ann-benchmarks VS AspNetCoreDiagnosticScenarios

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.

This one is a pretty decent guide - https://github.com/davidfowl/AspNetCoreDiagnosticScenarios/blob/master/AsyncGuidance.md

The C# (mostly applicat community has e.g. https://github.com/davidfowl/AspNetCoreDiagnosticScenarios/blob/master/AsyncGuidance.md written by one of the Asp.Net architects. I found this in this lovely thread https://news.ycombinator.com/item?id=36785691 which expresses my views/confusion more clearly than I can express.

No it doesn't, hence why there are best practices guidelines written by the .NET architects, and there was a research project to add Go/Java co-routines as well.

https://github.com/davidfowl/AspNetCoreDiagnosticScenarios/b...

https://twitter.com/davidfowl/status/1532880744732758018?lan...

https://github.com/dotnet/runtimelab/issues/2057

https://github.com/dotnet/runtimelab/issues/2398

They don't work just fine in C#, there is a reason why one of ASP.NET architects has written a guide of best practices.

https://github.com/davidfowl/AspNetCoreDiagnosticScenarios/b...

Many that praise async/await in C#, kind of forget it took about 10 years to spread across all the layer of the language and runtime, since it was done via IL rewriting, it caused several issues with F# async tasks, due to the age of the ecosystem plenty of code isn't async/await friendly and needs to be wrapped into Task.Run() or similar.

There is a best practices guideline from one of the ASP.NET architects, https://github.com/davidfowl/AspNetCoreDiagnosticScenarios/b...

During last year they researched adding Go/Java's approach to .NET, but now it is too late. See the ASP.NET Q&A session at BUILD 2023.

The best guidance I have found was from here: AsyncGuidance.md

To expand upon this thought, here is the AsyncGuidance doc[1] on why not to use .Result to get the return value of a completed Task in C#.

To make this simple they introduced async Main[2] a few years ago.

[1]: https://github.com/davidfowl/AspNetCoreDiagnosticScenarios/b...

Async Guidance