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| # HidrogenGPT 📑 | |
|  | |
| HidrogenGPT based on PrivateGPT, 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](https://zylon.ai) or [request a demo](https://cal.com/zylon/demo?source=pgpt-readme). | |
| > 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](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. | |
| ## 🎞️ Overview | |
| >[!WARNING] | |
| > 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/zylon-ai/private-gpt/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/zylon-ai/private-gpt/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{Zylon_PrivateGPT_2023, | |
| author = {Zylon by PrivateGPT}, | |
| license = {Apache-2.0}, | |
| month = may, | |
| title = {{PrivateGPT}}, | |
| url = {https://github.com/zylon-ai/private-gpt}, | |
| year = {2023} | |
| } | |
| ``` | |
| #### APA | |
| ``` | |
| Zylon by PrivateGPT (2023). PrivateGPT [Computer software]. https://github.com/zylon-ai/private-gpt | |
| ``` | |
| ## 🤗 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://github.com/hwchase17/langchain), | |
| [GPT4All](https://github.com/nomic-ai/gpt4all), | |
| [LlamaCpp](https://github.com/ggerganov/llama.cpp), | |
| [Chroma](https://www.trychroma.com/) | |
| and [SentenceTransformers](https://www.sbert.net/). | |