# Jan - Self-Hosted AI Platform

<p align="center">
  <img alt="janlogo" src="https://user-images.githubusercontent.com/69952136/266827788-b37d6f41-fc34-4677-aa1f-3e2ca6d3c91a.png">
</p>

<p align="center">
  <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
  <img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/m/janhq/jan"/>
  <img alt="Github Last Commit" src="https://img.shields.io/github/last-commit/janhq/jan"/>
  <img alt="Github Contributors" src="https://img.shields.io/github/contributors/janhq/jan"/>
  <img alt="GitHub closed issues" src="https://img.shields.io/github/issues-closed/janhq/jan"/>
  <img alt="Discord" src="https://img.shields.io/discord/1107178041848909847?label=discord"/>
</p>

<p align="center">
  <a href="https://docs.jan.ai/">Getting Started</a> - <a href="https://docs.jan.ai">Docs</a> 
  - <a href="https://docs.jan.ai/changelog/">Changelog</a> - <a href="https://github.com/janhq/jan/issues">Bug reports</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>

> ⚠️ **Jan is currently in Development**: Expect breaking changes and bugs!

Jan helps you run Local AI on your computer, with 1-click installs for the latest models. Easy-to-use yet powerful, with helpful tools to monitor and manage software-hardware performance. 

Jan runs on a wide variety of hardware. We run on consumer-grade GPUs and Mac Minis, as well as datacenter-grade DGX H100 clusters. 

Jan can be run as a server or cloud-native application for enterprise. We offer enterprise plugins for LDAP integration and Audit Logs. Contact us at [hello@jan.ai](mailto:hello@jan.ai) for more details. 

Jan is free, [open core](https://en.wikipedia.org/wiki/Open-core_model), and licensed under a Sustainable Use License. 

## Demo

<p align="center">
  <img style='border:1px solid #000000' src="https://github.com/janhq/jan/assets/69952136/1f9bb48c-2e70-4633-9f68-7881cd925972" alt="Jan Web GIF">
</p>

## Features

**Self-Hosted AI**
- [x] Self-hosted Llama2 and LLMs 
- [ ] Self-hosted StableDiffusion and Controlnet
- [ ] 1-click installs for Models (coming soon)

**3rd-party AIs**
- [ ] Connect to ChatGPT, Claude via API Key (coming soon)
- [ ] Security policy engine for 3rd-party AIs (coming soon)
- [ ] Pre-flight PII and Sensitive Data checks (coming soon)

**Multi-Device**
- [x] Web App
- [ ] Jan Mobile support for custom Jan server (in progress)
- [ ] Cloud deployments (coming soon)

**Organization Tools**
- [x] Multi-user support 
- [ ] Audit and Usage logs (coming soon)
- [ ] Compliance and Audit policy (coming soon)

**Hardware Support**

- [x] Nvidia GPUs 
- [x] Apple Silicon (in progress)
- [x] CPU support via llama.cpp
- [ ] Nvidia GPUs using TensorRT (in progress)

## Documentation

👋 https://docs.jan.ai (Work in Progress)

## Installation

> ⚠️ **Jan is currently in Development**: Expect breaking changes and bugs!

### Step 1: Install Docker

Jan is currently packaged as a Docker Compose application. 

- Docker ([Installation Instructions](https://docs.docker.com/get-docker/))
- Docker Compose ([Installation Instructions](https://docs.docker.com/compose/install/))

### Step 2: Clone Repo

```bash
git clone https://github.com/janhq/jan.git
cd jan
```

### Step 3: Configure `.env`

We provide a sample `.env` file that you can use to get started.

```shell
cp sample.env .env
```

You will need to set the following `.env` variables

```shell
# TODO: Document .env variables
```

### Step 4: Install Models

> Note: These step will change soon as we will be switching to [Nitro](https://github.com/janhq/nitro), an Accelerated Inference Server written in C++

#### Step 4.1: Install Mamba

> For complete Mambaforge installation instructions, see [miniforge repo](https://github.com/conda-forge/miniforge)

Install Mamba to handle native python binding (which can yield better performance on Mac M/ NVIDIA)

```bash
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh
rm Mambaforge-$(uname)-$(uname -m).sh

# Create environment
conda create -n jan python=3.9.16
conda activate jan
```

Uninstall any previous versions of `llama-cpp-python`
```bash
pip uninstall llama-cpp-python -y
```

#### Step 4.2: Install `llama-cpp-python`

> Note: This step will change soon once [Nitro](https://github.com/janhq/nitro) (our accelerated inference server written in C++) is released

- On Mac

```bash
# See https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md
CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir
pip install 'llama-cpp-python[server]'
```

- On Linux with NVIDIA GPU Hardware Acceleration

```bash
# See https://github.com/abetlen/llama-cpp-python#installation-with-hardware-acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python
pip install 'llama-cpp-python[server]'
```

- On Linux with Intel/ AMD CPU (support for AVX-2/ AVX-512)

```bash
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" FORCE_CMAKE=1 pip install llama-cpp-python
pip install 'llama-cpp-python[server]'
```

We recommend that Llama2-7B (4-bit quantized) as a basic model to get started.

You will need to download the models to the `models` folder at root level.

```shell
# Downloads model (~4gb)
# Download time depends on your internet connection and HuggingFace's bandwidth
# In this part, please head over to any source contains `.gguf` format model - https://huggingface.co/models?search=gguf
wget https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf -P models
```

- Run the model in host machine
```bash
# Please change the value of --model key as your corresponding model path
# The --n_gpu_layers 1 means using acclerator (can be Metal on Mac, NVIDIA GPU on on linux with NVIDIA GPU)
# This service will run at `http://localhost:8000` in host level
# The backend service inside docker compose will connect to this service by using `http://host.docker.internal:8000`
python3 -m llama_cpp.server --model models/llama-2-7b.Q4_0.gguf --n_gpu_layers 1
```

### Step 5: `docker compose up`

Jan utilizes Docker Compose to run all services:

```shell
docker compose up -d # Detached mode
```

The table below summarizes the services and their respective URLs and credentials.

| Service                                          | Container Name       | URL and Port          | Credentials                                                                        |
| ------------------------------------------------ | -------------------- | --------------------- | ---------------------------------------------------------------------------------- |
| Jan Web                                          | jan-web-*            | http://localhost:3000 | Set in `conf/keycloak_conf/example-realm.json` <br />- Default Username / Password |
| [Hasura](https://hasura.io) (Backend)            | jan-graphql-engine-* | http://localhost:8080 | Set in `conf/sample.env_app-backend` <br /> - `HASURA_GRAPHQL_ADMIN_SECRET`        |
| [Keycloak](https://www.keycloak.org/) (Identity) | jan-keycloak-*       | http://localhost:8088 | Set in `.env` <br />- `KEYCLOAK_ADMIN` <br />- `KEYCLOAK_ADMIN_PASSWORD`           |                                                                     |
| PostgresDB                                       | jan-postgres-*       | http://localhost:5432 | Set in `.env`                                                                      |

### Step 6: Configure Keycloak

- [ ] Refactor [Keycloak Instructions](KC.md) into main README.md
- [ ] Changing login theme

### Step 7: Use Jan

- Launch the web application via `http://localhost:3000`.
- Login with default user (username: `username`, password: `password`)

### Step 8: Deploying to Production

- [ ] TODO

## About Jan

Jan is a commercial company with a [Fair Code](https://faircode.io/) business model. This means that while we are open-source and can used for free, we require commercial licenses for specific use cases (e.g. hosting Jan as a service). 

We are a team of engineers passionate about AI, productivity and the future of work. We are funded through consulting contracts and enterprise licenses. Feel free to reach out to us!

### Repo Structure

Jan comprises of several repositories: 

| Repo                                                    | Purpose                                                                                                                                                       |
| ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Jan](https://github.com/janhq/jan)                     | AI Platform to run AI in the enterprise. Easy-to-use for users, and packed with useful organizational and compliance features.                                |
| [Jan Mobile](https://github.com/janhq/jan-react-native) | Mobile App that can be pointed to a custom Jan server.                                                                                                        |
| [Nitro](https://github.com/janhq/nitro)                 | Inference Engine that runs AI on different types of hardware. Offers popular API formats (e.g. OpenAI, Clipdrop). Written in C++ for blazing fast performance |

### Architecture

Jan builds on top of several open-source projects:

- [Keycloak Community](https://github.com/keycloak/keycloak) (Apache-2.0)
- [Hasura Community Edition](https://github.com/hasura/graphql-engine) (Apache-2.0)

We may re-evaluate this in the future, given different customer requirements. 


### Contributing

Contributions are welcome! Please read the [CONTRIBUTING.md](CONTRIBUTING.md) file for guidelines on how to contribute to this project.

Please note that Jan intends to build a sustainable business that can provide high quality jobs to its contributors. If you are excited about our mission and vision, please contact us to explore opportunities. 

### Contact

- For support: please file a Github ticket
- For questions: join our Discord [here](https://discord.gg/FTk2MvZwJH)
- For long form inquiries: please email hello@jan.ai