PrivateGPT-0.2.0版本 资源，完全私有的内部文档资源对话，可立即投入生产的 AI 项目

# 🔒 PrivateGPT 📑 [](https://github.com/imartinez/privateGPT/actions/workflows/tests.yml?query=branch%3Amain) [](https://docs.privategpt.dev/) [](https://discord.gg/bK6mRVpErU) [](https://twitter.com/PrivateGPT_AI) > Install & usage docs: https://docs.privategpt.dev/ > > Join the community: [Twitter](https://twitter.com/PrivateGPT_AI) & [Discord](https://discord.gg/bK6mRVpErU)  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](https://openai.com/blog/openai-api), 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](https://www.gradio.app/) 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](https://forms.gle/4cSDmH13RZBHV9at7) > and we'll try to help! We are refining PrivateGPT through your feedback. ## 🎞️ Overview DISCLAIMER: This README is not updated as frequently as the [documentation](https://docs.privategpt.dev/). 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](https://github.com/imartinez/privateGPT/tree/primordial) 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](https://github.com/imartinez/privateGPT/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](https://fastapi.tiangolo.com/) and follows [OpenAI's API scheme](https://platform.openai.com/docs/api-reference). * The RAG pipeline is based on [LlamaIndex](https://www.llamaindex.ai/). 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. Main building blocks: * APIs are defined in `private_gpt:server:<api>`. Each package contains an `<api>_router.py` (FastAPI layer) and an `<api>_service.py` (the service implementation). Each *Service* uses LlamaIndex base abstractions instead of specific implementations, decoupling the actual implementation from its usage. * Components are placed in `private_gpt:components:<component>`. Each *Component* is in charge of providing actual implementations to the base abstractions used in the Services - for example `LLMComponent` is in charge of providing an actual implementation of an `LLM` (for example `LlamaCPP` or `OpenAI`). ## 💡 Contributing Contributions are welcomed! To ensure code quality we have enabled several format and typing checks, just run `make check` before committing to make sure your code is ok. Remember to test your code! You'll find a tests folder with helpers, and you can run tests using `make test` command. Don't know what to contribute? Here is the public [Project Board](https://github.com/users/imartinez/projects/3) with several ideas. Head over to Discord #contributors channel and ask for write permissions on that Github project. ## 💬 Community Join the conversation around PrivateGPT on our: - [Twitter (aka X)](https://twitter.com/PrivateGPT_AI) - [Discord](https://discord.gg/bK6mRVpErU) ## 📖 Citation If you use PrivateGPT in a paper, check out the [Citation file](CITATION.cff) for the correct citation. You can also use the "Cite this repository" button in this repo to get the citation in different formats. Here are a couple of examples: #### BibTeX ```bibtex @software{Martinez_Toro_PrivateGPT_2023, author = {Martínez Toro, Iván and Gallego Vico, Daniel and Orgaz, Pablo}, license = {Apache-2.0}, month = may, title = {{PrivateGPT}}, url = {https://github.com/imartinez/privateGPT}, year = {2023} } ``` #### APA ``` Martínez Toro, I., Gallego Vico, D., & Orgaz, P. (2023). PrivateGPT [Computer software]. https://github.com/imartinez/privateGPT ``` ## 🤗 Partners & Supporters PrivateGPT is actively supported by the teams behind: * [Qdrant](https://qdrant.tech/), providing the default vector database * [Fern](https://buildwithfern.com/), providing Documentation and SDKs * [LlamaIndex](https://www.llamaindex.ai/), providing the base RAG framework and abstractions This project has been strongly influenced and supported by other amazing projects like [LangChain](https://git