Run LLMs Locally: Build Your Own AI Chat Assistant

This week, I explored how to run an AI chatbot locally using open-source models like CodeLlama with Ollama. The goal was to create an AI assistant that works entirely offline, just like ChatGPT, but without relying on any cloud-based services.

If you’ve never worked with LLMs before, don’t worry—this guide will take you from zero to a working local AI chat assistant step by step.

Step 1: Install Ollama

Ollama makes it incredibly easy to run open-source AI models on your own machine. If you haven’t installed it yet, just run:

curl -fsSL https://ollama.com/install.sh | sh

This will install Ollama and its dependencies.

Once installed, you can pull any model you want. For example, to download CodeLlama:

ollama pull codellama

Now you have an AI model running locally! 🎉

Step 2: Run Your First AI Query

To test it, simply run:

ollama run codellama "What is the capital of France?"

Your local AI will respond just like an online chatbot!

Step 3: Expose Ollama's API

Ollama provides an HTTP API at localhost:11434 to interact with models. You can send requests like this:

curl -X POST http://localhost:11434/api/generate \
-H "Content-Type: application/json" \
-d '{ "model": "codellama", "prompt": "What is 2 + 2?", "stream": false }'

This returns a JSON response with the AI’s answer.

Step 4: Build a Chat UI

Instead of using the command line, I built a React-based chat interface that connects to Ollama’s API and streams responses in real time. You can find the code here:

🔗 GitHub Repo

How It Works

✅ Sends messages to Ollama’s local API (localhost:11434).
✅ Streams responses in real time, so text appears word by word.
✅ Keeps chat history so the AI remembers context.

Step 5: Adding Context History

By default, each request is independent, meaning the AI forgets previous messages. To fix this, we send the full conversation history as the prompt.

1️⃣ Store messages in an array
2️⃣ Send the entire chat history as the prompt
3️⃣ Limit message history to avoid long input sizes

Here’s an example of a JSON request that includes previous messages:

{
  "model": "codellama",
  "prompt": "User: Hello!\\nAI: Hi there!\\nUser: How are you?\\nAI:",
  "stream": true
}

The AI now remembers context and responds accordingly.

Final Thoughts

Running LLMs locally is easier than ever. You now have a fully offline AI assistant that can chat like ChatGPT, but without sending data to external servers. 🚀

Next step? Adding persistent memory by storing chat history in a database!

Let me know if you try this! Would love to hear how it works for you. 🙌