Morpheus
Intelligent document reasoning system with semantic search and source citations. Upload private documents, ask questions in natural language, and receive accurate answers with citations.
Project Overview
Morpheus is a Retrieval-Augmented Generation (RAG) system featuring a Matrix-themed interface. Users can upload private documents and ask questions in natural language, receiving accurate answers with source citations from their uploaded content.
The system uses session-based isolation with fresh Pinecone vector namespaces per user, ensuring complete data privacy. When sessions end, all data is automatically deleted with no permanent storage.
Built with a focus on cost efficiency, Morpheus offers a token-based pricing model compared to traditional subscription alternatives, making it accessible for researchers and developers.
Technical Stack
Key Features
Document Upload
Upload and process multiple document formats for intelligent querying
Natural Language Q&A
Ask questions in plain English and get accurate, cited responses
RAG Modes
Multiple retrieval modes including semantic search and hybrid approaches
Session Isolation
Complete data privacy with session-based namespaces and automatic cleanup
Vector Search
Semantic search using Pinecone for accurate document retrieval
Fast Performance
Optimized chunking and embedding for quick responses
How It Works
1. Document Processing
Documents are chunked and converted to vector embeddings using OpenAI's embedding models
2. Vector Storage
Embeddings are stored in Pinecone under session-specific namespaces for isolation
3. Semantic Retrieval
User queries are embedded and matched against stored vectors for relevant context
4. Response Generation
Claude generates context-informed responses with source citations
5. Session Cleanup
All data is automatically deleted when sessions end, preventing persistence
6. Privacy First
No permanent storage ensures complete data privacy and security
Project Screenshots
Implementation Highlights
Frontend Architecture
- •Next.js with TypeScript for type-safe development
- •Matrix-themed UI with Tailwind CSS
- •Real-time chat interface for document querying
- •Responsive design for all devices
Backend Processing
- •FastAPI for high-performance async operations
- •LangChain for RAG pipeline orchestration
- •Document chunking with overlap for context preservation
- •RESTful API design for clean integration
Privacy & Security
- •Session-based isolation with unique namespaces
- •Automatic data deletion on session end
- •No permanent storage of user documents
- •Secure API key management
RAG Implementation
- •Pinecone vector database for semantic search
- •OpenAI embeddings for document vectorization
- •Claude for context-aware response generation
- •Multiple RAG modes for different use cases