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Articles tagged with #vector-similarity
Building applications with Large Language Models don’t have to be complicated. A lot has been going on recently to simplify the development, so you can utilize already pre-trained models and support even complex pipelines with a few lines of code. La...
Bi-encoders are probably the most efficient way of setting up a semantic Question Answering system. This architecture relies on the same neural model that creates vector embeddings for both questions and answers. The assumption is, both question and ...
Qdrant is designed as an efficient vector database, allowing for a quick search of the nearest neighbours. But, you may find yourself in need of applying some extra filtering on top of the semantic search. Up to version 0.10, Qdrant was offering supp...
In a real case scenario, a single object might be described in several different ways. If you run an e-commerce business, then your items will typically have a name, longer textual description and also a bunch of photos. While cooking, you may care a...
Model fine-tuning allows you to improve the quality of the pre-trained models with just a fraction of the resources spent on training the original model. But there is a trade-off between the number of layers you tune and the precision you get. Using ...
As an Open Source vector search engine, we are often compared to the competitors and asked about our performance vs the other tools. But the answer was never simple, as the world of vector databases lacked a unified open benchmark that would show the...