Vectors Dataset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
Vectors Dataset has 9 facts recorded in Dontopedia across 1 reference, with 1 live disagreement.
Mostly:used for(2), has number of vectors(1), has vector dimension(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.
requiresRequires(2)
- Addition Step
ex:addition-step - Training Step
ex:training-step
usesDatasetUses Dataset(2)
- Addition Operation
ex:addition-operation - Training Operation
ex:training-operation
builtOnBuilt on(1)
- Index Ivf Flat Index
ex:IndexIVFFlat-index
generatesGenerates(1)
- Random Generation
ex:random-generation
hasVariableHas Variable(1)
- Code Document
ex:code-document
partOfPart of(1)
- Subset of 10 Vectors
ex:subset-of-10-vectors
performedOnSubsetPerformed on Subset(1)
- Search Operation
ex:search-operation
usesSameDatasetUses Same Dataset(1)
- Training and Adding
ex:training-and-adding
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.
| Predicate | Value | Ref |
|---|---|---|
| Used for | Training Operation | [1] |
| Used for | Addition Operation | [1] |
| Has Number of Vectors | 100000 | [1] |
| Has Vector Dimension | 128 | [1] |
| Has Data Type | float32 | [1] |
| Generated by | Random Generation | [1] |
| Undergoes Conversion | Type Conversion | [1] |
| Used in Multiple Steps | 2 | [1] |
| Distribution | uniform-random | [1] |
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 (1)
ctx:claims/beam/2b210dd9-dd14-4daf-ba9f-ea7913237b0a- full textbeam-chunktext/plain1 KB
doc:beam/2b210dd9-dd14-4daf-ba9f-ea7913237b0aShow excerpt
Here's an optimized version of your code using `IndexIVFFlat` and enabling multi-threading: ```python import faiss import numpy as np # Assume we have a dataset of 100,000 vectors vectors = np.random.rand(100000, 128).astype('float32') #…
See also
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