1. CLIP vectorizer
CLIP vectorizer use CLIP models that can retrieve text and images from text queries.
Create a docker compose file from Weaviate wizzard : select the CLIP transformers vectorizer, with a specific transformer model among the list. Then start the docker container.
- (1) Select the “Clip” vectorizer type
- (3) Select a transformer CLIP model in the list.
Download and execute the docker-compose file generated by the wizard (docker-compose up -d):
Now, let’s create our index:
- (1) Select the multi2vec-clip module
- (2) Set a name for you index, visible in WPSOLR admin
- (3) Set a name for your Weaviate class (index)
- (4) Set the url of your Weaviate docker container
- (5) Create the index. Done!
All documents indexed with A CLIP model will index their title text, description text and embedded images (internal and external) , featured image, WooCommerce gallery images.
Below is a video of the configuration for searching in admin medias:
Connect to the Weaviate GraphQL console at https://console.semi.technology/console with url https://localhost:8080, to check our new index (class):
2 Select your data
- (1) (2) (3) select the index you just created
- (4) Choose a filter: “Near Text” to perform a vector search (search on concepts), or “Where” to perform a keywords search (classic search that works with words)
- (5) Set a similarity for your “Near Text” search. The closer to “1”, the more precise is the vector search.
- (6) Select Replace media archive “Media library” to be able to search media library images from text
3 Index your data