Jonex

AI Multimodal Domain Knowledge Engine Built for Enterprise Agents · Powered by the Dual Engines of Jonex Wiki and Graph Ontology

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Overview | Quick Start | Local Development | API Quickstart | Integration profile | Document and video parsing | Runtime requirements | Community & Security | License

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Overview

Jonex unifies an all-in-one multimodal parsing engine with an AI-ready knowledge engine. Ontology compiles domain reasoning into the knowledge layer before retrieval begins.

It is an end-to-end enterprise AI knowledge platform that turns raw content into reusable knowledge services. Jonex connects data ingestion, multimodal parsing, domain knowledge compilation, vector and graph indexing, source-grounded retrieval, feedback loops, and business applications in one governed system.

Quick Start

Docker Compose is the fastest way to run the complete platform.

Docker requirements

• Docker Engine or Docker Desktop
• Docker Compose v2 with Buildx
• make on macOS or Linux
• Sufficient disk space and time for the first build, which downloads container images, Python dependencies, and RAG models

1. Clone the repository

git clone https://github.com/yuezhiai/jonex.git
cd jonex

2. Initialize configuration

make init

This creates:

• deploy/.env for the platform, database, object storage, and LLM Gateway
• deploy/.env.rag for LightRAG, embeddings, and parsing
• deploy/.env.mcp for the MCP server
• Frontend .env files for Shell, Core Business, Ecosystem Management, Platform Management, and Dev Gateway

3. Configure model connections

Configure at least one OpenAI-compatible LLM and embedding provider in deploy/.env:

LLMGW_UPSTREAM_LLM_HOST=https://your-openai-compatible-host/v1
LLMGW_UPSTREAM_LLM_API_KEY=your_llm_api_key

LLMGW_UPSTREAM_EMBED_HOST=https://your-embedding-host/v1
LLMGW_UPSTREAM_EMBED_API_KEY=your_embedding_api_key

If your model names differ from the defaults, update the matching LLM_MODEL / EMBEDDING_MODEL keys — they are defined in both deploy/.env and deploy/.env.rag, and EMBEDDING_MODEL must be kept identical in the two files (it is used to build the vector index).

Keep LIGHTRAG_API_KEY identical in deploy/.env and deploy/.env.rag. For audio, video, or advanced image processing, also configure the VLM and ASR connections in deploy/.env.

4. Build and start

make build
make up
make ps

The first build creates the shared jonex/python-base:local image before Compose builds the platform services in parallel.

Use make logs to follow service logs when troubleshooting; press Ctrl+C to stop following them without stopping the platform.

5. Open Jonex

Visit:

http://localhost/

Local demo credentials:

Username: admin
Password: admin123
Tenant: tenant_jonex_demo

Security warning: These credentials are for local evaluation only. Change or remove the default administrator account before binding Jonex to a non-loopback interface, sharing the deployment, or exposing it to any network. Complete the production checklist in SECURITY.md before deployment.

Windows PowerShell

.\jonex.ps1 init
.\jonex.ps1 build
.\jonex.ps1 up
.\jonex.ps1 ps