PrivateGPT is a production-ready, offline-capable AI application enabling users to interact with their documents using LLMs without data leakage. It targets developers and organizations concerned with data privacy, offering a secure, local alternative to cloud-based AI services.

How It Works

PrivateGPT implements a Retrieval Augmented Generation (RAG) pipeline using LlamaIndex, wrapped in a FastAPI server that adheres to the OpenAI API standard. This architecture allows for easy integration and extension. Key components include document ingestion (parsing, embedding, storage) and a chat/completion interface that retrieves relevant context from ingested documents before generating responses. Dependency injection and LlamaIndex abstractions facilitate swapping out components like LLMs or vector stores.

Quick Start & Requirements

• Install via pip: pip install private-gpt
• Requires Python 3.10+.
• Supports local LLMs (e.g., via GPT4All, LlamaCpp) and vector stores (default: Qdrant).
• Full documentation: https://docs.private-gpt.dev/

Highlighted Details

• Production-ready, offline-capable RAG pipeline.
• FastAPI server with OpenAI-compatible API.
• Extensible architecture leveraging LlamaIndex.
• Includes Gradio UI client and utility scripts.

Maintenance & Community

• Actively supported by Zylon, Qdrant, Fern, and LlamaIndex.
• Community channels: Discord, Twitter.

Licensing & Compatibility

• License: Apache-2.0.
• Permissive license suitable for commercial use and integration into closed-source applications.

Limitations & Caveats

The project's README notes that it is not updated as frequently as the official documentation, which should be consulted for the latest information.