# Privacy-First Command-Line AI for Linux

![AI_ENV](logo.webp)

Unlock the power of AI—right from your Linux terminal.

This project delivers a fully local AI environment, running open source language models directly on your machine. 

No cloud. No GAFAM. Just full privacy, control, and the freedom to manipulate commands in your shell.

## How it works

* [Ollama](https://ollama.com/) run language models on the local machine.
* [openedai-speech](https://github.com/matatonic/openedai-speech) provides text-to-speech capability.
* [speaches-ai](https://github.com/speaches-ai/speaches) provide transcription, translation, and speech generation.
* [nginx](https://nginx.org/en/) add an authentication to the API.
* [AIChat](https://github.com/sigoden/aichat) is used as LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI Tools & Agents.

Everything is free, open-source and automated using Docker Compose and shell scripts.

## Requirements

To run this project efficiently, a powerful computer with a recent NVIDIA GPU is required.

As an example, I achieved good performance with an Intel(R) Core(TM) i7-14700HX, a GeForce RTX 4050, and 32GB of RAM using the [qwen2.5:7b](https://ollama.com/library/qwen2.5) model.

The model [qwen2.5-coder:32b](https://ollama.com/library/qwen2.5-coder:32b) is usable but slow with this configuration.

Note that more modest models like [llama3.2:3b](https://ollama.com/library/llama3.2) require much fewer resources and still allow you to do a lot of things.

On GNU/Linux, you must use the [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit).

Note that it is probably possible to run the project on other GPUs or modern MacBooks, but this is not the purpose of this project.

## How to launch the server

Choose the models you wish to use in the docker-compose.yaml file.

```
environment:
      - MODELS=....
```

Add an API key to secure server access by adding a `.env` file like this:

```
LLM_API_KEY=1234567890
```

Next, start the servers and their configuration with Docker Compose:

```bash
docker compose up --build -d
```

## How to use

The `setup_desktop.sh` script allows for copying a compiled static version of [AIChat](https://github.com/sigoden/aichat) from a container to your host and configuring the tool.

### AIChat essentials

A request to populate a demo database:
```bash
aichat "10 fictitious identities with username, firstname, lastname and email then display in json format. The data must be realistic, especially from known email domains."
```

Request a snippet of code:
```
aichat -m ollama:qwen2.5-coder:32b -c "if a docker image exist in bash"
```

To launch a chatbot while maintaining context:
```bash
aichat -s
```

Pipe a command and interpret the result:
```
ps aux | aichat 'which process use the most memory'
```

Using roles:
```
aichat -r short "tcp port of mysql"
./tools/speech.sh synthesize --play --lang en --voice bryce "$(aichat -r english-translator 'Bienvenue dans le monde de l\' AI et de la ligne de commande.')"
```

Go to the [AIChat](https://github.com/sigoden/aichat) website for other possible use cases.

### Text To Speech & Speech To Text

For these two features, use the speech.sh script like this:

```
./speech.sh synthesize --play --lang fr --voice pierre "Bonjour, aujourd'hui nous somme le $(date +%A\ %d\ %B\ %Y)."
./speech.sh transcript --lang fr --filename speech.wav
```

## How to Use Remotely

The API authentication via Nginx allows you to open the API on the internet and use it remotely.

By adding a reverse proxy like Caddy in front of it, you can also add TLS encryption. 

This way, you can securely use this environment remotely.

To use script tools in a remote context, use the environment variables TTS_API_HOST and STT_API_HOST and modify AIChat config (~/.config/aichat/config.yaml) .

Example:
```
export TTS_API_HOST="https://your-remote-domain"
export STT_API_HOST="https://your-remote-domain"
./tools/speech.sh ... 
```