elasticsearch-learning-to-rank VS qdrant

I worked on a learning-to-rank problem at a previous job (which unfortunately never got deployed womp, womp). This was early days, so at the time I was looking at using LambdaMART with solr or elasticsearch for reranking with a Bayesian click model to get pseudo-labels for relevance.

I'm modifying learning to rank plugin to add some custom BM25 algo and I want to add explain like that, but I can't find documents/tutorial that clearly show how it is done.

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.