diff --git a/qdrant-landing/content/blog/superlinked-multimodal-search.md b/qdrant-landing/content/blog/superlinked-multimodal-search.md index 2ba06bde1..43578603f 100644 --- a/qdrant-landing/content/blog/superlinked-multimodal-search.md +++ b/qdrant-landing/content/blog/superlinked-multimodal-search.md @@ -51,6 +51,7 @@ Then, a user query is also embedded and sent as a query vector to the engine for **Qdrant Vector Database:** The easiest way to store vectors is to [**create a free Qdrant Cloud cluster**](https://cloud.qdrant.io/login). We have simple docs that show you how to [**grab the API key**](/documentation/quickstart-cloud/) and upsert your new vectors and run some basic searches. For this demo, we have deployed a live Qdrant Cloud cluster. ### 1. Vector Spaces: The Building Blocks of Intelligent Search +![superlinked-hotel-1](/blog/superlinked-multimodal-search/superlinked-hotel-1.jpg) At the heart of Superlinked's innovation are [**Spaces**](https://docs.superlinked.com/concepts/overview) - specialized vector embedding environments designed for different data types. Unlike conventional approaches that force all data into a single embedding format, these spaces respect the inherent characteristics of different data types. diff --git a/qdrant-landing/static/blog/superlinked-multimodal-search/superlinked-hotel-1.jpg b/qdrant-landing/static/blog/superlinked-multimodal-search/superlinked-hotel-1.jpg new file mode 100644 index 000000000..380a3bc8c Binary files /dev/null and b/qdrant-landing/static/blog/superlinked-multimodal-search/superlinked-hotel-1.jpg differ