RaGenie - Agentic AI Platform

RaGenie is an agentic AI system that extends Ragbot with advanced orchestration, multi-agent workflows, and a modern web UI.

Relationship with Ragbot

┌─────────────────────────────────────────────────────────────┐
│                        RaGenie                               │
│  - Agentic workflows (LangGraph)                            │
│  - Multi-agent orchestration                                │
│  - FastAPI backend + React/Next.js frontend                 │
│  - Production-ready microservices                           │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                        Ragbot                                │
│  - Core RAG engine                                          │
│  - LLM integration (OpenAI, Anthropic, Google)              │
│  - CLI + Web UI + API                                       │
│  - AI Knowledge content compilation                         │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    AI Knowledge Repos                        │
│  ai-knowledge-ragbot (public templates/runbooks)            │
│  ai-knowledge-rajiv, ai-knowledge-flatiron, etc. (private)  │
└─────────────────────────────────────────────────────────────┘

Ragbot = Core RAG-enabled assistant (CLI + Web UI + API) RaGenie = Agentic extension layer (multi-agent workflows, advanced orchestration)

Both products share the same AI Knowledge content from the ai-knowledge-* repositories.

What RaGenie Adds

Capability	Ragbot	RaGenie
RAG-powered chat	Yes	Yes (via Ragbot)
CLI interface	Yes	No
Web UI	Yes	Yes
REST API	Yes	Yes
Agentic workflows	No	Yes (LangGraph)
Multi-agent orchestration	No	Yes

Architecture

Backend Services (FastAPI)

Service	Port	Purpose
Auth Service	8001	JWT authentication, user management
User Service	8002	Profile management, preferences
Document Service	8003	File storage, embedding generation
Conversation Service	8004	Chat management, RAG context assembly
LLM Gateway	8005	Unified LLM interface, cost tracking
File Watcher	-	Monitors AI Knowledge content
Embedding Worker	-	Generates vector embeddings

Infrastructure

• PostgreSQL - Primary database
• Redis - Caching, message queue
• Qdrant - Vector database for RAG
• Nginx - API gateway
• Prometheus/Grafana - Monitoring

Current Status

Component	Status
Backend services	Complete
RAG pipeline (Qdrant)	Complete
LangGraph workflows	Complete
Streaming SSE	Complete
React frontend	Not started
Ragbot integration	Not started

Getting Started

Prerequisites

• Docker and Docker Compose
• OpenAI API key (for embeddings)
• Optionally: Anthropic, Google API keys

Quick Start

# Clone and setup
cd ragenie
cp .env.example .env
# Edit .env with your API keys

# Start all services
docker-compose up -d

# Run database migrations
docker-compose exec auth-service alembic upgrade head

# Check status
docker-compose ps

API Documentation

Each service provides interactive docs:

• Auth: http://localhost:8001/docs
• User: http://localhost:8002/docs
• Document: http://localhost:8003/docs
• Conversation: http://localhost:8004/docs
• LLM Gateway: http://localhost:8005/docs

Project Documentation

See projects/ for detailed architecture and development docs:

• Architecture Overview
• LangGraph Integration
• Testing Guide

Development

RaGenie is built using Synthesis Engineering—systematically integrating human expertise with AI capabilities. Learn more:

• The Professional Practice
• Technical Implementation

Related Repositories

Repository	Purpose
ragbot	Core RAG engine (RaGenie extends this)
ai-knowledge-ragbot	Open-source templates, runbooks, guides
ai-knowledge-* (private)	Personal/workspace AI Knowledge repos

License

Same as Ragbot