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VideoStudy.app - Phase 1: References Analysis

Date: 2026-02-06 Objective: Document architecture, dependencies, and design patterns from reference projects

1. YTExtractor Analysis

1.1 Project Overview

Type: ASP.NET Core Web API
Framework: .NET 8.0
Purpose: Extract YouTube video metadata and transcripts
Deployment: Docker container with Linux support

1.2 Services Architecture

A. YoutubeService (Primary)

Purpose: YouTube video extraction using yt-dlp command-line tool
Key Methods:
- IsValidYouTubeUrl(string url) - Regex validation
- GetVideoInfo(string url, string workingDir) - Extract metadata (title, thumbnail)
- GetSubtitles(string url, string language, string workingDir) - Download captions in VTT format
Execution Model: System.Diagnostics process execution (yt-dlp)
Platform Support: Windows (.exe) and Linux/macOS (command)

B. YoutubeDataService (Alternative)

Purpose: Google YouTube API v3 integration
Methods: Same interface as YoutubeService
API Key: Environment variable YOUTUBE_API_KEY
Status: Currently not integrated in main endpoint (backup option)

C. ConvertTranscriptService

Purpose: VTT transcript conversion and cleaning
Key Methods:
- ExtractPlainText(string vttContent) - Remove timestamps, tags, music markers
- ConvertToSrt(string vttContent) - VTT → SRT format conversion
Processing: 7-step regex-based cleaning pipeline
- Remove WEBVTT header
- Remove timestamps (HH:MM:SS.mmm format)
- Strip HTML/VTT style tags (<00:00:00.280>, <c>)
- Remove music brackets [Música]
- Remove positioning markers
- Remove all remaining tags
- Normalize whitespace

D. MongoDBConnector

Purpose: Data persistence layer
Database: MongoDB "YTExtractor" database, "videos" collection
Methods:
- CRUD operations (Insert, Get, Update, Delete)
- GetVideoByUrl(string url) - Caching mechanism
Caching Strategy: Check cache before download to prevent redundant operations

1.3 API Endpoint

POST /api/video-info

Request:
{
  "url": "https://www.youtube.com/watch?v=VIDEO_ID",
  "language": "en"
}

Response:
{
  "url": "...",
  "title": "...",
  "thumbnailUrl": "...",
  "subtitles": "Plain text transcript..."
}

1.4 Data Flow

HTTP POST → Validate URL → Check MongoDB cache
  ├─ FOUND: Return cached response
  └─ NOT FOUND:
      ├─ Execute yt-dlp (metadata) → Get title + thumbnail
      ├─ Execute yt-dlp (subtitles) → Download .vtt file
      ├─ Clean VTT → Plain text via ConvertTranscriptService
      ├─ Store in MongoDB
      └─ Return response

1.5 Key Dependencies

Package	Version	Purpose
`Google.Apis.YouTube.v3`	1.69.0.3707	YouTube API client (backup)
`MongoDB.Driver`	3.1.0	Database access
`MongoDB.Bson`	3.1.0	BSON serialization
`Serilog.AspNetCore`	9.0.0	Structured logging
`Serilog.Sinks.Seq`	9.0.0	Centralized logging (Seq server)
`Swashbuckle.AspNetCore`	6.6.2	Swagger/OpenAPI UI

1.6 Configuration

{
  "MongoDbConnection": "mongodb://localhost:27017",
  "Serilog": {
    "MinimumLevel": "Information",
    "Sinks": ["Console", "Seq"],
    "Properties": {
      "Application": "YTExtractor",
      "Workspace": "Dev"
    }
  }
}

1.7 External Dependencies

yt-dlp - Command-line video tool (included in Docker image)
MongoDB - Document database
Seq Server - Log aggregation (dev only, optional)

1.8 Design Patterns Used

Repository Pattern - MongoDBConnector
Service Layer - Business logic separation
Process Execution - System.Diagnostics for yt-dlp
Caching - MongoDB-based result caching
Regex Parsing - Text processing pipeline

2. vcart.me.novo Analysis

2.1 Project Overview

Type: Full-stack ASP.NET Core MVC application
Framework: .NET 8.0
Purpose: LinkTree clone (business card/link aggregation platform)
CSS Framework: Bootstrap 5.3.2 + Custom CSS

2.2 CSS Architecture

A. Framework

Primary: Bootstrap 5.3.2 (grid, components, utilities)
Approach: Custom CSS + Bootstrap utilities (NO Tailwind, NO SCSS/LESS)
Preprocessor: Vanilla CSS only
CSS Variables: Custom properties for theming

B. Design Tokens

Color Palette:

/* Primary */
--primary-color: #007bff;           /* Bootstrap blue */
--secondary-color: #0056b3;         /* Darker shade */

/* Accents */
--accent-purple: #764ba2;           /* Gradients */
--accent-blue: #667eea;

/* Semantic */
--success: #28a745;
--warning: #ffc107;
--danger: #dc3545;

/* Text & Backgrounds */
--text-color: #212529;
--bg-light: #f8f9fa;
--bg-white: #ffffff;
--border-color: #dee2e6;

/* Dark Mode */
@media (prefers-color-scheme: dark) {
  --bg-dark: #121212;
  --bg-card-dark: rgba(33, 37, 41, 0.95);
}

Gradients:

/* Hero section gradient */
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);

/* Loading shimmer animation */
background: linear-gradient(90deg, rgba(255,255,255,0.1), rgba(255,255,255,0.3), rgba(255,255,255,0.1));

C. Typography System

Font Sizes (Responsive):

Base: 14px (mobile) → 16px (768px+)
Display (Hero): 3.5rem
Headings: Scales responsively
Body/Links: 0.9rem - 1.2rem

Font Weights:

Regular: 400
Medium: 500 (buttons, links)
Semi-bold: 600 (titles)
Bold: 700 (headings)

Line Heights:

Standard: 1.5
Relaxed: 1.6

D. Component Styles

Buttons:

.btn {
  border-radius: 50px;              /* Pill shape */
  font-weight: 500;
  padding: 0.6rem 1.5rem;
  transition: all 0.3s ease;
}

.btn-primary:hover {
  transform: translateY(-2px);
  box-shadow: 0 5px 15px rgba(37, 140, 251, 0.3);
}

Cards:

.card {
  border-radius: 15px;
  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
  overflow: hidden;
}

.profile-card {
  background-color: rgba(255, 255, 255, 0.95);
  backdrop-filter: blur(10px);      /* Glassmorphism */
}

Forms:

.form-control {
  border-radius: 10px;
  border: 2px solid #e9ecef;
  transition: all 0.3s ease;
}

.form-control:focus {
  border-color: #007bff;
  box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
}

Badges & Alerts:

.badge {
  border-radius: 20px;              /* Pill shaped */
}

.alert {
  border-radius: 15px;
  border: none;
}

E. Layout Patterns

Spacing System:

Bootstrap utilities (m-, p-, gap)
Custom: 1rem, 1.5rem, 2rem
Mobile-first approach

Breakpoints:

xs: Default (mobile-first)
sm: 576px+
md: 768px+
lg: 992px+
xl: 1200px+

Flexbox Patterns:

Navigation: flex row with gap
Profile cards: centered flex column
Link buttons: block-level

Hero Section:

.hero-section {
  background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
  color: white;
  padding: 2rem (mobile) - 5rem (desktop);
}

2.3 Frontend Structure

Key Pages:

Home/Index - Landing page
User Pages - Public profiles
Dashboard - Management interface
Manage Page - Page editor (complex)
Pricing - Subscription tiers
Authentication - Login/OAuth
Admin - Admin dashboard
Moderation - Content review

View Components:

ModerationMenu - Conditional navigation
SupportFab - Floating Action Button
_ThemeStyles - Dynamic CSS generation

JavaScript (Vanilla, no frameworks):

site.js - Global utilities (slug generation, validation, toasts)
support-fab.js - Support button
cookie-consent.js - Cookie banner
email-handler.js - Form handling
rating.js - Rating modal

2.4 Backend Architecture

Project Structure:

Controllers/     (16 MVC controllers)
Views/          (30 Razor templates)
Models/         (15 domain entities)
Services/       (39 business logic services)
Repositories/   (12 data access layer)
Areas/          (Artigos, Tutoriais, Support)
Middleware/     (Custom request handling)

Key Services:

UserPageService, ModerationService, PaymentService
ThemeService (dynamic CSS generation)
AuthService, EmailService, ImageStorageService
LivePageService, DowngradeService, TrialExpirationService
PlanConfigurationService

Database: MongoDB 7.0

Collections: users, userpages, categories, subscriptions
GridFS for file storage
Compound indexes for optimization

2.5 Key Dependencies

Package	Version	Purpose
`MongoDB.Driver`	3.4.2	Database driver
`Stripe.net`	48.4.0	Payment processing
`Microsoft.AspNetCore.Authentication.Google`	8.0.4	OAuth
`SendGrid`	9.29.3	Email service
`SixLabors.ImageSharp.Web`	3.1.0	Image manipulation
`Serilog`	8.0.0	Structured logging
`Serilog.Sinks.OpenSearch`	1.3.0	Log aggregation
`Markdig`	0.43.0	Markdown parsing
`HtmlAgilityPack`	1.11.54	HTML parsing
`YamlDotNet`	16.3.0	YAML configuration
`Bootstrap`	5.3.2	CSS framework
`jQuery`	3.7.1	DOM utilities
`Font Awesome`	6.4.0	Icons (CDN)

2.6 Design Patterns

Glassmorphism - Semi-transparent cards with backdrop blur
Micro-interactions - Smooth transitions (0.2s-0.5s)
Gradient accents - Purple-blue gradients
Responsive typography - Fluid font sizing
Color psychology - Blue for trust, purple for premium
Accessibility - Dark mode, reduced motion, high contrast

2.7 Build & Deployment

Build Targets:

Debug, Release, Testing configurations
Linux x64, ARM64 (Docker support)
Publishing profiles included

Static Assets:

Version-controlled via asp-append-version="true"
Content folder for Markdown
Resources folder for localization

3. ChatAPI / ChatRAG Analysis

3.1 Project Overview

ChatAPI:

Type: ASP.NET Core Web API
Framework: .NET 8.0
Purpose: Classification-based chat routing (Chat/Company/HR)
LLM Provider: OpenChat 3.5 via ServerSpace

ChatRAG:

Type: ASP.NET Core Web API
Framework: .NET 8.0
Purpose: RAG system with vector search and document retrieval
LLM Provider: Groq (llama-3.1-8b-instant)

3.2 Semantic Kernel Integration

Version

Semantic Kernel: 1.26.0 (current latest)
Connectors:
- Microsoft.SemanticKernel.Connectors.OpenAI 1.26.0 (ChatGPT, Groq, OpenChat)
- Microsoft.SemanticKernel.Connectors.Ollama 1.26.0-alpha (Local Ollama)
- Microsoft.SemanticKernel.Connectors.Google 1.26.0-alpha (Gemini)
- Lost.SemanticKernel.Connectors.Anthropic 1.25.0-alpha3 (Claude)

Registration Pattern

// In Program.cs
builder.Services.AddKernel();

// Add LLM provider (choose one):
// OpenAI-compatible (OpenChat, Groq, DeepInfra):
builder.Services.AddOpenAIChatCompletion(
    modelId: "openchat-3.5-0106",
    endpoint: new Uri("https://gpt.serverspace.com.br/v1/chat/completions"),
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")
);

// Or Ollama (local):
builder.Services.AddOllamaChatCompletion(
    modelId: "llama3.1:latest",
    endpoint: new Uri("http://localhost:11434")
);

// Or Google Gemini:
builder.Services.AddGoogleAIGeminiChatCompletion(
    modelId: "gemini-1.5-flash-latest",
    apiKey: Environment.GetEnvironmentVariable("GOOGLE_API_KEY")
);

// Add text embeddings (recommend Ollama):
builder.Services.AddOllamaTextEmbeddingGeneration(
    modelId: "all-minilm",
    endpoint: new Uri("http://localhost:11434")
);

3.3 Stepwise Planner Status

ANSWER: NO - Stepwise Planner is NOT used

Reason: Stepwise Planner is designed for multi-step function calling workflows. This architecture uses RAG with hierarchical query analysis instead.

Alternative Orchestration Pattern:

ChatAPI: Simple classification routing

Query Classification → Strategy Selection → Response Service Selection
Example: "RH salary question" → RH Service → ChatBotRHCall → External API

ChatRAG: Hierarchical RAG pipeline

Query Analysis (intent detection + strategy selection)
Multi-stage search (overview/specific/detailed/out-of-scope)
Confidence verification
Response generation with context assembly

3.4 Provider Switching Mechanism

Method: Configuration-driven with commented alternatives in Program.cs

ChatAPI Configuration:

// Active: OpenChat 3.5
builder.Services.AddOpenAIChatCompletion(
    "openchat-3.5-0106",
    new Uri("https://gpt.serverspace.com.br/v1/chat/completions"),
    "API_KEY"
);

// Available alternatives (commented):
// Local Ollama: builder.Services.AddOllamaChatCompletion("llama3.1:latest", new Uri("http://192.168.0.150:11434"));
// OpenAI GPT-4: builder.Services.AddOpenAIChatCompletion("gpt-4o-mini", "sk-proj-...");
// Google Gemini: builder.Services.AddGoogleAIGeminiChatCompletion("gemini-1.5-flash-latest", "AIzaSy...");
// Claude: builder.Services.AddAnthropicChatCompletion("claude-3-5-sonnet-latest", "sk-ant-...");

// Embeddings: Ollama
builder.Services.AddOllamaTextEmbeddingGeneration("all-minilm", new Uri("http://192.168.0.150:11434"));

ChatRAG Configuration:

// Active: Groq
var model = "llama-3.1-8b-instant";
var url = "https://api.groq.com/openai/v1";
builder.Services.AddOpenAIChatCompletion(model, new Uri(url), key);

// Embeddings: Ollama
builder.Services.AddOllamaTextEmbeddingGeneration("all-minilm", new Uri("http://localhost:11434"));

Switch Process:

Comment out current provider line
Uncomment desired provider
Update model ID and endpoint
Provide API key (environment variable or secret)
Run: dotnet build && dotnet run

3.5 RAG Implementation (ChatRAG)

Vector Database Support

Pluggable via Factory Pattern:

Qdrant (Primary) - High-performance vector DB
MongoDB (Secondary) - Embedded vectors in documents
Chroma (Alternative) - Lightweight vector DB
Hybrid - Combine multiple sources

Configuration:

"VectorDatabase": {
  "Provider": "Hybrid",
  "Qdrant": {
    "Host": "localhost",
    "Port": 6334,
    "CollectionName": "texts-whats",
    "VectorSize": 384,
    "Distance": "Cosine"
  }
}

RAG Strategies

HierarchicalRAGService: 4-stage search

Overview - Fetch all docs, categorize, summarize
Specific - Direct similarity search + context expansion
Detailed - Multi-concept search + knowledge gap filling
Out-of-scope - Fallback for unrelated queries

ConfidenceAwareRAGService: Multi-dimensional RAG

Intent detection (comparative, definition)
Heuristic search with query candidates
Dynamic confidence threshold adjustment
Confidence relaxation for specific intent types

Document Ingestion Pipeline

Document Upload
    ↓
Validation & Sanitization
    ↓
Auto-Enrichment (Groq LLM) - Optional
    ├─ Generate aliases
    ├─ Extract topics
    └─ Create summary
    ↓
Metadata Generation
    ├─ Title normalization
    ├─ Acronym extraction
    ├─ Search term building (no diacritics)
    └─ Structured metadata
    ↓
Vector Embedding
    └─ Ollama all-minilm (384 dimensions)
    ↓
Store in Vector Database
    ├─ Document content
    ├─ Metadata
    ├─ Vector embedding
    └─ Searchable indices

3.6 Services Architecture

ChatAPI Services:

ResponseChatService - General chat
ResponseCompanyService - Company queries
ResponseBotRHService - HR integration
ChatHistoryService - Conversation memory
ClassifierPersistence - Query classification storage
ResponseFactory - Service selection

ChatRAG Services:

ConfidenceAwareRAGService - Confidence-verified RAG
HierarchicalRAGService - Multi-strategy RAG
QdrantVectorSearchService - Qdrant operations
MongoVectorSearchService - MongoDB vectors
ChromaVectorSearchService - Chroma operations
DocumentIngestionService - Document preprocessing
PromptConfigurationService - Domain/language templates
ConfidenceVerifier - Quality assurance metrics
VectorDatabaseFactory - Provider instantiation

3.7 Key Interfaces (Semantic Kernel)

// Core abstractions
IChatCompletionService              // LLM chat operations
ITextEmbeddingGenerationService     // Vector embedding generation
IVectorSearchService                // Vector database queries
ITextDataService                    // Document storage operations

3.8 Key Dependencies

Common:

Package	Version	Purpose
`Microsoft.SemanticKernel`	1.26.0	Core Semantic Kernel
`Microsoft.SemanticKernel.Connectors.OpenAI`	1.26.0	OpenAI-compatible providers
`Microsoft.SemanticKernel.Connectors.Ollama`	1.26.0-alpha	Local LLM support
`MongoDB.Driver`	3.0.0	Database client
`Swashbuckle.AspNetCore`	6.6.2	Swagger/OpenAPI

ChatRAG-Specific:

Package	Version	Purpose
`Qdrant.Client`	1.14.0	Qdrant vector DB
`Microsoft.Extensions.VectorData.Abstractions`	9.0.0-preview	Vector storage abstraction
`BlazMapper`	0.0.5	Object mapping

3.9 Data Flow Diagrams

ChatAPI Flow:

HTTP Request
    ↓
Classifier (determines type: Chat/Company/HR)
    ↓
ResponseFactory (selects service)
    ↓
IResponseService (delegates)
    ├─ ResponseChatService (general queries)
    ├─ ResponseCompanyService (company info)
    └─ ResponseBotRHService (HR)
    ↓
IChatCompletionService (Kernel)
    ↓
LLM Provider (OpenChat/Ollama/GPT-4)
    ↓
Response

ChatRAG Flow:

HTTP Request (ProjectId + Question)
    ↓
Query Analysis (intent detection, strategy selection)
    ↓
Hierarchical Search (4 strategies based on complexity)
    ├─ IVectorSearchService (retrieve similar documents)
    └─ ITextEmbeddingGenerationService (generate embeddings)
    ↓
Context Building & Enrichment
    ├─ Document summarization (concurrent)
    ├─ Gap identification
    └─ Related document expansion
    ↓
Confidence Verification (multi-dimensional)
    ├─ Calculate confidence metrics
    ├─ Apply relaxation rules (intent-based)
    └─ Decide: respond or fallback
    ↓
Response Generation
    ├─ PromptConfigurationService (template selection)
    ├─ IChatCompletionService (Kernel)
    ├─ LLM Provider (Groq/Ollama)
    └─ Format response with metadata
    ↓
HTTP Response

3.10 Configuration Management

Provider Selection (appsettings.json):

{
  "LLM": {
    "Provider": "Groq",
    "Model": "llama-3.1-8b-instant"
  },
  "Embeddings": {
    "Provider": "Ollama",
    "Model": "all-minilm"
  },
  "VectorDatabase": {
    "Provider": "Hybrid",
    "Qdrant": { ... },
    "MongoDB": { ... },
    "Chroma": { ... }
  }
}

4. Summary: Objects for Modification (Per CLAUDE.md Instructions)

Following your instruction "não quero que você mude nada, mas que aponte quais objetos terão que ser alterados", here are the objects that would need modification for various VideoStudy.app implementation scenarios:

4.1 If Integrating YTExtractor Services

Objects to Modify/Create:

YoutubeService interface - Extract interface from YTExtractor.YoutubeService
YoutubeDataService interface - Alternative implementation
ConvertTranscriptService - Adapt for VideoStudy domain
IVideoRepository - MongoDB access for cached videos
API Endpoint: POST /api/videos/analyze - Accept video URL + analysis parameters
VideoAnalysisRequest model - Input model with URL, language, analysis type
VideoAnalysisResponse model - Output model with metadata + transcript

4.2 If Integrating CSS/Design from vcart.me.novo

Objects to Modify/Create:

Copy CSS framework structure (Bootstrap 5.3.2)
Create site.css - Global application styles
Create video-player.css - Video display styles
Create analysis-panel.css - Analysis results styles
Reuse color palette (Bootstrap blue + purple accents)
Reuse component patterns (buttons, cards, forms)
Reuse typography system (responsive, semantic)

4.3 If Integrating Semantic Kernel (ChatRAG Pattern)

Objects to Modify/Create:

IAnalysisService interface - Semantic Kernel-based analysis
VideoAnalysisService - RAG service for transcript analysis
DocumentIngestionService - Adapt from ChatRAG for video metadata
PromptService - Template management for different analysis types
EmbeddingService - Text embeddings for similarity search
IVectorSearchService - Vector database abstraction
API Endpoint: POST /api/analysis/semantic - Analysis request handler
AnalysisResult model - Structured analysis output
VectorDatabaseFactory - Provider selection (Qdrant/MongoDB/Chroma)

4.4 Objects NOT to Create (Avoid Over-Engineering)

Custom authentication system (use existing patterns from vcart.me.novo)
Caching layer beyond MongoDB (trust Kernel caching)
Complex middleware stack
Custom logging infrastructure (Serilog is sufficient)
Multiple response types (start with simple JSON)

5. Recommended Implementation Strategy

Phase 1 - Minimal Viable Structure

Copy YTExtractor Services → VideoStudy.API
- YoutubeService
- ConvertTranscriptService
- MongoDB repository pattern
Implement Minimal Semantic Kernel Integration
- Single endpoint: /api/analysis/analyze
- Accept: video URL
- Return: transcript + basic analysis
- Use Semantic Kernel 1.26.0
Use vcart.me.novo Design System
- Bootstrap 5.3.2
- Color palette (blue + purple)
- Responsive typography
- Button/card styles
Create Blazor Hybrid Desktop App
- Single page with video URL input
- Button to submit
- Text area for results
- Use CSS from API project

Phase 2 - RAG Implementation (Future)

Integrate HierarchicalRAGService pattern from ChatRAG
Add vector database (Qdrant or MongoDB)
Implement document ingestion pipeline
Add confidence verification

Phase 3 - Advanced Features (Future)

Accordion for result sections
Drag & drop for file uploads
PDF preview
Progress bar
Multiple analysis strategies

6. Questions to Answer (Per Phase Requirements)

These questions will be answered in the next phase after implementation:

Semantic Kernel Version: 1.26.0 (recommended, latest stable)
Stepwise Planner Alternative: Hierarchical query analysis + multi-stage execution (from ChatRAG)
YTExtractor Essential Dependencies:
- yt-dlp tool (external executable)
- MongoDB.Driver 3.1.0
- Google.Apis.YouTube.v3 1.69.0.3707 (optional backup)
vcart.me.novo Design: Bootstrap 5.3.2 (NOT Tailwind)

Document Version History

Version	Date	Changes
1.0	2026-02-06	Initial comprehensive reference analysis

EOF

22 KiB Raw Blame History

VideoStudy.app - Phase 1: References Analysis

1. YTExtractor Analysis

1.1 Project Overview

1.2 Services Architecture

A. YoutubeService (Primary)

B. YoutubeDataService (Alternative)

C. ConvertTranscriptService

D. MongoDBConnector

1.3 API Endpoint

1.4 Data Flow

1.5 Key Dependencies

1.6 Configuration

1.7 External Dependencies

1.8 Design Patterns Used

2. vcart.me.novo Analysis

2.1 Project Overview

2.2 CSS Architecture

A. Framework

B. Design Tokens

C. Typography System

D. Component Styles

E. Layout Patterns

2.3 Frontend Structure

2.4 Backend Architecture

2.5 Key Dependencies

2.6 Design Patterns

2.7 Build & Deployment

3. ChatAPI / ChatRAG Analysis

3.1 Project Overview

3.2 Semantic Kernel Integration

Version

Registration Pattern

3.3 Stepwise Planner Status

3.4 Provider Switching Mechanism

3.5 RAG Implementation (ChatRAG)

Vector Database Support

RAG Strategies

Document Ingestion Pipeline

3.6 Services Architecture

3.7 Key Interfaces (Semantic Kernel)

3.8 Key Dependencies

3.9 Data Flow Diagrams

3.10 Configuration Management

4. Summary: Objects for Modification (Per CLAUDE.md Instructions)

4.1 If Integrating YTExtractor Services

4.2 If Integrating CSS/Design from vcart.me.novo

4.3 If Integrating Semantic Kernel (ChatRAG Pattern)

4.4 Objects NOT to Create (Avoid Over-Engineering)

5. Recommended Implementation Strategy

Phase 1 - Minimal Viable Structure

Phase 2 - RAG Implementation (Future)

Phase 3 - Advanced Features (Future)

6. Questions to Answer (Per Phase Requirements)

Document Version History

22 KiB

Raw Blame History