zettabrain-rag 0.1.5

Local private RAG pipeline — LangChain + Ollama + ChromaDB + NFS support

ZettaBrain RAG

Local private RAG pipeline — your documents, your hardware, zero cloud.

Chat with your documents using a fully local AI. No API keys. No data leaving your machine. Runs on your own server or laptop.

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Install

Choose the method that suits your environment:

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Option 1 — pipx (Recommended for developers)

pipx installs ZettaBrain into an isolated environment and exposes the CLI globally. No virtualenv management needed.

# Install pipx if you don't have it
apt install -y pipx          # Ubuntu / Debian
brew install pipx            # macOS

# Install ZettaBrain
pipx install zettabrain-rag

# Verify
zettabrain --version

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Option 2 — One-line installer (Recommended for servers)

The fastest path on a fresh Linux server. Handles Python, Ollama, NFS client, and all dependencies automatically.

curl -fsSL https://raw.githubusercontent.com/YOUR_USERNAME/zettabrain-rag/main/install.sh | sudo bash

What it does:

• Detects your OS (Ubuntu, Debian, Amazon Linux, RHEL)
• Installs Python, NFS client, and system dependencies
• Creates an isolated Python environment at /opt/zettabrain/venv
• Installs zettabrain-rag from PyPI
• Registers all CLI commands in /usr/local/bin
• Installs and starts Ollama
• Pulls the nomic-embed-text embedding model

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First-time setup (both options)

1. Pull the LLM model

ollama pull llama3.1:8b

2. Mount your NFS document store

sudo zettabrain-setup

Prompts for your NFS server IP and export path, mounts at /mnt/Rag-data, and builds the vector store automatically.

3. Start chatting

zettabrain-chat

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Commands

Command	Description
sudo zettabrain-setup	NFS mount wizard + auto vector store build
zettabrain-chat	Start interactive RAG chat
zettabrain-chat --rebuild	Rebuild vector store then start chat
zettabrain-chat --debug	Show retrieved chunks on every query
zettabrain-chat --model mistral:7b	Use a different LLM
zettabrain-ingest	Ingest documents without starting chat
zettabrain-ingest --folder /path	Ingest a specific folder
zettabrain-ingest --file /path/doc.pdf	Ingest a single file
zettabrain-ingest --rebuild	Force full re-embed of all documents
zettabrain-ingest --stats	Show what is in the vector store
zettabrain-ingest --clear	Wipe the vector store
zettabrain-status	Show install paths and store statistics

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Chat commands

While inside zettabrain-chat:

Type	Action
Any question	Query your documents
sources	Show which document chunks were used
debug on	Show retrieved chunks on every query
debug off	Hide debug output
quit	Exit

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Configuration

All settings can be overridden via environment variables:

export ZETTABRAIN_DOCS=/mnt/Rag-data
export ZETTABRAIN_CHROMA=./zettabrain_vectorstore
export ZETTABRAIN_LLM_MODEL=llama3.1:8b
export ZETTABRAIN_EMBED_MODEL=nomic-embed-text
export ZETTABRAIN_CHUNK_SIZE=1500
export ZETTABRAIN_CHUNK_OVERLAP=200
export OLLAMA_HOST=http://localhost:11434

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Supported document formats

.pdf .txt .md .docx

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Hardware requirements

RAM	Recommended model	Performance
8GB	llama3.2:3b	Basic
16GB	llama3.1:8b	Recommended
32GB	mistral-nemo:12b	Better reasoning
Apple M3/M4 (16GB+)	llama3.1:70b-q4	Excellent

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Diagnostics

# Verify ChromaDB is working
python3 /opt/zettabrain/src/01_chromadb_setup.py

# Verify embedding model is working
python3 /opt/zettabrain/src/02_embeddings_test.py

# Check Ollama is running
curl http://localhost:11434

# Check downloaded models
ollama list

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Uninstall

pipx

pipx uninstall zettabrain-rag

One-line installer

rm -rf /opt/zettabrain
rm -f /usr/local/bin/zettabrain*

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License

MIT — © ZettaBrain