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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Summarize and query from a lot of heterogeneous documents. Any LLM provider, any filetype, advanced RAG, advanced summaries, scriptable, etc
| Date | Stars |
|---|---|
| 2026-07-31 | 520 |
| 2026-08-01 | 520 |
| 2026-08-06 | 520 |
Today
— stars today
This week
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Momentum
0.0
growth rate 0.00%/day
[](https://badge.fury.io/py/wdoc)
[](https://deepwiki.com/thiswillbeyourgithub/wdoc)
# wdoc
<p align="center"><img src="https://github.com/thiswillbeyourgithub/wdoc/blob/main/images/icon.png?raw=true" width="512" style="background-color: transparent !important"></p>
> *I'm wdoc. I solve RAG problems.*
> - wdoc, imitating Winston "The Wolf" Wolf
`wdoc` is a powerful RAG (Retrieval-Augmented Generation) system designed to summarize, search, and query documents across various file types. It's particularly useful for handling large volumes of diverse document types, making it ideal for researchers, students, and professionals dealing with extensive information sources.
Created by a psychiatry resident who needed a way to get a definitive answer from multiple sources at the same time (audio recordings, video lectures, [Anki flashcards](https://apps.ankiweb.net/), PDFs, EPUBs, etc.). `wdoc` was born from frustration with existing RAG solutions for querying and summarizing. Note: `wdoc` was coded mostly by hand, without LLM assistance as they didn't exist at the time, Claude Code will probably be used to refactor the code as it evolves.
*(The online documentation can be found [here](https://wdoc.readthedocs.io/en/stable))*
* **Goal and project specifications**: `wdoc`'s goal is to create **perfectly useful** summaries and **perfectly useful** sourced answers to questions on heterogeneous corpus. It's capable of querying **tens of thousands** of documents across [various file types](#filetypes) at the same time. The project also includes an opinionated summary feature to help users efficiently keep up with large amounts of information. It uses mostly [LangChain](https://python.langchain.com/) and [LiteLLM](https://docs.litellm.ai/docs/) as backends.
* **Current status**: **usable, tested, still under active development, tens of planned features**
* I don't plan on stopping to read anytime soon so if you find it promising, stick around as I have many improvements planned (see roadmap section).
* **I would greatly benefit from testing by users as it's the quickest way for me to find the many minor quick-to-fix bugs.**
* The main branch is more stable than the dev branch, which in turns offers more features.
* Open to feature requests and pull requests. All feedbacks, including reports of typos, are highly appreciated
* Please open an issue before making a PR, as there may be ongoing improvements in the pipeline.
* **Key Features**:
* **Docker Web UI**: Easy deployment with a [Gradio-based web interface](./docker/README.md) for simplified document processing without CLI interaction.
* **High recall and specificity**: it was made to find A LOT of documents using carefully designed embedding search then carefully aggregate gradually each answer using semantic batch to produce a single answer that mentions the source pointing to the exact portion of the source document.
* Use both an expensive and cheap LLM to make recall as high as possible because we can afford fetching a lot of documents per query (via embeddings)
* Supports **virtually any LLM providers**, including local ones, and even with extra layers of security for super secret stuff.
* Aims to **support *any* filetypes** and query from all of them at the same time (**15+** are already implemented!)
* **Actually *useful* AI powered summary**: get the thought process of the author instead of nebulous takeaways.
* **Actually *useful* AI powered queries**: get the **sourced** indented markdown answer to your questions instead of hallucinated nonsense.
* **Extensible**: this is both a tool and a library. It was even turned into [an Open-WebUI Tool](https://openwebui.com/t/qqqqqqqqqqqqqqqqqqqq/wdoctool). Also available as a [Docker web UI](./docker/README.md) for easy deployment.
* **Web Search**: Preliminary web search suExcerpt of 50,220 characters
Read on GitHub
Olivier Cornelis · olicorp · France
4.6k
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d364a16dffeac397, topic:rag, topic:question-answering
matched fp:d364a16dffeac397, topic:llm