guard-rag 1.0.5

Privacy-first, fully offline AI document assistant secured by tiered safety guardrails

     

GuardRAG

A privacy-first, fully offline AI document assistant — secured by a tiered safety guardrails system

Upload any document. Ask anything. Get answers — entirely on your machine.
No cloud. No API keys. No data leaves your device.

📦 Installation · 📖 Quick Ref · 🤝 Contribute

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Architecture

The GuardRAG package provides a command-line interface for building and querying RAG (Retrieval-Augmented Generation) chatbots with local LLMs.

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Why GuardRAG?

Most RAG chatbots rely on cloud APIs, which creates privacy risks for sensitive documents — contracts, medical records, internal reports. GuardRAG solves that by:

• Running the LLM locally via Ollama (no data transmitted)
• Embedding documents offline using HuggingFace sentence-transformers
• Enforcing tiered safety policies with 4 sensitivity levels
• Providing a simple CLI interface for easy usage

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Feature Highlights

Core 100% Offline — zero external network calls at runtime Multi-format ingestion — PDF, TXT, DOCX Persistent FAISS cache — same file re-uploads skip re-embedding Multi-turn conversation — full history-aware retrieval Any Ollama model — Gemma, Llama3, Mistral, Phi, and more CLI Interface — easy command-line usage

Safety 4-Tier Data Sensitivity System — Public → Internal → Confidential → Restricted Jailbreak / prompt injection detection — always active Credential & API key protection — Internal+ PII protection — SSN, email, phone, DOB, credit card (Confidential+) Regulated data guards — HIPAA / GDPR / financial categories (Restricted)

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Data Sensitivity Levels

Level	Badge	What is Protected
Public		Jailbreak & prompt injection only
Internal		+ API keys, credentials, passwords, tokens
Confidential		+ SSN, email, phone number, DOB, credit card
Restricted		+ Medical records, diagnoses, financials, HIPAA/GDPR

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Tech Stack

Layer	Technology
CLI Interface	Python argparse + rich console output
LLM Engine	Ollama — local model inference
Embeddings	HuggingFace sentence-transformers/all-MiniLM-L6-v2
Vector Store	FAISS — disk-persisted
RAG Pipeline	LangChain — retrieval chains + chat history
Safety Rails	Custom tiered guardrails system (input + output)

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Prerequisites

• Python 3.9+
• Ollama installed and running locally
• At least one model pulled via Ollama:

ollama pull gemma3:1b
# or any other model: llama3.1, phi3, mistral, etc.

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Installation

Install GuardRAG from PyPI:

pip install guard-rag

Or install from source:

git clone https://github.com/sowmiyan-s/GUADRAILS-RAG-CHAT-TOOL.git
cd GUADRAILS-RAG-CHAT-TOOL
pip install .

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Quick Start

After installation, run the CLI:

guard-rag --pdf path/to/your/document.pdf

This will start an interactive chat session with your document.

CLI Options

guard-rag --pdf <file>             Load and chat with a PDF document
          --model <model>          Ollama model to use (default: gemma3:1b)
          --ollama-host <url>      Ollama server URL (default: http://localhost:11434)
          --chunk-size <int>       Document chunk size (default: 1000)
          --chunk-overlap <int>    Chunk overlap (default: 200)
          --sensitivity <level>    Data sensitivity: Public | Internal | Confidential | Restricted
          --no-guardrails          Disable safety guardrails
          --help                   Show this help message

Example Session

# Start with a PDF using Llama 3.1
guard-rag --pdf report.pdf --model llama3.1 --sensitivity Confidential

# You: What are the key findings?
# Chatbot: Based on the document, the key findings are...

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Project Structure

GUADRAILS-RAG-CHAT-TOOL/
│
├── guardrag/                 # Main installable package
│   ├── api/                  # FastAPI local server
│   ├── cli/                  # Command-line interface
│   ├── rag/                  # RAG pipeline logic
│   └── utils/                # General utilities
│
├── docs/                     # Documentation (INSTALL, QUICK_REFERENCE)
├── tests/                    # Unit and integration tests
├── scripts/                  # Development and maintenance scripts
├── extras/                   # Experimental / legacy components
│
├── pyproject.toml             # Modern build configuration
├── setup.py                   # Legacy support configuration
├── README.md                  # Project overview
├── CONTRIBUTING.md            # Contribution guidelines
├── CODE_OF_CONDUCT.md         # Community standards
└── LICENSE                    # MIT License open source

.guardrag_storage/ is auto-generated on first document load (FAISS cache).

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Configuration

Environment Variables

Copy .env.example to .env and adjust as needed:

cp .env.example .env

Variable	Default	Description
OLLAMA_HOST	http://localhost:11434	Ollama API endpoint
NO_PROXY	huggingface.co,...	Bypass proxy for local+HF calls
PORT	8000	Server port (auto-set by PaaS)

Chunking Parameters

Adjustable per-session via the sidebar in the UI:

• Chunk Size (default 1000 chars)
• Chunk Overlap (default 200 chars)

Different chunk settings for the same file produce a separate FAISS index automatically.

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Deployment

From PyPI (recommended)

pip install guard-rag

From Source

git clone https://github.com/sowmiyan-s/GUADRAILS-RAG-CHAT-TOOL.git
cd GUADRAILS-RAG-CHAT-TOOL
pip install .

In a virtual environment (best practice)

python -m venv .venv
# Windows:
.venv\Scripts\activate
# macOS / Linux:
source .venv/bin/activate

pip install guard-rag

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Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

Bug reports and feature requests are welcome via GitHub Issues.

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License

This project is licensed under the MIT License — see LICENSE for details.

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Built with ❤️ by Sowmiyan S

FastAPI · LangChain · Ollama · HuggingFace · FAISS · Vanilla JS