vector
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
vector has 20 facts recorded in Dontopedia across 8 references, with 3 live disagreements.
20 facts·11 predicates·8 sources·3 in dispute
Mostly:rdf:type(6), has data type(2), has data type(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (10)
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(5)
- Article Class
ex:article-class - Document Class
ex:Document-class - Document Class
ex:Document-class - Near Vector Object
ex:near-vector-object - Vector Class
ex:vector-class
containsContains(1)
- Vector Class
ex:vector-class
has-propertyHas Property(1)
- Vector Class
ex:vector-class
hasValueForHas Value for(1)
- Near Vector Object
ex:near-vector-object
inverseHasPropertyInverse Has Property(1)
- Document Class
ex:Document-class
usesPathUses Path(1)
- Near Vector Filter
ex:near-vector-filter
Other facts (17)
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.
17 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Vector Property | [1] |
| Rdf:type | Schema Property | [2] |
| Rdf:type | Vector Property | [3] |
| Rdf:type | Object Property | [4] |
| Rdf:type | Weaviate Property | [6] |
| Rdf:type | Vector Property | [8] |
| Has Data Type | float[] | [7] |
| Has Data Type | Float Array Type | [7] |
| Has Data Type | Vector Type | [1] |
| Vector Size | 512 | [1] |
| Example Value | Example Vector | [1] |
| Data Type | Vector Type | [2] |
| Dimension | 128 | [2] |
| Inverse of | Is Vector of | [2] |
| Has Dimension | 128 | [3] |
| Required for | compliance | [5] |
| Belongs to | Vector Class | [6] |
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.
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typebeam/60ab9372-9811-442b-9f99-a99ec6e6717e
ex:VectorProperty
—
hasDataTypebeam/60ab9372-9811-442b-9f99-a99ec6e6717e
ex:vector-type
—
vectorSizebeam/60ab9372-9811-442b-9f99-a99ec6e6717e
512
—
exampleValuebeam/60ab9372-9811-442b-9f99-a99ec6e6717e
ex:example-vector
—
typebeam/2fce069a-0714-4bf1-b525-b39dea374779
ex:SchemaProperty
—
labelbeam/2fce069a-0714-4bf1-b525-b39dea374779
vector
—
dataTypebeam/2fce069a-0714-4bf1-b525-b39dea374779
ex:vector-type
—
dimensionbeam/2fce069a-0714-4bf1-b525-b39dea374779
128
—
inverseOfbeam/2fce069a-0714-4bf1-b525-b39dea374779
ex:isVectorOf
—
typebeam/70bbc43a-27da-4ee6-abde-0b83af52d874
ex:VectorProperty
—
labelbeam/70bbc43a-27da-4ee6-abde-0b83af52d874
vector
—
hasDimensionbeam/70bbc43a-27da-4ee6-abde-0b83af52d874
128
—
typebeam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8
ex:ObjectProperty
—
labelbeam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8
vector property
—
requiredForbeam/cdd51d1c-232b-4579-bc7b-6fee02a86cab
compliance
—
typebeam/149dec1b-3c49-4cff-a826-bc9175d778ec
ex:WeaviateProperty
—
belongsTobeam/149dec1b-3c49-4cff-a826-bc9175d778ec
ex:vector-class
—
has-data-typebeam/5e937662-abc6-4623-b5b6-7b168728e324
float[]
—
has-data-typebeam/5e937662-abc6-4623-b5b6-7b168728e324
ex:float-array-type
—
typebeam/7a9ac19a-33f6-4bf6-abb1-90a9206a55a1
ex:VectorProperty
References (8)
8 references
ctx:claims/beam/60ab9372-9811-442b-9f99-a99ec6e6717e- full textbeam-chunktext/plain1 KB
doc:beam/60ab9372-9811-442b-9f99-a99ec6e6717eShow excerpt
{"name": "vector", "dataType": ["vector", "512"]} # Adjust vector size as needed ] } ) # Add data data_object = DataObject(client) data_object.create( { "class": "Article", "properties": { …
ctx:claims/beam/2fce069a-0714-4bf1-b525-b39dea374779- full textbeam-chunktext/plain1 KB
doc:beam/2fce069a-0714-4bf1-b525-b39dea374779Show excerpt
- Use a managed service or deploy on a cloud provider to achieve the desired uptime. 2. **Define Schema**: - Define the schema for your vectors and metadata. 3. **Insert Vectors**: - Insert vectors into Weaviate using the appropr…
ctx:claims/beam/70bbc43a-27da-4ee6-abde-0b83af52d874ctx: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…
ctx:claims/beam/cdd51d1c-232b-4579-bc7b-6fee02a86cabctx:claims/beam/149dec1b-3c49-4cff-a826-bc9175d778ec- full textbeam-chunktext/plain1 KB
doc:beam/149dec1b-3c49-4cff-a826-bc9175d778ecShow excerpt
[Turn 4940] User: I'm trying to assess Weaviate 1.20.0 for its search time on 300K vectors, but I'm having trouble understanding how it compares to other alternatives like FAISS 1.7.4, which I've also been testing for its 180ms search time …
ctx:claims/beam/5e937662-abc6-4623-b5b6-7b168728e324- full textbeam-chunktext/plain1 KB
doc:beam/5e937662-abc6-4623-b5b6-7b168728e324Show excerpt
print(f"Weaviate time: {weaviate_time} seconds") print(f"FAISS time: {faiss_time} seconds") ``` ->-> 5,10 [Turn 4941] Assistant: Certainly! To properly benchmark Weaviate and FAISS, you'll want to measure both the indexing time and the sea…
ctx:claims/beam/7a9ac19a-33f6-4bf6-abb1-90a9206a55a1
See also
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