PrivateGPT is a production-ready, open-source AI application that enables users to ask questions about their local documents using Large Language Models (LLMs) without ever sending data to external servers. Designed for privacy-sensitive industries like healthcare, legal, and finance, it eliminates data leakage risks by running entirely offline. Built on FastAPI and LlamaIndex, it provides a complete RAG (Retrieval-Augmented Generation) pipeline that ingests, embeds, and retrieves document content locally. The project evolved from a simple local chatbot prototype into a robust API gateway for private AI applications, offering both high-level abstractions and low-level components for developers to build custom solutions.
PrivateGPT supports multiple LLM backends (including LlamaCPP and OpenAI-compatible models), integrates with Qdrant as the default vector store, and provides a Gradio-based UI for immediate testing. It is ideal for teams that need full control over their data and cannot rely on cloud-based AI services due to compliance or security requirements.
What You Get
- Full offline RAG pipeline - Ingest, embed, and query documents without internet access; all processing occurs locally using LlamaIndex and Qdrant.
- OpenAI API-compatible endpoints - Supports /chat/completions and /embeddings endpoints following OpenAI’s standard, enabling seamless integration with existing tools.
- Document ingestion system - Automatically parses PDFs, DOCX, TXT, and other formats; extracts metadata and splits content into contextual chunks for retrieval.
- Low-level RAG primitives - Access to embedding generation, vector store operations, and contextual chunk retrieval for building custom AI workflows.
- Gradio UI interface - Built-in web interface to interact with your documents via chat without writing code.
- Configurable LLM and embedding backends - Swap models like LlamaCPP, OpenAI, or others via configuration without code changes.
- Bulk document ingestion and folder watching - Automate ingestion of entire directories and monitor folders for new files to index automatically.
Common Use Cases
- Building a secure legal document assistant - Law firms use PrivateGPT to query case files, contracts, and precedents without exposing sensitive client data to cloud providers.
- Creating a private healthcare knowledge base - Hospitals deploy PrivateGPT internally to let staff ask questions about patient records or medical literature while complying with HIPAA.
- Problem: Sensitive documents can’t be uploaded to cloud AI services → Solution: PrivateGPT runs entirely on-premise, keeping data isolated in a secure environment - Organizations with strict data governance policies use PrivateGPT to leverage LLMs without violating compliance.
- DevOps teams managing internal knowledge systems - Teams use PrivateGPT to build searchable documentation portals for internal wikis, codebases, and runbooks accessible via chat interface.
Under The Hood
PrivateGPT is a Python-based solution designed for building private AI workflows, focusing on document ingestion, LLM interaction, and vector storage. It enables users to process and retrieve information from private documents using a modular architecture that supports multiple LLMs, embeddings, and vector stores.
Architecture
This project adopts a layered monolithic architecture with clear separation between ingestion, processing, and storage components. It emphasizes component-based design with strategy and factory patterns to support flexible provider selection.
- Modular structure with distinct layers for ingestion, LLM handling, and storage
- Strategy and factory patterns enable easy switching between different LLMs and embeddings
- Service-oriented routers manage API endpoint routing with extensible configuration
- Clear separation of concerns enhances maintainability and scalability
Tech Stack
Built with Python 3.11, the project leverages modern tools and frameworks to support AI-powered document workflows.
- Uses FastAPI for web server capabilities and LlamaIndex as the core document processing framework
- Integrates with Qdrant, Milvus, and Postgres for vector storage solutions
- Employs Poetry for dependency management and Ruff/Black for linting and formatting
- Includes MyPy for type checking and pytest for comprehensive test coverage
Code Quality
The codebase reflects a well-organized Python project with strong emphasis on modularity and testability.
- Comprehensive test suite ensures reliability across components and integration points
- Dependency injection promotes flexibility and decouples system components
- Type annotations and linting practices improve code consistency and readability
- Extensive use of configuration-driven design supports easy customization
What Makes It Unique
PrivateGPT distinguishes itself through its extensible and modular architecture tailored for private AI use cases.
- Component-based abstraction allows seamless integration of custom LLMs and vector stores like Ollama or SageMaker
- Clean API and configuration system simplify deployment and customization for private workflows
- Flexible architecture supports switching between providers with minimal code changes
- Designed for developers who want to build private AI solutions without extensive infrastructure overhead