Ambar VS Searx

AnyTXT.net for PC. https://github.com/RD17/ambar and https://docfetcher.sourceforge.net/en/index.html for Linux

One product could be "Ambar: Document Search Engine"

https://github.com/RD17/ambar

> Ambar is an open-source document search engine with automated crawling, OCR, tagging and instant full-text search

> *Easily deploy Ambar with a single docker-compose file; *Perform Google-like search through your documents and contents of your images; *Tag your documents; *Use a simple REST API to integrate Ambar into your workflow

There was an open-source program called Ambar that was pretty chill. It let you full-text search documents, perform OCR on these documents, and tag. There surely exist apps that do each, but the nice thing about Ambar was it incorporated all of the above into one nice GUI.

I don't need any other features (other than batch uploading). The best ones I've found so far are Ambar, I, librarian, and Lodestone with their multiple 'snippet' search functionality, however for various reasons they won't work with my use case.[1]

Ambar - Document Search Engine · An open-source document search engine with automated crawling, OCR, tagging and instant full-text search

Amber Main Page and GitHub

There is also stuff like Mayan EDMS which is much more enterprise-oriented or Ambar which targeted more for individual users.

I think searx was largely built by a single person.

https://github.com/searx/searx

I use Recoll to index all of it. Recoll WebUI exposes an API, which I plugged into Searx.

The quality of Google search results has been awful for many years now, but if you still want to use it, the only usable way is via a frontend like Searx[1]. Using any of Google's frontends for any of their services is an exercise in frustration from dodging ads and fighting their hostile UI.

[1]: https://github.com/searx/searx

RSS-Bridge has a Google search adapter. You can also fake it with Searx (which offers RSS feeds of search results).

I've recently wondered how effective local models were at chaining together thoughts as proposed in https://ofir.io/self-ask.pdf. Turns out they are indeed capable of doing so while also creating reasonable chains of thoughts that are easily as good as OpenAI's models. To make it completely free to run I used SearX running inside a Docker container with a second model curating the search results for the main model to get answers from the web.