Project Manager	Michael Anderson	Oversees project execution and manages timelines.
Vendor Management Specialist	Sarah Johnson	Provides expertise in vendor management processes and practices.
Training Coordinator	Daniel Lee	Develops and conducts vendor management training programs.
Process Analyst	Emily Brown	Assesses current vendor management processes and recommends improvements.
Change Management Lead	Olivia Green	Manages communication and engagement during the transformation.
Project Analyst	Lucas White	Assists with project documentation and reporting.
Role	Name	Responsibilities
Project Sponsor	David Smith	Provides project funding and strategic direction.
Operations Manager	Jessica Thompson	Coordinates with FWC on operational requirements.
Supplier Relationship Manager	Kevin Davis	Ensures alignment between vendor and PMS objectives.
IT Manager	Rachel Kim	Supports any technology-related changes needed for vendor management.
Risk	Likelihood	Impact	Mitigation Strategy
Resistance to Change from Employees	High	Medium	Develop engaging training materials and involve employees in the process.
Incomplete Implementation of New Processes	Medium	High	Conduct regular check-ins and provide support throughout the transformation.
Insufficient Leadership Support	Medium	High	Ensure project sponsor engagement and communication of project goals.
Inadequate Vendor Participation	Medium	Medium	Build strong relationships and involve vendors early in the process.
Item	Estimated Cost (USD)
Vendor Assessment Framework Development	$35,000
Training Program Development	$20,000
Process Redesign Consulting	$40,000
Collaboration Tools Implementation	$25,000
Project Management and Administration	$50,000
Total Estimated Budget	$170,000
--------------------------------------------------------------------------------
Extracting text from DOCX file: SDLC Documentation of GenAIus KT.docx

Paragraphs:
Table of Contents
Introduction
Phase 1: Planning
Purpose
Objectives
Stakeholders
Scope
Assumptions & Constraints
Risks
Phase 2: System Analysis
Requirements Analysis
Functional Requirements
Non-Functional Requirements
Use Case Diagrams
Phase 3: System Design
High-Level Design
System Architecture
Database Design
User Interface Design
Security Considerations
Phase 4: Development
Frontend Development
Backend Development
Integration of Frontend and Backend
Data Preprocessing & Management
Phase 5: Testing
Unit Testing
Integration Testing
User Acceptance Testing (UAT)
Security Testing
Performance Testing
Phase 6: Deployment
Deployment Strategy
Environment Setup
Version Control
Monitoring & Logging
Phase 7: Maintenance
Bug Fixes & Enhancements
Performance Optimization
Backup & Disaster Recovery
Documentation Updates
Future Development Initiatives
Conclusion
References














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 designed 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
Develop a user-friendly interface for interacting with the AI-powered chatbot.
Integrate the frontend and backend seamlessly using Next.js and Flask, respectively.
Implement a backend that leverages Google’s Gemini model for generating accurate responses.
Facilitate easy integration and data management using cleaned and preprocessed data.
Ensure the chatbot is professional, formal, and relevant to domain-specific queries.
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.
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:
The system should allow users to ask domain-specific questions.
The chatbot must use the cleaned dataset to answer queries.
Generate responses using Google’s Generative AI model.
Display results in a professional and aesthetically pleasing interface.
Support Markdown formatting for enhanced text readability.
Manage chat history for multiple users.
The chatbot should initialize with a professional welcome message.
Non-Functional Requirements:
Performance: The system must handle 100 concurrent users without noticeable lag.
Reliability: 99% uptime for the chatbot interface.
Security: Ensure secure handling of data, especially when interacting with AI APIs.
Usability: Intuitive interface with clear guidance for new users.
Maintainability: Code should be modular and easy to update.
Scalability: Backend should handle additional AI API integrations if needed.
Use Case Diagrams
Primary Use Cases:
User initiates a chat session.
User asks a query.
Backend retrieves relevant data.
AI processes the query and generates a response.
Response is displayed with proper formatting.

4. Phase 3: System Design
High-Level Design
The system is composed of three main components:
Frontend (UI): Built using Next.js for a responsive and user-friendly interface.
Backend: Flask-based server responsible for handling user queries and interacting with Google’s AI API.
Data Storage: Text files or a database containing cleaned data for query resolution.
System Architecture
Client-Side:
User interacts with the chatbot through a web-based interface.
The interface uses a RESTful API to communicate with the backend.
Server-Side:
Flask handles incoming API requests.
Preprocessed data (all_cleaned_data.txt) is read and used for generating AI responses.
Google Gemini API is invoked for generating responses.
Integration:
REST API for communication between frontend and backend.
Backend communicates with Google Gemini AI using the provided API key.
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:
Chat Input Box (fixed at the bottom)
Chat History Panel (20% of screen width)
Response Display Area (80% of screen width)
Light/Dark Mode Toggle (Top-left corner)
Design Principles:
Professional color scheme.
Simple, clean, and intuitive layout.
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
Endpoints:
/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.
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.
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 sources 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 inquiries.
Use Cases: Besides the holiday calendar, other potential real-time data integrations may include:
Company events and announcements.
Dynamic project timelines.
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.
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, allowing 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 contextually 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, enhancing 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:
Natural language understanding for more nuanced user interactions.
Advanced contextual awareness, allowing the model to retain and refer to past conversations while respecting privacy constraints.
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 development, making the system reliable and scalable for future enhancements.
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.























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 }]);
    }
  }, [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
    }

    setInput('');
  };

  const toggleTheme = () => {
    setIsDarkMode((prevMode) => !prevMode);
  };

  const handleNewChat = () => {
    window.location.reload(); // Reloads the page
  };

  const selectChat = (index) => {
    setMessages(chatHistory[index]);
    setCurrentChat(index);
  };

  return (
    <div className="container">
      <div className="left-panel">
        <h2>GenAIus KT</h2>
        <button className="new-chat" onClick={handleNewChat}>+ New Chat</button>
        
        <hr className="divider" />

        <ul className="chat-history">
          {chatHistory.map((_, index) => (
            <li key={index} onClick={() => selectChat(index)}>Chat Message {index + 1}</li>
          ))}
        </ul>
      </div>
      <div className="chatbot-container">
        <div className="header">
          <span className="TitleHeader">
            <img src="/logo.png" alt="Chatbot Logo" className="header-logo" />
            <h1>GenAIus KT</h1>
          </span>
          <IconButton onClick={toggleTheme} style={{ color: isDarkMode ? '#ffc107' : '#000' }}>
            {isDarkMode ? <LightModeIcon /> : <DarkModeIcon />}
          </IconButton>
        </div>

        <div className="chat-window">
          {messages.map((msg, index) => (
            <div key={index} className={`message ${msg.sender} ${isDarkMode ? 'dark' : 'light'}`}>
              {msg.logo && <img src="/logo.png" alt="Chatbot Logo" className="bot-logo" />}
              <span dangerouslySetInnerHTML={{ __html: msg.text }} />
            </div>
          ))}
          {loading && (
            <div className="loading-message">
              <CircularProgress size={24} />
              <span className="loading-text">Working On It...</span>
            </div>
          )}
          <div ref={chatEndRef} />
        </div>

        <div className="input-container">
          <input
            type="text"
            className={isDarkMode ? 'dark' : 'light'}
            value={input}
            onChange={(e) => setInput(e.target.value)}
            placeholder="Type your message..."
            onKeyDown={(e) => e.key === 'Enter' && handleSend()}
          />
          <IconButton onClick={handleSend} style={{ color: isDarkMode ? '#ffc107' : '#246ffe' }}>
            <SendIcon />
          </IconButton>
        </div>
      </div>

      <style jsx>{`
        .container {
          display: flex;
          height: 100vh;
          width: 100vw;
        }

        .left-panel {
          width: 20%;
          background-color: var(--sidebar-bg);
          padding: 20px;
          border-right: 1px solid var(--border-color);
          display: flex;
          flex-direction: column;
        }

        .left-panel h2 {
          font-size: 1.5rem;
          margin-bottom: 20px;
        }

        .new-chat {
          background-color: #246ffe;
          border: none;
          padding: 10px;
          margin-bottom: 20px;
          cursor: pointer;
          border-radius: 8px;
          color: #fff;
          font-size: 1rem;
          box-shadow: 0px 3px 8px rgba(0, 0, 0, 0.1);
          transition: background-color 0.3s ease;
        }

        .new-chat:hover {
          background-color: #205ce4;
        }

        .divider {
          border: 0;
          height: 1px;
          background-color: var(--border-color);
          margin: 20px 0;
        }

        .chat-history {
          list-style: none;
          padding: 0;
        }

        .chatbot-container {
          width: 80%;
          display: flex;
          flex-direction: column;
          background-color: var(--background-color);
          padding: 20px;
          overflow: hidden;
        }

        .header {
          display: flex;
          justify-content: space-between;
          align-items: center;
          margin-bottom: 10px;
        }

