Private Document AI

ZettaBrain lets you have natural language conversations with your own documents — running entirely on your own infrastructure, with no API keys and no data sent to any cloud service.

What It Does

It connects to your existing file storage (local disk, NFS, SMB, or S3-compatible), indexes your documents locally, and lets you query them in plain English through a web interface or CLI. Every answer includes the source chunks it came from.

Supported Platforms

Quick Links

• Quick Install (per-OS commands)
• Sample test data — try ZettaBrain without your own documents
• Retrieval pipeline
• Storage sources
• CLI commands
• GitHub repository

Quick Install

Select your operating system — the installer handles Python, Ollama, and model download automatically.

Ubuntu Red Hat Linux macOS Windows

curl -fsSL https://zettabrain.app/install.sh | sudo bash

curl -fsSL https://zettabrain.app/install.sh | sudo bash

curl -fsSL https://zettabrain.app/install.sh | bash

# Download: https://zettabrain.app/install.cmd
# Right-click → Run as administrator

[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12
irm https://zettabrain.app/install.ps1 | iex

What the installer does

• Detects your OS and package manager (apt / dnf / yum / brew / winget)
• Installs Python 3.9+ and system dependencies including zstd
• Installs zettabrain-rag via pipx (isolated environment)
• Installs and starts Ollama as a background service
• Downloads the nomic-embed-text embedding model (~275 MB)
• On NVIDIA hardware: installs CUDA runtime and drivers

Install via pip / pipx

pip install zettabrain-rag
# or isolated:
pipx install zettabrain-rag
zettabrain --version

After install

sudo zettabrain-setup       # configure storage + model
zettabrain-server           # launch web UI at :7860
zettabrain-chat             # or use CLI chat

First-Time Setup

After installation, the setup wizard configures storage, selects a model based on your hardware, and enables HTTPS.

1. Run the Wizard

sudo zettabrain-setup

2. Launch the Web Interface

zettabrain-server

Open https://local.zettabrain.app:7860 — trusted HTTPS, fully private.

3. Or Use the CLI

zettabrain-chat

Type any question to query your documents. Type sources to see which chunks were used. Type quit to exit.

Retrieval Pipeline

Five stages process every query — combining keyword and semantic search before passing only the best context to the local LLM.

1. Adaptive chunking — Chunk size tuned per document type and text density.
2. MMR semantic search — Maximum Marginal Relevance via ChromaDB: relevant and diverse.
3. BM25 keyword search — Exact term matching alongside vector search.
4. Merge & deduplicate — Both result sets merged; duplicates removed by content hash.
5. Cross-encoder re-ranking — FlashRank (ms-marco-MiniLM-L-12-v2) scores candidates against the actual query.

Supported Document Formats

CLI Commands

In-Session Commands

GPU & Model Selection

Ollama auto-detects your GPU. No configuration is needed beyond having the correct drivers installed.

Supported Hardware

• NVIDIA — CUDA 12+
• AMD — ROCm 5.7+
• Apple Silicon — Metal (M1/M2/M3, built-in)
• CPU-only — Works on any x86; smaller models recommended

Model Selection Wizard

Hardware detected: NVIDIA RTX 3080 (10 GB VRAM)
Recommended: llama3.1:8b

  1) llama3.2:3b    ~2 GB   fastest, good for quick Q&A
  2) llama3.1:8b    ~5 GB   balanced        ← default
  3) mistral:7b     ~4 GB   strong reasoning
  4) llama3.1:13b   ~8 GB   better, needs 12 GB+
  5) qwen2.5:14b    ~9 GB   excellent, needs 16 GB+
  6) Custom

Approximate Performance

Configuration

Settings via environment variables or /opt/zettabrain/src/zettabrain.env.

System Requirements

Note on RAM: llama3.1:8b (Q4) needs roughly 5 GB, plus ~2–3 GB for OS and ChromaDB. Below 8 GB you'll hit swap and response times increase significantly.

Storage Sources

• Local disk — Default. Any local path.
• NFS — Network File System mounts.
• SMB — Windows / Samba shares.
• Object Storage — S3-compatible: AWS S3, MinIO, Ceph.

sudo zettabrain-storage add   # add a new source
zettabrain-storage list       # list configured sources

Diagnostics

zettabrain-status
python3 /opt/zettabrain/src/01_chromadb_setup.py
python3 /opt/zettabrain/src/02_embeddings_test.py
curl http://localhost:11434
ollama list
journalctl -u zettabrain -f

Uninstall

Linux / macOS

pipx uninstall zettabrain-rag
sudo rm -rf /opt/zettabrain
sudo systemctl disable --now zettabrain 2>/dev/null || true

Windows

pipx uninstall zettabrain-rag
Remove-Item -Recurse -Force "$env:LOCALAPPDATA\ZettaBrain"

Sample Test Data

If you don't have your own documents handy, you can use our pre-built test corpora to evaluate ZettaBrain RAG against realistic enterprise content.

Download the Document Sets

How to Use

1. Download and unzip

Linux / macOS Windows

mkdir -p ~/zettabrain-samples && cd ~/zettabrain-samples
curl -fsSLO https://www.zettabrain.io/sample-data/zettabrain-financial-test-docs.zip
curl -fsSLO https://www.zettabrain.io/sample-data/zettabrain-healthcare-test-docs.zip
unzip zettabrain-financial-test-docs.zip  -d financial
unzip zettabrain-healthcare-test-docs.zip -d healthcare

mkdir $HOME\zettabrain-samples; cd $HOME\zettabrain-samples
irm https://www.zettabrain.io/sample-data/zettabrain-financial-test-docs.zip  -OutFile financial.zip
irm https://www.zettabrain.io/sample-data/zettabrain-healthcare-test-docs.zip -OutFile healthcare.zip
Expand-Archive financial.zip  -DestinationPath financial
Expand-Archive healthcare.zip -DestinationPath healthcare

2. Ingest one industry into ZettaBrain

zettabrain-ingest --folder ~/zettabrain-samples/financial --rebuild

3. Ask the test prompts

zettabrain-chat
> What is the pre-clearance process for personal securities trades?
> sources       # see the exact chunks the answer was drawn from