A revolutionary PDF chatbot that uses no vector embeddings or traditional RAG. Instead, it leverages Large Language Models for intelligent document selection and page relevance detection, providing a completely stateless and privacy-first experience.

Traditional PDF chatbots convert documents into vector embeddings for semantic search. This approach:

• ❌ Requires expensive vector databases
• ❌ Needs pre-processing and indexing
• ❌ Stores document data on servers
• ❌ Loses context and nuance in embeddings

Our Vectorless approach:

• ✅ Uses LLM reasoning instead of vectors
• ✅ Processes documents in real-time
• ✅ Completely stateless - no server storage
• ✅ Preserves full document context
• ✅ Privacy-first - documents stay in your browser

graph TD
    A[📄 User uploads PDFs] --> B[🧠 LLM Document Selection]
    B --> C[🎯 LLM Page Relevance Detection]
    C --> D[💬 Contextual Answer Generation]
    
    B --> B1[Analyzes collection description<br/>+ document filenames<br/>+ user question]
    C --> C1[Examines actual page content<br/>from selected documents<br/>in parallel processing]
    D --> D1[Generates comprehensive answer<br/>with proper citations]

• LLM reads your collection description and document filenames
• Intelligently selects which documents are likely to contain relevant information
• No embeddings needed - uses reasoning and context understanding

• LLM examines actual page content from selected documents
• Processes multiple documents in parallel for speed
• Identifies the most relevant pages based on question context

• Uses only the relevant pages to generate accurate answers
• Maintains full document context and nuance
• Provides proper citations and references

• Zero Server Storage: Documents processed and stored entirely in your browser
• LocalStorage Persistence: Your documents persist across browser sessions
• No Data Leakage: Document content never persists on servers
• Serverless-Friendly: Perfect for Vercel/Netlify deployments

• Up to 100 PDF documents per session
• Chunked Upload System: Automatically handles large file sets (>4.5MB)
• 4.5MB per file limit - processes substantial documents
• Real-time Processing: No pre-indexing required

• Multi-Model Support: GPT-4, GPT-5-mini, and more
• Parallel Processing: Multiple documents analyzed simultaneously
• Context Preservation: Full document context maintained throughout
• Dynamic Descriptions: Edit collection descriptions anytime

• Responsive Design: Works on desktop and mobile
• Real-time Progress: Visual feedback during uploads and processing
• GitHub Integration: Easy access to source code
• Error Handling: Comprehensive error messages and recovery

• Next.js 15: React framework with App Router
• TypeScript: Type safety and better development experience
• Tailwind CSS: Modern utility-first styling
• Lucide React: Beautiful, consistent icons

• Python Functions: Serverless API endpoints
• PyPDF2: Reliable PDF text extraction
• OpenAI GPT: Advanced language models for reasoning
• Chunked Processing: Handle large uploads efficiently

• Vercel Deployment: Seamless serverless hosting
• No Databases: Completely stateless architecture
• Automatic Scaling: Handle traffic spikes effortlessly

• Node.js 18+ and npm
• OpenAI API key

git clone https://github.com/roe-ai/vectorless-chatbot.git
cd vectorless-chatbot
npm install

Create .env.local:

# Only needed for local development
OPENAI_API_KEY=your_openai_api_key_here

npm run dev

Visit http://localhost:3000

1. Push to GitHub
2. Connect to Vercel
3. Set environment variable: OPENAI_API_KEY=your_key
4. Deploy! ✅

• Click "Add Your First Document" or "Add Files"
• Select up to 100 PDF files (4.5MB each max)
• Add a description of your document collection
• Large uploads are automatically chunked for reliability

• Ask questions about your documents in natural language
• Watch the 3-step process: Document Selection → Page Detection → Answer Generation
• Get detailed answers with timing and cost breakdowns

• Add more documents anytime
• Edit collection descriptions
• Start new sessions as needed
• All data stays in your browser

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Browser       │    │  Vercel Functions │    │   OpenAI API    │
│                 │    │                  │    │                 │
│ • LocalStorage  │◄──►│ • /api/upload    │◄──►│ • GPT Models    │
│ • Document Data │    │ • /api/chat/stream│    │ • Real-time     │
│ • Chat History  │    │ • No Storage     │    │   Processing    │
│ • Session State │    │ • Stateless      │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘

When uploading large document sets:

1. Size Detection: Frontend calculates total upload size
2. Automatic Chunking: Splits into 3.5MB chunks if needed
3. Parallel Processing: Each chunk processed independently
4. Progressive Results: Documents become available as chunks complete
5. Error Recovery: Failed chunks can be retried individually

Upload and process PDF documents

• Input: FormData with files and description
• Output: Processed documents with extracted text
• Features: Automatic chunking, progress tracking

Stream chat responses in real-time

• Input: Question, documents, chat history
• Output: Server-sent events with processing steps
• Features: Real-time progress, cost tracking, citations

Service health check

• Output: System status and mode information

1. Upload: Marketing team uploads 50 company PDFs
2. Describe: "Company policies, procedures, and guidelines"
3. Ask: "What is our remote work policy?"
4. Process:
   • 🧠 LLM selects "HR Handbook" and "Remote Work Guidelines"
   • 🎯 Identifies relevant pages about remote work
   • 💬 Generates comprehensive answer with citations
5. Result: Accurate answer in ~15 seconds with cost breakdown

• Multi-format Support: Word docs, PowerPoint, Excel
• Advanced Citations: Highlight exact text passages
• Collaboration Features: Share sessions with team members
• Analytics Dashboard: Usage patterns and insights
• Custom Models: Support for local and custom LLMs
• Batch Operations: Process multiple questions simultaneously

We welcome contributions! This project showcases how modern LLMs can replace traditional vector-based approaches while providing better accuracy and user experience.

MIT License - see LICENSE file for details.

⭐ Star us on GitHub if you find this vectorless approach interesting!

Built with ❤️ by ROE AI Inc.