126 lines
4.3 KiB
Markdown
126 lines
4.3 KiB
Markdown
# Privacy-First Command-Line AI for Linux
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Unlock the power of AI right from your Linux terminal.
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This project delivers a fully local AI environment, running open source language models directly on your machine.
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No cloud. No GAFAM. Just full privacy, control, and the freedom to manipulate commands in your shell.
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## How it works
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* [Ollama](https://ollama.com/) run language models on the local machine.
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* [speaches.ai](https://speaches.ai) provides text-to-speech capability.
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* [nginx](https://nginx.org/en/) adds an authentication to the API.
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* [AIChat](https://github.com/sigoden/aichat) is used as LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI Tools & Agents.
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Everything is free, open-source, and automated using Docker Compose and shell scripts.
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It doesn't require an internet connection to work once the models have been downloaded.
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## Requirements
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To run this project efficiently, a powerful computer with a recent NVIDIA GPU is required.
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As an example, I achieved good performance with an Intel(R) Core(TM) i7-14700HX, a GeForce RTX 4050 (6GB VRAM), and 32GB of RAM using the [gemma3:12b-it-qat](https://ollama.com/library/gemma3:12b-it-qat) model.
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On GNU/Linux, you must use the [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit).
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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.
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## How to launch the server
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Choose the models you wish to use in the `.env` file. Put the embedding model first and the default model second.
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```bash
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MODELS=nomic-embed-text,gemma3n:e4b,gpt-oss:20b,llama3.1:8b,llama3.2:3b,gemma3:12b-it-qat
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```
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Add an API key to secure server access by adding a `.env` file like this (you can generate one with `openssl rand -hex 16`):
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```bash
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LLM_API_KEY=1234567890
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```
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Create a user authentication for aichat Web UI:
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```bash
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htpasswd -c src/nginx/htpasswd user
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```
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Next, start the servers and their configuration with Docker Compose:
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```bash
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docker compose up --build -d
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```
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Then wait for the models to finish downloading using the following command to display the status:
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```bash
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docker-compose logs -f ollama_provision
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```
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## How to use
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The `setup_desktop.sh` script allows you to copy a compiled static version of [AIChat](https://github.com/sigoden/aichat) from a container to your host and configure the tool.
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### AIChat essentials
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A request to populate a demo database:
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```bash
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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."
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```
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Request a snippet of code:
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```bash
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aichat -m ollama:gemma3:12b-it-qat -c "if condition to check if a docker image exist in bash"
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```
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To launch a chatbot while maintaining context:
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```bash
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aichat -s
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```
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Using shell pipe:
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```bash
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cat README.md | aichat -m ollama:llama3.1:8b 'Check the spelling, grammar, and phrasing. Anwser the correction using diff format'
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```
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Using roles:
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```bash
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aichat -r short "tcp port of mysql"
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./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.')"
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```
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Go to the [AIChat](https://github.com/sigoden/aichat) website for other possible use cases.
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### Text To Speech
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For this features, use the speech.sh script like this:
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```bash
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./tools/speech.sh synthesize --play --lang fr --voice pierre "Bonjour, aujourd'hui nous somme le $(date +%A\ %d\ %B\ %Y)."
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```
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## How to Use Remotely
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The API authentication via Nginx allows you to open the API on the internet and use it remoteli.
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By adding a reverse proxy like Caddy in front of it, you can also add TLS encryption.
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This way, you can securely use this environment remotely.
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To use script tools in a remote context, use the environment variables TTS_API_HOST and modify AIChat config (~/.config/aichat/config.yaml) .
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Example:
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```bash
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export TTS_API_HOST="https://your-remote-domain"
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./tools/speech.sh ...
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```
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## Web UI
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A web application to interact with supported LLMs directly from your browser is available at [http://127.0.0.1:8001/playground](http://127.0.0.1:8001/playground).
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A web platform to compare different LLMs side-by-side is available at [http://127.0.0.1:8001/arena](http://127.0.0.1:8001/arena).
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