Birbla

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Hacker News

How do you feel about supporting an S3 compatible target as a feature request?

by toomuchtodo

Honestly. This with Paperless-NGX might be game changing if both pointed to the same folder.

by nickweb

Anyone getting a bunch of permission errors when running (e.g. Traceback (most recent call last): File "/Users/tron/Applications/DocuBrowse/venv/lib/python3.9/site-packages/psutil/_psosx.py", line 347, in wrapper return fun(self, args, *kwargs) File "/Users/tron/Applications/DocuBrowse/venv/lib/python3.9/site-packages/psutil/_psosx.py", line 508, in net_connections rawlist = cext.proc_net_connections(self.pid, families, types) PermissionError: [Errno 1] Operation not permitted (originated from proc_pidinfo(PROC_PIDLISTFDS) 1/2)

by appstorelottery

Not a fan of pushing every personal document to someone else's cloud. Nice to see a tool that keeps everything on disk instead.

by mune2gu-chan

We need projects like this. Automatically classifying the files is smart.

I'm working on a similar application called Hister (https://github.com/asciimoo/hister). I should borrow some of your ideas. =]

by asciimoo

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.

[0] https://docs.anythingllm.com/features/embedding-models

by karmakaze

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.

by linuxrebe1

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  • Hacker News
  • How do you feel about supporting an S3 compatible target as a feature request?
    by toomuchtodo
  • Honestly. This with Paperless-NGX might be game changing if both pointed to the same folder.
    by nickweb
  • Anyone getting a bunch of permission errors when running (e.g. Traceback (most recent call last): File "/Users/tron/Applications/DocuBrowse/venv/lib/python3.9/site-packages/psutil/_psosx.py", line 347, in wrapper return fun(self, args, *kwargs) File "/Users/tron/Applications/DocuBrowse/venv/lib/python3.9/site-packages/psutil/_psosx.py", line 508, in net_connections rawlist = cext.proc_net_connections(self.pid, families, types) PermissionError: [Errno 1] Operation not permitted (originated from proc_pidinfo(PROC_PIDLISTFDS) 1/2)
    by appstorelottery
  • Not a fan of pushing every personal document to someone else's cloud. Nice to see a tool that keeps everything on disk instead.
    by mune2gu-chan
  • Wanted to share Antfly which I think serves a similar niche:

    https://antfly.io/

    https://github.com/antflydb/antfly

    They’ve put a lot of effort into optimizing the local llm pipelines and I have a lot of faith in the devs working on it.

    by kamranjon
  • We need projects like this. Automatically classifying the files is smart.

    I'm working on a similar application called Hister (https://github.com/asciimoo/hister). I should borrow some of your ideas. =]

    by asciimoo
  • 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.

    [0] https://docs.anythingllm.com/features/embedding-models

    by karmakaze
  • 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.

    by linuxrebe1