UdditdocGPT 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 my Portfolio or contact me for a demo. Crafted by Uddit Kant Sinha, UdditdocGPT is a best-in-class AI document analyzer 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.
Warning
This README is not updated as frequently as the documentation. Please check the docs 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.
As a PatentTech innovator and AI Engineer, I created UdditdocGPT to address these concerns head-on.
UdditdocGPT is evolving towards becoming a gateway to generative AI models and primitives, including completions, document ingestion, RAG pipelines and other low-level building blocks. I 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 the 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 in the project documentation.
Conceptually, UdditdocGPT 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 UdditdocGPT 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,BaseEmbeddingorVectorStore, 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
udditdoc_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
udditdoc_gpt:components:<component>. Each Component is in charge of providing actual implementations to the base abstractions used in the Services - for exampleLLMComponentis in charge of providing an actual implementation of anLLM(for exampleLlamaCPPorOpenAI).
Contributions are welcomed! To ensure code quality I 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? Check out the Project Board with several ideas.
Head over to our communication channels and ask for write permissions on the GitHub project.
Join the conversation around UdditdocGPT on:
If you use UdditdocGPT in a paper, please cite it appropriately.
Here are a couple of examples:
@software{Uddit_UdditdocGPT_2025,
author = {Uddit Kant Sinha},
license = {Apache-2.0},
month = april,
title = {{UdditdocGPT}},
url = {https://github.com/UDDITwork/UdditdocGPT},
year = {2025}
}Uddit Kant Sinha (2025). UdditdocGPT [Computer software]. https://github.com/UDDITwork/UdditdocGPT
UdditdocGPT complements my other AI initiatives:
- Patent Diagram Generator Tool: Converts patent text into FreeCAD/AutoCAD diagrams
- ABC_AI: Smart AI Assistant for Hiring & Task Automation
- Document Analyzer with OCR + Gemini API: Chat with PDFs, DOCX, TXT using Tesseract + NLP
UdditdocGPT is actively supported by various technologies:
- Qdrant, providing the default vector database
- LlamaIndex, providing the base RAG framework and abstractions
This project has been strongly influenced and supported by other amazing projects like LangChain, GPT4All, LlamaCpp, Chroma and SentenceTransformers.
Want to collaborate or hire me for your AI/ML projects?
- Email: udditkantsinha2@gmail.com
- LinkedIn: lorduddit-
- Portfolio: udditwork.github.io/PORTFOLIO-Uddit