peritext VS ann-benchmarks

This doesn’t appear to support rich text formatting ranges like bold, italic, etc - unless I’m missing something in the API. AFAIK Peritext is still the state of the art in rich text CRDT algorithms https://www.inkandswitch.com/peritext/

I’d love to see this build the rich text stuff from the Peritext algorithm.

The work Ink & Switch (unaffiliated) do has been an inspiration to my with regard to local-first and decentralized software: https://www.inkandswitch.com

They have a quasi-manifesto on local-first (https://www.inkandswitch.com/local-first/) and have published the best rich text CRDT around, Peritext: https://www.inkandswitch.com/peritext/

Lots of interesting work happening in this space.

Take a look at https://automerge.org/ and the stack those folks are building. You're exactly right that it's a difficult balance (specifically the trick is proving commutativity for the domain-specific data of your application). But automerge (and then https://github.com/inkandswitch/peritext) show it's at least possible. Good stuff.

Yes - The BFT problem only matters when you have Byzantine actors. But I think users deserve and expect the system to be reasonably well behaved and predictable in all situations. Anything publically writable, for example, needs BFT resilience. Or any video game.

As for the prosemirror problem, I assume you’re talking about weird merges from users putting markdown in a text crdt? You’re totally right - this is a problem. Text CRDTs treat documents as a simple sequence of characters. And that confuses a lot of structured formats. For example, if two users concurrently bold the same word, the system should see that users agree that it should be bolded. But if that “bold” intent is translated into “insert double asterisks here and here”, you end up with 4 asterisks before and after the text, and that confused markdown parsers. The problem is that a text crdt doesn’t understand markdown.

JSON editing has similar problems. I’ve heard of plenty of people over the years putting json text into a text crdt, only to find that when concurrent edits happen, the json grows parse errors. Eg if two users concurrently insert “a” and “b” into an empty list. The result is [“a””b”] which can’t be parsed.

The answer to both of these problems is to use CRDTs which understand the shape of your data structure. Eg, use a json OT/crdt system for json data (like sharedb or automerge). Likewise, if the user is editing rich text in prosemirror then you want a rich text crdt like peritext. Rich text CRDTs add the concept of annotations - so if two users bold overlapping regions of text, the crdt understands that the result should be that the entire region is bolded. And that can be translated back to markdown if you want.

The ink & switch people did a great write up of how this sort of crdt works here: https://www.inkandswitch.com/peritext/

> I'm looking out for practical CRDT ideas that works well with richtext.

Have you seen Peritext from Ink & Switch? https://www.inkandswitch.com/peritext/ It's relatively new, but is a CRDT aimed at rich text!

To put it in a different perspective, plain text editing has well-solved CRDT patterns. But, semantic data-structures like rich-text or syntax trees is what's tricky and has unsolved challenges.

Peritext[1] is the only one that came close to solving rich-text, but even that one left out important aspect of rich-text editing like handling list & table operations as "work to be done later".

For people interested on why it's difficult to build CRDTs for richtext, here's a piece I wrote a year back: https://writer.zohopublic.com/writer/published/grcwy5c699d67...

Related HN discussion: https://news.ycombinator.com/item?id=29433896

[1] https://github.com/inkandswitch/peritext

Anyone unsure of what a CRDT is, this is the perfect intro: https://www.inkandswitch.com/peritext/

The two most widely used CRDT implementations (combining JSON like general purpose types and rich text editing types) are:

- Automerge https://github.com/automerge/automerge

- Yjs https://github.com/yjs/yjs

Svelte is, but that is your smallest problem. You want to look into CRDTs (conflict-free replicated data types) to offer true (offline) collaboration. A popular JS library to solve this complex problem is called [automerge](Conflict-free replicated data type). A rather recent development in that area specifically for text-based content is Peritext. Also check out this interactive tutorial about CRDTs.

On top of my head, pgvector only supports 2 indexes, those are running in memory only. They don't support GPU indexing, nor Disk based indexing, they also don't have separation of query and insertions.

Also with different people I've talked to, they struggle with scale past 100K-1M vector.

You can also have a look yourself from a performance perspective: https://ann-benchmarks.com/

https://ann-benchmarks.com/ is a good resource covering those libraries and much more.

We utilized the ANN Benchmarks methodology, a standard for benchmarking vector databases. Our tests used the dbpedia dataset of 1,000,000 OpenAI embeddings (1536 dimensions) and inner product distance metric for both Pinecone and pgvector.

Data warehouses are columnar stores. They are very different from row-oriented databases - like Postgres, MySQL. Operations on columns - e.g., aggregations (mean of a column) are very efficient.

Most vector databases use one of a few different vector indexing libraries - FAISS, hnswlib, and scann (google only) are popular. The newer vector dbs, like weaviate, have introduced their own indexes, but i haven't seen any performance difference -

Reference: https://ann-benchmarks.com/

(Blog author here). Thanks for the question. In this case the index for both DiskANN and pgvector HNSW is small enough to fit in memory on the machine (8GB RAM), so there's no need to touch the SSD. We plan to test on a config where the index size is larger than memory (we couldn't this time due to limitations in ANN benchmarks [0], the tool we use).

To your question about RAM usage, we provide a graph of index size. When enabling PQ, our new index is 10x smaller than pgvector HNSW. We don't have numbers for HNSWPQ in FAISS yet.

[0]: https://github.com/erikbern/ann-benchmarks/

In terms of "All You Need" for Vector Search, ANN Benchmarks (https://ann-benchmarks.com/) is a good site to review when deciding what you need. As with anything complex, there often isn't a universal solution.

txtai (https://github.com/neuml/txtai) can build indexes with Faiss, Hnswlib and Annoy. All 3 libraries have been around at least 4 years and are mature. txtai also supports storing metadata in SQLite, DuckDB and the next release will support any JSON-capable database supported by SQLAlchemy (Postgres, MariaDB/MySQL, etc).

pg_vector doesn't perform well compared to other methods, at least according to ANN-Benchmarks (https://ann-benchmarks.com/).

txtai is more than just a vector database. It also has a built-in graph component for topic modeling that utilizes the vector index to autogenerate relationships. It can store metadata in SQLite/DuckDB with support for other databases coming. It has support for running LLM prompts right with the data, similar to a stored procedure, through workflows. And it has built-in support for vectorizing data into vectors.

For vector databases that simply store vectors, I agree that it's nothing more than just a different index type.

The reason https://ann-benchmarks.com is so good, is that we can see a plot of recall vs latency. I can see you have some latency numbers in the leaderboard at the bottom, but it's very difficult to make a decision.

As a practitioner that works with vector databases every day, just latency is meaningless to me, because I need to know if it's fast AND accurate, and what the tradeoff is! You can't have it both ways. So it would be helpful if you showed plots showing this tradeoff, similar to ann-benchmarks.