Text2Vec Contextionary
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
Text2Vec Contextionary has 8 facts recorded in Dontopedia across 3 references.
Mostly:rdf:type(3), used for(1), described as(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
configuresVectorizerConfigures Vectorizer(1)
- Example Schema Definition
ex:example-schema-definition
describesDescribes(1)
- Comment Default Vectorizer
ex:comment-default-vectorizer
hasVectorizerHas Vectorizer(1)
- My Class
ex:MyClass
hasVectorizerConfigHas Vectorizer Config(1)
- Schema
ex:schema
Other facts (7)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Vectorizer | [1] |
| Rdf:type | Vectorizer | [2] |
| Rdf:type | Vectorizer | [3] |
| Used for | text | [2] |
| Described As | default vectorizer | [2] |
| Has Description | default vectorizer for text | [3] |
| Has Comment | Comment Default Vectorizer | [3] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (3)
ctx:claims/beam/7d88293f-b412-4a42-9fde-d4ff46d757a3- full textbeam-chunktext/plain1 KB
doc:beam/7d88293f-b412-4a42-9fde-d4ff46d757a3Show excerpt
[Turn 2238] User: hmm, what if I need to handle different vector sizes for my data? Can Weaviate accommodate that? [Turn 2239] Assistant: Weaviate can handle different vector sizes for your data, but it requires some additional configurati…
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/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 …
See also
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