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--- |
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title: KnowledgeBridge |
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emoji: 📚 |
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colorFrom: yellow |
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colorTo: red |
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sdk: docker |
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pinned: false |
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license: mit |
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short_description: 'A sophisticated AI-powered knowledge retrieval and analysis ' |
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tags: |
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- agent-demo-track |
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--- |
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# KnowledgeBridge |
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🚀 **An AI-Enhanced Knowledge Discovery Platform with Document Processing & Vector Search** |
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A production-ready AI-powered knowledge retrieval system featuring real document upload, OCR processing, vector embeddings, and distributed computing for large-scale document analysis and semantic search. |
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## 🎯 Hackathon Submission |
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**🤖 Track 3: Agentic Demo Showcase** |
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**Submitted to**: [Hugging Face Agents-MCP-Hackathon](https://huggingface.co/Agents-MCP-Hackathon) |
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**Live Demo**: [Try KnowledgeBridge on Hugging Face Spaces](https://huggingface.co/spaces/Agents-MCP-Hackathon/KnowledgeBridge |
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[Video Link]{https://drive.google.com/drive/folders/1iQafhb7PmO6zWW-JDq1eWGo8KN10Ctdf?usp=sharing} |
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### **🚀 "Show us the most incredible things that your agents can do!"** |
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KnowledgeBridge demonstrates sophisticated AI agent orchestration through multi-modal knowledge discovery, intelligent query enhancement, and autonomous research synthesis. |
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## 🤖 Agentic Capabilities Showcase |
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### 🧠 **Multi-Agent Orchestration** |
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- **Coordinated Search Agents**: Simultaneous deployment across GitHub, Wikipedia, ArXiv, and web sources |
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- **Intelligent Load Balancing**: Agents dynamically distribute workload based on query type and source availability |
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- **Fallback Agent Strategy**: Backup agents activate when primary sources fail or timeout |
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- **Real-Time Coordination**: Agents communicate results and adapt search strategies collaboratively |
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### 🔍 **Query Enhancement Agents** |
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- **Intent Recognition Agents**: AI agents analyze user intent and suggest optimal search strategies |
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- **Semantic Expansion Agents**: Agents enhance queries with related terms and concepts |
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- **Context-Aware Agents**: Agents consider previous searches and user preferences |
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- **Multi-Modal Query Agents**: Agents adapt search approach based on content type (code, academic, general) |
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### 📊 **Document Processing & Analysis Agents** |
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- **OCR Processing Agents**: Autonomous PDF and image text extraction using Modal's distributed Tesseract OCR |
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- **Vector Embedding Agents**: Generate 1536-dimensional embeddings and build FAISS indices at scale |
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- **Batch Processing Agents**: Coordinate distributed document processing across Modal compute nodes |
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- **Research Synthesis Agents**: AI agents combine insights from multiple sources into coherent analysis |
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- **Quality Assessment Agents**: Agents evaluate source credibility and content relevance |
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### 🛡️ **Security & Validation Agents** |
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- **URL Validation Agents**: Intelligent agents verify link accessibility and content authenticity |
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- **Rate Limiting Agents**: Protective agents prevent API abuse (100 requests/15min, 10/min for sensitive endpoints) |
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- **Input Sanitization Agents**: Security agents validate and clean all user inputs |
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- **Error Recovery Agents**: Resilient agents handle failures gracefully and maintain system stability |
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### 🌐 **Intelligent Integration Agents** |
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- **ArXiv Academic Agents**: Specialized agents for academic paper validation and retrieval |
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- **GitHub Repository Agents**: Code-focused agents with author filtering and relevance scoring |
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- **Wikipedia Knowledge Agents**: Authoritative content agents with intelligent caching strategies |
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- **Cross-Platform Synthesis Agents**: Agents that combine and rank results across all sources |
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## 🏗️ Technical Architecture |
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### **Frontend Stack** |
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- **React 18** with TypeScript for type-safe development |
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- **Wouter Router** for lightweight client-side routing |
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- **TanStack Query** for efficient data fetching and caching |
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- **Radix UI + Tailwind CSS** for accessible, modern components |
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- **Framer Motion** for smooth animations and transitions |
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### **Backend Stack** |
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- **Node.js + Express** with comprehensive middleware |
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- **SQLite Database** with real document storage and metadata |
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- **File Upload System** supporting PDFs, images, text files (50MB each) |
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- **Express Rate Limit** for API protection |
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- **Helmet.js** for security headers |
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### **AI & Distributed Computing** |
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- **Nebius AI Platform** - Advanced LLM and embedding capabilities |
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- **DeepSeek-R1-0528** for chat completions and document analysis |
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- **BAAI/bge-en-icl** for embedding generation (1536 dimensions) |
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- **Query Enhancement** and intelligent content analysis |
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- **Modal.com Platform** - Production heavy workloads |
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- **OCR Processing**: PDF/image text extraction with PyPDF2 + Tesseract |
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- **FAISS Vector Indexing**: Distributed index building for large document collections |
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- **High-Performance Search**: Sub-second similarity search across millions of vectors |
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- **Batch Processing**: Concurrent document processing with 2-4GB memory per task |
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- **Persistent Storage**: Modal volumes for cross-session index storage |
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## 🚀 Quick Start |
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### **Environment Configuration** |
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Create a `.env` file in the project root: |
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```bash |
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# Nebius AI Configuration (Required) |
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NEBIUS_API_KEY=your_nebius_api_key_here |
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# Modal Configuration (Optional - for advanced processing) |
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MODAL_TOKEN_ID=your_modal_token_id |
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MODAL_TOKEN_SECRET=your_modal_token_secret |
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MODAL_BASE_URL=https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run |
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# GitHub Configuration (Optional - for repository search) |
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GITHUB_TOKEN=your_github_token_here |
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# Node Environment |
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NODE_ENV=development |
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``` |
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### **Development Setup** |
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```bash |
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# Install dependencies |
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npm install |
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# Start development server |
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npm run dev |
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# Build for production |
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npm run build |
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# Type checking |
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npm run check |
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``` |
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The application will be available at `http://localhost:5000` |
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## 🎯 Usage Guide |
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### **Document Upload & Processing** |
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1. **Upload Documents**: Drag and drop PDFs, images, text files (up to 50MB each) |
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2. **Automatic Processing**: OCR extraction via Modal for PDFs/images, embedding generation |
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3. **Status Tracking**: Monitor processing status (pending → processing → completed) |
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4. **Batch Operations**: Process multiple documents and build vector indices |
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### **Vector Search** |
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1. **Semantic Search**: Query your processed documents using vector similarity |
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2. **Index Management**: Build FAISS indices from your document collections |
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3. **Performance Comparison**: Side-by-side vector vs. keyword search results |
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4. **Relevance Scoring**: AI-powered relevance scores with detailed metrics |
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### **AI-Enhanced Search** |
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1. **Traditional Search**: Natural language queries across web sources |
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2. **Query Enhancement**: AI-powered query improvement suggestions |
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3. **Multi-Source Results**: Combined results from GitHub, Wikipedia, ArXiv |
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4. **Research Synthesis**: AI analysis and synthesis of search results |
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### **Knowledge Management** |
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- **Document Library**: Manage uploaded documents with metadata |
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- **Citation Generation**: Export results in multiple academic formats |
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- **Knowledge Graph**: Interactive visualization of document relationships |
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## 🔧 API Reference |
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### **Document Management** |
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```typescript |
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POST /api/documents/upload |
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// Multipart form data with files[] |
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// Optional: title, source |
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GET /api/documents/list |
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// Query params: limit, offset, sourceType, processingStatus |
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POST /api/documents/process/:id |
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{ |
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operations: ["extract_text", "generate_embedding", "build_index"]; |
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indexName?: string; |
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} |
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POST /api/documents/process/batch |
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{ |
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documentIds: number[]; |
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operations: ["extract_text", "generate_embedding"]; |
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indexName?: string; |
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} |
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DELETE /api/documents/:id |
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// Deletes document and associated file |
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``` |
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### **Vector Search & Indexing** |
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```typescript |
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POST /api/documents/search/vector |
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{ |
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query: string; |
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indexName?: string; |
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maxResults?: number; |
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} |
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POST /api/documents/index/build |
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{ |
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documentIds?: number[]; // Optional: specific documents |
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indexName?: string; |
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} |
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GET /api/documents/status/:id |
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// Returns processing status and metadata |
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``` |
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### **Traditional Search & AI** |
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```typescript |
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POST /api/search |
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{ |
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query: string; |
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searchType: "semantic" | "keyword" | "hybrid"; |
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limit: number; |
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filters?: { sourceTypes?: string[]; }; |
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} |
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POST /api/analyze-document |
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{ |
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content: string; |
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analysisType: "summary" | "classification" | "key_points"; |
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useMarkdown?: boolean; |
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} |
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POST /api/enhance-query |
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{ |
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query: string; |
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context?: string; |
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} |
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``` |
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### **Health Check** |
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```typescript |
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GET /api/health |
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// Returns comprehensive health status of all services including: |
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// - Nebius AI (embeddings, chat completions) |
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// - Modal.com (API connectivity, function availability) |
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// - External APIs (GitHub, Wikipedia, ArXiv) |
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``` |
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## 🚀 Performance & Reliability |
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### **Performance Metrics** |
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- **Document Upload**: <1s for files up to 50MB with progress tracking |
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- **OCR Processing**: 5-15 seconds per PDF/image via Modal distributed computing |
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- **Vector Search**: <500ms for similarity search across large document collections |
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- **Index Building**: 10-60 seconds for 100-1000 documents using FAISS |
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- **Nebius AI**: |
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- Document analysis: 3-5 seconds for comprehensive analysis |
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- Embedding generation: 500ms-1s per document |
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- Query enhancement: 1-2 seconds |
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- **Traditional Search**: <100ms for local database queries |
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### **Production Scalability** |
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- **Distributed Computing**: Modal automatically scales compute resources (2-4GB per task) |
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- **Concurrent Processing**: Parallel document processing across multiple nodes |
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- **Persistent Storage**: SQLite for metadata, Modal volumes for vector indices |
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- **Batch Operations**: Process hundreds of documents simultaneously |
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- **Intelligent Caching**: Optimized repeated operations and query results |
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- **Graceful Fallbacks**: Continues operation when external services unavailable |
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- **Resource Optimization**: Automatic cleanup and memory management |
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### **Error Handling** |
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- React Error Boundaries prevent UI crashes |
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- Comprehensive API error responses |
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- Automatic retry logic for network requests |
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- User-friendly error messages |
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## 🔒 Security Features |
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### **Input Protection** |
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- Request body size limits (10MB) |
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- Comprehensive input sanitization |
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- SQL injection prevention |
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- XSS protection with CSP headers |
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### **API Security** |
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- Rate limiting on all endpoints |
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- Secure environment variable handling |
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- No hardcoded credentials |
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- Proper error logging without information disclosure |
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### **Infrastructure Security** |
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- Helmet.js security headers |
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- CORS configuration |
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- Secure cookie handling |
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- Production-ready error handling |
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## 🛠️ Development |
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### **Code Quality** |
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- 100% TypeScript coverage |
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- ESLint + Prettier configuration |
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- Comprehensive error handling |
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- Type-safe API contracts with Zod validation |
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### **Testing** |
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```bash |
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# Type checking |
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npm run check |
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# Development server |
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npm run dev |
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# Production build |
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npm run build |
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``` |
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## 🎉 Latest Features |
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- ✅ **Document Upload System**: Real file upload with drag-and-drop, supporting PDFs, images, text files |
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- ✅ **OCR Processing Pipeline**: Modal-powered text extraction from PDFs and images using Tesseract |
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- ✅ **Vector Search Engine**: FAISS-based semantic search with distributed index building |
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- ✅ **SQLite Database**: Persistent storage replacing in-memory data with full metadata tracking |
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- ✅ **Batch Processing**: Concurrent document processing across Modal's distributed compute nodes |
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- ✅ **Production Ready**: Real heavy workloads utilizing Modal's computational capabilities |
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## 📚 Production Architecture |
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### **Complete Document Processing Pipeline** |
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**📄 Document Upload → 🔄 Processing → 🔍 Search → 📊 Analysis** |
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1. **Upload & Storage**: |
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- Multi-file drag-and-drop interface (PDFs, images, text files) |
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- SQLite database with full metadata tracking |
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- File validation and organization by date |
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2. **Modal Distributed Processing**: |
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- OCR text extraction using Tesseract for images/PDFs |
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- Parallel processing across compute nodes (2-4GB per task) |
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- Batch operations for large document collections |
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3. **AI Analysis & Embeddings**: |
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- Nebius AI generates 1536-dimensional embeddings |
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- Document classification and content analysis |
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- Quality assessment and metadata enrichment |
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4. **Vector Index & Search**: |
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- FAISS index building via Modal's distributed computing |
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- High-performance semantic similarity search |
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- Persistent storage across sessions |
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### **Service Integration & Division of Responsibilities** |
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## **🧠 Nebius AI: Language Intelligence & AI Reasoning** |
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### **Used For:** |
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- **📝 Document Analysis**: Classification, summarization, key points extraction, quality scoring |
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- **🔍 Search Intelligence**: Query enhancement, intent understanding, relevance scoring |
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- **💭 AI Reasoning**: Research synthesis, explanations, conversational responses |
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- **🎯 Embeddings**: Real-time text-to-vector conversion using BAAI/bge-en-icl model |
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- **📊 Content Understanding**: All language comprehension and semantic analysis |
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### **Specific Endpoints:** |
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- `/api/analyze-document` - Document analysis with DeepSeek-R1 model |
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- `/api/enhance-query` - AI-powered query improvement |
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- `/api/embeddings` - Generate vector embeddings |
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- `/api/research-synthesis` - Combine insights from multiple sources |
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- `/api/ai-search` - Enhanced semantic search |
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--- |
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## **⚡ Modal.com: Heavy Computation & Distributed Processing** |
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### **Used For:** |
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- **📄 OCR Processing**: PDF and image text extraction using Tesseract |
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- **🔧 Vector Operations**: FAISS index building and high-performance search |
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- **📦 Batch Processing**: Concurrent processing of large document collections |
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- **💾 Infrastructure**: Serverless scaling, persistent storage, distributed compute |
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- **🚀 Heavy Workloads**: All computationally intensive operations |
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### **Specific Endpoints:** |
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- `/api/documents/process/:id` - OCR text extraction via Modal |
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- `/api/documents/index/build` - FAISS vector index creation |
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- `/api/documents/search/vector` - High-performance vector search |
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- `/api/documents/process/batch` - Distributed batch processing |
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### **Live Deployment**: [Modal App](https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run) |
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--- |
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## **🔄 How They Work Together** |
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### **Document Processing Pipeline:** |
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1. **Upload** → Local file storage |
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2. **OCR** → **Modal** extracts text from PDFs/images |
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3. **Analysis** → **Nebius** analyzes content and generates embeddings |
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4. **Indexing** → **Modal** builds FAISS vector index |
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5. **Search** → **Modal** performs vector search, **Nebius** scores relevance |
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### **Search Workflow:** |
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1. **Query Enhancement** → **Nebius** improves user queries |
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2. **Vector Search** → **Modal** finds similar documents |
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3. **Traditional Search** → Local database + external APIs |
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4. **Ranking** → **Nebius** scores and ranks combined results |
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5. **Synthesis** → **Nebius** generates insights |
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--- |
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## **📊 Clear Division:** |
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| Feature | Nebius AI | Modal.com | |
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|---------|-----------|-----------| |
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| **OCR Processing** | ❌ | ✅ | |
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| **Document Analysis** | ✅ | ❌ | |
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| **Vector Search** | ❌ | ✅ | |
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| **Query Enhancement** | ✅ | ❌ | |
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| **Batch Processing** | ❌ | ✅ | |
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| **Embeddings** | ✅ | ✅* | |
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| **Research Synthesis** | ✅ | ❌ | |
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*Modal only for batch embeddings, Nebius for real-time |
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**Nebius = "The Brain"** (AI intelligence) |
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**Modal = "The Engine"** (computational power) |
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### **Intelligent Fallbacks** |
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- **Modal Unavailable**: Local processing for text files, basic search |
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- **Nebius Unavailable**: Mock embeddings, simplified analysis |
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- **Network Issues**: Cached results and offline functionality |
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## 🏆 Track 3: Agentic Demo Showcase Features |
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### **🤖 "Show us the most incredible things that your agents can do!"** |
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KnowledgeBridge demonstrates sophisticated multi-agent systems in action: |
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### **🧠 Autonomous Agent Workflows** |
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- **Smart Agent Coordination**: Multiple specialized agents work together to fulfill complex research tasks |
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- **Adaptive Agent Behavior**: Agents dynamically adjust strategies based on query complexity and source availability |
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- **Multi-Modal Agent Processing**: Different agent types (search, analysis, validation) collaborate seamlessly |
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- **Intelligent Agent Fallbacks**: Backup agents activate automatically when primary agents encounter issues |
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### **🔍 Real-Time Agent Decision Making** |
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- **Query Analysis Agents**: Instantly determine optimal search strategies across 4+ sources |
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- **Load Balancing Agents**: Distribute workload intelligently based on API response times and rate limits |
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- **Quality Control Agents**: Evaluate and filter results in real-time for relevance and authenticity |
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- **Synthesis Agents**: Combine disparate information sources into coherent, actionable insights |
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### **📊 Advanced Agent Orchestration** |
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- **Parallel Agent Execution**: Simultaneous deployment of search agents across GitHub, Wikipedia, ArXiv |
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- **Agent Communication Protocols**: Real-time coordination between agents for optimal resource utilization |
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- **Adaptive Agent Learning**: Agents improve performance based on user interactions and feedback |
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- **Error Recovery Agents**: Autonomous problem-solving when individual agents encounter failures |
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### **🛡️ Production-Grade Agent Infrastructure** |
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- **Security Agent Monitoring**: Continuous protection against abuse with intelligent rate limiting |
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- **Validation Agent Networks**: Multi-layer content verification and URL authenticity checking |
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- **Performance Agent Optimization**: Automatic scaling and resource management for enterprise workloads |
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- **Resilience Agent Systems**: Graceful degradation and fault tolerance across all agent operations |
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### **⚡ Agent Performance Metrics** |
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- **Sub-second Agent Response**: Query analysis and routing in <100ms |
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- **Concurrent Agent Processing**: 4+ agents working simultaneously on complex research tasks |
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- **Intelligent Agent Caching**: Smart result storage and retrieval for enhanced performance |
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- **Scalable Agent Architecture**: Horizontal scaling support for enterprise deployment |
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## 📄 License |
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MIT License - see [LICENSE](LICENSE) file for details. |
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## 🔗 Related Resources |
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### **AI Services** |
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- [Nebius AI Documentation](https://docs.nebius.ai/) - Advanced language models and embeddings |
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- [Modal Documentation](https://modal.com/docs) - Serverless computing platform |
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- **Live Modal App**: [https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run](https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run) |
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- **Modal API Docs**: [https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run/docs](https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run/docs) |
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### **Frontend Technologies** |
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- [React Query Documentation](https://tanstack.com/query/latest) |
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- [Radix UI Components](https://www.radix-ui.com/) |
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- [Tailwind CSS](https://tailwindcss.com/) |
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### **AI Models** |
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- [DeepSeek Models](https://platform.deepseek.com/) - Advanced reasoning capabilities |
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- [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) - Embedding model for semantic search |
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--- |
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## 🚀 Agents-MCP-Hackathon Submission Summary |
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**KnowledgeBridge** showcases the incredible power of AI agents through: |
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🤖 **Multi-Agent Orchestration** - Coordinated intelligence across search, analysis, and synthesis agents |
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🔍 **Real-Time Decision Making** - Agents adapt strategies and optimize performance dynamically |
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📊 **Advanced Agent Workflows** - Complex multi-step processes handled autonomously |
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🛡️ **Production-Ready Agent Infrastructure** - Enterprise-grade security and resilience |
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**Track 3: Agentic Demo Showcase** - Demonstrating what happens when sophisticated AI agents work together to revolutionize knowledge discovery and research workflows. |
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**Built for the Hugging Face Agents-MCP-Hackathon** 🏆 |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
