LLM-Chainlit Chat Application

A streamlined chatbot application built with Chainlit and OpenAI's GPT-3.5 Turbo, designed to provide helpful responses in a pleasant conversational tone.

View application here

• Interactive chat interface powered by Chainlit
• Integration with OpenAI's GPT-3.5 Turbo model
• Real-time streaming responses
• Docker containerization support
• Environment variable configuration for secure API key management

• Python
• Chainlit (UI Framework)
• OpenAI API
• Docker
• uv (Package Manager)

As large language models (LLMs) like GPT and Claude become core components in modern AI applications, developers need tools that streamline their integration, testing, and deployment. Chainlit is one such open-source framework designed to bridge the gap between LLM backends and user-facing interfaces — quickly, efficiently, and with minimal code overhead.

Chainlit is a lightweight Python framework that helps you build and deploy conversational LLM applications with ease. It provides an interface to connect LLM workflows (using LangChain, LlamaIndex, or custom chains) to an interactive frontend — all within a developer-friendly local environment or deployable app.

• Zero-config UI: Automatically spins up a local UI for testing your chatbot or assistant.
• LangChain & LlamaIndex Integration: Natively supports popular LLM frameworks and agents.
• Real-time streaming: Offers token-level output rendering for a responsive chat experience.
• UI Customization: Allows you to customize chat behavior, avatars, and inputs.
• Easy Deployment: Share your apps via local hosting or deploy to platforms like Streamlit, Hugging Face Spaces, or your own servers.

import chainlit as cl

@cl.on_message
def handle_message(message: cl.Message):
    response = f"Echo: {message.content}"
    cl.Message(content=response).send()

In just a few lines, you get a working chat interface that echoes user input.

• Prototyping and debugging LLM applications
• Teaching and demoing AI agents
• Building internal tools or research assistants
• Wrapping LangChain pipelines into interactive apps

Install Chainlit with:

pip install chainlit

Run your app with:

chainlit run app.py

Chainlit simplifies the journey from LLM-powered logic to a usable, interactive interface — empowering AI developers to focus on logic and workflows instead of frontend scaffolding. Whether you're building a smart assistant, research tool, or LLM wrapper, Chainlit is a great place to start.

Would you like a tutorial article showing how to use Chainlit with LangChain or OpenAI for a specific use case?

• Python 3.x
• OpenAI API key
• Docker (optional, for containerization)

1. Clone the repository:

git clone <repository-url>
cd <repository-name>

2. Create and activate a virtual environment:

# Create virtual environment
uv venv

# Activate the environment
# On macOS/Linux:
source .venv/bin/activate
# On Windows:
# .venv\Scripts\activate

3. Install dependencies:

uv sync

4. Create a .env file in the root directory and add your OpenAI API key:

OPENAI_API_KEY=your-api-key-here

uv run chainlit run app.py -w

1. Build the Docker image:

docker build -t llm-app .

2. Run the container:

docker run -p 7860:7860 llm-app

Visit http://localhost:7860 in your browser to access the application.

Once the application is running:

1. Open the provided URL in your browser
2. Type your message in the chat interface
3. Receive responses from the AI assistant in real-time

The application can be configured through the following settings in app.py:

• Model: gpt-3.5-turbo
• Temperature: 0 (for consistent responses)
• Max tokens: 500
• Other OpenAI parameters can be adjusted in the settings dictionary

The project includes a Dockerfile for containerization, making it easy to deploy in any environment. The container exposes port 7860 for the web interface.

• API keys are managed through environment variables
• Never commit your .env file to version control
• Use secrets management when deploying to production environments

A big shoutout to the @AI Makerspace for all making this possible. Couldn't have done it without the incredible community there. 🤗🙏

Looking forward to building with the community! 🙌✨ Here's to many more creations ahead! 🥂🎉