private-gpt (fullstack，but not overwin dify)

private-gpt

https://github.com/zylon-ai/private-gpt?tab=readme-ov-file

 

PrivateGPT

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.

 

Tip

If you are looking for an enterprise-ready, fully private AI workspace check out Zylon's website or request a demo. Crafted by the team behind PrivateGPT, Zylon is a best-in-class AI collaborative workspace that can be easily deployed on-premise (data center, bare metal...) or in your private cloud (AWS, GCP, Azure...).

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.

🎞️ Overview

 

Warning

This README is not updated as frequently as the documentation. Please check it out for the latest updates!

Motivation behind PrivateGPT

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.

Primordial version

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.

Present and Future of PrivateGPT

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.

📄 Documentation

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/

🧩 Architecture

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. Some key architectural decisions are:

• Dependency Injection, decoupling the different components and layers.
• Usage of LlamaIndex abstractions such as LLM, BaseEmbedding or VectorStore, making it immediate to change the actual implementations of those abstractions.
• Simplicity, adding as few layers and new abstractions as possible.
• Ready to use, providing a full implementation of the API and RAG pipeline.