PrivateGPT 2026: The Fully Offline Document Chat Expert

100% privacy protection—data never leaves your machine. Build enterprise-grade secure knowledge bases with PrivateGPT.

In this AI era, data privacy is the top concern for enterprises and individuals. PrivateGPT lives up to its name—it’s all about “absolute privacy.”

Unlike ChatPDF or other online document analysis tools, PrivateGPT can run without any internet connection. Your financial reports, legal contracts, medical records—all sensitive data stays only on your hard drive, invisible to anyone, including AI vendors.

What is PrivateGPT?

PrivateGPT is a production-grade AI project that provides a complete set of APIs and a beautiful UI, enabling you to chat with your private documents.

2026 Version Highlights:

  • Architecture Overhaul: Deep refactoring based on LlamaIndex, more extensible.
  • Hybrid Mode: Supports both local models (Ollama/LlamaCpp) and cloud connections (OpenAI/Azure) if you’re willing to trade privacy for stronger inference.
  • Ultra-Fast Indexing: Document parsing and vectorization speed improved by 300%.

Core Features

  1. Ingest API: Powerful document ingestion system supporting PDF, CSV, DOCX, EPUB, PPTX, and almost all formats.
  2. Context-aware Chat: True contextual conversation, not just one-shot Q&A.
  3. High-level API: Developer-friendly interface—integrate RAG capabilities into your app with just a few lines of code.

Installation Guide

PrivateGPT recommends using Poetry for dependency management.

1. Prepare Environment

You need Python 3.11.

# Install Poetry (if not already installed)
curl -sSL https://install.python-poetry.org | python3 -

# Clone the repository
git clone https://github.com/zylon-ai/private-gpt
cd private-gpt

2. Install Dependencies

PrivateGPT supports multiple “Extras.” Here we install the version supporting local Ollama.

poetry install --extras "ui llms-ollama embeddings-ollama vector-stores-qdrant"

3. Configure Local Models

First run Ollama and download models:

ollama pull llama3
ollama pull nomic-embed-text

Then modify the settings-ollama.yaml file (or set environment variables):

llm:
  mode: ollama
  ollama:
    base_url: http://localhost:11434
    model: llama3

embedding:
  mode: ollama
  ollama:
    base_url: http://localhost:11434
    model: nomic-embed-text

4. Run

PGPT_PROFILES=ollama poetry run python -m private_gpt

Open your browser to http://localhost:8001, and you’ll see a minimalist chat interface.

Hands-On Experience

”Interrogate” Your Documents

  1. In the left sidebar, click “Upload Files” and select some PDFs.
  2. Wait for the “Ingesting” progress bar to complete.
  3. Switch the mode to “Query Documents”.
  4. Ask: “What’s the core conflict across these documents?”

You’ll be amazed by its precision—it lists source citations for every point, and you can click to view the original text, ensuring AI isn’t making things up.

Developer Guide: API Calls

PrivateGPT isn’t just a UI—it’s a backend service. After starting the service, visit http://localhost:8001/docs for Swagger documentation.

Python Example:

import requests

# 1. Upload document
file = {'file': open('contract.pdf', 'rb')}
requests.post('http://localhost:8001/v1/ingest/file', files=file)

# 2. Chat
payload = {
  "messages": [{"role": "user", "content": "What's the penalty clause in the contract?"}],
  "use_context": True
}
response = requests.post('http://localhost:8001/v1/chat/completions', json=payload)
print(response.json()['choices'][0]['message']['content'])

Use Cases

  • Law firms/Hospitals: Handle highly sensitive cases or medical records.
  • Offline Environments: Deploy AI assistants in submarines, mines, and other no-network environments.
  • Personal Knowledge Base: Organize years of notes and ebooks, building a “second brain.”

PrivateGPT proves that convenient AI experience and ultimate data privacy can coexist.