Deliver Better Recommendations with Qdrant’s new API

Qdrant, a vector search engine, has enhanced its Recommendation API in version 1.6, offering greater flexibility and control for users. The updated API allows for the use of both vector IDs and embeddings for positive and negative examples, enabling more sophisticated recommendation strategies. The previous API required at least one positive example and used an average vector approach, which has now been supplemented with a new "best score" strategy that selects recommendations based on the closest distances to positive and negative samples. This advancement is exemplified by the Food Discovery demo, which demonstrates the API's application in recommending meals based on user preferences and dislikes, allowing for the inclusion of multiple queries and strategy switching. The demo also highlights challenges with multimodal data, such as combining text and image embeddings, which can affect the results depending on the proximity of the embeddings in the dataset. The open-source demo is available online, with precomputed embeddings for easy deployment on platforms like Qdrant Cloud.