my_text_property
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
my_text_property has 11 facts recorded in Dontopedia across 5 references, with 1 live disagreement.
Mostly:rdf:type(5), data type(1), has name(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (13)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
hasPropertyHas Property(7)
- Data Example 1
ex:data-example-1 - Data Example 2
ex:data-example-2 - Data Item 1
ex:data-item-1 - Data Item 2
ex:data-item-2 - Data Object 1
ex:data-object-1 - My Class
ex:my-class - Schema Variable
ex:schema-variable
retrievesPropertiesRetrieves Properties(3)
- Query Example
ex:query-example - Query Operation
ex:query-operation - Vector Search Example
ex:vector-search-example
containsElementContains Element(2)
- Properties List
ex:properties-list - Property List
ex:property-list
hasElementHas Element(1)
- Properties Array
ex:properties-array
Other facts (9)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
Timeline
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References (5)
ctx:claims/beam/e3b0d393-cb26-4e01-b5f0-47981803de05- full textbeam-chunktext/plain1 KB
doc:beam/e3b0d393-cb26-4e01-b5f0-47981803de05Show excerpt
client = weaviate.Client("http://localhost:8080") # Define the schema schema = { "class": "MyClass", "properties": [ {"name": "my_text_property", "dataType": ["text"]}, {"name": "my_vector_property", "dataType": ["v…
ctx:claims/beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0- full textbeam-chunktext/plain1 KB
doc:beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0Show excerpt
# Create the schema in Weaviate client.schema.create_class(schema) print("Schema created successfully.") ``` #### Inserting Data When inserting data, you can specify which vector property to use based on the vector size. ```python # Add …
ctx:claims/beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c- full textbeam-chunktext/plain1 KB
doc:beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138cShow excerpt
.with_near_vector(near_vector_128) .with_limit(10) .do() ) print("Vector search query successful (size 128):") print(result_128) query_vector_256 = [0.5, 0.6, 0.7, 0.8] * 64 # Example query vector of size 256 near_vector_256 …
ctx:claims/beam/ea34a816-3421-425e-97a9-50206b2c6248ctx:claims/beam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8- full textbeam-chunktext/plain821 B
doc:beam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8Show excerpt
print("Vector search query successful (size 128):") print(result_128) query_vector_256 = [0.5, 0.6, 0.7, 0.8] * 64 # Example query vector of size 256 near_vector_256 = {"vector": query_vector_256} result_256 = ( client.query.get("MyC…
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