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# KnowledgeBridge System Flow - Visual Guide for Demo |
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## π― Overview for Demo |
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This document provides a detailed breakdown of the technical architecture and data flow for KnowledgeBridge that you can reference during live demos or system presentations. |
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## π Main Data Flow (Left to Right) |
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``` |
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User Query β AI Enhancement β Multi-Source Search β URL Validation β Results Display |
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``` |
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## π Detailed Process Flow |
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### Stage 1: Input Processing & Enhancement |
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**Visual Elements for Demo:** |
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- User icon with speech bubble: "How does semantic search work?" |
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- Arrow pointing to React Enhanced Search Interface |
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- API endpoint box: `POST /api/search` |
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**Technical Details:** |
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- React captures user input with real-time validation |
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- TypeScript validation and sanitization |
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- Express.js endpoint with security middleware |
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- Optional AI query enhancement using Nebius |
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### Stage 2: AI Query Enhancement (Optional) |
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**Visual Elements for Demo:** |
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- Text box: "How does semantic search work?" |
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- Transformation arrow with Nebius AI logo |
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- Enhanced query output with keywords and suggestions |
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**Technical Details:** |
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- Nebius API call: `deepseek-ai/DeepSeek-R1-0528` |
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- Query analysis and improvement suggestions |
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- Intent recognition and keyword extraction |
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- Fallback to original query if enhancement fails |
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### Stage 3: Document Index (Pre-computed) |
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**Visual Elements for Miro:** |
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- Document icons flowing into a processor |
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- Chunking visualization (document β smaller pieces) |
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- FAISS index cylinder/database icon |
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**Technical Details:** |
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- LlamaIndex processes documents |
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- Text chunking for optimal retrieval |
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- Batch embedding generation |
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- FAISS index storage for fast search |
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### Stage 4: Similarity Search |
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**Visual Elements for Miro:** |
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- Query vector vs Document vectors |
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- Cosine similarity calculation visual |
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- Top-K selection (show top 5 results) |
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**Technical Details:** |
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- FAISS performs cosine similarity |
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- Mathematical formula: `cos(ΞΈ) = AΒ·B / (||A|| ||B||)` |
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- Ultra-fast: millions of comparisons/second |
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- Returns relevance scores (0.0 to 1.0) |
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### Stage 5: Document Retrieval |
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**Visual Elements for Miro:** |
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- Ranked list of documents |
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- Metadata extraction |
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- Snippet generation process |
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**Technical Details:** |
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- Retrieve top-scored document chunks |
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- Extract metadata (source, author, date) |
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- Generate context-aware snippets |
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- Prepare structured response |
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### Stage 6: AI Response Generation (Optional) |
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**Visual Elements for Miro:** |
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- GPT-4 brain icon |
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- Context window with query + documents |
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- Generated explanation output |
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**Technical Details:** |
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- LLM receives query + retrieved context |
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- Prompt engineering for accurate responses |
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- Citation and source attribution |
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- Structured JSON response |
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### Stage 7: Results Display |
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**Visual Elements for Miro:** |
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- UI cards showing results |
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- Relevance scores and rankings |
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- Citation tracking interface |
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**Technical Details:** |
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- React components render results |
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- Real-time UI updates |
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- Interactive result cards |
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- Citation management system |
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## π¨ Color Coding for Miro Board |
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### Technology Stack Colors: |
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- **Frontend (Blue)**: React, TypeScript, TailwindCSS |
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- **Backend (Green)**: Express.js, Node.js |
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- **AI/ML (Purple)**: OpenAI, Embeddings, LlamaIndex |
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- **Storage (Orange)**: FAISS, Vector Database |
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- **External APIs (Red)**: GitHub API, OpenAI API |
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### Data Flow Colors: |
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- **User Input (Light Blue)**: Query, interactions |
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- **Processing (Yellow)**: Transformations, calculations |
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- **Storage (Gray)**: Cached data, indexes |
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- **Output (Light Green)**: Results, responses |
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## π Key Performance Metrics to Highlight |
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### Speed Benchmarks: |
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- **Embedding Generation**: ~100ms per query |
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- **Vector Search**: <50ms for millions of documents |
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- **Total Response Time**: <500ms end-to-end |
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- **Concurrent Users**: Scales horizontally |
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### Accuracy Metrics: |
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- **Semantic Similarity**: 0.85+ for relevant results |
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- **Precision**: 90%+ relevant results in top-5 |
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- **Recall**: Finds relevant docs even with different wording |
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## π οΈ Architecture Diagrams for Miro |
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### High-Level Architecture: |
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``` |
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[Frontend] ββ [API Gateway] ββ [Search Engine] ββ [Vector DB] |
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β β β β |
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[React UI] [Express.js] [LlamaIndex] [FAISS] |
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``` |
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### Data Flow Sequence: |
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1. User Input β 2. Embedding β 3. Search β 4. Retrieval β 5. Display |
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``` |
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### Technology Stack: |
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Presentation: React + TypeScript + TailwindCSS |
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Business Logic: Express.js + Node.js |
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AI/ML: OpenAI API + LlamaIndex |
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Storage: FAISS Vector Store + In-Memory Cache |
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``` |
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## π Demo Script Suggestions |
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### Opening Hook: |
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"What if you could ask questions in natural language and get precise, cited answers from a curated knowledge base? Let me show you how this works under the hood." |
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### Technical Deep Dive: |
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1. **Show the query**: "Watch as 'How does RAG work?' becomes mathematics" |
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2. **Demonstrate embedding**: "This text becomes a 1536-dimensional vector" |
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3. **Visualize search**: "We're comparing meaning, not just keywords" |
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4. **Highlight speed**: "Searched 10,000+ documents in 50 milliseconds" |
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5. **Show accuracy**: "Notice the relevance scores and source citations" |
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### Closing Impact: |
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"This isn't just search - it's semantic understanding at scale, making knowledge truly accessible." |
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## π Scalability Points for Judges |
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- **Horizontal Scaling**: Add more vector storage nodes |
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- **Caching Strategy**: Embedding cache for repeated queries |
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- **API Rate Limiting**: Handles high concurrency |
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- **Real-time Updates**: New documents indexed automatically |
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- **Multi-modal Support**: Ready for images, audio, video |
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Use this guide to create compelling visuals that showcase both the technical sophistication and practical impact of your knowledge base system! |
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