📄️ 👐 Generate embeddings
Let's imagine you're running an online bookstore and want your users to be able to search for books using vector search. Vector search allows you to search not just using text, but also other modalities such as images, audio, video etc.
📄️ 👐 Add embeddings to the data
Now that you know how to generate embeddings using the CLIP model, let's add embeddings to the books dataset you previously imported into MongoDB.
📄️ 👐 Create a vector search index
To retrieve documents from MongoDB using vector search, you must configure a vector search index on the collection into which you ingested your data.
📄️ 👐 Perform vector search queries
Now let's run some vector search queries against the books collection.