In this project, we’ll walk through building a chatbot using DeepSeek AI and deploying it in a Docker container. This guide is designed for beginners and includes step-by-step instructions and full code snippets to help you create and deploy your chatbot.

Project Overview

We’ll build a simple conversational chatbot using DeepSeek AI’s NLP capabilities. The chatbot will be deployed as a web application using Flask (a Python web framework) and containerized using Docker for easy deployment and scalability.

Prerequisites

Before starting, ensure you have the following installed:

1. Python 3.8+
2. Docker
3. DeepSeek AI SDK (or API access)
4. Flask (for the web interface)
5. Postman (optional, for testing the API)

Step 1: Set Up Your Project

1. Create a project directory:

mkdir deepseek-chatbot
cd deepseek-chatbot

2. Set up a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3.  Install required Python packages:

pip install flask deepseek-sdk

4. Step 2: Create the Chatbot Backend

Create a file named app.py:

from flask import Flask, request, jsonify
from deepseek import DeepSeekClient  # Import DeepSeek AI SDK

app = Flask(__name__)

# Initialize DeepSeek AI client
deepseek_client = DeepSeekClient(api_key="your_deepseek_api_key")

@app.route("/chat", methods=["POST"])
def chat():
    # Get user input from the request
    user_input = request.json.get("message")

    # Send the input to DeepSeek AI for processing
    response = deepseek_client.generate_response(user_input)

    # Return the chatbot's response
    return jsonify({"response": response})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

Replace "your_deepseek_api_key" with your actual DeepSeek API key.

Step 3: Test the Chatbot Locally

1. Run the Flask application:

python app.py

2. Use Postman or curl to test the API:

curl -X POST http://127.0.0.1:5000/chat -H "Content-Type: application/json" -d '{"message": "Hello, how are you?"}'

3. You should receive a response from the chatbot:

{
    "response": "I'm doing well, thank you! How can I assist you today?"
}

Step 4: Dockerize the Application

1. Create a Dockerfile in your project directory:

# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the requirements file into the container
COPY requirements.txt .

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Copy the current directory contents into the container
COPY . .

# Make port 5000 available to the world outside this container
EXPOSE 5000

# Run app.py when the container launches
CMD ["python", "app.py"]

2. Create a requirements.txt file:

flask==2.3.2
deepseek-sdk==1.0.0

3. Build the Docker image:

docker build -t deepseek-chatbot .

4. Run the Docker container:

docker run -p 5000:5000 deepseek-chatbot

5. Test the chatbot using the same curl command as before:

curl -X POST http://127.0.0.1:5000/chat -H "Content-Type: application/json" -d '{"message": "Hello, how are you?"}'

Step 5: Deploy the Chatbot

1. Push the Docker image to a container registry (e.g., Docker Hub)

docker tag deepseek-chatbot your_dockerhub_username/deepseek-chatbot
docker push your_dockerhub_username/deepseek-chatbot

2. Deploy the container to a cloud platform (e.g., AWS ECS, Google Cloud Run, or Heroku).

Step 6: Future Enhancements

1. Add a Frontend:

• Use HTML/JavaScript to create a simple web interface for the chatbot.
• Integrate with Flask using templates.

2. Improve the Chatbot:

• Fine-tune the DeepSeek AI model for specific use cases.
• Add context management for more natural conversations.

3. Add Logging and Monitoring:

• Use tools like Elasticsearch or Grafana to monitor the chatbot’s performance.

The project code is available at Github.

This project demonstrates how to create a scalable and portable AI-powered application. By containerizing the chatbot, you can easily deploy it to any environment, making it a versatile solution for various use cases.