Memoriva
AI-Powered Document Management & RAG System
Project Overview
🏗️ Multi-Repository Architecture
Memoriva consists of two separate repositories: frontend (Next.js) and backend (Go). Installation and setup instructions are available in each repository's README.
Memoriva is an intelligent document management and retrieval system that leverages advanced RAG (Retrieval-Augmented Generation) technology to help users organize, search, and interact with their documents in natural language.
The platform combines powerful vector search capabilities with AI-powered document analysis to provide contextual answers and insights from your document collection. Built with modern web technologies and optimized for performance and scalability.
Technology Stack
Frontend Technologies
- Next.js 14 with App Router
- TypeScript for type safety
- Tailwind CSS styling
- React Query for state management
Backend & Database
- Go backend services
- PostgreSQL with pgvector
- Redis for caching
- Docker containerization
AI & Vector Search
- OpenAI Embeddings API
- Vector similarity search
- RAG pipeline implementation
- Document chunking & indexing
Infrastructure
- RESTful API design
- File upload handling
- Authentication & authorization
- Scalable microservices
Core Features
RAG Pipeline
- • Document ingestion & processing
- • Intelligent text chunking
- • Vector embedding generation
- • Semantic similarity search
- • Context-aware retrieval
Vector Database
- • PostgreSQL with pgvector
- • High-dimensional vector storage
- • Efficient similarity queries
- • Metadata filtering
- • Scalable indexing
Document Processing
- • Multi-format support (PDF, DOCX, TXT)
- • Text extraction & cleaning
- • Metadata preservation
- • Batch processing capabilities
- • Error handling & validation
Natural Language Query
- • Conversational search interface
- • Context-aware responses
- • Source attribution
- • Query refinement
- • Multi-turn conversations
Security & Privacy
- • User authentication
- • Document access control
- • Data encryption
- • Privacy-first design
- • Secure file handling
Performance
- • Fast vector similarity search
- • Caching layer with Redis
- • Optimized query processing
- • Concurrent request handling
- • Scalable architecture
Source Code
Demo Video
RAG Pipeline Demo
Document Processing & Search
Watch how documents are processed, indexed, and searched using natural language queries with contextual answers.