Build Your Own Chat Application with Ollama Cloud Models and Chainlit

Learn how to create your own AI chat application using Ollama Cloud's powerful models and Chainlit's intuitive framework.

Introduction

While Ollama provides the cognitive engine, Chainlit provides the interface. As LLM applications move beyond simple chatbot interactions into complex, multi-step agentic workflows, traditional dashboarding tools like Streamlit have proven insufficient. Chainlit has emerged as the purpose-built framework for this new paradigm.

What You'll Need

• Python 3.13+ installed

• Basic Python knowledge

• uv package manager

• Ollama Cloud Account (free tier)

• PostgreSQL

• Create an Auth0 Application

• Code editor of choice

Understanding the Tech Stack

What is Ollama Cloud?

• Managed version of Ollama - no local setup required
• Access to popular models (deepseek-v3.1, gpt-oss, qwen3-vl, etc.)
• API-based, scalable, and easy to integrate

What is Chainlit?

• Python framework for building chat interfaces
• Pre-built UI components
• Easy integration with AI models
• Real-time updates and streaming

AI-Powered Chat Application with Ollama Cloud and Chainlit

A sophisticated conversational AI application built with Chainlit and Ollama, featuring multi-modal document processing, MCP (Model Context Protocol) tool integration, and persistent chat sessions. Supports voice input, file analysis (PDF, DOCX, images), and seamless LLM model switching through customizable chat profiles.

Key Features

• MCP Integration: Connect external tools and services via Model Context Protocol
• Multi-format Document Processing : PDF, DOCX, TXT, and image analysis
• Voice Input: Audio transcription with ElevenLabs integration
• Multiple Chat Profiles: Switch between different LLM models and configurations
• Persistent Sessions: Resume conversations with SQLAlchemy + Azure Blob storage
• OAuth Authentication: Secure user management
• Modern UI: Clean, responsive interface with custom themes
• Tech Stack: Python, Chainlit, Ollama, SQLAlchemy, Azure Storage, ElevenLabs, PyMuPDF, OCR

Perfect for building intelligent document analysis tools, customer support bots, or educational AI assistants with enterprise-grade persistence and tool integration capabilities.

Bellow, you'll find the final home page

All my code can be found in my GitHub repository

Advanced Implementation: How to combine Chainlit UI with MCP Server

Chainlit supports three types of MCP connections:

1. SSE (Server-Sent Events): Connect to a remote service via HTTP
2. Streamable HTTP: Send HTTP requests to a server and receive JSON responses or connect using SSE streams
3. stdio: Execute a local command and communicate via standard I/O

End to end example showcasing MCP tool calling with Ollama

Conclusion

The combination of Ollama's Cloud Models with Chainlit's dynamic, event-driven interface marks a significant step forward in modern AI application development. Together, they deliver a unified workflow that blends the privacy and flexibility of local execution with the power, scalability, and reliability of cloud-hosted LLMs. Chainlit provides the rich, developer-friendly UI layer needed to rapidly iterate, while Ollama's cloud infrastructure ensures consistent performance and elastic scaling for real-world deployment.

Resources

• Chainlit docs
• Ollama docs
• MCP docs
• Chainlit Application