search_vector

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

search_vector has 34 facts recorded in Dontopedia across 3 references, with 8 live disagreements.

34 facts·21 predicates·3 sources·8 in dispute

Mostly:parameter(4), returns(3), rdf:type(2)

Inbound mentions (8)

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.

`computedBy`Computed by(2)

Distancesex:distances
Indicesex:indices

`usedIn`Used in(2)

Kex:k
Vectorex:vector

`demonstrates`Demonstrates(1)

Example Usageex:example-usage

`foundUsing`Found Using(1)

Nearest Neighborsex:nearest-neighbors

`identifiedBy`Identified by(1)

Nearest Neighborsex:nearest-neighbors

`validates`Validates(1)

Example Usageex:example-usage

Other facts (32)

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.

32 facts

Predicate	Value	Ref
Parameter	Vector Parameter	[2]
Parameter	K Parameter	[2]
Parameter	Vector Parameter	[3]
Parameter	K Parameter	[3]
Returns	Distances and Indices	[2]
Returns	Distances	[3]
Returns	Indices	[3]
Rdf:type	Function	[2]
Rdf:type	Search Function	[3]
Return Values	D	[2]
Return Values	I	[2]
Defined in	Code Example	[2]
Defined in	Searching Nearest Neighbors	[3]
Has Output	Distances	[3]
Has Output	Indices	[3]
Computes	Distances	[3]
Computes	Indices	[3]
Takes	Vector	[3]
Takes	K Parameter	[3]
Contains Comment	Search for Nearest Neighbors Comment	[1]
Parameter Count	2	[2]
Calls	index.search()	[2]
Wraps Vector	true	[2]
Wrapping Method	np.array()	[2]
Passes K	true	[2]
Comment	Search for nearest neighbors	[2]
Return Type	Tuple	[2]
Return Element Count	2	[2]
Performs Wrapping	true	[2]
Called in	Example Usage	[3]
Validates	Faiss Process	[3]
Encapsulates	Searching Nearest Neighbors	[3]

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.

34 undated

—

containsCommentbeam/6260578c-fa34-4b5f-871e-0d090a2956db

ex:search-for-nearest-neighbors-comment

—

labelbeam/6260578c-fa34-4b5f-871e-0d090a2956db

search_vector

—

typebeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:Function

—

labelbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

search_vector

—

parameterbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:vector-parameter

—

parameterbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:k-parameter

—

returnsbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:distances-and-indices

—

returnValuesbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:D

—

returnValuesbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:I

—

definedInbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:code-example

—

parameterCountbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

—

callsbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

index.search()

—

wrapsVectorbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

true

—

wrappingMethodbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

np.array()

—

passesKbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

true

—

commentbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

Search for nearest neighbors

—

returnTypebeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:tuple

—

returnElementCountbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

—

performsWrappingbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

true

—

typebeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:SearchFunction

—

parameterbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:vector-parameter

—

parameterbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:k-parameter

—

returnsbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:distances

—

returnsbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:indices

—

hasOutputbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:distances

—

hasOutputbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:indices

—

calledInbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:example-usage

—

definedInbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:searching-nearest-neighbors

—

computesbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:distances

—

computesbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:indices

—

validatesbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:faiss-process

—

encapsulatesbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:searching-nearest-neighbors

—

takesbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:vector

—

takesbeam/88bd05bd-f58b-4516-adae-bf469048d980

ex:k-parameter

References (3)

3 references

[1]6260578c Fa34 4b5f 871e 0d090a2956db2 facts
ctx:claims/beam/6260578c-fa34-4b5f-871e-0d090a2956db
- full textbeam-chunk
  text/plain848 Bdoc:beam/6260578c-fa34-4b5f-871e-0d090a2956db
  Show excerpt
  [Turn 7202] User: I'm working on a project where I need to integrate vector search with approximate nearest neighbors for our hybrid retrieval prototype, and I want to know how I can optimize the performance of this integration to achieve b…
[2]2fcc4e7a D497 4bfa B889 84fb8a9dfe4017 facts
ctx:claims/beam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
[3]88bd05bd F58b 4516 Adae Bf469048d98015 facts
ctx:claims/beam/88bd05bd-f58b-4516-adae-bf469048d980
- full textbeam-chunk
  text/plain1 KBdoc:beam/88bd05bd-f58b-4516-adae-bf469048d980
  Show excerpt
  - The `100` parameter specifies the number of clusters. 3. **Training the Index**: - We train the index using the dataset. This step is crucial for the index to learn the structure of the data. 4. **Adding Vectors**: - We add the…

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search_vector

Inbound mentions (8)

computedByComputed by(2)

usedInUsed in(2)

demonstratesDemonstrates(1)

foundUsingFound Using(1)

identifiedByIdentified by(1)

validatesValidates(1)

Other facts (32)

Timeline

References (3)

See also

Keep researching

`computedBy`Computed by(2)

`usedIn`Used in(2)

`demonstrates`Demonstrates(1)

`foundUsing`Found Using(1)

`identifiedBy`Identified by(1)

`validates`Validates(1)