lucene VS faiss

First, a review of the landscape. As said in the introduction, there aren't a ton of good options. Apache Lucene is by far the best traditional search index from a speed, performance and functionality standpoint. It's the base for Elasticsearch/OpenSearch and many other projects. But it requires Java.

https://github.com/apache/lucene/issues/10047

2. The Panama Vector API allows CPU's that support it to accelerate vector operations: https://openjdk.org/jeps/438

So this allows fast ANN on Lucene for semantic search!

How did people do this before Lucene supported it? Only through entirely different tools?

Are they forking Lucene or somehow getting the Lucene devs to increase that limit? Because this PR has been open for over a year now: https://github.com/apache/lucene/issues/11507

Lucene's adaptation of Roaring uses the complement idea on a block-wise basis:

https://github.com/apache/lucene/blob/84cae4f27cfd3feb3bb42d...

Like someone said, it's in locations as specified in the path.data. Depending on sharing and replication, it could be on more than one host. Elastic uses Apache Lucene to store documents, since it's open source, that rabbit hole will welcome research :-)

It is pretty clear to me that Elastic is planning to build their ANN features differently than OpenDistro's k-NN implementation, or other plugins modules that extend Easticsearch in similar ways. They now will build on the Apache Lucene capabilities that were collaboratively built "upstream" by a number of individuals, some that work for Amazon and some that work for Elastic.

From the linked issue, it seemed that they were originally planning to develop this as a proprietary feature of Elasticsearch, without contributing the functionality to Apache Lucene, but then changed direction when the Apache Lucene developers (some of which are currently employed to do such work by Amazon) started to build its approximate nearest neighbor (ANN) vector search capabilities. [1]

It's great to see folks that work for Elastic collaborating and building on what is in Apache Lucene to extend the utility of ANN with Hierarchical Navigable Small World Graphs (HNSW) [2]! From this, I think it should be possible to implement an Open Source version of the functionality with a compatible API, if that is something that OpenSearch users seek.

[1] https://issues.apache.org/jira/browse/LUCENE-9004

[2] https://github.com/apache/lucene/pull/250

Or just use FAISS https://github.com/facebookresearch/faiss

You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.

This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).

For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.

faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}

If you want to experiment with vector stores, you can do that locally with something like faiss which has good platform support: https://github.com/facebookresearch/faiss

Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and there's no standard best-practice. When you use a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those to an LLM like a normal prompt.

The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://news.ycombinator.com/item?id=36725982

According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.

Indexes for vector databases in high dimensions are nowhere near are effective as the 2-d indexes used in GIS or the 1-d B-tree indexes that are commonly used in databases.

Back around 2005 I was interested in similarity search and read a lot of conference proceedings on the top and was basically depressed at the state of vector database indexes and felt that at least for the systems I was prototyping I was OK with a full scan and later in 2013 I had the assignment of getting a search engine for patents using vector embeddings in front of customers and we got performance we found acceptable with full scan.

My impression today is that the scene is not too different than it was in 2005 but I can't say I haven't missed anything. That is, you have tradeoffs between faster algorithms that miss some results and slower algorithms that are more correct.

I think it's already a competitive business. You have Pinecone which had the good fortune of starting before the gold rush. Many established databases are adding vector extension. I know so many engineering managers who love postgresql and they're just going to load a vector extension and go. My RSS reader YOShInOn uses SBERT embeddings to cluster and classify text and certainly More Like This and semantic search are on the agenda, I'd expect it to take about an hour to get

https://github.com/facebookresearch/faiss

up and working, I could spend more time stuck on some "little" front end problem like getting something to look right in Bootstrap than it would take to get working.

I can totally believe somebody could make a better vector db than what's out there but will it be better enough? A startup going through YC now could spend 2-3 to get a really good product and find customers and that is forever in a world where everybody wants to build AI applications right now.

As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.

But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...

https://github.com/facebookresearch/faiss

Not a model, but I would use this Dense Passage Retrieval for Open Domain QA simply fine-tuning two BERT models, one for questions and one for queries, and then fine-tuning using contrastive loss between positive key/value pairs of document embeddings (the [CLS]) token. You can then use a vector database (Like Faiss, Elasticsearch, Vespa or similar) for querying the question.