1. Transformers module

Transformers vectorizer use models children of the “BERT” model brought by Google, which led to the current machine learning/artificial intelligence revolution.

Create a docker compose file from Weaviate wizzard : select the Text transformers vectorizer, with a specific transformer model among the list. Then start the docker container.

• (1) Select the “Text” vectorizer type
• (2) Select the “Transformers” vectorizer
• (3) Select a transformer 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 text2vec-transformers
• (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!

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.

3 Index your data