feat: Implement OpenAI-powered Chainlit chat app with Docker

This commit adds:
- Initial Chainlit chat application with async OpenAI integration
- Streaming response support for real-time AI chat experience
- Full conversation context management
- Docker containerization with UV package management
- Environment variable handling for secure API key management
- Error handling and resilient architecture

The application provides a production-ready ChatGPT-like interface that
can be easily deployed using Docker.

.gitignore

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+.env
+.venv
+.chainlit
+__pycache__

Dockerfile

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+# Use Python 3.10 as the base image
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install uv
+RUN pip install uv
+
+# Copy requirements file
+COPY requirements.txt .
+
+# Install dependencies system-wide
+RUN uv pip install --system -r requirements.txt
+
+# Copy the application code
+COPY . .
+
+# Expose the port Chainlit runs on
+EXPOSE 8000
+
+# Command to run the application
+CMD ["chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "8000"]
+
+# Add this line to your Dockerfile
+RUN --mount=type=secret,id=openai_api_key \
+    cat /run/secrets/openai_api_key > /app/.env
+
+ARG OPENAI_API_KEY
+ENV OPENAI_API_KEY=$OPENAI_API_KEY 

app.py

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+import os
+from dotenv import load_dotenv
+import chainlit as cl
+from openai import AsyncOpenAI
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Default model settings
+DEFAULT_SETTINGS = {
+    "model": "gpt-3.5-turbo",
+    "temperature": 0.7,
+    "max_tokens": 500,
+    "top_p": 1,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+}
+
+SYSTEM_PROMPT = "You are a helpful, friendly AI assistant. Provide clear and concise responses."
+
+@cl.on_chat_start
+async def start():
+    """
+    Initialize the chat session:
+    - Create OpenAI client
+    - Set up message history with system prompt
+    - Configure model settings
+    - Send welcome message
+    """
+    # Initialize OpenAI client
+    client = AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+    cl.user_session.set("client", client)
+    
+    # Initialize message history with system prompt
+    message_history = [{"role": "system", "content": SYSTEM_PROMPT}]
+    cl.user_session.set("message_history", message_history)
+    
+    # Save model settings
+    cl.user_session.set("settings", DEFAULT_SETTINGS)
+    
+    # Send welcome message
+    await cl.Message(
+        content="Hello! I'm your AI assistant powered by OpenAI. How can I help you today?"
+    ).send()
+
+
+@cl.on_message
+async def main(user_message: cl.Message):
+    """
+    Process user messages and generate AI responses:
+    - Update message history with user input
+    - Call OpenAI API with current conversation context
+    - Stream the response back to the user
+    - Update message history with AI response
+    
+    Args:
+        user_message: The message sent by the user
+    """
+    # Retrieve session data
+    client = cl.user_session.get("client")
+    message_history = cl.user_session.get("message_history")
+    settings = cl.user_session.get("settings")
+    
+    # Add user message to history
+    message_history.append({"role": "user", "content": user_message.content})
+    
+    # Prepare response message with loading state
+    response_message = cl.Message(content="")
+    await response_message.send()
+    
+    try:
+        # Call OpenAI API to get response
+        stream = await client.chat.completions.create(
+            messages=message_history,
+            stream=True,
+            **settings
+        )
+        
+        # Stream the response
+        full_response = ""
+        async for chunk in stream:
+            if chunk.choices[0].delta.content:
+                content_chunk = chunk.choices[0].delta.content
+                full_response += content_chunk
+                
+                # Update message in real-time
+                response_message.content = full_response
+                await response_message.update()
+        
+        # Add AI response to message history
+        message_history.append({"role": "assistant", "content": full_response})
+        cl.user_session.set("message_history", message_history)
+        
+    except Exception as e:
+        # Handle errors
+        response_message.content = f"Error: {str(e)}"
+        await response_message.update() 

chainlit.md

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+# Welcome to Chainlit! 🚀🤖
+
+Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
+
+## Useful Links 🔗
+
+- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
+- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬
+
+We can't wait to see what you create with Chainlit! Happy coding! 💻😊
+
+## Welcome screen
+
+To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.

requirements.txt

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+chainlit
+openai>=1.0.0