Query Vectors
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
Query Vectors has 22 facts recorded in Dontopedia across 5 references, with 5 live disagreements.
Mostly:rdf:type(3), used by(2), dtype(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
generatesGenerates(2)
- Random Vector Generation
ex:random-vector-generation - Search Operation
ex:search-operation
createsCreates(1)
- Example Usage
ex:example-usage
describesDescribes(1)
- 1000
ex:1000
hasEntityHas Entity(1)
- 1000 Query Vectors
ex:1000-query-vectors
processesInBatchesProcesses in Batches(1)
- Batch Search Function
ex:batch-search-function
requiresRequires(1)
- Search Operation
ex:search-operation
Other facts (22)
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 | Vector Collection | [2] |
| Rdf:type | Numpy Array | [3] |
| Rdf:type | Array | [4] |
| Used by | Weaviate | [2] |
| Used by | Faiss | [2] |
| Dtype | float32 | [3] |
| Dtype | np.float32 | [4] |
| Dimension | 2 | [4] |
| Dimension | 3 | [4] |
| Contains | [1, 2, 3] | [4] |
| Contains | [4, 5, 6] | [4] |
| Generated Once | true | [1] |
| Shared Between | Weaviate and Faiss Search | [1] |
| Has Count | 1000 | [2] |
| Generated by | Search Operation | [2] |
| Has Exact Count | 1000 | [2] |
| Is Part of | 1000 Query Vectors | [2] |
| Shape | 10000x128 | [3] |
| Overlaps With | Example Vectors | [4] |
| Used for | search | [4] |
| Vector Count | 2 | [4] |
| Is Required by | Search Operation | [5] |
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 (5)
ctx:claims/beam/9d96f8cb-54e9-48bd-a699-50a1796601b9ctx:claims/beam/7a9ac19a-33f6-4bf6-abb1-90a9206a55a1ctx:claims/beam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9- full textbeam-chunktext/plain1 KB
doc:beam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9Show excerpt
from concurrent.futures import ThreadPoolExecutor # Create a FAISS index d = 128 # dimension index = faiss.IndexFlatL2(d) # Add vectors to the index vectors = np.random.rand(10000, d).astype('float32') index.add(vectors) # Function to p…
ctx:claims/beam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042ctx:claims/beam/9170f193-72c4-43d3-9c09-87f869d91b8b- full textbeam-chunktext/plain1 KB
doc:beam/9170f193-72c4-43d3-9c09-87f869d91b8bShow excerpt
index.nprobe = nprobe return index # Example usage: vectors = np.random.rand(10000, 128).astype(np.float32) index = create_ivfpq_index(vectors, nlist=200, m=8, nprobe=15) print(index.ntotal) # Test the index query_vectors = np.ran…
See also
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