ZettaBrain RAG

Query documents on your NFS shares, SMB servers, or local drive using local AI. No cloud, no Docker, no API keys — the only RAG skill that reads from enterprise storage directly. Runs on your own machine with Ollama + ChromaDB.

ZettaBrain RAG Skill

Chat with your own documents using a local AI. Document data stays on your machine when you use local storage and a local Ollama endpoint (the default). Remote storage (S3, NFS, SMB) and a remote OLLAMA_HOST are optional and will move data off-device — see the Privacy section.

Supports PDF, DOCX, TXT, Markdown. Works on Linux, macOS (including EC2 Mac Apple Silicon), and Windows.

• Source code: https://github.com/zettabrain/zettabrain-rag (MIT)
• Installer scripts: https://github.com/zettabrain/zettabrain-rag/blob/main/install.sh | install.ps1
• PyPI: https://pypi.org/project/zettabrain-rag/

Install

Recommended — pipx (no elevated privileges, fully inspectable)

bash

pipx install zettabrain-rag
sudo zettabrain-setup

One-line installer (review source before running)

The installer scripts are open source and auditable at the links above before execution.

bash

# Linux — review first: https://github.com/zettabrain/zettabrain-rag/blob/main/install.sh
curl -fsSL https://zettabrain.app/install.sh | sudo bash

# macOS — review first: https://github.com/zettabrain/zettabrain-rag/blob/main/install.sh
curl -fsSL https://zettabrain.app/install.sh | bash

# Windows — review first: https://github.com/zettabrain/zettabrain-rag/blob/main/install.ps1
irm https://zettabrain.app/install.ps1 | iex

The Linux installer requires sudo to install Ollama system-wide and register a systemd service. The macOS installer does not require sudo for the package install step.

Setup

Run the interactive setup wizard once after install:

bash

sudo zettabrain-setup

This will:

1. Configure your document storage (local, NFS, SMB, or S3)
2. Install and start Ollama locally
3. Pull the recommended AI model for your hardware
4. Generate a self-signed TLS certificate (stays on-device)
5. Register ZettaBrain as a background service (see Service Management to stop or remove it)

Commands

Command	Description
zettabrain-chat	Interactive CLI chat with your documents
zettabrain-server	Start the web GUI server (HTTPS on port 7860)
zettabrain-ingest	Index documents into the vector store
zettabrain-ingest --rebuild	Wipe and re-index all documents
zettabrain-status	Show Ollama, vector store, and storage status
zettabrain-storage add	Add an additional storage source
zettabrain-setup	Re-run the setup wizard

Usage Examples

Chat via CLI:

bash

zettabrain-chat
# > What does our Q3 report say about cloud costs?

Start the web GUI (https://localhost:7860):

bash

zettabrain-server

Ingest a specific folder:

bash

ZETTABRAIN_DOCS=/path/to/docs zettabrain-ingest

Vector Store — Location, Retention & Deletion

The vector index (document embeddings) is stored only on your local machine:

Item	Location
Vector database	/opt/zettabrain/src/zettabrain_vectorstore/
Ingestion log (MD5 hashes)	/opt/zettabrain/src/ingested_files.json
Configuration	/opt/zettabrain/src/zettabrain.env

Embeddings are never transmitted to any remote service. They are derived from your documents and stored locally in ChromaDB.

Delete the vector index:

bash

# Via CLI
zettabrain-server &
curl -X DELETE http://localhost:7860/api/vectorstore

# Or directly
rm -rf /opt/zettabrain/src/zettabrain_vectorstore
rm -f  /opt/zettabrain/src/ingested_files.json

Rebuild from scratch:

bash

zettabrain-ingest --rebuild

Exclude files or folders by not including them in ZETTABRAIN_DOCS — only files under that path are indexed.

Service Management

ZettaBrain registers a background service so the web GUI auto-starts on boot. Here is how to control or fully remove it:

Linux (systemd)

bash

# Stop the service
sudo systemctl stop zettabrain

# Disable auto-start on boot
sudo systemctl disable zettabrain

# Check status
sudo systemctl status zettabrain

# View logs
journalctl -u zettabrain -f

# Remove service completely
sudo systemctl stop zettabrain
sudo systemctl disable zettabrain
sudo rm /etc/systemd/system/zettabrain.service
sudo systemctl daemon-reload

macOS (launchd)

bash

# Stop the service
sudo launchctl unload /Library/LaunchDaemons/io.zettabrain.server.plist

# Remove auto-start on boot
sudo rm /Library/LaunchDaemons/io.zettabrain.server.plist

# View logs
tail -f /opt/zettabrain/logs/server.log

Uninstall completely

bash

# Remove the package
pipx uninstall zettabrain-rag

# Stop and remove service (Linux)
sudo systemctl stop zettabrain && sudo systemctl disable zettabrain
sudo rm -f /etc/systemd/system/zettabrain.service && sudo systemctl daemon-reload

# Stop and remove service (macOS)
sudo launchctl unload /Library/LaunchDaemons/io.zettabrain.server.plist
sudo rm -f /Library/LaunchDaemons/io.zettabrain.server.plist

# Remove all data, config, and vector index
sudo rm -rf /opt/zettabrain

Privacy

Privacy depends on your configuration:

Configuration	Data stays local?
Local storage + OLLAMA_HOST=http://localhost:11434 (default)	✅ Yes — fully on-device
NFS or SMB network storage	⚠️ Documents fetched over your LAN
S3 / object storage	⚠️ Documents streamed from cloud storage
Remote OLLAMA_HOST	⚠️ Queries and retrieved document chunks sent to remote Ollama

Default setup is fully local. The setup wizard defaults to local storage and a localhost Ollama endpoint. Remote options are opt-in and clearly labelled during setup.

Document embeddings (vector index) are always stored locally regardless of storage configuration.

Configuration

Settings file: /opt/zettabrain/src/zettabrain.env

Variable	Default	Description
ZETTABRAIN_DOCS	set during setup	Path to documents folder
ZETTABRAIN_LLM_MODEL	set during setup	Ollama model name
ZETTABRAIN_EMBED_MODEL	nomic-embed-text	Embedding model
OLLAMA_HOST	http://localhost:11434	Ollama API endpoint (keep local for full privacy)
ZETTABRAIN_CHUNK_SIZE	1000	Document chunk size
ZETTABRAIN_CHUNK_OVERLAP	200	Chunk overlap

Supported Platforms

Platform	Notes
Ubuntu 22.04 / 24.04	Full GPU support (NVIDIA auto-installed)
Amazon Linux 2 / 2023	Full support
RHEL / Rocky / AlmaLinux 8/9	Full support
macOS 12+ Apple Silicon	Metal GPU via Ollama (mac2.metal, mac2-m2.metal)
macOS 12+ Intel	CPU inference (mac1.metal)
Windows 10/11	Via PowerShell installer

Links

• GitHub (source + installer scripts): https://github.com/zettabrain/zettabrain-rag
• PyPI: https://pypi.org/project/zettabrain-rag/
• Website: https://zettabrain.io
• Issues: https://github.com/zettabrain/zettabrain-rag/issues