making high -level decisions on policy and strategy.  
o Responsibilities:  
▪ Define the company vision and goals.  
▪ Oversee operations and ensure alignment with strategic objectives.  
▪ Communicate with stakeholders, including clients, board members, 
and employees.  
• Chief Operating Officer (COO)  
o Role:  Manages day -to-day operations and ensures that business processes run 
smoothly.  
o Responsibilities:  
▪ Implement operational strategies.  
▪ Oversee project management and resource allocation.  
▪ Ensure compliance with regulatory standards.  
• Chief Financial Officer (CFO)  
o Role:  Manages the company’s finances, including budgeting, forecasting, and 
financial reporting.  
o Responsibilities:  
▪ Develop financial strategies and monitor financial performance.  
▪ Oversee financial audits and risk management.  
▪ Manage investor relations.  
2. Management Level  
• Department Heads/Directors (e.g., IT Director, HR Director)  
o Role:  Lead specific departments and ensure alignment with overall business 
objectives.  
o Responsibilities:  
▪ Develop departmental strategies and manage budgets.  
▪ Supervise managers and oversee departmental performance.  
▪ Liaise with other departments to ensure cross -functional collaboration.  
Page 2:
• Project Managers  
o Role:  Responsible for planning, executing, and closing projects within the 
organization.  
o Responsibilities:  
▪ Define project scope and objectives.  
▪ Coordinate team efforts and manage project timelines.  
▪ Communicate project status to stakeholders and manage project risks.  
• Team Leaders/Supervisors  
o Role:  Oversee specific teams or units within departments.  
o Responsibilities:  
▪ Guide team members and ensure task completion.  
▪ Conduct performance evaluations and provide feedback.  
▪ Facilitate communication between team members and management.  
3. Technical Staff  
• Software Engineers/Developers  
o Role:  Design, develop, and maintain software applications and systems.  
o Responsibilities:  
▪ Write code and conduct unit testing.  
▪ Collaborate with product managers to define features.  
▪ Troubleshoot and resolve software issues.  
• System Administrators  
o Role:  Manage and maintain IT infrastructure, including servers, networks, and 
databases.  
o Responsibilities:  
▪ Monitor system performance and security.  
▪ Perform system upgrades and backups.  
▪ Support users with technical issues.  
• Data Analysts  
o Role:  Analyze data to provide insights and support decision -making processes.  
o Responsibilities:  
▪ Collect and clean data from various sources.  
Page 3:
▪ Create reports and visualizations to communicate findings.  
▪ Collaborate with stakeholders to identify data needs.  
4. Support Staff  
• Human Resources (HR) Specialists  
o Role:  Manage employee -related processes, including recruitment, onboarding, 
and performance management.  
o Responsibilities:  
▪ Handle employee relations and resolve workplace issues.  
▪ Implement training and development programs.  
▪ Ensure compliance with labor laws and company policies.  
• Customer Support Representatives  
o Role:  Provide assistance and support to clients and customers.  
o Responsibilities:  
▪ Address customer inquiries and resolve issues.  
▪ Document support requests and escalate when necessary.  
▪ Gather feedback to improve service offerings.  
• Administrative Assistants  
o Role:  Support daily administrative operations within the organization.  
o Responsibilities:  
▪ Manage schedules, correspondence, and documentation.  
▪ Assist in organizing meetings and events.  
▪ Perform general office duties as needed.  
5. Interns/Entry -Level Employees  
• Interns/Trainees  
o Role:  Assist various departments while gaining practical experience and 
training.  
o Responsibilities:  
▪ Support projects under the supervision of experienced staff.  
▪ Participate in training sessions and workshops.  
▪ Contribute to team efforts and tasks as assigned.  
 
--------------------------------------------------------------------------------
Extracting text from PDF file: SDLC Documentation of GenAIus KT.pdf

Page 1:
Table of Contents  
1. Introduction  
2. Phase 1: Planning  
o Purpose  
o Objectives  
o Stakeholders  
o Scope  
o Assumptions & Constraints  
o Risks  
3. Phase 2: System Analysis  
o Requirements Analysis  
o Functional Requirements  
o Non-Functional Requirements  
o Use Case Diagrams  
4. Phase 3: System Design  
o High -Level Design  
o System Architecture  
o Database Design  
o User Interface Design  
o Security Considerations  
5. Phase 4: Development  
o Frontend Development  
o Backend Development  
o Integration of Frontend and Backend  
o Data Preprocessing & Management  
6. Phase 5: Testing  
o Unit Testing  
o Integration Testing  
o User Acceptance Testing (UAT)  
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o Security Testing  
o Performance Testing  
7. Phase 6: Deployment  
o Deployment Strategy  
o Environment Setup  
o Version Control  
o Monitoring & Logging  
8. Phase 7: Maintenance  
o Bug Fixes & Enhancements  
o Performance Optimization  
o Backup & Disaster Recovery  
o Documentation Updates  
9. Future Development Initiatives  
10. Conclusion  
11. References  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Page 3:
1. Introduction  
Project Name : GenAIus KT - Knowledge Management System  
GenAIus KT is a sophisticated Generative AI -driven chatbot project that serves as a Knowledge 
Management System (KMS). It utilizes Google’s Generative AI, particularly the Gemini 
model, to facilitate knowledge management and transfer. The chatbot is design ed to respond to 
queries related to educational content, domain -specific information, and other professional 
needs. This documentation provides a detailed walkthrough of the SDLC phases for the project.  
 
2. Phase 1: Planning  
Purpose  
The primary purpose of the GenAIus KT project is to create a reliable, efficient, and 
professional chatbot interface that serves educational and domain -specific queries using a 
structured knowledge base.  
Objectives  
1. Develop a user -friendly interface for interacting with the AI -powered chatbot.  
2. Integrate the frontend and backend seamlessly using Next.js and Flask, respectively.  
3. Implement a backend that leverages Google’s Gemini model for generating accurate 
responses.  
4. Facilitate easy integration and data management using cleaned and preprocessed data.  
5. Ensure the chatbot is professional, formal, and relevant to domain -specific queries.  
6. Implement proper support for Markdown -formatted responses.  
Stakeholders  
• Project Owner : FWC  
• Project Manager : [Your Name]  
• Developers : Backend Developer, Frontend Developer  
• Designers : UI/UX Designer  
• End Users : Educational institutions, IT professionals, FWC employees.  
Scope  
• In-Scope : Development of the chatbot interface, backend integration with Google 
Gemini API, data cleaning, and preprocessing.  
• Out-of-Scope : Advanced NLP tasks beyond Google’s model, third -party integrations 
other than Gemini AI, content creation beyond cleaning existing data.  
Page 4:
Assumptions & Constraints  
Assumptions : 
• The dataset for training is clean, structured, and suitable for processing.  
• The Google Gemini API provides reliable outputs for generative responses.  
• Users have basic knowledge of educational and professional domain -specific topics.  
Constraints : 
• Limited access to real -time data updates.  
• Budget restrictions for additional AI API integrations.  
• Dependence on external AI models (Google Gemini).  
Risks  
• Model Risks : AI model may not generate accurate responses for vague queries.  
• Integration Risks : Frontend and backend integration might face compatibility issues.  
• Performance Risks : High data loads may cause system delays.  
• Security Risks : Potential vulnerabilities in data handling and API access.  
 
3. Phase 2: System Analysis  
Requirements Analysis  
Functional Requirements : 
1. The system should allow users to ask domain -specific questions.  
2. The chatbot must use the cleaned dataset to answer queries.  
3. Generate responses using Google’s Generative AI model.  
4. Display results in a professional and aesthetically pleasing interface.  
5. Support Markdown formatting for enhanced text readability.  
6. Manage chat history for multiple users.  
7. The chatbot should initialize with a professional welcome message.  
Non-Functional Requirements : 
1. Performance : The system must handle 100 concurrent users without noticeable lag.  
2. Reliability : 99% uptime for the chatbot interface.  
3. Security : Ensure secure handling of data, especially when interacting with AI APIs.  
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4. Usability : Intuitive interface with clear guidance for new users.  
5. Maintainability : Code should be modular and easy to update.  
6. Scalability : Backend should handle additional AI API integrations if needed.  
Use Case Diagrams  
• Primary Use Cases : 
o User initiates a chat session.  
o User asks a query.  
o Backend retrieves relevant data.  
o AI processes the query and generates a response.  
o Response is displayed with proper formatting.  
 
4. Phase 3: System Design  
High -Level Design  
The system is composed of three main components:  
1. Frontend (UI) : Built using Next.js for a responsive and user -friendly interface.  
2. Backend : Flask -based server responsible for handling user queries and interacting with 
Google’s AI API.  
3. Data Storage : Text files or a database containing cleaned data for query resolution.  
System Architecture  
1. Client -Side: 
o User interacts with the chatbot through a web -based interface.  
o The interface uses a RESTful API to communicate with the backend.  
2. Server -Side: 
o Flask handles incoming API requests.  
o Preprocessed data ( all_cleaned_data.txt ) is read and used for generating AI 
responses.  
o Google Gemini API is invoked for generating responses.  
3. Integration : 
o REST API for communication between frontend and backend.  
o Backend communicates with Google Gemini AI using the provided API key.  
Page 6:
Database Design  
Currently, preprocessed data is stored in a flat file format ( all_cleaned_data.txt ). Future 
iterations may include database integration (e.g., MongoDB) for scalable and structured data 
handling.  
User Interface Design  
• Components : 
o Chat Input Box (fixed at the bottom)  
o Chat History Panel (20% of screen width)  
o Response Display Area (80% of screen width)  
o Light/Dark Mode Toggle (Top -left corner)  
• Design Principles : 
o Professional color scheme.  
o Simple, clean, and intuitive layout.  
o Fixed header and footer for consistency.  
Security Considerations  
• Use HTTPS for secure communication.  
• Secure API keys in environment variables ( .env). 
• Implement CORS to control API access.  
• Consider data encryption for sensitive user interactions.  
 
5. Phase 4: Development  
Frontend Development  
Framework : Next.js  
• Design Elements : Professional and user -friendly.  
• Theme Toggle : Light/Dark mode for accessibility.  
• Chat Interface : Auto -scroll enabled for overflow, with a fixed input box.  
• Welcome Message : Implemented to start at the bottom and move up as new messages 
appear.  
Backend Development  
Framework : Flask  
Page 7:
• Endpoints : 
o /api/chat : Main endpoint to receive and respond to user queries.  
• Data Handling : Reads preprocessed knowledge base data and passes it to the AI model.  
• Markdown : Integrated to convert AI -generated responses into HTML for frontend 
compatibility.  
• Google API Integration : Utilizes Gemini API for response generation.  
Integration of Frontend and Backend  
• RESTful API setup to enable frontend to communicate with Flask backend.  
• Response data is formatted using Markdown and sent to the frontend.  
• API key securely stored in .env file. 
Data Preprocessing & Management  
• Raw data was cleaned and stored in all_cleaned_data.txt . 
• Data includes educational content, JSON data about transactions, reviews, and product 
pricing.  
• Preprocessing ensures content is relevant, structured, and optimized for AI processing.  
 
6. Phase 5: Testing  
Unit Testing  
• Objective : Ensure individual components like UI elements, backend routes, and API 
responses function as expected.  
• Tools : Jest for frontend testing, PyTest for backend.  
• Test Cases : Chat UI rendering, Markdown response handling, API response validation.  
Integration Testing  
• Objective : Verify that the integrated system components (frontend and backend) work 
together seamlessly.  
• Scenarios : Data retrieval from all_cleaned_data.txt , API response to frontend, 
Markdown rendering.  
User Acceptance Testing (UAT)  
• Objective : Validate that the system meets the end -users' expectations.  
• Stakeholders Involved : IT professionals, educational experts.  
• Feedback : Tweaks in UI, minor adjustments in AI response formatting.  
Page 8:
Security Testing  
• Objective : Identify vulnerabilities in data handling and API interactions.  
• Tools : OWASP ZAP, Postman for API security.  
• Results : API keys securely handled, CORS policies enforced.  
Performance Testing  
• Objective : Measure system performance under load.  
• Tools : Apache JMeter, LoadRunner.  
• Metrics : Response time under varying user loads, resource usage during high traffic.  
 
7. Phase 6: Deployment  
Deployment Strategy  
• Environment : Staging for internal testing, Production for public release.  
• Tools : Docker for containerization, NGINX for load balancing.  
• Version Control : GitHub repository for tracking code changes and version history.  
Environment Setup  
• Frontend : Hosted on Vercel for a scalable and managed Next.js environment.  
• Backend : Hosted on AWS EC2 with Flask and Python environment.  
• APIs : Integrated with Google Gemini using stored API keys.  
Monitoring & Logging  
• Tools : ELK Stack (Elasticsearch, Logstash, Kibana) for backend monitoring.  
• Metrics : Error rates, response times, user traffic.  
 
8. Phase 7: Maintenance  
Bug Fixes & Enhancements  
• Monitor user feedback for bug identification.  
• Plan periodic feature enhancements.  
• Schedule updates for AI model improvements.  
Performance Optimization  
• Optimize query handling and data processing.  
Page 9:
• Refactor code for efficiency based on monitoring feedback.  
Backup & Disaster Recovery  
• Daily backups of preprocessed data.  
• Use cloud storage solutions for redundancy.  
Documentation Updates  
• Maintain detailed documentation for all updates.  
• Include API changes, new features, and bug fixes.  
 
9. Future Development Initiatives  
i) Real -time Data  
To enhance the functionality of the GenAIus KT  system, we plan to integrate real -time data 
functionalities. This will involve implementing various functions that can call and retrieve real -
time information relevant to the organization's operations and needs. For instance, one of the 
critical data sourc es will be the company's holiday calendar, which is essential for scheduling 
and operational planning.  
• Function Implementation : We will create distinct functions that will make API calls 
to fetch real -time data. For example, when an employee inquires about the upcoming 
holidays, the system will automatically access the holiday calendar API and provide the 
latest updates.  
• API Integration : This integration will not only streamline the information retrieval 
process but also ensure that users receive the most current and accurate data available. 
This capability will enhance user experience by reducing the need for manual updates 
and inquirie s. 
• Use Cases : Besides the holiday calendar, other potential real -time data integrations may 
include:  
o Company events and announcements.  
o Dynamic project timelines.  
o Employee leave status updates.  
ii) Privacy of Confidential Information  
In our ongoing commitment to safeguarding sensitive corporate information, we plan to 
leverage Google Vertex AI . This initiative will utilize advanced machine learning capabilities, 
particularly through the Gemini model, to ensure privacy and security while handling 
confidential data.  
Page 10:
• Gemini Model : By using Gemini , we will implement function calling mechanisms that 
allow the system to interact with real -time APIs without exposing sensitive information. 
This model supports large language models (LLMs) that can effectively perform real -
time data retrieval without the  typical limitations associated with traditional models.  
• Tenant Project Architecture : The architecture will be designed to accommodate 
tenant -based implementations, where different tenants (or clients) can utilize the system 
while ensuring that their data remains separate and secure. Each tenant will have 
distinct access controls, allowin g for customized experiences while maintaining robust 
data protection measures.  
• Reasoning Engine : A robust reasoning engine will be integrated to facilitate logical 
processing and decision -making capabilities. This engine will analyze user queries in 
real-time and determine the best course of action, ensuring that responses are both 
accurate and cont extually relevant.  
• Multi -Agent System : The multi -agent framework will allow multiple instances of the 
chatbot to operate concurrently, each handling different aspects of user queries. This 
design will enable the system to respond more effectively to a variety of requests 
simultaneously, enhan cing overall responsiveness and user satisfaction.  
• Data Security : Utilizing Vertex AI not only supports efficient data processing but also 
strengthens data security protocols. The implementation of stringent privacy measures 
will ensure that confidential information remains protected during API calls and data 
exchanges , adhering to the principles of tenant -based security.  
• Exploring Functionality : As we dive deeper into the integration of Vertex AI, we will 
explore how it can support additional functionalities, such as:  
o Natural language understanding for more nuanced user interactions.  
o Advanced contextual awareness, allowing the model to retain and refer to past 
conversations while respecting privacy constraints.  
o Enhanced feedback mechanisms for continuous learning and improvement.  
These initiatives represent a significant step forward in enhancing the functionality, security, 
and user experience of the GenAIus KT  system. By prioritizing real -time data access and the 
protection of confidential information, we are committed to developing a robust and user -
friendly knowledge management system.  
 
10.  Conclusion  
The GenAIus KT project successfully implemented a professional Knowledge Management 
System using AI technology, focusing on user -friendly interaction and accurate response 
generation. The adherence to SDLC principles ensured a systematic approach to develo pment, 
making the system reliable and scalable for future enhancements.  
Page 11:
11. References  
• FWC Company Profile and Dataset.  
• Google Gemini AI Documentation.  
• Next.js Official Documentation.  
• Flask REST API Best Practices.  
• OWASP Guidelines for API Security.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Page 12:
Codes Explained with Functions:  
 
Front  End Code:  
 
1.Chatbot. js 
 
import  { useState , useEffect , useRef  } from  'react' ; 
import  axios  from  'axios' ; 
import  { IconButton , CircularProgress  } from  '@mui/material' ; 
import  DarkModeIcon  from  '@mui/icons -material/DarkMode' ; 
import  LightModeIcon  from  '@mui/icons -material/LightMode' ; 
import  SendIcon  from  '@mui/icons -material/Send' ; 
 
const  Chatbot  = () => { 
  const  [chatHistory , setChatHistory ] = useState ([]); 
  const  [currentChat , setCurrentChat ] = useState (0); 
  const  [messages , setMessages ] = useState ([]); 
  const  [input , setInput ] = useState (''); 
  const  [isDarkMode , setIsDarkMode ] = useState (false ); 
  const  [loading , setLoading ] = useState (false ); // Added loading state  
  const  chatEndRef  = useRef (null); 
 
  const  welcomeMessage  = "Greetings! I'm GenAIus KT, your Onboarding Buddy. How can I 
assist you?" ; 
 
  useEffect (() => { 
    document .body .className  = isDarkMode  ? 'dark'  : 'light' ; 
    if (messages .length  === 0 && chatHistory .length  === 0) { 
      setMessages ([{ sender:  'bot', text: welcomeMessage , logo:  true }]); 
    } 
Page 13:
  }, [isDarkMode ]); 
 
  useEffect (() => { 
    chatEndRef .current ?.scrollIntoView ({ behavior:  "smooth"  }); 
  }, [messages ]); 
 
  const  handleSend  = async  () => { 
    if (input .trim() === '') return ; 
 
    setMessages ((prevMessages ) => [...prevMessages , { sender:  'user' , text: input  }]); 
    setLoading (true); // Set loading to true  
 
    try { 
      const  response  = await  axios .post('http://127.0.0.1:5000/api/chat' , { message:  input  }); 
      setMessages ((prevMessages ) => [ 
        ...prevMessages ,  
        { sender:  'bot', text: response .data.reply , logo:  true } 
      ]); 
    } catch  (error ) { 
      console .error ('Error:' , error ); 
      setMessages ((prevMessages ) => [...prevMessages , { sender:  'bot', text: 'Something went 
wrong.' , logo:  false  }]); 
    } finally  { 
      setLoading (false ); // Set loading to false after response  
