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ELI
ELI

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Build Your Own Chat Application with Ollama Cloud Models and Chainlit

#python #ai #ollama #mcp

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

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

User Interface

All my code can be found in my GitHub repository

Getting Started: Installation & Setup

1.Clone the Repository

git clone https://github.com/EliAbdiel/ollama-chat-application.git

cd ollama-chat-application
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2.Install dependencies

uv sync
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3.Create and fill .env

# Windows
copy .env.example .env

# Linux/Mac
cp .env.example .env
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4.Run the app

# Linux/Mac
source .venv/bin/activate

# Windows (CMD)
.venv\Scripts\activate

# Windows (PowerShell)
.venv\Scripts\Activate.ps1

chainlit run mani.py -w
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How the App Works

The Ollama Chat Application is a Chainlit-based conversational AI interface that processes text, files, and voice input through local or cloud-hosted Ollama models with MCP tool integration and persistent storage.

Open the UI in your browser

The Ollama Chat Application uses Chainlit's built-in OAuth authentication system with support for GitHub and Auth0 providers.

User Authentication

Model Selection

Chat profiles in the Ollama Chat Application provide a user-friendly interface for selecting different LLM models while abstracting the underlying model identifiers used by the Ollama API.

Chat profiles

Document Processing

Document processing in the Ollama Chat Application handles file uploads, extracts content from various formats (PDF, DOCX, images), and integrates the extracted text into the conversation flow.

Document processing

MCP Integration

The MCP (Model Context Protocol) system in the Ollama Chat Application enables external tool integration, allowing the LLM to execute functions from connected MCP servers and incorporate results into conversations.

MCP Connection is Established

GitHub MCP

GitHub MCP Server

GitHub MCP Server

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.

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