grub-2.0 VS tantivy

Not finished, but the Selenium based crawler works pretty well to combat most blocks: https://github.com/kordless/grub-2.0

For IP blocks, try this: https://github.com/kordless/mitta-screenshot

I started https://mitta.us as this, but am pivoting to prompt management for GPT-3. I've Open Sourced the code for the crawler here: https://github.com/kordless/grub-2.0. The entire system uses Google Vision for extracting text. I dislike fiddling with the DOM...

If you are interested in using Solr for this, I can provide instructions to you. I'm kordless at the gmails ... com.

Depending on the use case you might try imaging the page, then send the image to an ML model for full text before indexing. If you need links extracted, Selenium also supports parsing the assembled DOM: https://github.com/kordless/grub-2.0/tree/main/aperture

I’ve found imaging the page and doing OCR on the image is quite good for text extraction. Many pages on the Internet render with JavaScript, which means BS may not see the text in the DOM.

Here is the code to do some of that: https://github.com/kordless/grub-2.0

Tantivy - a full-text indexing library written in Rust. Has a great performance and featureset.

By this I presume you mean build a search index that can retrieve results based on keywords? I know certain databases use Lucene to build a keyword-based index on top of unstructured blobs of data. Another alternative is to use Tantivy (https://github.com/quickwit-oss/tantivy), a Rust version of Lucene, if building search indices via Java isn't your cup of tea :)

Both libraries offer multilingual support for keywords, I believe, so that's a benefit to vector search where multilingual embedding models are rather expensive.

We also implemented our schemaless columnar storage optimized for object storage.

The inverted index and columnar storage are part of tantivy [0], which is the fastest search library out there. We maintain it and we decided to build the distributed engine on top of it.

[0] tantivy github repo: https://github.com/quickwit-oss/tantivy

The issue for geo search is here: https://github.com/quickwit-oss/tantivy/issues/44

Search index : Custom-built using tantivy.

The roaring bitmap variant is used only for the optional index (1 docid => 0 or 1 value) in the columnar storage (DocValues), not for the inverted index. Since this is used for aggregation, some queries may be a full scan.

The inverted index in tantivy uses bitpacked values of 128 elements with a skip index on top.

> I didn't follow the rest of your comment, select is what EF is good at, every other data structure needs a lot more scanning once you land on the right chunk. With BMI2 you can also use the PDEP instruction to accelerate the final select on a 64-bit block

The select for the sparse codec is a [simple array index access](https://github.com/quickwit-oss/tantivy/blob/main/columnar/s...), that is hard to beat. Compression is not good near the 5k threshold though.

Hi /r/rust, I’m a SWE on Etsy’s Retrieval Systems team where we’re building a platform based on rust and tantivy (https://github.com/quickwit-oss/tantivy). We’re looking to bring two new engineers onto the team.

Quick Search: Easily find specific notes with Velo's fuzzy-search feature, powered by tantivy. tantivy might have been a little overkill, but it was really easy to integrate.