Orcah Studio

Orcah Studio: Local Video Knowledge Base

Orcah Studio (formerly Edit Mind) is a self-hosted video knowledge base. It indexes your videos — including transcription, frame analysis, and multi-model embedding — then lets you search videos or individual scenes with natural language.

Development Status: Edit Mind is currently in active development and not yet production-ready. Expect incomplete features and occasional bugs. We welcome contributors to help us reach v1.0!

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Note on the name: Orcah Studio was previously called Edit Mind. The repository, package names, and some historical material still use the former name while the rename is rolled out.

Sponsors of this project

Showcase Video

Click to watch a walkthrough of Orcah Studio's core features.

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Why Orcah Studio?

• Search videos by spoken words, objects, faces, etc...
• Runs fully locally, respecting privacy.
• Works on any computer or server with Docker installed.
• Uses AI for rich metadata extraction and semantic search.

Core Features

• Video Indexing and Processing: A background service watches for new video files and queues them for AI-powered analysis.
• AI-Powered Video Analysis: Extracts metadata like face recognition, transcription, object & text detection, scene analysis, and more.
• Vector-Based Semantic Search: Powerful natural language search capabilities on video content using ChromaDB.

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Core Technologies

Area	Technology
Monorepo	pnpm workspaces
Containerization	Docker, Docker Compose
Web Service	React Router V7, TypeScript, Vite
Background Jobs Service	Node.js, Express.js, BullMQ
ML Sevice	Python, PyAV, PyTorch, OpenAI Whisper, Google Gemini or Ollama (Used for NLP)
Vector Database	ChromaDB
Relational DB	PostgreSQL (via Prisma ORM)

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Getting Started

The self-hosted Orcah Studio edition uses Docker Compose to run everything in containers.

Setup Video

Click to watch a walkthrough of the self-hosted setup guide.

Prerequisites

• Docker Desktop installed and running.
• That's it! Everything else runs in containers.

Quick install (recommended)

With Docker Desktop open, paste this command into Terminal (macOS/Linux) or Git Bash/WSL (Windows):

bash <(curl -fsSL https://raw.githubusercontent.com/IliasHad/edit-mind/main/install.sh)

The installer asks where your videos are stored and whether to use local Ollama, Gemini, or skip AI setup for now. It creates the required configuration, then starts Orcah Studio. Your library remains on your computer; the installer only gives Docker access to the folder you choose.

Prefer to configure every setting by hand? Follow the manual setup steps below.

1. Project Setup

mkdir edit-mind
cd edit-mind

2. Configure Docker File Sharing

Important: Before proceeding, configure Docker to access your media folder.

macOS/Windows:

1. Open Docker Desktop
2. Go to Settings → Resources → File Sharing
3. Add the path where your videos are stored (e.g., /Users/yourusername/Videos)
4. Click Apply & Restart

Linux: File sharing is typically enabled by default.

3. Configure Environment Variables

The self-hosted edition uses a two-file environment configuration:

• .env - Your personal configuration (required)
• .env.system - System defaults (required)

Step 3.1: Create Your Personal Configuration

Copy the example file and customize it:

curl -L https://raw.githubusercontent.com/IliasHad/edit-mind/refs/heads/main/.env.example -o .env
curl -L https://raw.githubusercontent.com/IliasHad/edit-mind/refs/heads/main/.env.system.example -o .env.system
curl -L https://raw.githubusercontent.com/IliasHad/edit-mind/refs/heads/main/docker-compose.yml -o docker-compose.yml

If you have NVIDIA GPU, use docker-compose.cuda.yml file instead

curl -L https://raw.githubusercontent.com/IliasHad/edit-mind/refs/heads/main/docker-compose.cuda.yml -o docker-compose.yml

Edit the .env file and configure these critical settings:

# 1. SET YOUR VIDEO FOLDER PATH (REQUIRED)
# Must match the path you added to Docker File Sharing
HOST_MEDIA_PATH="/Users/yourusername/Videos"

# 2. CHOOSE AI MODEL (Pick one option)
# Option A: Use Ollama (more private, requires model download)
USE_OLLAMA_MODEL="true"
OLLAMA_HOST="http://172.17.0.1"
OLLAMA_PORT="11434"
OLLAMA_MODEL="qwen2.5:7b-instruct"

# Please make sure to run ollama server first using this command 

# OLLAMA_HOST=0.0.0.0:11434 ollama serve
# and pull the ollama model first 
# ollama pull qwen2.5:7b-instruct

# Option B: Use Gemini API (requires API key)
USE_GEMINI="true"
GEMINI_API_KEY="your-gemini-api-key-from-google-ai-studio"

# Option C: Skip AI setup for now (AI-powered features remain unavailable)
USE_OLLAMA_MODEL="false"
USE_GEMINI="false"

# 3. GENERATE SECURITY KEYS (REQUIRED)
# Generate with: openssl rand -base64 32
ENCRYPTION_KEY="your-random-32-char-base64-key"
# Generate with: openssl rand -hex 32
SESSION_SECRET="your-random-session-secret"

Quick Key Generation:

# Generate ENCRYPTION_KEY
openssl rand -base64 32

# Generate SESSION_SECRET
openssl rand -hex 32

4. Start the Services

Start all services with a single command:

docker compose up

5. Access the Applications

Once all services are running (look for "ready" messages in logs):

• Web App: http://localhost:3745

If you're using Safari, use http://127.0.0.1:3745

6. Add Your First Videos

1. Navigate to the web app at http://localhost:3745
2. Create an account
3. Navigate to the web app at http://localhost:3745/app/settings
4. Click "Add Folder"
5. Select a folder from your HOST_MEDIA_PATH location
6. Navigate to the folder details page and click on Rescan
7. The background job service will automatically start processing your videos and will start watching for new video file events

Special Thanks

A huge thank you to the r/selfhosted community on Reddit for their amazing support, valuable feedback, and encouragement.

Original discussion: https://www.reddit.com/r/selfhosted/comments/1ogis3j/i_built_a_selfhosted_alternative_to_googles_video/

Contributing

We welcome contributions of all kinds! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

Development Setup

Follow the steps below if you want to extend the app functionality or fix bugs.

1. Clone the Repository

git clone https://github.com/iliashad/edit-mind
cd edit-mind

2. Setup dev environment

cp .env.system.example docker/.env.system
cp .env.example docker/.env.dev

3. Start docker container in dev mode

pnpm install
cd docker 
docker-compose -f docker-compose.dev.yml up --build

macOS App

This repository is the self-hosted edition. If you prefer a native macOS app, see orcah.app. GitHub users can use code GITHUB for 5% off at checkout.

Community Presentations and Videos

Watch Orcah Studio, then known as Edit Mind, featured at Syntax.fm (starts at 41:21)

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Watch Orcah Studio, then known as Edit Mind, presented at Twelve Labs (starts at 21:12)

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License

This project is licensed under the AGPL-3.0 License - see the LICENSE.md file for details.