gpt-home

ChatGPT at home! Basically a better Google Nest Hub or Amazon Alexa home assistant. Built on the Raspberry Pi using the OpenAI API.

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GPT Home is a project that allows users to build their own home assistant using Raspberry Pi and OpenAI API. It serves as a guide for setting up a smart home assistant similar to Google Nest Hub or Amazon Alexa. The project integrates various components like OpenAI, Spotify, Philips Hue, and OpenWeatherMap to provide a personalized home assistant experience. Users can follow the detailed instructions provided to build their own version of the home assistant on Raspberry Pi, with optional components for customization. The project also includes system configurations, dependencies installation, and setup scripts for easy deployment. Overall, GPT Home offers a DIY solution for creating a smart home assistant using Raspberry Pi and OpenAI technology.

README:

🏠 GPT Home 🤖💬

ChatGPT at home! Basically a better Google Nest Hub or Amazon Alexa home assistant. Built on the Raspberry Pi using the OpenAI API.

This guide will explain how to build your own. It's pretty straight forward. You can also use this as a reference for building other projects on the Raspberry Pi.

Theoretically, the app should run on any linux system thanks to docker, but I can only vouch for the versions listed in the compatibility table. You should be able use any plug-and-play USB/3.5mm speaker or microphone as long as it's supported by ALSA or PortAudio.

📦 Integrations
✅ OpenAI ✅ Spotify ✅ Philips Hue ✅ OpenWeatherMap ✅ Open-Meteo	✅ Alarms ✅ Reminders ✅ Calendar (CalDAV) ✅ LiteLLM 🔲 Home Assistant

🔧 Use Cases
☁️ Weather ⏰ Alarms ⌚ Reminders 📆 Calendar ☑️ To-Do List	📚 General Knowledge 🗣️ Translation 🎵 Music 💡 Lights 😆 Fun & Games

🚀 TL;DR

Run the setup script with the --no-build flag to pull the latest image from DockerHub:

curl -s https://raw.githubusercontent.com/judahpaul16/gpt-home/main/contrib/setup.sh | \
    bash -s -- --no-build

Required for Semantic Routing: Update the openai_api_key field in settings.json with your API key for the OpenAI and make sure you have at least a few dollars in your billing tab. You can also update this later in the web interface at gpt-home.local/settings or [local_ip_address]/settings.

docker exec -it gpt-home bash -c "sed -i 's/\"openai_api_key\": \"[^\"]*\"/\"openai_api_key\": \"YOUR_API_KEY_HERE\"/' src/settings.json"

Optional: If you want to use a model not provided by OpenAI, make sure to update the litellm_api_key field in settings.json. See the LiteLLM docs for a list of all supported providers. You can also update this later in the web interface at gpt-home.local/settings or [local_ip_address]/settings.

docker exec -it gpt-home bash -c "sed -i 's/\"litellm_api_key\": \"[^\"]*\"/\"litellm_api_key\": \"YOUR_API_KEY_HERE\"/' src/settings.json"

🔌 Schematics

⚠️ Caution: Battery Connection

IMPORTANT: The image on the left is for illustration purposes. Do not connect the battery directly to the Raspberry Pi. Use a UPS or power supply with a battery like this one. Connecting the battery directly to the Raspberry Pi can cause damage to the board from voltage fluctuations.

Before connecting the battery, ensure that the polarity is correct to avoid damage to your Raspberry Pi or other components. Disconnect power sources before making changes.

[click to enlarge]

🛠 My Parts List

This is the list of parts I used to build my first GPT Home. You can use this as a reference for building your own. I've also included optional parts that you can add to enhance your setup. To be clear you can use any system that runs Linux.

👈 View My Parts List

Core Components

Raspberry Pi 4B: Link - $50-$70
Mini Speaker: Link - $18
128 GB MicroSD card: Link - $13
USB 2.0 Mini Microphone: Link - $8

Optional Components

128x32 OLED Display: Link - $13-$14
Standoff Spacer Column M3x40mm: Link - $14
M1.4 M1.7 M2 M2.5 M3 Screw Kit: Link - $15
Raspberry Pi UPS Power Supply with Battery: Link - $30
Cool Case for Raspberry Pi 4B: Link - $16

💲 Total Price Range

Core Components: $102-$123
Optional Components: $75
Total (Without Optional): $102-$123
Total (With Optional): $177-$198

📶 Configuring Wi-Fi via wpa_supplicant

To configure Wi-Fi on your Raspberry Pi, you'll need to edit the wpa_supplicant.conf file and ensure the wireless interface is enabled at boot. This method supports configuring multiple Wi-Fi networks and is suitable for headless setups. You could also use the raspi-config or the nmcli utility to configure Wi-Fi; or simply use an Ethernet connection if you prefer.

👈 View Instructions

Step 1: Create the Bash Script

sudo nano /usr/local/bin/start_wifi.sh

Add the following content to the script:

#!/bin/bash

# Set the interface and SSID details
INTERFACE="wlan0"
SSID="your_wifi_ssid"
PASSWORD="your_wifi_password"

# Make sure no previous configuration interferes
sudo killall wpa_supplicant
sudo dhcpcd -x $INTERFACE

# Ensure the wireless interface is up
sudo ip link set $INTERFACE up

# Create a wpa_supplicant configuration file
WPA_CONF="/etc/wpa_supplicant/wpa_supplicant.conf"
wpa_passphrase "$SSID" "$PASSWORD" | sudo tee $WPA_CONF > /dev/null

# Start wpa_supplicant
sudo wpa_supplicant -B -i $INTERFACE -c $WPA_CONF

# Obtain an IP address
sudo dhcpcd $INTERFACE

Make sure to replace your_wifi_ssid and your_wifi_password with your actual WiFi network's SSID and password.

Step 2: Make the Script Executable

sudo chmod +x /usr/local/bin/start_wifi.sh

Step 3: Create a Systemd Service File

sudo nano /etc/systemd/system/start_wifi.service

Add the following content to the service file:

[Unit]
Description=Start WiFi at boot
After=network.target

[Service]
ExecStart=/usr/local/bin/start_wifi.sh
RemainAfterExit=true

[Install]
WantedBy=multi-user.target

Step 4: Reload Systemd and Enable the Service

sudo systemctl daemon-reload
sudo systemctl enable start_wifi.service
sudo systemctl start start_wifi.service

Your Raspberry Pi should now connect to the Wi-Fi network automatically on boot.

If you want to connect to hidden networks or multiple networks, edit the wpa_supplicant.conf file located at /etc/wpa_supplicant/wpa_supplicant.conf and add the following configuration:

network={
    priority=1 # Higher priority networks are attempted first
    ssid="Your_Wi-Fi_Name"
    psk="Your_Wi-Fi_Password"
    key_mgmt=WPA-PSK
    scan_ssid=1 # Hidden network

    priority=2
    ssid="Enterprise_Wi-Fi_Name"
    key_mgmt=WPA-EAP
    eap=PEAP # or TTLS, TLS, FAST, LEAP
    identity="Your_Username"
    password="Your_Password" 
    phase1="peaplabel=0" # or "peapver=0" for PEAPv0
    phase2="auth=MSCHAPV2" # or "auth=MSCHAP" for MSCHAPv1
}

Restart the wpa_supplicant service to apply the changes:

sudo systemctl restart wpa_supplicant

See the wpa_supplicant example file for more information on the configuration options.

🛠 System Dependencies

Before running this project on your system, ensure your system clock is synchronized, your package lists are updated, and NGINX and Docker are installed. The setup script will take care of this for you but you can also do this manually.

👈 View Instructions

Synchronize your system clock:
Install chrony for time synchronization:

sudo apt install -y chrony       # For Debian/Ubuntu
sudo yum install -y chrony       # For RHEL/CentOS/Alma
sudo dnf install -y chrony       # # For RHEL/CentOS/Alma 9^
sudo zypper install -y chrony    # For openSUSE
sudo pacman -S chrony            # For Arch Linux

Activate and synchronize time immediately with chrony:

sudo chronyc makestep

Update your package list:
Regular updates to your package list ensure access to the latest software and security patches.

sudo apt update                   # For Debian/Ubuntu
sudo yum makecache                # For RHEL/CentOS/Alma
sudo dnf makecache                # For RHEL/CentOS/Alma 9^
sudo zypper refresh               # For openSUSE
sudo pacman -Sy                   # For Arch Linux

Enable additional repositories:
For systems that utilize EPEL and other special repositories, you may need to enable them to access a wider range of available packages.

For Debian/Ubuntu:

sudo add-apt-repository universe
sudo apt update

For RHEL/CentOS/Alma and Fedora:

sudo yum install -y epel-release   # For RHEL/CentOS/Alma
sudo dnf install -y epel-release   # For RHEL/CentOS/Alma 9^
sudo yum makecache --timer            # For RHEL/CentOS/Alma
sudo dnf makecache --timer            # For RHEL/CentOS/Alma 9^

Install Development Tools:
Development tools are essential for building packages and compiling software. Ensure you have the necessary tools installed.

For Debian/Ubuntu:

sudo apt install -y build-essential

For RHEL/CentOS/Alma and Fedora:

sudo yum groupinstall -y "Development Tools"   # For RHEL/CentOS/Alma 
sudo dnf groupinstall -y "Development Tools"   # For RHEL/CentOS/Alma 9^

Install System Dependencies

Docker: Required for containerization.

sudo apt-get install -y docker.io  # For Debian/Ubuntu
sudo yum install -y docker         # For RHEL/CentOS/Alma
sudo dnf install -y docker         # For RHEL/CentOS/Alma 9^
sudo zypper install -y docker      # For openSUSE
sudo pacman -S docker              # For Arch Linux

then sudo systemctl enable --now docker

NGINX: Required for reverse proxy for the web interface.

sudo apt-get install -y nginx   # For Debian/Ubuntu
sudo yum install -y nginx       # For RHEL/CentOS/Alma
sudo dnf install -y nginx       # For RHEL/CentOS/Alma 9^
sudo zypper install -y nginx    # For openSUSE
sudo pacman -S nginx            # For Arch Linux

🐳 Building the Docker Container

Note: Update the openai_api_key field in settings.json with your API key for the OpenAI and make sure you have at least a few dollars in your billing tab. You can also update this later in the web interface at gpt-home.local/settings or [local_ip_address]/settings.

Optionally, if you want to use a model not provided by OpenAI, make sure to update the litellm_api_key field in settings.json. See the LiteLLM docs for a list of all supported providers.

Optional: Add these aliases to your .bashrc file for easier management of the container.

alias gpt-start="docker exec -it gpt-home supervisorctl start app"
alias gpt-restart="docker exec -it gpt-home supervisorctl restart app"
alias gpt-stop="docker exec -it gpt-home supervisorctl stop app"
alias gpt-status="docker exec -it gpt-home supervisorctl status app"
alias gpt-log="docker exec -it gpt-home tail -n 100 -f /app/src/events.log"

alias wi-start="docker exec -it gpt-home supervisorctl start web-interface"
alias wi-restart="docker exec -it gpt-home supervisorctl restart web-interface && sudo systemctl restart nginx"
alias wi-stop="docker exec -it gpt-home supervisorctl stop web-interface"
alias wi-status="docker exec -it gpt-home supervisorctl status web-interface"
alias wi-build="docker exec -it gpt-home bash -c 'cd /app/src/frontend && npm i && npm run build'"
alias wi-log="tail -n 100 -f /var/log/nginx/access.log"
alias wi-error="tail -n 100 -f /var/log/nginx/error.log"

alias spotifyd-start="docker exec -it gpt-home supervisorctl start spotifyd"
alias spotifyd-restart="docker exec -it gpt-home supervisorctl restart spotifyd"
alias spotifyd-stop="docker exec -it gpt-home supervisorctl stop spotifyd"
alias spotifyd-status="docker exec -it gpt-home supervisorctl status spotifyd"
alias spotifyd-log="docker exec -it gpt-home tail -n 100 -f /var/log/spotifyd.log"

Run source ~/.bashrc to apply the changes to your current terminal session.

The setup script will take quite a while to run (900.0s+ to build and setup dependencies on my quad-core Raspberry Pi 4B w/ 1G RAM). It will install all the dependencies and build the Docker container. However, you can skip the build process by passing the --no-build flag to the script; it will install the dependencies, set up the firewall and NGINX, and pull the container from Docker Hub and run it.

curl -s https://raw.githubusercontent.com/judahpaul16/gpt-home/main/contrib/setup.sh | \
    bash -s -- --no-build

Alternatively, for development purposes, running setup.sh without the --no-build flag mounts the project directory to the container by adding -v ~/gpt-home:/app to the docker run command. This allows you to make changes to the project files on your Raspberry Pi and see the changes reflected in the container without rebuilding the image. This is useful for testing changes to the codebase. Run directly with:

curl -s https://raw.githubusercontent.com/judahpaul16/gpt-home/main/contrib/setup.sh | \
    bash -s

You can also run the container interactively if you need to debug or test changes to the codebase with the -it (interactive terminal), --entrypoint /bin/bash, and --rm (remove on process exit) flags. This will drop you into a shell session inside the container. Alternatively, if the conatiner is already running:

docker exec -it gpt-home bash

This will start the container and drop you into a shell session inside the container.

Explanation of Docker Run Flags

--tmpfs /run:
    Mounts a tmpfs at /run for transient runtime data.
--tmpfs /run/lock:
    Mounts a tmpfs at /run/lock for lock files.
--privileged:
    Grants extended privileges to the container
    Necessary for accessing host audio devices.
--net=host:
    Uses the host network stack directly.
    May be necessary for avahi-daemon services.
-v /dev/snd:/dev/snd:
    Provides access to the host's sound devices.
-v /dev/shm:/dev/shm:
    Provides access to shared memory.
-v /usr/share/alsa:/usr/share/alsa:ro:
    Maps the ALSA shared data as read-only.
-v /var/run/dbus:/var/run/dbus:
    Provides access to the D-Bus system for inter-process communication.
--mount type=bind,source=/etc/asound.conf,target=/etc/asound.conf:
    Binds the host's ALSA configuration to the container.

🐚 setup.sh

If you prefer to run the setup script manually, you can do so. Create a script in your home folder with vim ~/setup.sh or nano ~/setup.sh and paste in the following:

👈 View Script

#!/bin/bash

latest_release=$(curl -s https://api.github.com/repos/judahpaul16/gpt-home/releases/latest | grep 'tag_name' | cut -d\" -f4)

# Colors
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
BLUE='\033[0;34m'
MAGENTA='\033[0;35m'
CYAN='\033[0;36m'
WHITE='\033[0;37m'
NC='\033[0m' # No Color

echo ""
echo -e "${MAGENTA}"
echo "GPT Home $latest_release"
echo "Created by Judah Paul"
echo "More info @ https://github.com/judahpaul16/gpt-home/"
echo -e "${NC}"

echo -e "${GREEN}"
echo "  ____ ____ _____   _   _                      "
echo " / ___|  _ \\_   _| | | | | ___  _ __ ___   ___ "
echo "| |  _| |_) || |   | |_| |/ _ \\| '_ \` _ \\ / _ \\"
echo "| |_| |  __/ | |   |  _  | (_) | | | | | |  __/"
echo " \\____|_|    |_|   |_| |_|\\___/|_| |_| |_|\\___|"
echo -e "${NC}"

echo -e "${CYAN}"
echo "         ______________"
echo "        | how may I    |"
echo "        | assist you   |"
echo "        | today?       |"
echo "        |______________|"
echo "                 \\                       |"
echo "                  \\                      |"
echo "                   \\                     |"
echo "   _______                   ________    |"
echo "  |ooooooo|      ____       | __  __ |   |"
echo "  |[]+++[]|     [____]      |/  \\/  \\|   |"
echo "  |+ ___ +|     ]()()[      |\\__/\\__/|   |"
echo "  |:|   |:|   ___\\__/___    |[][][][]|   |"
echo "  |:|___|:|  |__|    |__|   |++++++++|   |"
echo "  |[]===[]|   |_|/  \\|_|    | ______ |   |"
echo "_ ||||||||| _ | | __ | | __ ||______|| __|"
echo "  |_______|   |_|[::]|_|    |________|   \\"
echo "              \\_|_||_|_/                  \\"
echo "                |_||_|                     \\"
echo "               _|_||_|_                     \\"
echo "      ____    |___||___|                     \\"
echo -e "${NC}"

# Mask systemd-networkd-wait-online.service to prevent boot delays
sudo systemctl mask systemd-networkd-wait-online.service

# Set Permissions
sudo chown -R $(whoami):$(whoami) .
sudo chmod -R 755 .

# Function to install system dependencies
function install() {
    local package=$1
    echo "Ensuring package '$package' is installed..."

    # Detect the package management system
    if command -v apt-get >/dev/null; then
        if ! dpkg -s "$package" >/dev/null 2>&1; then
            sudo yes | add-apt-repository universe >/dev/null 2>&1 || true
            sudo apt update || true
            if [ "$package" == "docker" ]; then
                sudo apt-get install -y docker.io
            else
                sudo apt-get install -y "$package"
            fi
        fi
    elif command -v yum >/dev/null; then
        if ! rpm -q "$package" >/dev/null 2>&1; then
            sudo yum install -y epel-release >/dev/null 2>&1 || true
            sudo yum makecache --timer || true
            sudo yum install -y "$package"
        fi
    elif command -v dnf >/dev/null; then
        if ! dnf list installed "$package" >/dev/null 2>&1; then
            sudo dnf install -y epel-release >/dev/null 2>&1 || true
            sudo dnf makecache --timer || true
            sudo dnf install -y "$package"
        fi
    elif command -v zypper >/dev/null; then
        if ! zypper se -i "$package" >/dev/null 2>&1; then
            sudo zypper refresh || true
            sudo zypper install -y "$package"
        fi
    elif command -v pacman >/dev/null; then
        if ! pacman -Q "$package" >/dev/null 2>&1; then
            sudo pacman -Sy
            sudo pacman -S --noconfirm "$package"
        fi
    else
        echo "Package manager not supported."
        return 1
    fi

    if [ "$package" == "docker" ]; then
        if ! docker ps >/dev/null 2>&1; then
            echo "Docker installed. Adding $(whoami) to the 'docker' group..."
            sudo usermod -aG docker $(whoami)
            echo -e "${RED}User added to \`docker\` group but the session must be reloaded to access the Docker daemon. Please log out, log back in, and rerun the script. Exiting...${NC}"
            exit 0
        fi
    fi
}

install chrony
install nginx
install containerd
install docker
install docker-buildx-plugin
install alsa-utils
sudo systemctl enable docker
sudo systemctl start docker

# Create ALSA config (asound.conf, adjust as needed)
sudo tee /etc/asound.conf > /dev/null <<EOF
pcm.!default { type hw card Headphones device 0 }
ctl.!default { type hw card Headphones }
EOF

# Install Docker Buildx plugin
mkdir -p $HOME/.docker/cli-plugins
curl -Lo $HOME/.docker/cli-plugins/docker-buildx https://github.com/docker/buildx/releases/download/v0.14.0/buildx-v0.14.0.linux-arm64
sudo chmod +x $HOME/.docker/cli-plugins/docker-buildx
docker buildx version

# Setup UFW Firewall
echo "Setting up UFW Firewall..."
if which firewalld >/dev/null; then
    sudo systemctl stop firewalld
    sudo systemctl disable firewalld
    sudo yum remove firewalld -y 2>/dev/null || sudo apt-get remove firewalld -y 2>/dev/null || sudo zypper remove firewalld -y 2>/dev/null
fi
if ! which ufw >/dev/null; then
    sudo yum install ufw -y 2>/dev/null || sudo apt-get install ufw -y 2>/dev/null || sudo zypper install ufw -y 2>/dev/null
fi
sudo ufw allow ssh
sudo ufw allow 80,443/tcp
sudo ufw allow 5353/udp
echo "y" | sudo ufw enable

# Setup NGINX for reverse proxy
echo "Setting up NGINX..."
sudo mkdir -p /etc/nginx/sites-available /etc/nginx/sites-enabled
sudo tee /etc/nginx/sites-available/gpt-home <<EOF
server {
    listen 80;
    location / {
        proxy_pass http://127.0.0.1:8000/;
        proxy_set_header Host \$host;
        proxy_set_header X-Real-IP \$remote_addr;
        proxy_set_header X-Forwarded-For \$proxy_add_x_forwarded_for;
    }
}
EOF

# Remove gpt-home site symlink if it exists
[ -L "/etc/nginx/sites-enabled/gpt-home" ] && sudo unlink /etc/nginx/sites-enabled/gpt-home

# Remove the default site if it exists
[ -L "/etc/nginx/sites-enabled/default" ] && sudo unlink /etc/nginx/sites-enabled/default

# Create a symlink to the gpt-home site and reload NGINX
sudo ln -s /etc/nginx/sites-available/gpt-home /etc/nginx/sites-enabled
sudo systemctl enable nginx
sudo nginx -t && sudo systemctl restart nginx

sudo systemctl status --no-pager nginx

if [[ "$1" != "--no-build" ]]; then
    [ -d ~/gpt-home ] && rm -rf ~/gpt-home
    git clone https://github.com/judahpaul16/gpt-home ~/gpt-home
    cd ~/gpt-home
    echo "Checking if the container 'gpt-home' is already running..."
    if [ $(docker ps -q -f name=gpt-home) ]; then
        echo "Stopping running container 'gpt-home'..."
        docker stop gpt-home
    fi

    echo "Checking for existing container 'gpt-home'..."
    if [ $(docker ps -aq -f status=exited -f name=gpt-home) ]; then
        echo "Removing existing container 'gpt-home'..."
        docker rm -f gpt-home
    fi

    echo "Pruning Docker system..."
    docker system prune -f

    # Check if the buildx builder exists, if not create and use it
    if ! docker buildx ls | grep -q mybuilder; then
        docker buildx create --name mybuilder --use
        docker buildx inspect --bootstrap
    fi

    # Building Docker image 'gpt-home' for ARMhf architecture
    echo "Building Docker image 'gpt-home' for ARMhf..."
    timeout 3600 docker buildx build --platform linux/arm64 -t gpt-home --load .

    if [ $? -ne 0 ]; then
        echo "Docker build failed. Exiting..."
        exit 1
    fi

    echo "Container 'gpt-home' is now ready to run."

    echo "Running container 'gpt-home' from image 'gpt-home'..."
    docker run --restart unless-stopped -d --name gpt-home \
        --mount type=bind,source=/etc/asound.conf,target=/etc/asound.conf \
        --privileged \
        --net=host \
        --tmpfs /run \
        --tmpfs /run/lock \
        -v ~/gpt-home:/app \
        -v /dev/snd:/dev/snd \
        -v /dev/shm:/dev/shm \
        -v /usr/share/alsa:/usr/share/alsa \
        -v /var/run/dbus:/var/run/dbus \
        gpt-home

    echo "Container 'gpt-home' is now running."

    # Show status of the container
    docker ps -a | grep gpt-home

    sleep 10

    # Show status of all programs managed by Supervisor
    docker exec -i gpt-home supervisorctl status
fi

if [[ "$1" == "--no-build" ]]; then
    docker ps -aq -f name=gpt-home | xargs -r docker rm -f
    docker pull judahpaul/gpt-home
    docker run --restart unless-stopped -d --name gpt-home \
        --mount type=bind,source=/etc/asound.conf,target=/etc/asound.conf \
        --privileged \
        --net=host \
        --tmpfs /run \
        --tmpfs /run/lock \
        -v /dev/snd:/dev/snd \
        -v /dev/shm:/dev/shm \
        -v /usr/share/alsa:/usr/share/alsa \
        -v /var/run/dbus:/var/run/dbus \
        judahpaul/gpt-home
    docker ps -a | grep gpt-home
    sleep 10
    docker exec -i gpt-home supervisorctl status
fi

Be sure to make the script executable to run it

chmod +x setup.sh
./setup.sh

✅ Compatibility

Raspberry Pi
3B	✅
3B+	✅
4B	✅
5	❔
Zero 2 W	✅
Orange Pi 3B	✅
Blinka only supports the Orange Pi PC and R1 if you're using i2c

Python
3.7	❌
3.8	✅
3.9	✅
3.10	✅
3.11	✅
3.12	❌

Operating System
Ubuntu 22.04	✅
Ubuntu 23.04	✅ (EOL)
Ubuntu 24.04	✅
Debian Buster	✅
Debian Bullseye	✅
Alma Linux 8	✅
Alma Linux 9	✅
Users of Raspberry Pi OS should enable i2c manually

Node.js
17.x	❔
18.x	✅
19.x	❔
20.x	❔
21.x	❔
22.x	❔

📚 Useful Documentation

🤝 Contributing

Contributions are certainly welcome! Please read the contributing guidelines for more information on how to contribute.

📜 License

This project is licensed under the GNU GPL v3.0 License - see the LICENSE file for details.

🌟 Star History

For Tasks:

Click tags to check more tools for each tasks

build home assistant configure wi-fi install dependencies setup services create virtual environment

For Jobs:

software developer system administrator iot developer ai engineer web developer

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Simba is an open source, portable Knowledge Management System (KMS) designed to seamlessly integrate with any Retrieval-Augmented Generation (RAG) system. It features a modern UI and modular architecture, allowing developers to focus on building advanced AI solutions without the complexities of knowledge management. Simba offers a user-friendly interface to visualize and modify document chunks, supports various vector stores and embedding models, and simplifies knowledge management for developers. It is community-driven, extensible, and aims to enhance AI functionality by providing a seamless integration with RAG-based systems.

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obsei

Obsei is an open-source, low-code, AI powered automation tool that consists of an Observer to collect unstructured data from various sources, an Analyzer to analyze the collected data with various AI tasks, and an Informer to send analyzed data to various destinations. The tool is suitable for scheduled jobs or serverless applications as all Observers can store their state in databases. Obsei is still in alpha stage, so caution is advised when using it in production. The tool can be used for social listening, alerting/notification, automatic customer issue creation, extraction of deeper insights from feedbacks, market research, dataset creation for various AI tasks, and more based on creativity.

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bark.cpp

Bark.cpp is a C/C++ implementation of the Bark model, a real-time, multilingual text-to-speech generation model. It supports AVX, AVX2, and AVX512 for x86 architectures, and is compatible with both CPU and GPU backends. Bark.cpp also supports mixed F16/F32 precision and 4-bit, 5-bit, and 8-bit integer quantization. It can be used to generate realistic-sounding audio from text prompts.

github

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evalscope

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

github

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evalplus

EvalPlus is a rigorous evaluation framework for LLM4Code, providing HumanEval+ and MBPP+ tests to evaluate large language models on code generation tasks. It offers precise evaluation and ranking, coding rigorousness analysis, and pre-generated code samples. Users can use EvalPlus to generate code solutions, post-process code, and evaluate code quality. The tool includes tools for code generation and test input generation using various backends.

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wzry_ai

This is an open-source project for playing the game King of Glory with an artificial intelligence model. The first phase of the project has been completed, and future upgrades will be built upon this foundation. The second phase of the project has started, and progress is expected to proceed according to plan. For any questions, feel free to join the QQ exchange group: 687853827. The project aims to learn artificial intelligence and strictly prohibits cheating. Detailed installation instructions are available in the doc/README.md file. Environment installation video: (bilibili) Welcome to follow, like, tip, comment, and provide your suggestions.

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fastserve-ai

FastServe-AI is a machine learning serving tool focused on GenAI & LLMs with simplicity as the top priority. It allows users to easily serve custom models by implementing the 'handle' method for 'FastServe'. The tool provides a FastAPI server for custom models and can be deployed using Lightning AI Studio. Users can install FastServe-AI via pip and run it to serve their own GPT-like LLM models in minutes.

github

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ollama4j

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It facilitates communication with the Ollama server and provides models for deployment. The tool requires Java 11 or higher and can be installed locally or via Docker. Users can integrate Ollama4j into Maven projects by adding the specified dependency. The tool offers API specifications and supports various development tasks such as building, running unit tests, and integration tests. Releases are automated through GitHub Actions CI workflow. Areas of improvement include adhering to Java naming conventions, updating deprecated code, implementing logging, using lombok, and enhancing request body creation. Contributions to the project are encouraged, whether reporting bugs, suggesting enhancements, or contributing code.

github

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auto-subs

Auto-subs is a tool designed to automatically transcribe editing timelines using OpenAI Whisper and Stable-TS for extreme accuracy. It generates subtitles in a custom style, is completely free, and runs locally within Davinci Resolve. It works on Mac, Linux, and Windows, supporting both Free and Studio versions of Resolve. Users can jump to positions on the timeline using the Subtitle Navigator and translate from any language to English. The tool provides a user-friendly interface for creating and customizing subtitles for video content.

github

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Noi

Noi is an AI-enhanced customizable browser designed to streamline digital experiences. It includes curated AI websites, allows adding any URL, offers prompts management, Noi Ask for batch messaging, various themes, Noi Cache Mode for quick link access, cookie data isolation, and more. Users can explore, extend, and empower their browsing experience with Noi.

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yolo-flutter-app

Ultralytics YOLO for Flutter is a Flutter plugin that allows you to integrate Ultralytics YOLO computer vision models into your mobile apps. It supports both Android and iOS platforms, providing APIs for object detection and image classification. The plugin leverages Flutter Platform Channels for seamless communication between the client and host, handling all processing natively. Before using the plugin, you need to export the required models in `.tflite` and `.mlmodel` formats. The plugin provides support for tasks like detection and classification, with specific instructions for Android and iOS platforms. It also includes features like camera preview and methods for object detection and image classification on images. Ultralytics YOLO thrives on community collaboration and offers different licensing paths for open-source and commercial use cases.

github

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KsanaLLM

KsanaLLM is a high-performance engine for LLM inference and serving. It utilizes optimized CUDA kernels for high performance, efficient memory management, and detailed optimization for dynamic batching. The tool offers flexibility with seamless integration with popular Hugging Face models, support for multiple weight formats, and high-throughput serving with various decoding algorithms. It enables multi-GPU tensor parallelism, streaming outputs, and an OpenAI-compatible API server. KsanaLLM supports NVIDIA GPUs and Huawei Ascend NPU, and seamlessly integrates with verified Hugging Face models like LLaMA, Baichuan, and Qwen. Users can create a docker container, clone the source code, compile for Nvidia or Huawei Ascend NPU, run the tool, and distribute it as a wheel package. Optional features include a model weight map JSON file for models with different weight names.

github

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HuixiangDou

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

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comfy-cli

comfy-cli is a command line tool designed to simplify the installation and management of ComfyUI, an open-source machine learning framework. It allows users to easily set up ComfyUI, install packages, manage custom nodes, download checkpoints, and ensure cross-platform compatibility. The tool provides comprehensive documentation and examples to aid users in utilizing ComfyUI efficiently.

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crewAI-tools

The crewAI Tools repository provides a guide for setting up tools for crewAI agents, enabling the creation of custom tools to enhance AI solutions. Tools play a crucial role in improving agent functionality. The guide explains how to equip agents with a range of tools and how to create new tools. Tools are designed to return strings for generating responses. There are two main methods for creating tools: subclassing BaseTool and using the tool decorator. Contributions to the toolset are encouraged, and the development setup includes steps for installing dependencies, activating the virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. Enhance AI agent capabilities with advanced tooling.

github

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aipan-netdisk-search

Aipan-Netdisk-Search is a free and open-source web project for searching netdisk resources. It utilizes third-party APIs with IP access restrictions, suggesting self-deployment. The project can be easily deployed on Vercel and provides instructions for manual deployment. Users can clone the project, install dependencies, run it in the browser, and access it at localhost:3001. The project also includes documentation for deploying on personal servers using NUXT.JS. Additionally, there are options for donations and communication via WeChat.

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Agently-Daily-News-Collector

Agently Daily News Collector is an open-source project showcasing a workflow powered by the Agent ly AI application development framework. It allows users to generate news collections on various topics by inputting the field topic. The AI agents automatically perform the necessary tasks to generate a high-quality news collection saved in a markdown file. Users can edit settings in the YAML file, install Python and required packages, input their topic idea, and wait for the news collection to be generated. The process involves tasks like outlining, searching, summarizing, and preparing column data. The project dependencies include Agently AI Development Framework, duckduckgo-search, BeautifulSoup4, and PyYAM.

github

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comfy-cli

Comfy-cli is a command line tool designed to facilitate the installation and management of ComfyUI, an open-source machine learning framework. Users can easily set up ComfyUI, install packages, and manage custom nodes directly from the terminal. The tool offers features such as easy installation, seamless package management, custom node management, checkpoint downloads, cross-platform compatibility, and comprehensive documentation. Comfy-cli simplifies the process of working with ComfyUI, making it convenient for users to handle various tasks related to the framework.

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BentoDiffusion

BentoDiffusion is a BentoML example project that demonstrates how to serve and deploy diffusion models in the Stable Diffusion (SD) family. These models are specialized in generating and manipulating images based on text prompts. The project provides a guide on using SDXL Turbo as an example, along with instructions on prerequisites, installing dependencies, running the BentoML service, and deploying to BentoCloud. Users can interact with the deployed service using Swagger UI or other methods. Additionally, the project offers the option to choose from various diffusion models available in the repository for deployment.

github

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md-agent

MD-Agent is a LLM-agent based toolset for Molecular Dynamics. It uses Langchain and a collection of tools to set up and execute molecular dynamics simulations, particularly in OpenMM. The tool assists in environment setup, installation, and usage by providing detailed steps. It also requires API keys for certain functionalities, such as OpenAI and paper-qa for literature searches. Contributions to the project are welcome, with a detailed Contributor's Guide available for interested individuals.

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The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

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ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

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classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

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chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

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BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

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uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

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