Interact with your documents using the power of GPT, 100% privately, no data leaks
docs.privategpt.dev
Install & usage docs: https://docs.privategpt.dev/
PrivateGPT is a production-ready AI project that allows you to ask questions about your documents using the power of Large Language Models (LLMs), even in scenarios without an Internet connection. 100% private, no data leaves your execution environment at any point.
The project provides an API offering all the primitives required to build private, context-aware AI applications. It follows and extends the OpenAI API standard, and supports both normal and streaming responses.
The API is divided into two logical blocks:
High-level API, which abstracts all the complexity of a RAG (Retrieval Augmented Generation) pipeline implementation:
- Ingestion of documents: internally managing document parsing, splitting, metadata extraction, embedding generation and storage.
- Chat & Completions using context from ingested documents: abstracting the retrieval of context, the prompt engineering and the response generation.
Low-level API, which allows advanced users to implement their own complex pipelines:
- Embeddings generation: based on a piece of text.
- Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested documents.
In addition to this, a working Gradio UI client is provided to test the API, together with a set of useful tools such as bulk model download script, ingestion script, documents folder watch, etc.
👂 Need help applying PrivateGPT to your specific use case? Let us know more about it and we'll try to help! We are refining PrivateGPT through your feedback.
DISCLAIMER: This README is not updated as frequently as the documentation. Please check it out for the latest updates!
Generative AI is a game changer for our society, but adoption in companies of all sizes and data-sensitive domains like healthcare or legal is limited by a clear concern: privacy. Not being able to ensure that your data is fully under your control when using third-party AI tools is a risk those industries cannot take.
The first version of PrivateGPT was launched in May 2023 as a novel approach to address the privacy concerns by using LLMs in a complete offline way.
That version, which rapidly became a go-to project for privacy-sensitive setups and served as the seed for thousands of local-focused generative AI projects, was the foundation of what PrivateGPT is becoming nowadays; thus a simpler and more educational implementation to understand the basic concepts required to build a fully local -and therefore, private- chatGPT-like tool.
If you want to keep experimenting with it, we have saved it in the primordial branch of the project.
It is strongly recommended to do a clean clone and install of this new version of PrivateGPT if you come from the previous, primordial version.
PrivateGPT is now evolving towards becoming a gateway to generative AI models and primitives, including completions, document ingestion, RAG pipelines and other low-level building blocks. We want to make it easier for any developer to build AI applications and experiences, as well as provide a suitable extensive architecture for the community to keep contributing.
Stay tuned to our releases to check out all the new features and changes included.
Full documentation on installation, dependencies, configuration, running the server, deployment options, ingesting local documents, API details and UI features can be found here: https://docs.privategpt.dev/
Conceptually, PrivateGPT is an API that wraps a RAG pipeline and exposes its primitives.
- The API is built using FastAPI and follows OpenAI's API scheme.
- The RAG pipeline is based on LlamaIndex.
The design of PrivateGPT allows to easily extend and adapt both the API and the RAG implementation.
from https://github.com/zylon-ai/private-gpt
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