GitHub

PrivateGPT

What is the project about?

PrivateGPT is a production-ready AI project that enables users to ask questions about their documents using Large Language Models (LLMs) while maintaining privacy. It operates without an internet connection, ensuring no data leaves the user's environment.

What problem does it solve?

The project addresses the privacy concerns associated with using third-party AI tools, especially in data-sensitive domains like healthcare or legal. It allows companies and individuals to leverage the power of generative AI without compromising the confidentiality of their data.

What are the features of the project?

  • Provides an API (following the OpenAI API standard) for building private, context-aware AI applications.
  • Supports both normal and streaming responses.
  • Offers a high-level API for easy implementation of Retrieval Augmented Generation (RAG) pipelines, including document ingestion and context-aware chat/completions.
  • Provides a low-level API for advanced users to create custom pipelines, including embeddings generation and contextual chunk retrieval.
  • Includes a Gradio UI client for testing the API.
  • Offers tools for bulk model downloads, document ingestion, and folder monitoring.

What are the technologies used in the project?

  • FastAPI: For building the API.
  • LlamaIndex: As the base RAG framework.
  • Langchain, GPT4All, LlamaCpp, Chroma, Sentence Transformers: Influence and support.
  • Qdrant: Default vector database.
  • Gradio: UI

What are the benefits of the project?

  • Privacy: 100% private, with no data leaving the user's execution environment.
  • Offline Capability: Functions without an internet connection.
  • Production-Ready: Designed for real-world applications.
  • Extensible: Allows for easy adaptation and extension of both the API and RAG implementation.
  • Easy to Use: Provides high-level abstractions for common tasks.
  • Flexibility: Offers low-level control for advanced users.

What are the use cases of the project?

  • Querying documents in data-sensitive industries (healthcare, legal, finance).
  • Building private, context-aware AI applications.
  • Creating chatbots that can answer questions based on internal documents.
  • Developing AI-powered tools for research and analysis.
  • Any scenario where privacy and data control are paramount when using LLMs.
privateGPT screenshot