I had an issue. A documents folder with over 12k objects in it. A hodgepodge of folders and sub-folders. That over time had created a mess that no amount of file movement was ever going to make it usable. I wanted:
1) To keep my data local
2) be able to filter out PII and other data
3) Be able to find and delete duplicates
4) Get short synopsis of what a document is
5) Semantic and keyword search
6) All of this kept local to me requiring no internet access and no tokens spent to train someone elses AI.
The result I call DocuBrowser and in it's current form is FOSS (GPL-3) licensed for your personal use. The UI is in your browser. The AI models used are held local and are tiny, Available for Linux(RPM,Deb, and tgz) Windows and Mac. Let me know what you think and thanks for taking the time to try it out.
I've run it on a VM with 4G ram and no GPU. It runs, But I really recommend 8G ram at least. If you have a GPU (like I do) with 4G vRAM that is ideal. Will get this in the readme. Thanks for the suggestion. I really tried to build this to minimal spec.
Could it be extended so it also extracts pictures from pptx and xlsx and run vision to get a description to be added to the text content before indexing?
I've been working on something related - extracting tons of data from various formats to allow searching them - and the solution I chose for xlxs and xls files was headless LibreOffice to convert them to CSV. There's also exceljs but I found it didn't work for many old xls files.
I didn't find screenshotting of spreadsheets worked well, vision wasn't very accurate on them. I do use it for PDFs though. For docx it's probably fine either way but I went with LibreOffice -> markdown.
I went with the python libraries (pydoc and pyxls for example), because it's portable and doesn't require a big download to a users system if they don't already have it installed.
By restricted for personal use I mean it's not networked. It's running on your system only. It's not a networked commercial product able to do SSO etc. It's not an enterprise level product.
I learned a solution is to turn the documents into vectors in say PostgreSQL (with pgvector) and do a cosine similarity search with a search vector. Doing a search for embed models on HuggingFace shows nomic-ai/nomic-embed-text-v1.5 and Qwen/Qwen3-Embedding-0.6B. I might have used a larger one like Qwen/Qwen3-Embedding-4B.
There's some info for AnythingLLM[0] which supports RAG. AnythingLLM has LanceDB out of the box but also supports others including pgvector.
I just had a wild thought. Combine Hister with my RepoSearch app. Point it at a companies Internal github/gitlab and have a searchable knowledge base of your git repos.
I have not set up Hister yet but it's on my list to try out. How would I do something like host it on my Unraid box but have it index/persist my local MacBook browsing history?
Living in bizarro world of AI. Install open source project, fails, feed into OpenCode w/DeepSeekFlash 4 -> feed error into it get fixed.
The kill_port function only catches ImportError from the psutil block, so when psutil is installed but raises AccessDenied (common on macOS), it crashes instead of falling back to lsof.
In platform_paths.py - add two lines after line 250:
except psutil.Error:
pass
Fixed. Now when psutil raises AccessDenied (as happens on macOS without elevated privileges), it falls through to the lsof/fuser fallback instead of crashing. Try docubrowser start again.
Disappointed that it wasn't returning a list of paragraphs from eBooks that semantically match; search only appears to list the publications - not the actual match within the document.
I just installed this and, after a few hiccups, got it up and running on my Ubuntu system. Works great, looks great. Thank you for this.
Half of my documents are OpenDocument format. Is there any chance you'll be supporting ODF in the future?
I'm actually thinking of this for a commercial product feature. However, if you use a tool like Rclone on Windows, Linux or Mac. Mount the s3 bucket and you can then run DocuBrowse as if the s3 bucket were local.
Consider it yes, However having experience in this ... not really. For now there is a file called Decisions.md in the repo that is my "notes to self" if you will about where and what I need to do.
- Filling a need I personally have.
- Learning how to leverage AI for real world use not just to fill up a data center.
- Personal knowledge
-developing skills
The result I call DocuBrowser and in it's current form is FOSS (GPL-3) licensed for your personal use. The UI is in your browser. The AI models used are held local and are tiny, Available for Linux(RPM,Deb, and tgz) Windows and Mac. Let me know what you think and thanks for taking the time to try it out.
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