zettabrain-teams 0.1.0

Multi-user RAG server with team access control — built on zettabrain-rag

ZettaBrain Teams

Multi-user RAG server with team access control — the commercial layer built on zettabrain-rag.

• JWT-based login with admin / user roles
• Teams with manager / member / viewer roles
• Per-team isolated document collections (ChromaDB)
• Full audit log — every query recorded
• Confidence scoring on every answer
• Single-page web UI with admin console

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Quick install (Linux server, run as root)

# 1. Install pipx if needed
python3 -m pip install pipx && python3 -m pipx ensurepath

# 2. Install zettabrain-teams
pipx install git+https://github.com/zettabrain/zettabrain-teams

# 3. Run the one-command setup (installs Ollama, pulls models, creates systemd service)
sudo zettabrain-teams-setup

That's it. The setup script:

• Installs Ollama if not present
• Pulls llama3.1:8b (~5 GB) and nomic-embed-text (~274 MB)
• Creates /opt/zettabrain-teams/{data,chromadb,certs}
• Registers and starts a zettabrain-teams systemd service

Then open http://<your-server-ip>:7861 in a browser.
The default admin password is printed to the console on first boot.

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Setup options

sudo zettabrain-teams-setup \
  --port 7861 \
  --llm  llama3.1:8b \
  --embed nomic-embed-text \
  --no-systemd        # skip systemd, start manually instead

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Starting / stopping

# Managed by systemd after setup
sudo systemctl start   zettabrain-teams
sudo systemctl stop    zettabrain-teams
sudo systemctl restart zettabrain-teams
sudo journalctl -u zettabrain-teams -f   # live logs

# Or start manually (e.g. dev/testing)
zettabrain-teams --port 7861

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Firewall

Open port 7861 on your firewall or cloud security group so users can reach the UI.

AWS example:

aws ec2 authorize-security-group-ingress \
  --group-id sg-xxxxxxxx \
  --protocol tcp --port 7861 --cidr 0.0.0.0/0

UFW example:

sudo ufw allow 7861/tcp

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Admin workflow

1. Log in at http://<ip>:7861 with admin + the generated password
2. Admin → Users — create user accounts
3. Admin → Teams & Members — create a team, expand it, add users with roles
4. Ingest documents for a team:

   ZETTABRAIN_CHROMA=/opt/zettabrain-teams/chromadb/<team-slug> \
   ZETTABRAIN_DOCS=/path/to/your/documents \
   zettabrain-ingest
   

5. Users log in, select their team from the sidebar, and start chatting

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Configuration

/opt/zettabrain-teams/teams.env is created on first setup:

Variable	Default	Description
ZBT_PORT	7861	Server port
ZETTABRAIN_LLM_MODEL	llama3.1:8b	Ollama LLM model
ZETTABRAIN_EMBED_MODEL	nomic-embed-text	Ollama embed model
OLLAMA_HOST	http://localhost:11434	Remote Ollama if not local
ZBT_JWT_SECRET	auto-generated	JWT signing secret (auto-created)
ZBT_TOKEN_EXPIRE	480	Token expiry in minutes

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Requirements

• Linux (Ubuntu 20.04+ recommended)
• Python 3.9+
• 8 GB RAM minimum (16 GB recommended for llama3.1:8b)
• ~10 GB disk for models

GPU is optional — Ollama runs in CPU-only mode if no GPU is detected.

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Architecture

zettabrain-teams (port 7861)
├── FastAPI + SQLModel (SQLite)     — users, teams, audit log
├── JWT auth                         — login / token
├── ChromaDB per team               — /opt/zettabrain-teams/chromadb/<slug>/
└── zettabrain-rag                  — hybrid_retrieve(), RAG prompt, embeddings
        └── Ollama (port 11434)     — LLM + embeddings

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Upgrading

pipx upgrade zettabrain-teams
# or reinstall from GitHub for latest commit:
pipx reinstall zettabrain-teams
sudo systemctl restart zettabrain-teams