Not able to see vectors in collection

Support
,
1

Description

I am trying to setup schema that may look like this.
I am using Weaviate 4.X setup.
My use case needs multitenancy setup.

self.client.collections.create(

            name=self.collection_name,
            vectorizer_config=Configure.Vectorizer.text2vec_openai(    
                                        model="text-embedding-3-small"
                                       ),               

            multi_tenancy_config= Configure.multi_tenancy(
                enabled=True,
                auto_tenant_creation= True,
                auto_tenant_activation= True
            ),

properties=[ Property(
name=“title”,
data_type=DataType.TEXT,
vectorize_property_name= True,
),
Property(
name=“Content”,
data_type=DataType.TEXT,
vectorize_property_name= True,

                        ),
                        
                        Property(
                            name="Stage",
                            data_type=DataType.TEXT,
                            vectorize_property_name= False,
                        ),
                        Property(
                            name="Version",
                            data_type=DataType.TEXT,
                            vectorize_property_name= False,
                        )]

)
I am expecting Title and Content field to be stored as vector and vector search should happen on them when I fire user query for search.
But when I see Weaviate UI , I see these fields as raw text and so i am not sure if they have been vectorized at all.
can you tell me whats wrong here.

Server Setup Information

  • Weaviate Server Version: Trial version / Weaviate Sandbox
  • Deployment Method:
  • Multi Node? Number of Running Nodes: No
  • Client Language and Version: Python
  • Multitenancy?: Yes

Any additional Information

2

As per my understanding,

Weaviate is not working like this. You have to generate embedding for specific fields (in your case title and content) and then store it in weaviate.

here I am storing embedding in vector while adding object and weaviate_ojb is whole data.

# here I am storing embedding in vector while adding object
batch.add_object(
    collection="[YOUR COLLECTION NAME]",
    properties=weaviate_obj,
    uuid=obj_uuid,
    vector=embedding
)

After that when you query within your vectorstore, then you can create embedding for your query and do hybrid search.

result = collection_name.query.hybrid(
    query=query,
    vector=query_embedding,
    limit=k,
    return_metadata=MetadataQuery(score=True)
)

Or you can use weaviate vectorizer config settings: Vectorizer and vector index config | Weaviate Documentation