The goal behind this capstone project is not to solve a specific problem but to find new problems to solve and to learn new things in the process.
1. Introduction
- 1.1 Project Background:
- I am technically savvy in networking, compute, python, Linux, AWS, virtualization, cybersecurity with experience in using python with API calls to Generative AI including; open-ai, Gemini, and AWSs Bard. I would like to challenge myself with a custom (made for me) capstone project that focuses on AI, docker, cybersecurity, and includes single sign on, and my experience with AWS public cloud, python and Linux.
- Highlight the importance of security, scalability, and user experience in chatbot development.
- How to create a containerized, Internet accessible chatbot, behind a secure, SSO with MFA website, leveraging my knowledge of Python, API, LLM, AWS, Docker and extending my understanding of LLM training, custom models, TensorFlow and RAG. Stretch goal to include multi-cloud docker environment and include GPU based hardware with TensorFlow integration.
- Learn new things durring the process of development.
- 1.2 Problem Statement:
- How to create, a containierized Define the challenges and limitations of existing chatbot solutions, such as security vulnerabilities, limited scalability, and inadequate user experience.
- 1.3 Project Objectives:
- Develop a secure and scalable AI-powered chatbot application with enhanced features.
- Implement robust security measures, including SSO, 2FA, and secure access controls.
- Integrate advanced features like RAG, file uploads, and a user-friendly web interface.
- Deploy the chatbot on a cloud platform (AWS) for scalability and reliability.
2. Literature Review
- 2.1 AI Chatbot Technologies:
- Discuss the evolution of chatbot technologies, including rule-based systems, machine learning models, and large language models (LLMs).
- Explore different types of LLMs and their applications.
- 2.2 Security Best Practices:
- Review security principles and best practices for web applications, including authentication, authorization, data encryption, and vulnerability management.
- Discuss the importance of SSO, 2FA, and access control in enhancing security.
- 2.3 Cloud Computing Technologies:
- Explore relevant AWS services (e.g., ECS, EKS, Lambda, S3) and their suitability for deploying and scaling chatbot applications.
- 2.4 RAG Architectures:
- Discuss the concept of Retrieval Augmented Generation (RAG) and its benefits for improving chatbot performance and accuracy.
3. Methodology
- 3.1 System Design:
- Design the overall system architecture, including components such as:
- Frontend (Web Interface)
- Backend (API Gateway, Chatbot Logic, Database, etc.)
- AI Model Integration
- Security Modules (SSO, 2FA, Authorization)
- Cloud Infrastructure (AWS services)
- Create detailed system diagrams and flowcharts.
- Design the overall system architecture, including components such as:
- 3.2 Technology Stack:
- Frontend: Select a suitable frontend framework (e.g., React, Vue.js, Angular).
- Backend: Utilize Python with frameworks like Flask or Django.
- Database: Choose a suitable database (e.g., PostgreSQL, MongoDB) for storing user data and chatbot interactions.
- AI Model: Select and integrate a suitable LLM (e.g., GPT models, Gemini, Bard).
- Cloud Services: Utilize AWS services for deployment, scaling, and management.
- 3.3 Development Process:
- Agile development methodology (e.g., Scrum) with iterative development and testing.
- Version control system (e.g., Git) for tracking code changes and collaboration.
4. Implementation
- 4.1 Development:
- Develop the frontend, backend, and chatbot logic.
- Integrate the chosen LLM and implement the RAG architecture.
- Develop and implement security features (SSO, 2FA, access control).
- Implement file upload and processing functionality.
- Develop and test the API endpoints.
- 4.2 Dockerization:
- Containerize the application and its dependencies using Docker.
- 4.3 AWS Deployment:
- Deploy the Dockerized application on AWS using the selected service (e.g., ECS, EKS).
- Configure and manage the deployment using AWS tools and services.
5. Testing and Evaluation
- 5.1 Unit Testing:
- Conduct unit tests for individual components of the application.
- 5.2 Integration Testing:
- Test the integration of different components of the system.
- 5.3 User Acceptance Testing (UAT):
- Involve users in testing the application's functionality and usability.
- 5.4 Performance Testing:
- Evaluate the application's performance under different load conditions.
- 5.5 Security Testing:
- Conduct security audits and penetration testing to identify and address vulnerabilities.
6. Results and Discussion
- 6.1 Project Outcomes:
- Present the final deployed chatbot application and its key features.
- Demonstrate the effectiveness of the implemented security measures.
- Analyze the performance and scalability of the deployed application.
- 6.2 Discussion:
- Discuss the challenges encountered during development and how they were addressed.
- Analyze the strengths and weaknesses of the implemented solution.
- Discuss the potential for future improvements and enhancements.
7. Conclusion
- 7.1 Summary:
- Summarize the key findings and accomplishments of the project.
- 7.2 Contributions:
- Discuss the contributions of the project to the field of chatbot development and AI applications.
- 7.3 Future Work:
- Suggest potential future research directions and areas for improvement.
8. Appendices
- Include supporting documentation such as:
- System architecture diagrams
- Code snippets
- Test results
- User manuals
Technical Outcomes:
- A fully functional and secure AI-powered chatbot application.
- A robust and scalable backend architecture.
- A user-friendly and secure web interface.
- Successful implementation of SSO, 2FA, and other security measures.
- Integration of RAG architecture for improved chatbot performance.
- Successful deployment of the application on the AWS cloud.
- A well-documented and maintainable codebase.