In this post, I will show you how to set up a local AI chatbot using Ollama, DeepSeek-R1, and the Open WebUI. This approach offers flexibility, privacy, and control over your AI interactions without relying on cloud-based services.

I’ll keep it simple and concise so we can quickly learn how to install Ollama, run LLM models like Llama 3.3 and DeepSeek-R1, and use the web interface to interact with the LLMs locally.

What is Ollama?

Olama is an open-source tool that helps run Large Language Models (LLMs), like ChatGPT, on a self-hosted server or even on a local machine.

It supports Llama 3.3, DeepSeek-R1, Phi-4, Mistral, Gemma 2, and other models locally.

You can find the list of supported LLMs by Olama here: https://ollama.com/search.

What is Deepseek-R1?

deepseek-r1 is a powerful open-source LLM available for anyone to download, copy, and build upon. In this post, I will run deepseek-r1 locally using Ollama. In real-life scenarios, you can run any LLM model available at https://ollama.com/search.

What is Open WebUI?

Open WebUI, formerly known as Ollama WebUI, is an extensible, feature-rich, and user-friendly self-hosted web interface designed to operate entirely offline or self-hosted server. It supports various Large Language Model (LLM) runners, making it a versatile tool for deploying and interacting with language models.

How to Build a Local AI Chatbot with Ollama, DeepSeek-R1, and Open WebUI?

I will explain below how to build and run the Local AI Chatbot with Ollama, DeepSeek-R1, and Open WebUI in the following steps.

1. Install Ollama Locally

To install Ollama locally you can go to https://ollama.com/, download and install appropriate version in your local machine.

I am using M1 Mac how I will be installing via brew

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brew install ollama

After installing Ollama, check the version by running below command to ensure that it was installed successfully.

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ollama --version

If Ollama was successfully installed you will see like this:

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ollama --version
ollama version is 0.5.11

You can run ollama --help command to see the all the available CLI commands that is used to interact with LLMs

There are a couple of very useful Ollama commands. I will explain them in step 2 after installing deepseek-r1

2. Pull and Run deepseek-r1 model

Once Ollama is up and running in your machine nw you can run any supported LLMs on you machine. Here I am going to use the very popular LLM model called deepseek-r1. To run this model we can run ollama run deepseek-r1 command.

In this post, I am using deepseek-r1 locally. In real-life scenarios, you can run any LLM model available at https://ollama.com/search with same ollama run {model name} command.

If your LLM model is locally installed, the command ollama run {model name} will instantly run the model; otherwise, it will pull a model from a registry, which may take a couple of minutes because it will perform ollama pull {model name} and ollama run {model name} together.

In our case, we are running ollama run deepseek-r1, which might take a couple of minutes because deepseek-r1 is not available locally.

Alternatively, you can do it in two steps :

1. ollama pull deepseek-r1
2. ollama run deepseek-r1

3. Interact with deepseek-r1/others model using Ollama

Once you run the model you can see above screen (step 2), and you can directly interact with the deepseek-r1 via CLI tool. You can ask any question. I have just tried with “hello”. In Right hand side you can also see the logs tail -f ~/.ollama/logs/server.log

Instead of CLI you can also use REST API. Ollama has a REST API for running and managing models.

Generate a response:

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 curl http://localhost:11434/api/generate -d '{
  "model": "deepseek-r1", 
  "prompt": "hello",                                                           
  "stream": false
}' | jq .

You can do many more using REST API. Here is full documentation https://github.com/ollama/ollama/blob/main/docs/api.md

Response generated from LLM for above REST API request:

But, Web interface is always more interactive than CLI tools. So, in next step I will be installing and using

Open WebUI to interact with LLMs.

Some of the useful Ollama commands:

List all the running LLM models

Below command will list all the locally running models.

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ollama ps

Result of above command

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NAME                  ID              SIZE      PROCESSOR    UNTIL              
deepseek-r1:latest    0a8c26691023    6.0 GB    100% GPU     4 minutes from now  

Stop running LLM Model

Below command will stop the locally running model.

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 ollama stop {model name}

In our case I need to stop deepseek-r1 I will be running below command.

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 ollama stop deepseek-r1

If I ran above command, locally running models will be empty. Something like this.

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NAME    ID    SIZE    PROCESSOR    UNTIL 

I will not be stopping because I am going to demonstrate using deepseek-r1.

List all the models installed locally

Below command will list all the models installed locally whether it’s running or not.

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 ollama lsit

Result of above command

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NAME                  ID              SIZE      MODIFIED    
deepseek-r1:latest    0a8c26691023    4.7 GB    5 hours ago    

4. Install Open WebUI

Open WebUI can be installed using pip, the Python package installer. Before proceeding, ensure you’re using Python 3.11 to avoid compatibility issues.

Install Open WebUI: Open your terminal and run the following command to install Open WebUI:

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pip install open-webui

Running Open WebUI: After installation, you can start Open WebUI by executing:

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open-webui serve

This will start the Open WebUI server, which you can access at http://localhost:8080.

5. Test Chatbot Locally

Once all above steps are successfully completed then you can access at http://localhost:8080. First, you need to set up the Admin account

Once you have set up an admin account, you can interact with the LLMs. Here, I have asked the LLM to write a 'hello world' Node js app and received this response

This is how you can run the ChatGPT like AI Models locally. This helps you to maintain privacy, and control over your AI interactions.

Conclusion

In this post we have learned to set up Ollama, DeepSeek-R1, and Open WebUI to run ChatGPT-like AI models locally with ease. This approach offers flexibility, privacy, and control over your AI interactions without relying on cloud-based services.