Ollama server (e.g., http://localhost:11434). If you are using Flowise in a Docker container and Windows use http://host.docker.internal:11434
Model Name: Select a model like llama3.2. For this example we wil use SARA that we created in this blog.
Temperature: Adjust creativity. Use lower values for precise answers (e.g., 0.2) and higher values for more varied creative responses.
This integration allows your chatbot to understand user input and generate dynamic, relevant replies.
Connect all the points up as in the image above and click the save icon, give your file a name and we have a basic chatbot. Congratulations! Lets test it out.
Step 5: Testing Your Chatbot
Once your chatbot is ready, it’s time to test it. Now click on the chat icon on the right, you will have a chatbox popup.
Here’s an example scenario as we are using SARA:
We can ask "How do we make a strong password?" Your chatbot wil respond with the answer if it has all worked.
Step 6: Fine-Tuning and Deployment
Fine-Tuning Tips
Start with a simple design, then gradually add complexity.
Test your chatbot with different local models.
Adjust Ollama’s settings to refine your chatbot’s tone and responsiveness.
Deploying Your Chatbot (This is for another blog! )
Flowise makes it easy to deploy your chatbot. You can:
Embed it on your website.
Host it locally for personal use.
Deploy it on platforms like AWS or Docker for larger-scale access.
Conclusion: Your Chatbot Journey Begins
Building a chatbot is more than just a technical project; it’s an opportunity to create something truly interactive and engaging. With tools like Flowise and Ollama, the process is streamlined, allowing you to focus on crafting meaningful interactions.
I remember the first time my chatbot greeted me with, “Hello! How can I help you today?” It was a small moment, but it represented hours of work and a clear vision coming to life. Now it’s your turn.
If you would like more help with Flowise I have created a GPT called Flowise Ally, Chat with it here.
Get started and see where your chatbot can take you! Check out my GitHub for the JSON.
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