private-gpt

Interact with your documents using the power of GPT, 100% privately, no data leaks

open-sourceagent-frameworks
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Overview

PrivateGPT is a production-ready AI project that enables users to interact with their documents using Large Language Models (LLMs) while maintaining complete privacy. Built by Zylon, this tool operates entirely offline, ensuring that no data ever leaves your execution environment. The system implements a full RAG (Retrieval Augmented Generation) pipeline, allowing users to ask questions about their documents and receive contextually relevant answers powered by local LLMs. The project provides both high-level and low-level APIs that follow the OpenAI API standard, supporting both normal and streaming responses. The high-level API abstracts the complexity of document ingestion, parsing, splitting, metadata extraction, embedding generation, and storage, while also handling chat completions using context from ingested documents. For advanced users, the low-level API offers direct access to embeddings generation and contextual chunk retrieval. PrivateGPT includes a working Gradio UI for testing and interaction, along with useful utilities like bulk model download scripts, document ingestion tools, and folder monitoring capabilities. This makes it particularly valuable for organizations in regulated industries such as financial services, healthcare, government, and defense, where data privacy and security are paramount concerns.

Pros

  • + Complete data privacy with 100% local processing and no external data transmission
  • + Production-ready with comprehensive API following OpenAI standards and streaming support
  • + Flexible architecture offering both high-level RAG pipeline and low-level API for custom implementations

Cons

  • - Requires significant local compute resources to run LLMs effectively
  • - Setup complexity may be challenging for non-technical users
  • - Limited to documents that can be processed and stored locally

Use Cases

Getting Started

1. Install PrivateGPT following the documentation setup instructions 2. Ingest your documents using the provided ingestion script or API 3. Start querying your documents through the Gradio UI or API endpoints