Discovery needs context

Qdrant's version 1.7 introduces a new Discovery Search API that enhances search functionality by allowing users to constrain searches within a vector space using context, which consists of pairs of positive and negative vectors. This approach allows for a controlled exploration of vector spaces, enabling searches to identify points that are relevant but not necessarily closest to a target. The API facilitates searches by partitioning the vector space into areas that favor positive over negative vectors, inspired by the triplet loss concept in machine learning. The Discovery Search can be applied in various real-world scenarios, such as searching for images using a multimodal encoder like CLIP, which embeds text and images into the same vector space, allowing for refined searches by excluding unwanted results. Context Search, a component of Discovery Search, enables exploration without a specific target, which can be useful for breaking out of recommendation bubbles in services like music streaming. By incorporating positive and negative feedback, users can refine searches to yield more diverse and controlled results.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.