ayankumar/SDLC-Assistant-MultiAgent
0
1 <style>2 #agent-output {font-family: "Segoe UI", sans-serif; color: #333; }3 4 #agent-output table {width: 100%; border-collapse: collapse; margin-top: 1rem;}5 #agent-output th, 6 #agent-output td {border-top: 1px solid #ccc; border-bottom: 1px solid #ccc; border-left: none; border-right: none; padding: 10px; text-align: left;}7 #agent-output tr:nth-child(even) {background-color: #f9f9f9;}8 #agent-output tr:hover {background-color: #e6f7ff; }9 #agent-output th {background-color: #111; color: white;}10 11 #agent-output h1, 12 #agent-output h2,13 #agent-output h3 { border-bottom: 2px solid #ddd; padding-bottom: 6px; margin-top: 1.5rem; color: #111; }14 #agent-output ul { padding-left: 1.5rem; margin-bottom: 1rem; list-style-type: disc;}15 #agent-output ul li { margin-bottom: 8px; padding-left: 0.5rem; border-left: 3px; background-color: #f7faff; border-radius: 4px; transition: background-color 0.3s ease; }16 #agent-output ul li:hover { background-color: #e6f2ff;}17 #agent-output ol {padding-left: 1.5rem; margin-bottom: 1rem; list-style-type: decimal; counter-reset: item;}18 #agent-output ol li {margin-bottom: 8px; padding-left: 0.5rem; position: relative; background-color: #fffdf7; border-left: 3px; border-radius: 4px; transition: background-color 0.3s ease;}19 #agent-output ol li:hover { background-color: #fff3e0; }20 #agent-output p { font-size: 1rem; line-height: 1.7; margin-bottom: 1.2rem; padding: 0.5rem 0.75rem; border-left: 4px;}21 #agent-output pre { background-color: #f4f4f4; padding: 10px; border-radius: 6px; overflow-x: auto; }22 </style> 23<div id="agent-output"><h1>Design</h1>24<h2>Software Design Document: File Size Optimization System</h2>25<p><strong>1. High-Level Design</strong></p>26<p>This system aims to reduce the average file size of images, videos, and documents by 25% while maintaining acceptable subjective quality. It will operate in an Azure cloud environment.</p>27<p><strong>Component Overview:</strong></p>28<ul>29<li><strong>Ingestion Service:</strong> Receives files from various sources (e.g., user uploads, APIs). Performs initial validation and metadata extraction.</li>30<li><strong>Optimization Engine:</strong> Core component responsible for applying size reduction techniques based on file type and content. This includes intelligent compression algorithms, resolution scaling (for images/videos), and potentially format conversion.</li>31<li><strong>Quality Assessment Service:</strong> Evaluates the subjective quality of the optimized files using a machine learning model trained on user feedback data. This service flags files falling below the acceptable quality threshold.</li>32<li><strong>Storage Service:</strong> Stores both original and optimized files in Azure Blob Storage. Metadata including original size, optimized size, and quality score are also stored.</li>33<li><strong>Metadata Service:</strong> Manages metadata associated with files, including original and optimized file information, and quality assessment results. This will be critical for reporting and analysis.</li>34<li><strong>API Gateway:</strong> Provides a single entry point for external systems to interact with the system. Handles authentication and authorization.</li>35</ul>36<p><strong>Responsibilities:</strong></p>37<ul>38<li><strong>Ingestion Service:</strong> File validation, metadata extraction, initial quality checks.</li>39<li><strong>Optimization Engine:</strong> File size reduction using appropriate algorithms.</li>40<li><strong>Quality Assessment Service:</strong> Subjective quality scoring using a machine learning model.</li>41<li><strong>Storage Service:</strong> Secure and scalable storage of original and optimized files and metadata.</li>42<li><strong>Metadata Service:</strong> Management and retrieval of file metadata.</li>43<li><strong>API Gateway:</strong> Secure and controlled access to the system.</li>44</ul>45<p><strong>High-Level Data Flow:</strong></p>46<div class="codehilite"><pre><span></span><code><span class="p">[</span><span class="n">External</span><span class="w"> </span><span class="n">Systems</span><span class="p">]</span><span class="w"> </span><span class="o">--></span><span class="w"> </span><span class="p">[</span><span class="n">API</span><span class="w"> </span><span class="n">Gateway</span><span class="p">]</span><span class="w"> </span><span class="o">--></span><span class="w"> </span><span class="p">[</span><span class="n">Ingestion</span><span class="w"> </span><span class="n">Service</span><span class="p">]</span><span class="w"> </span><span class="o">--></span><span class="w"> </span><span class="p">[</span><span class="n">Optimization</span><span class="w"> </span><span class="n">Engine</span><span class="p">]</span><span class="w"> </span><span class="o">--></span><span class="w"> </span><span class="p">[</span><span class="n">Quality</span><span class="w"> </span><span class="n">Assessment</span><span class="w"> </span><span class="n">Service</span><span class="p">]</span><span class="w"> </span><span class="o">--></span><span class="w"> </span><span class="p">[</span><span class="n">Storage</span><span class="w"> </span><span class="n">Service</span><span class="p">]</span><span class="w"> </span><span class="o">&</span><span class="w"> </span><span class="p">[</span><span class="n">Metadata</span><span class="w"> </span><span class="n">Service</span><span class="p">]</span><span class="w"> </span><span class="o">--></span><span class="w"> </span><span class="p">[</span><span class="n">API</span><span class="w"> </span><span class="n">Gateway</span><span class="p">]</span><span class="w"> </span><span class="o">--></span><span class="w"> </span><span class="p">[</span><span class="n">External</span><span class="w"> </span><span class="n">Systems</span><span class="p">]</span>47</code></pre></div>48 49<p><strong>(Diagram will be provided at the end)</strong></p>50<p><strong>2. Low-Level Design</strong></p>51<ul>52<li><strong>Ingestion Service:</strong> Microservice written in Python using Flask/FastAPI. Integrates with Azure Blob Storage SDK for file upload and metadata management.</li>53<li><strong>Optimization Engine:</strong> Microservice, potentially using multiple libraries depending on file type (e.g., OpenCV for image processing, FFmpeg for video processing, libraries for document compression like Ghostscript). Designed for horizontal scalability.</li>54<li><strong>Quality Assessment Service:</strong> Microservice leveraging a pre-trained machine learning model (e.g., TensorFlow/PyTorch model deployed via Azure Machine Learning service) for subjective quality assessment.</li>55<li><strong>Storage Service:</strong> Azure Blob Storage.</li>56<li><strong>Metadata Service:</strong> NoSQL database like Azure Cosmos DB for flexible schema and scalability.</li>57<li><strong>API Gateway:</strong> Azure API Management.</li>58</ul>59<p><strong>Interfaces:</strong></p>60<ul>61<li>REST APIs (JSON) for communication between services and external systems.</li>62<li>Asynchronous message queues (Azure Service Bus) for decoupling services and handling high throughput.</li>63</ul>64<p><strong>3. Technical Architecture Description</strong></p>65<ul>66<li><strong>Deployment Layers:</strong> Microservices deployed in Azure Kubernetes Service (AKS) for scalability and resilience.</li>67<li><strong>Communication Paths:</strong> REST APIs and asynchronous message queues.</li>68<li><strong>External Integrations:</strong> Secure APIs for integration with existing security and data governance systems (details dependent on specific systems identified in clarifying questions).</li>69</ul>70<p><strong>4. List of Tools, Technologies, and Frameworks</strong></p>71<ul>72<li>Programming Languages: Python</li>73<li>Frameworks: Flask/FastAPI, TensorFlow/PyTorch</li>74<li>Databases: Azure Cosmos DB</li>75<li>Cloud Platform: Azure (AKS, Blob Storage, API Management, Service Bus, Machine Learning)</li>76<li>Containerization: Docker</li>77<li>Orchestration: Kubernetes</li>78</ul>79<p><strong>5. Assumptions and Constraints</strong></p>80<ul>81<li>Access to existing security and data governance systems APIs and documentation.</li>82<li>Sufficient training data for the quality assessment model.</li>83<li>Acceptable subjective quality degradation threshold will be defined based on user testing and feedback.</li>84<li>The system will handle a defined peak load and average daily file processing volume. This will need to be specified based on expected usage.</li>85</ul>86<p><strong>6. Notes on Future Scalability Considerations</strong></p>87<ul>88<li>Horizontal scaling of microservices using AKS.</li>89<li>Asynchronous processing using message queues to handle bursts of activity.</li>90<li>Database sharding and replication for improved database performance.</li>91<li>Content Delivery Network (CDN) for fast content delivery.</li>92</ul>93<p><strong>7. Data Quality Considerations</strong></p>94<ul>95<li>Data validation at ingestion to ensure file integrity.</li>96<li>Regular monitoring of the quality assessment model's accuracy and retraining as needed.</li>97<li>Logging and monitoring of processing errors and failures.</li>98</ul>99<p><strong>8. Data Governance Considerations</strong></p>100<ul>101<li>Adherence to GDPR and HIPAA compliance requirements through secure access controls and data encryption.</li>102<li>Integration with existing data governance systems.</li>103<li>Data retention policies and procedures.</li>104</ul>105<p><strong>9. Infrastructure Decisions</strong></p>106<ul>107<li>Azure cloud platform for scalability, reliability, and security.</li>108<li>AKS for container orchestration and microservice deployment.</li>109<li>Azure Blob Storage for secure and scalable file storage.</li>110<li>Azure Cosmos DB for flexible and scalable metadata storage.</li>111</ul>112<p><strong>10. Data Security Considerations</strong></p>113<ul>114<li>Encryption of data at rest and in transit.</li>115<li>Role-based access control (RBAC) for secure access to system resources.</li>116<li>Regular security audits and penetration testing.</li>117<li>Secure API gateways with authentication and authorization.</li>118</ul>119<p><strong>(Diagram)</strong></p>120<div class="codehilite"><pre><span></span><code><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span>121<span class="c">| External Systems |</span><span class="nb">----</span><span class="nv">></span><span class="c">| API Gateway |</span><span class="nb">----</span><span class="nv">></span><span class="c">| Ingestion Service|</span><span class="nb">----</span><span class="nv">></span><span class="c">|Optimization Engine|</span><span class="nb">----</span><span class="nv">></span><span class="c">| Quality Assessment|</span>122<span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span>123<span class="c"> ^ |</span>124<span class="c"> | v</span>125<span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span>126<span class="c"> | Storage Service |</span><span class="nb">----</span><span class="nv">></span><span class="c">| Metadata Service |</span>127<span class="c"> </span><span class="nb">+-----------------+</span><span class="c"> </span><span class="nb">+-----------------+</span>128</code></pre></div>129 130<p><strong>Clarifying Questions and Answers:</strong></p>131<p>The provided answers to the clarifying questions were rather vague. To proceed with a robust design, more concrete details are needed for points like specific APIs and data formats for the existing security and data governance systems. The "subjective quality score decrease" also needs a numerical target (e.g., maximum 10% decrease in a specific subjective quality metric). The expected file sizes and resolutions also need to be quantified for accurate capacity planning. This information will significantly impact storage needs, optimization algorithm selection, and system performance.</p></div>