PrivateGPT

What is PrivateGPT?

PrivateGPT is an open-source AI project designed to allow users to interact with their documents using the power of Large Language Models (LLMs) in a completely private manner. It ensures that no data leaves the user's execution environment, making it ideal for privacy-sensitive scenarios such as healthcare, legal, and enterprise applications.

How Does PrivateGPT Work?

PrivateGPT operates through a Retrieval Augmented Generation (RAG) pipeline, which combines document ingestion, contextual retrieval, and response generation. The system uses LlamaIndex as its base RAG framework and supports various LLMs and embeddings through a modular architecture.

Core Components

• High-level API: Abstracts the complexity of RAG pipelines, handling document parsing, splitting, metadata extraction, embedding generation, and storage.
• Low-level API: Provides advanced users with tools for embeddings generation and contextual chunk retrieval.
• Gradio UI Client: Offers a user-friendly interface to test the API and manage documents.

Key Features

• 100% Private: All processing occurs locally, ensuring no data is transmitted to external servers.
• OpenAI API Compatibility: Follows and extends the OpenAI API standard for seamless integration.
• Modular Architecture: Uses dependency injection to decouple components, allowing easy customization.
• Support for Multiple Models: Compatible with various LLMs and embeddings, including LlamaCPP, OpenAI, and Azure OpenAI.
• Document Ingestion Tools: Includes scripts for bulk model downloads, ingestion, and folder monitoring.

Practical Applications

PrivateGPT is suitable for:

• Enterprises: Needing secure, on-premise AI solutions for document analysis and querying.
• Developers: Building privacy-focused AI applications with minimal setup.
• Researchers: Handling sensitive data without compromising privacy.

Why Choose PrivateGPT?

• Security: Ensures data privacy with full local processing.
• Flexibility: Supports both high-level and low-level API usage for varied complexity.
• Community-Driven: Backed by active contributors and integrations with tools like Qdrant and Fern.

Getting Started

To use PrivateGPT, clone the repository from GitHub, follow the installation instructions, and configure the settings based on your preferred model providers. The documentation provides detailed guides for deployment, ingestion, and API usage.

For more information, visit the official documentation.