Using Ollama models (FastAPI + React Native)

What is Ollama

Ollama is a powerful, open-source tool that allows you to run large language models (LLMs) entirely on your local machine, without relying on cloud-based services. It provides an easy way to download, manage, and run AI models with optimized performance, leveraging GPU acceleration when available.

Key Features:

✅ Run LLMs Locally – No internet required after downloading models.
✅ Easy Model Management – Download, switch, and update models effortlessly.
✅ Optimized for Performance – Uses GPU acceleration for faster inference.
✅ Private & Secure – No data leaves your machine.
✅ Custom Model Support – Modify and fine-tune models for specific tasks.
✅ Simple API & CLI – Interact with models programmatically or via command line.

How It Works:

1. Install Ollama – A simple install command sets it up.
2. Pull a Model – Example: ollama pull mistral to download Mistral-7B.
3. Run a Model – Example: ollama run mistral to start interacting.
4. Integrate with Code – Use the API for automation and app development.

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Create a API microservice to interact with Ollama models

We'll use FastAPI to create a microservice that interacts with Ollama models.

FastAPI Code : Ollama.py

Start the API microservice

uvicorn Ollama:app --host 0.0.0.0 --port 8000

Output in Postman:

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Create a react native chat bot to call API microservice to process user query

Now, let's build a React Native chatbot that will communicate with the API microservice.

Main Chatbot UI : App.js

Chat Interface : ChatbotUI.js

Start the react native application

# npm install
# npm run web

Output :

Output can be watched at Video

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Conclusion

Building a chatbot using Ollama models provides a powerful and private AI experience by running large language models locally. By integrating Ollama with a FastAPI microservice and a React Native frontend, we created a seamless, interactive chatbot that processes user queries efficiently.

This approach offers:
✅ Full control over AI models without cloud dependencies.
✅ Optimized performance using GPU acceleration when available.
✅ Enhanced privacy, as no data is sent to external servers.

Whether you're developing an AI assistant, a customer support bot, or experimenting with LLMs, this setup provides a strong foundation for further improvements and customization. 🚀

Complete code can be found at GitHub