Near Vector Object
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
Near Vector Object has 13 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(3), contains key(1), contains value(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
takesArgumentTakes Argument(1)
- With Near Vector
ex:with-near-vector
usedByUsed by(1)
- Query Vector
ex:query-vector
usedInUsed in(1)
- Vector Key
ex:vector-key
Other facts (10)
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 | Dictionary | [1] |
| Rdf:type | Data Structure | [2] |
| Rdf:type | Configuration Object | [3] |
| Contains Key | "vector" | [1] |
| Contains Value | Query Vector | [1] |
| Used by | With Near Vector | [1] |
| Has Key | "vector" | [2] |
| Has Value | Query Vector 128 | [2] |
| Has Property | Vector Property | [3] |
| Has Value for | Vector Property | [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/cbaeb875-e16f-44dd-bc0f-36b3945d0935- full textbeam-chunktext/plain1 KB
doc:beam/cbaeb875-e16f-44dd-bc0f-36b3945d0935Show excerpt
print("Query successful:") print(result) ``` ### Example with Vector Search If you want to perform a vector search and retrieve both text and vector data, you can use the `nearVector` filter: ```python # Perform a vector search query_vec…
ctx:claims/beam/ea34a816-3421-425e-97a9-50206b2c6248ctx: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…
See also
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