Dontopedia

vectors

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

vectors has 31 facts recorded in Dontopedia across 7 references, with 3 live disagreements.

31 facts·21 predicates·7 sources·3 in dispute

Mostly:rdf:type(5), has method(3), created with(2)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (22)

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.

isMethodOfIs Method of(3)

isPropertyOfIs Property of(2)

iteratesOverIterates Over(2)

operatesOnOperates on(2)

addsVectorToAdds Vector to(1)

appliedToApplied to(1)

createsCreates(1)

dependsOnDepends on(1)

describesDescribes(1)

encapsulatesEncapsulates(1)

hasAttributeHas Attribute(1)

hasSameDimensionHas Same Dimension(1)

instantiatesInstantiates(1)

isIndependentOfIs Independent of(1)

passesArgumentPasses Argument(1)

receivesArgumentReceives Argument(1)

selectsSelects(1)

Other facts (28)

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.

28 facts
PredicateValueRef
Rdf:typeVariable[1]
Rdf:typeArray[2]
Rdf:typeNumpy Array[3]
Rdf:typeNumpy Array[4]
Rdf:typeArray[6]
Has MethodAdd Vector Method[3]
Has MethodGet Vectors Method[3]
Has MethodResize Method[3]
Created WithInitial Capacity Parameter[3]
Created WithVector Size Parameter[3]
Is Initialized WithRandom Array Generation[1]
Array Dimensions[10000, 128][1]
Has Shape[num_vectors, 128][1]
Has Second Dimension128[1]
Serves AsDatabase[1]
Is of TypeNumpy Array[3]
Has Initial Capacityspecified[3]
Has Vector Sizespecified[3]
Is Preallocatedtrue[4]
Preallocated Withzeros[4]
Created byNumpy Zeros Call[4]
Has Data Typenp.float32[5]
Initialized byNp Zeros Function[5]
UsesNumpy Random[7]
Has Dtypefloat32[7]
Generated byNumpy Random Rand[7]
Has Shape300000x128[7]
Has Element TypeFloat32 Type[7]

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.

typebeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
ex:Variable
labelbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
vectors
isInitializedWithbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
ex:random-array-generation
arrayDimensionsbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
[10000, 128]
hasShapebeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
[num_vectors, 128]
hasSecondDimensionbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
128
servesAsbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
ex:database
typebeam/202a3697-e562-4fba-bbf7-cecbb06b3cd0
ex:Array
isOfTypebeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
ex:numpy-array
hasInitialCapacitybeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
specified
hasVectorSizebeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
specified
typebeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
ex:NumpyArray
labelbeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
vectors
hasMethodbeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
ex:add-vector-method
hasMethodbeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
ex:get-vectors-method
hasMethodbeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
ex:resize-method
createdWithbeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
ex:initial-capacity-parameter
createdWithbeam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
ex:vector-size-parameter
typebeam/8db83f0d-819a-4f3b-b500-3a38a63092b2
ex:NumpyArray
labelbeam/8db83f0d-819a-4f3b-b500-3a38a63092b2
vectors
isPreallocatedbeam/8db83f0d-819a-4f3b-b500-3a38a63092b2
true
preallocatedWithbeam/8db83f0d-819a-4f3b-b500-3a38a63092b2
zeros
createdBybeam/8db83f0d-819a-4f3b-b500-3a38a63092b2
ex:numpy-zeros-call
hasDataTypebeam/1d97c824-a92f-4574-8a4f-ad59542ea9aa
np.float32
initializedBybeam/1d97c824-a92f-4574-8a4f-ad59542ea9aa
ex:np-zeros-function
typebeam/e84015fa-c493-4afc-989d-244a981b70fe
ex:Array
usesbeam/5e937662-abc6-4623-b5b6-7b168728e324
ex:numpy-random
has-dtypebeam/5e937662-abc6-4623-b5b6-7b168728e324
float32
generated-bybeam/5e937662-abc6-4623-b5b6-7b168728e324
ex:numpy-random-rand
has-shapebeam/5e937662-abc6-4623-b5b6-7b168728e324
300000x128
has-element-typebeam/5e937662-abc6-4623-b5b6-7b168728e324
ex:float32-type

References (7)

7 references
  1. ctx:claims/beam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
  2. ctx:claims/beam/202a3697-e562-4fba-bbf7-cecbb06b3cd0
    • full textbeam-chunk
      text/plain1 KBdoc:beam/202a3697-e562-4fba-bbf7-cecbb06b3cd0
      Show excerpt
      # Simulate memory usage and storage size memory_usage = len(vectors) * 128 * 8 / (1024 * 1024) # in MB storage_size = memory_usage # Assuming similar size for simplicity results['memory_usage'] = memory_usage results['
  3. ctx:claims/beam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0e98f2e1-cdc0-4a33-868b-98a143f5105d
      Show excerpt
      - A NumPy array `vectors` is created with the specified initial capacity and vector size. 2. **Adding Vectors**: - The `add_vector` method checks if the current number of vectors has reached the capacity. If so, it resizes the array
  4. ctx:claims/beam/8db83f0d-819a-4f3b-b500-3a38a63092b2
  5. ctx:claims/beam/1d97c824-a92f-4574-8a4f-ad59542ea9aa
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1d97c824-a92f-4574-8a4f-ad59542ea9aa
      Show excerpt
      2. **Performance**: Accessing and traversing a trie can be slower compared to direct array access. 3. **Alternative Data Structures**: Depending on your use case, other data structures like NumPy arrays, sparse matrices, or even specialized
  6. ctx:claims/beam/e84015fa-c493-4afc-989d-244a981b70fe
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e84015fa-c493-4afc-989d-244a981b70fe
      Show excerpt
      - The `add_vector` method checks if the current number of vectors has reached the capacity. If so, it resizes the array to accommodate more vectors. - The new vector is added to the array, and the count of vectors is incremented. 3.
  7. ctx:claims/beam/5e937662-abc6-4623-b5b6-7b168728e324
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5e937662-abc6-4623-b5b6-7b168728e324
      Show 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

See also

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