PrivateGPT — Overview
PrivateGPT is an open-source, production-ready project that exposes a RAG-oriented API to enable private, context-aware interactions with documents using large language models (LLMs). The project is designed so that no data leaves the execution environment, enabling deployment in fully offline or on-premise settings — a key requirement for privacy-sensitive domains like healthcare, legal, and regulated enterprises.
Key features
- API-first design: Implements a FastAPI-based service that follows and extends OpenAI's API patterns, providing both high-level chat/RAG convenience endpoints and low-level primitives (embeddings, retrieval) for advanced pipelines.
- Document ingestion pipeline: Handles parsing, chunking, metadata extraction, embedding generation and storage, making it straightforward to index local documents for retrieval.
- Retrieval & context management: Built around a RAG workflow (uses LlamaIndex abstractions) to retrieve relevant chunks and feed them as context to the LLM for contextualized answers.
- Local-first & privacy-focused: Designed to operate offline and keep all data within the execution environment. No automatic data leaks to third-party endpoints by default.
- Pluggable components: Decoupled components (LLM, embeddings, vector store) allow swapping implementations — common integrations include LlamaCPP, OpenAI, Qdrant and others.
- UI & tooling: Ships with a Gradio-based UI for interactive testing, plus scripts for bulk model download, document ingestion, and folder watching for automated ingestion.
Architecture highlights
- FastAPI server exposing API routers and services, structured to follow OpenAI-like routes.
- RAG implementation built on LlamaIndex abstractions (LLM, BaseEmbedding, VectorStore) so backend implementations can be changed with minimal friction.
- Dependency injection pattern to decouple components and allow custom providers for LLMs, embeddings, and vector stores.
- Default vector database support and community-backed integrations (Qdrant is listed as a partner in the project documentation).
Use cases
- Private document search and Q&A: Ask questions over company manuals, contracts, or patient records while keeping data on-premises.
- Prototyping local LLM apps: Developers can build private chat assistants, knowledge bases, or RAG-powered applications without relying on cloud-hosted model APIs.
- On-premise deployments for regulated industries: Deploy in private cloud, datacenter, or isolated environments where data exfiltration is not acceptable.
Getting started & extensibility
- Documentation: Comprehensive docs are available at the project’s official documentation site (https://docs.privategpt.dev/).
- Developer workflow: The repo includes tests, a Gradio client, ingestion scripts, and examples. Components and services are structured to be extended or replaced.
- Contributing: The project includes checks and tests and welcomes community contributions; maintainers provide a project board with ideas and a Discord community for contributors.
Notes
- The repository explicitly encourages checking the documentation for the latest updates and release notes.
- The project positions itself as a gateway to generative AI primitives (completions, embeddings, retrieval) with a focus on private deployments.
References
- Repository & docs: Official repo and documentation pages (see README and docs.privategpt.dev for deployment and API details).
