---
title: KnowledgeBridge
emoji: 📚
colorFrom: yellow
colorTo: red
sdk: docker
pinned: false
license: mit
short_description: 'A sophisticated AI-powered knowledge retrieval and analysis '
tags:
  - agent-demo-track
---

# KnowledgeBridge

🚀 **An AI-Enhanced Knowledge Discovery Platform with Document Processing & Vector Search**

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.

![Security Status](https://img.shields.io/badge/Security-Hardened-green)
![TypeScript](https://img.shields.io/badge/TypeScript-100%25-blue)
![AI Models](https://img.shields.io/badge/AI-Nebius%20DeepSeek-purple)
![License](https://img.shields.io/badge/License-MIT-yellow)

## 🎯 Hackathon Submission

**🤖 Track 3: Agentic Demo Showcase**

**Submitted to**: [Hugging Face Agents-MCP-Hackathon](https://huggingface.co/Agents-MCP-Hackathon)

**Live Demo**: [Try KnowledgeBridge on Hugging Face Spaces](https://huggingface.co/spaces/Agents-MCP-Hackathon/KnowledgeBridge

[Video Link]{https://drive.google.com/drive/folders/1iQafhb7PmO6zWW-JDq1eWGo8KN10Ctdf?usp=sharing}

### **🚀 "Show us the most incredible things that your agents can do!"**

KnowledgeBridge demonstrates sophisticated AI agent orchestration through multi-modal knowledge discovery, intelligent query enhancement, and autonomous research synthesis.

## 🤖 Agentic Capabilities Showcase

### 🧠 **Multi-Agent Orchestration**
- **Coordinated Search Agents**: Simultaneous deployment across GitHub, Wikipedia, ArXiv, and web sources
- **Intelligent Load Balancing**: Agents dynamically distribute workload based on query type and source availability
- **Fallback Agent Strategy**: Backup agents activate when primary sources fail or timeout
- **Real-Time Coordination**: Agents communicate results and adapt search strategies collaboratively

### 🔍 **Query Enhancement Agents**
- **Intent Recognition Agents**: AI agents analyze user intent and suggest optimal search strategies
- **Semantic Expansion Agents**: Agents enhance queries with related terms and concepts
- **Context-Aware Agents**: Agents consider previous searches and user preferences
- **Multi-Modal Query Agents**: Agents adapt search approach based on content type (code, academic, general)

### 📊 **Document Processing & Analysis Agents**
- **OCR Processing Agents**: Autonomous PDF and image text extraction using Modal's distributed Tesseract OCR
- **Vector Embedding Agents**: Generate 1536-dimensional embeddings and build FAISS indices at scale
- **Batch Processing Agents**: Coordinate distributed document processing across Modal compute nodes
- **Research Synthesis Agents**: AI agents combine insights from multiple sources into coherent analysis
- **Quality Assessment Agents**: Agents evaluate source credibility and content relevance

### 🛡️ **Security & Validation Agents**
- **URL Validation Agents**: Intelligent agents verify link accessibility and content authenticity
- **Rate Limiting Agents**: Protective agents prevent API abuse (100 requests/15min, 10/min for sensitive endpoints)
- **Input Sanitization Agents**: Security agents validate and clean all user inputs
- **Error Recovery Agents**: Resilient agents handle failures gracefully and maintain system stability

### 🌐 **Intelligent Integration Agents**
- **ArXiv Academic Agents**: Specialized agents for academic paper validation and retrieval
- **GitHub Repository Agents**: Code-focused agents with author filtering and relevance scoring
- **Wikipedia Knowledge Agents**: Authoritative content agents with intelligent caching strategies
- **Cross-Platform Synthesis Agents**: Agents that combine and rank results across all sources

## 🏗️ Technical Architecture

### **Frontend Stack**
- **React 18** with TypeScript for type-safe development
- **Wouter Router** for lightweight client-side routing
- **TanStack Query** for efficient data fetching and caching
- **Radix UI + Tailwind CSS** for accessible, modern components
- **Framer Motion** for smooth animations and transitions

### **Backend Stack**
- **Node.js + Express** with comprehensive middleware
- **SQLite Database** with real document storage and metadata
- **File Upload System** supporting PDFs, images, text files (50MB each)
- **Express Rate Limit** for API protection
- **Helmet.js** for security headers

### **AI & Distributed Computing**
- **Nebius AI Platform** - Advanced LLM and embedding capabilities
  - **DeepSeek-R1-0528** for chat completions and document analysis
  - **BAAI/bge-en-icl** for embedding generation (1536 dimensions)
  - **Query Enhancement** and intelligent content analysis
- **Modal.com Platform** - Production heavy workloads
  - **OCR Processing**: PDF/image text extraction with PyPDF2 + Tesseract
  - **FAISS Vector Indexing**: Distributed index building for large document collections
  - **High-Performance Search**: Sub-second similarity search across millions of vectors
  - **Batch Processing**: Concurrent document processing with 2-4GB memory per task
  - **Persistent Storage**: Modal volumes for cross-session index storage

## 🚀 Quick Start

### **Environment Configuration**

Create a `.env` file in the project root:

```bash
# Nebius AI Configuration (Required)
NEBIUS_API_KEY=your_nebius_api_key_here

# Modal Configuration (Optional - for advanced processing)
MODAL_TOKEN_ID=your_modal_token_id
MODAL_TOKEN_SECRET=your_modal_token_secret
MODAL_BASE_URL=https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run

# GitHub Configuration (Optional - for repository search)
GITHUB_TOKEN=your_github_token_here

# Node Environment
NODE_ENV=development
```

### **Development Setup**

```bash
# Install dependencies
npm install

# Start development server
npm run dev

# Build for production
npm run build

# Type checking
npm run check
```

The application will be available at `http://localhost:5000`

## 🎯 Usage Guide

### **Document Upload & Processing**
1. **Upload Documents**: Drag and drop PDFs, images, text files (up to 50MB each)
2. **Automatic Processing**: OCR extraction via Modal for PDFs/images, embedding generation
3. **Status Tracking**: Monitor processing status (pending → processing → completed)
4. **Batch Operations**: Process multiple documents and build vector indices

### **Vector Search**
1. **Semantic Search**: Query your processed documents using vector similarity
2. **Index Management**: Build FAISS indices from your document collections
3. **Performance Comparison**: Side-by-side vector vs. keyword search results
4. **Relevance Scoring**: AI-powered relevance scores with detailed metrics

### **AI-Enhanced Search**
1. **Traditional Search**: Natural language queries across web sources
2. **Query Enhancement**: AI-powered query improvement suggestions
3. **Multi-Source Results**: Combined results from GitHub, Wikipedia, ArXiv
4. **Research Synthesis**: AI analysis and synthesis of search results

### **Knowledge Management**
- **Document Library**: Manage uploaded documents with metadata
- **Citation Generation**: Export results in multiple academic formats
- **Knowledge Graph**: Interactive visualization of document relationships

## 🔧 API Reference

### **Document Management**
```typescript
POST /api/documents/upload
// Multipart form data with files[]
// Optional: title, source

GET /api/documents/list
// Query params: limit, offset, sourceType, processingStatus

POST /api/documents/process/:id
{
  operations: ["extract_text", "generate_embedding", "build_index"];
  indexName?: string;
}

POST /api/documents/process/batch
{
  documentIds: number[];
  operations: ["extract_text", "generate_embedding"];
  indexName?: string;
}

DELETE /api/documents/:id
// Deletes document and associated file
```

### **Vector Search & Indexing**
```typescript
POST /api/documents/search/vector
{
  query: string;
  indexName?: string;
  maxResults?: number;
}

POST /api/documents/index/build
{
  documentIds?: number[]; // Optional: specific documents
  indexName?: string;
}

GET /api/documents/status/:id
// Returns processing status and metadata
```

### **Traditional Search & AI**
```typescript
POST /api/search
{
  query: string;
  searchType: "semantic" | "keyword" | "hybrid";
  limit: number;
  filters?: { sourceTypes?: string[]; };
}

POST /api/analyze-document
{
  content: string;
  analysisType: "summary" | "classification" | "key_points";
  useMarkdown?: boolean;
}

POST /api/enhance-query
{
  query: string;
  context?: string;
}
```

### **Health Check**
```typescript
GET /api/health
// Returns comprehensive health status of all services including:
// - Nebius AI (embeddings, chat completions)
// - Modal.com (API connectivity, function availability)
// - External APIs (GitHub, Wikipedia, ArXiv)
```

## 🚀 Performance & Reliability

### **Performance Metrics**
- **Document Upload**: <1s for files up to 50MB with progress tracking
- **OCR Processing**: 5-15 seconds per PDF/image via Modal distributed computing
- **Vector Search**: <500ms for similarity search across large document collections
- **Index Building**: 10-60 seconds for 100-1000 documents using FAISS
- **Nebius AI**:
  - Document analysis: 3-5 seconds for comprehensive analysis
  - Embedding generation: 500ms-1s per document
  - Query enhancement: 1-2 seconds
- **Traditional Search**: <100ms for local database queries

### **Production Scalability**
- **Distributed Computing**: Modal automatically scales compute resources (2-4GB per task)
- **Concurrent Processing**: Parallel document processing across multiple nodes
- **Persistent Storage**: SQLite for metadata, Modal volumes for vector indices
- **Batch Operations**: Process hundreds of documents simultaneously
- **Intelligent Caching**: Optimized repeated operations and query results
- **Graceful Fallbacks**: Continues operation when external services unavailable
- **Resource Optimization**: Automatic cleanup and memory management

### **Error Handling**
- React Error Boundaries prevent UI crashes
- Comprehensive API error responses
- Automatic retry logic for network requests
- User-friendly error messages

## 🔒 Security Features

### **Input Protection**
- Request body size limits (10MB)
- Comprehensive input sanitization
- SQL injection prevention
- XSS protection with CSP headers

### **API Security**
- Rate limiting on all endpoints
- Secure environment variable handling
- No hardcoded credentials
- Proper error logging without information disclosure

### **Infrastructure Security**
- Helmet.js security headers
- CORS configuration
- Secure cookie handling
- Production-ready error handling

## 🛠️ Development

### **Code Quality**
- 100% TypeScript coverage
- ESLint + Prettier configuration
- Comprehensive error handling
- Type-safe API contracts with Zod validation

### **Testing**
```bash
# Type checking
npm run check

# Development server
npm run dev

# Production build
npm run build
```

## 🎉 Latest Features

- ✅ **Document Upload System**: Real file upload with drag-and-drop, supporting PDFs, images, text files
- ✅ **OCR Processing Pipeline**: Modal-powered text extraction from PDFs and images using Tesseract
- ✅ **Vector Search Engine**: FAISS-based semantic search with distributed index building
- ✅ **SQLite Database**: Persistent storage replacing in-memory data with full metadata tracking
- ✅ **Batch Processing**: Concurrent document processing across Modal's distributed compute nodes
- ✅ **Production Ready**: Real heavy workloads utilizing Modal's computational capabilities

## 📚 Production Architecture

### **Complete Document Processing Pipeline**

**📄 Document Upload → 🔄 Processing → 🔍 Search → 📊 Analysis**

1. **Upload & Storage**:
   - Multi-file drag-and-drop interface (PDFs, images, text files)
   - SQLite database with full metadata tracking
   - File validation and organization by date

2. **Modal Distributed Processing**:
   - OCR text extraction using Tesseract for images/PDFs
   - Parallel processing across compute nodes (2-4GB per task)
   - Batch operations for large document collections

3. **AI Analysis & Embeddings**:
   - Nebius AI generates 1536-dimensional embeddings
   - Document classification and content analysis
   - Quality assessment and metadata enrichment

4. **Vector Index & Search**:
   - FAISS index building via Modal's distributed computing
   - High-performance semantic similarity search
   - Persistent storage across sessions

### **Service Integration & Division of Responsibilities**

## **🧠 Nebius AI: Language Intelligence & AI Reasoning**

### **Used For:**
- **📝 Document Analysis**: Classification, summarization, key points extraction, quality scoring
- **🔍 Search Intelligence**: Query enhancement, intent understanding, relevance scoring  
- **💭 AI Reasoning**: Research synthesis, explanations, conversational responses
- **🎯 Embeddings**: Real-time text-to-vector conversion using BAAI/bge-en-icl model
- **📊 Content Understanding**: All language comprehension and semantic analysis

### **Specific Endpoints:**
- `/api/analyze-document` - Document analysis with DeepSeek-R1 model
- `/api/enhance-query` - AI-powered query improvement
- `/api/embeddings` - Generate vector embeddings
- `/api/research-synthesis` - Combine insights from multiple sources
- `/api/ai-search` - Enhanced semantic search

---

## **⚡ Modal.com: Heavy Computation & Distributed Processing**

### **Used For:**
- **📄 OCR Processing**: PDF and image text extraction using Tesseract
- **🔧 Vector Operations**: FAISS index building and high-performance search
- **📦 Batch Processing**: Concurrent processing of large document collections
- **💾 Infrastructure**: Serverless scaling, persistent storage, distributed compute
- **🚀 Heavy Workloads**: All computationally intensive operations

### **Specific Endpoints:**
- `/api/documents/process/:id` - OCR text extraction via Modal
- `/api/documents/index/build` - FAISS vector index creation  
- `/api/documents/search/vector` - High-performance vector search
- `/api/documents/process/batch` - Distributed batch processing

### **Live Deployment**: [Modal App](https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run)

---

## **🔄 How They Work Together**

### **Document Processing Pipeline:**
1. **Upload** → Local file storage
2. **OCR** → **Modal** extracts text from PDFs/images
3. **Analysis** → **Nebius** analyzes content and generates embeddings
4. **Indexing** → **Modal** builds FAISS vector index
5. **Search** → **Modal** performs vector search, **Nebius** scores relevance

### **Search Workflow:**
1. **Query Enhancement** → **Nebius** improves user queries
2. **Vector Search** → **Modal** finds similar documents
3. **Traditional Search** → Local database + external APIs
4. **Ranking** → **Nebius** scores and ranks combined results
5. **Synthesis** → **Nebius** generates insights

---

## **📊 Clear Division:**

| Feature | Nebius AI | Modal.com |
|---------|-----------|-----------|
| **OCR Processing** | ❌ | ✅ |
| **Document Analysis** | ✅ | ❌ |
| **Vector Search** | ❌ | ✅ |
| **Query Enhancement** | ✅ | ❌ |
| **Batch Processing** | ❌ | ✅ |
| **Embeddings** | ✅ | ✅* |
| **Research Synthesis** | ✅ | ❌ |

*Modal only for batch embeddings, Nebius for real-time

**Nebius = "The Brain"** (AI intelligence)  
**Modal = "The Engine"** (computational power)

### **Intelligent Fallbacks**
- **Modal Unavailable**: Local processing for text files, basic search
- **Nebius Unavailable**: Mock embeddings, simplified analysis
- **Network Issues**: Cached results and offline functionality

## 🏆 Track 3: Agentic Demo Showcase Features

### **🤖 "Show us the most incredible things that your agents can do!"**

KnowledgeBridge demonstrates sophisticated multi-agent systems in action:

### **🧠 Autonomous Agent Workflows**
- **Smart Agent Coordination**: Multiple specialized agents work together to fulfill complex research tasks
- **Adaptive Agent Behavior**: Agents dynamically adjust strategies based on query complexity and source availability  
- **Multi-Modal Agent Processing**: Different agent types (search, analysis, validation) collaborate seamlessly
- **Intelligent Agent Fallbacks**: Backup agents activate automatically when primary agents encounter issues

### **🔍 Real-Time Agent Decision Making**
- **Query Analysis Agents**: Instantly determine optimal search strategies across 4+ sources
- **Load Balancing Agents**: Distribute workload intelligently based on API response times and rate limits
- **Quality Control Agents**: Evaluate and filter results in real-time for relevance and authenticity
- **Synthesis Agents**: Combine disparate information sources into coherent, actionable insights

### **📊 Advanced Agent Orchestration**
- **Parallel Agent Execution**: Simultaneous deployment of search agents across GitHub, Wikipedia, ArXiv
- **Agent Communication Protocols**: Real-time coordination between agents for optimal resource utilization
- **Adaptive Agent Learning**: Agents improve performance based on user interactions and feedback
- **Error Recovery Agents**: Autonomous problem-solving when individual agents encounter failures

### **🛡️ Production-Grade Agent Infrastructure**
- **Security Agent Monitoring**: Continuous protection against abuse with intelligent rate limiting
- **Validation Agent Networks**: Multi-layer content verification and URL authenticity checking
- **Performance Agent Optimization**: Automatic scaling and resource management for enterprise workloads
- **Resilience Agent Systems**: Graceful degradation and fault tolerance across all agent operations

### **⚡ Agent Performance Metrics**
- **Sub-second Agent Response**: Query analysis and routing in <100ms
- **Concurrent Agent Processing**: 4+ agents working simultaneously on complex research tasks
- **Intelligent Agent Caching**: Smart result storage and retrieval for enhanced performance
- **Scalable Agent Architecture**: Horizontal scaling support for enterprise deployment

## 📄 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🔗 Related Resources

### **AI Services**
- [Nebius AI Documentation](https://docs.nebius.ai/) - Advanced language models and embeddings
- [Modal Documentation](https://modal.com/docs) - Serverless computing platform
- **Live Modal App**: [https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run](https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run)
- **Modal API Docs**: [https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run/docs](https://fazeelusmani18--knowledgebridge-main-fastapi-app.modal.run/docs)

### **Frontend Technologies**
- [React Query Documentation](https://tanstack.com/query/latest)
- [Radix UI Components](https://www.radix-ui.com/)
- [Tailwind CSS](https://tailwindcss.com/)

### **AI Models**
- [DeepSeek Models](https://platform.deepseek.com/) - Advanced reasoning capabilities
- [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) - Embedding model for semantic search

---

## 🚀 Agents-MCP-Hackathon Submission Summary

**KnowledgeBridge** showcases the incredible power of AI agents through:

🤖 **Multi-Agent Orchestration** - Coordinated intelligence across search, analysis, and synthesis agents  
🔍 **Real-Time Decision Making** - Agents adapt strategies and optimize performance dynamically  
📊 **Advanced Agent Workflows** - Complex multi-step processes handled autonomously  
🛡️ **Production-Ready Agent Infrastructure** - Enterprise-grade security and resilience  

**Track 3: Agentic Demo Showcase** - Demonstrating what happens when sophisticated AI agents work together to revolutionize knowledge discovery and research workflows.

**Built for the Hugging Face Agents-MCP-Hackathon** 🏆

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference