OpenContext

Self-Hosted RAG System for Secure Document Processing

OpenContext is an enterprise-grade RAG (Retrieval-Augmented Generation) system designed for organizations that require complete data sovereignty. Process confidential documents without external dependencies or security compromises.

Why OpenContext?

The Problem: Developers need AI assistance but can't upload sensitive code or documents to external cloud services due to security, compliance, or intellectual property concerns.

The Solution: OpenContext runs entirely within your infrastructure, ensuring your data never leaves your control while providing enterprise-grade search capabilities.

Key Benefits

• Zero External Dependencies: All processing happens within your network
• Self-Hosted Security: Keep sensitive data within your infrastructure
• Advanced Search: Hybrid keyword + semantic search with Korean language support
• Production Ready: Docker Compose deployment with monitoring and logging
• Developer Friendly: MCP protocol integration for AI assistants like Cursor

Demo

OpenContextDemo.mp4

Architecture

graph TB
    subgraph "Client Layer"
        UI[Admin Dashboard]
        AI[AI Assistant]
    end
    
    subgraph "Application Layer"
        Core[OpenContext Core<br/>Spring Boot]
        MCP[MCP Adapter<br/>Node.js]
    end
    
    subgraph "Data Layer"
        PG[(PostgreSQL)]
        ES[(Elasticsearch)]
        MinIO[(MinIO)]
        Ollama[Ollama]
    end
    
    UI --> Core
    AI --> MCP
    MCP --> Core
    Core --> PG
    Core --> ES
    Core --> MinIO
    Core --> Ollama

Loading

Quick Start

Prerequisites

docker --version          # 20.10+
docker compose version    # 2.0+
git --version             # Any recent version

System Requirements:

• Minimum 4GB RAM, 10GB disk space recommended

Installation

git clone https://github.com/OpenContextAI/open-context.git
cd open-context

# Start all services (model download included)
docker compose up -d

# Verify deployment
curl http://localhost:8080/actuator/health

Access Points

Service	URL	Purpose
Admin UI	http://localhost:3001	Document management dashboard
Core API	http://localhost:8080	REST API endpoints
MCP Adapter	http://localhost:3000	AI assistant integration
API Docs	http://localhost:8080/swagger-ui.html	Interactive API documentation

Usage

1. Upload Documents

Access the Admin UI at http://localhost:3001 and upload PDF or Markdown files. The system processes documents through multiple stages:

• PENDING: File uploaded, waiting for processing
• PARSING: Document structure extraction
• CHUNKING: Text segmentation into meaningful chunks
• EMBEDDING: Vector generation for semantic search
• INDEXING: Search index creation
• COMPLETED: Ready for search and retrieval

2. Efficient Search and Retrieve

OpenContext optimizes AI assistant token usage through a two-phase approach:

Phase 1 - Explore (Low Token Cost):

curl "http://localhost:8080/api/v1/search?query=spring%20security&topK=5"

Returns lightweight chunk summaries for the AI to evaluate relevance.

Phase 2 - Focus (Targeted Token Usage):

curl -X POST http://localhost:8080/api/v1/get-content \
  -H "Content-Type: application/json" \
  -d '{"chunkId": "your-chunk-id", "maxTokens": 25000}'

Retrieves full content only for selected chunks, minimizing unnecessary token consumption.

3. AI Assistant Integration

Configure your AI assistant (Cursor, VSCode) with MCP:

{
  "mcpServers": {
    "opencontext": {
      "url": "http://localhost:3000/mcp"
    }
  }
}

Technology Stack

Backend

• Java 21 + Spring Boot 3.3.11
• LangChain4j for RAG pipeline
• PostgreSQL for metadata
• Elasticsearch for search
• Ollama for local embeddings

Frontend

• React 19 + TypeScript
• Tailwind CSS + Vite
• TanStack Query for state management

Infrastructure

• Docker Compose orchestration
• MinIO for file storage
• Unstructured.io for document parsing

Configuration

API Key Setup

Default API key: dev-api-key-123 (change for production)

All administrative APIs require API key authentication via X-API-KEY header:

# Generate secure key
openssl rand -hex 32

# Set environment variable
export OPENCONTEXT_API_KEY="your-secure-key"

# Use in API calls
curl -H "X-API-KEY: your-secure-key" http://localhost:8080/api/v1/sources

Embedding Model

Default: dengcao/Qwen3-Embedding-0.6B:F16

# Install different model
docker compose exec ollama ollama pull nomic-embed-text

# Update configuration in application-docker.yml

Development

Local Development

# Backend
cd core && ./gradlew bootRun

# Frontend
cd admin-ui && npm install && npm run dev

# MCP Adapter
cd mcp-adapter && npm install && npm run dev

Testing

./gradlew test                    # Unit tests
./gradlew integrationTest        # Integration tests
cd admin-ui && npm test          # Frontend tests

Documentation

• Core Backend Guide - Backend development and API details
• MCP Adapter Guide - MCP protocol implementation
• Admin UI Guide - Frontend development guide
• Installation and Setup - Production deployment and configuration
• API Reference - Complete API documentation
• Interactive API Docs - Swagger UI for testing

Contributing

We welcome contributions! Please see our Contributing Guide for complete details.

Development Workflow

1. Fork the repository and clone locally
2. Create feature branch: feature/123-brief-description
3. Implement changes with comprehensive tests
4. Follow commit convention: type(scope): subject
5. Submit pull request with detailed description
6. Address code review feedback

Branch Strategy

• main: Production releases
• develop: Integration branch for features
• feature/*: New features and improvements
• hotfix/*: Critical production fixes

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Project Wiki

License

Licensed under the Apache License, Version 2.0. See LICENSE file for details.