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

search is Search for nearest neighbors.

32 facts·10 predicates·8 sources·7 in dispute

Mostly:returns(8), rdf:type(5), has parameter(5)

Inbound mentions (21)

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.

`assignedBy`Assigned by(4)

Distances Variableex:distances-variable
Distances Variableex:distances-variable
Indices Variableex:indices-variable
Indices Variableex:indices-variable

`calls`Calls(4)

Index Testingex:index-testing
Search Operationex:search-operation
Search Similar Vectors Functionex:search-similar-vectors-function
Search Similar Vectors Functionex:search-similar-vectors-function

`contains`Contains(3)

Annoy Methodsex:annoy-methods
Faiss Methodsex:faiss-methods
Faiss Sectionex:faiss-section

`returnedBy`Returned by(2)

Distancesex:distances
Indicesex:indices

`appliedBefore`Applied Before(1)

Faiss Normalize L2ex:faiss-normalize-L2

`calledBefore`Called Before(1)

Index Add Methodex:index-add-method

`delegatesTo`Delegates to(1)

Search Functionex:search-function

`hasMethod`Has Method(1)

Faiss Indexex:faiss-index

`method`Method(1)

Index Objectex:index-object

`usedBy`Used by(1)

Query Vector Variableex:query-vector-variable

`usesMethod`Uses Method(1)

Search Operationex:search-operation

`wraps`Wraps(1)

Search Vectorex:search_vector

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

Predicate	Value	Ref
Returns	Distances Variable	[1]
Returns	Indices Variable	[1]
Returns	Distances	[3]
Returns	Indices	[3]
Returns	Distances Indices Tuple	[4]
Returns	Distances	[5]
Returns	Indices	[5]
Returns	Distances and Indices	[7]
Rdf:type	Method	[1]
Rdf:type	Method	[2]
Rdf:type	Faiss Method	[3]
Rdf:type	Method	[4]
Rdf:type	Method	[6]
Has Parameter	Ef Search Parameter	[1]
Has Parameter	query_vector	[2]
Has Parameter	k	[2]
Has Parameter	Query Vector Reshaped	[4]
Has Parameter	K Argument	[4]
Parameter	Reshaped Query Vector	[3]
Parameter	K Parameter	[3]
Used by	Search Similar Vectors Function	[4]
Used by	Search Vector	[8]
Called With	Query Embedding Param	[6]
Called With	K Param	[6]
Description	Search for nearest neighbors	[1]
Function	Searches for the k nearest neighbors to the query vector	[2]
Called on	Reshaped Query Vector	[5]
Returns Tuple	Search Results	[6]

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.

32 undated

—

typebeam/42a434b2-95aa-4616-a1af-a5af03a4baf6

ex:Method

—

descriptionbeam/42a434b2-95aa-4616-a1af-a5af03a4baf6

Search for nearest neighbors

—

hasParameterbeam/42a434b2-95aa-4616-a1af-a5af03a4baf6

ex:efSearch-parameter

—

returnsbeam/42a434b2-95aa-4616-a1af-a5af03a4baf6

ex:distances-variable

—

returnsbeam/42a434b2-95aa-4616-a1af-a5af03a4baf6

ex:indices-variable

—

typebeam/a62e0ed1-9011-4f17-b311-aa52982c8569

ex:Method

—

labelbeam/a62e0ed1-9011-4f17-b311-aa52982c8569

index.search

—

hasParameterbeam/a62e0ed1-9011-4f17-b311-aa52982c8569

query_vector

—

hasParameterbeam/a62e0ed1-9011-4f17-b311-aa52982c8569

—

functionbeam/a62e0ed1-9011-4f17-b311-aa52982c8569

Searches for the k nearest neighbors to the query vector

—

typebeam/0acf2b58-c3f3-461c-bfe2-21a5cea3bfc9

ex:FaissMethod

—

labelbeam/0acf2b58-c3f3-461c-bfe2-21a5cea3bfc9

index.search

—

parameterbeam/0acf2b58-c3f3-461c-bfe2-21a5cea3bfc9

ex:reshaped-query-vector

—

parameterbeam/0acf2b58-c3f3-461c-bfe2-21a5cea3bfc9

ex:k-parameter

—

returnsbeam/0acf2b58-c3f3-461c-bfe2-21a5cea3bfc9

ex:distances

—

returnsbeam/0acf2b58-c3f3-461c-bfe2-21a5cea3bfc9

ex:indices

—

typebeam/4acac4d0-910b-4fa1-96b2-afff0416f947

ex:Method

—

labelbeam/4acac4d0-910b-4fa1-96b2-afff0416f947

—

hasParameterbeam/4acac4d0-910b-4fa1-96b2-afff0416f947

ex:query-vector-reshaped

—

hasParameterbeam/4acac4d0-910b-4fa1-96b2-afff0416f947

ex:k-argument

—

returnsbeam/4acac4d0-910b-4fa1-96b2-afff0416f947

ex:distances-indices-tuple

—

usedBybeam/4acac4d0-910b-4fa1-96b2-afff0416f947

ex:search-similar-vectors-function

—

returnsbeam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd

ex:distances

—

returnsbeam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd

ex:indices

—

calledOnbeam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd

ex:reshaped-query-vector

—

typebeam/f4875baf-2de8-4f32-b31f-0e5fd916dd32

ex:Method

—

labelbeam/f4875baf-2de8-4f32-b31f-0e5fd916dd32

index.search()

—

calledWithbeam/f4875baf-2de8-4f32-b31f-0e5fd916dd32

ex:query_embedding-param

—

calledWithbeam/f4875baf-2de8-4f32-b31f-0e5fd916dd32

ex:k-param

—

returnsTuplebeam/f4875baf-2de8-4f32-b31f-0e5fd916dd32

ex:search-results

—

returnsbeam/9170f193-72c4-43d3-9c09-87f869d91b8b

ex:distances-and-indices

—

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

ex:search_vector

References (8)

8 references

[1]42a434b2 95aa 4616 A1af A5af03a4baf65 facts
ctx:claims/beam/42a434b2-95aa-4616-a1af-a5af03a4baf6
- full textbeam-chunk
  text/plain1 KBdoc:beam/42a434b2-95aa-4616-a1af-a5af03a4baf6
  Show excerpt
  Here's an example using the `IndexHNSW` index, which is more scalable and efficient for large datasets: ```python import numpy as np import faiss # Assuming I have a dataset of vectors vectors = np.random.rand(1000, 128).astype('float32')…
[2]A62e0ed1 9011 4f17 B311 Aa52982c85695 facts
ctx:claims/beam/a62e0ed1-9011-4f17-b311-aa52982c8569
[3]0acf2b58 C3f3 461c Bfe2 21a5cea3bfc96 facts
ctx:claims/beam/0acf2b58-c3f3-461c-bfe2-21a5cea3bfc9
[4]4acac4d0 910b 4fa1 96b2 Afff0416f9476 facts
ctx:claims/beam/4acac4d0-910b-4fa1-96b2-afff0416f947
- full textbeam-chunk
  text/plain1 KBdoc:beam/4acac4d0-910b-4fa1-96b2-afff0416f947
  Show excerpt
  # Normalize the vectors for cosine similarity faiss.normalize_L2(vectors) # Create an HNSW index M = 16 # Number of links per node efConstruction = 200 # Number of neighbors to consider during construction efSearch = 64 # Number of neig…
[5]9c3d6c77 2b58 4a3b 9618 59e705c00dfd3 facts
ctx:claims/beam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd
- full textbeam-chunk
  text/plain1 KBdoc:beam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd
  Show excerpt
  # Normalize the vectors for cosine similarity faiss.normalize_L2(vectors) # Create an IVFPQ index nlist = 100 # Number of clusters m = 8 # Number of subquantizers index = faiss.IndexIVFPQ(faiss.IndexFlatL2(128), 128, nlist, m, 8) # 8 is…
[6]F4875baf 2de8 4f32 B31f 0e5fd916dd325 facts
ctx:claims/beam/f4875baf-2de8-4f32-b31f-0e5fd916dd32
[7]9170f193 72c4 43d3 9c09 87f869d91b8b1 fact
ctx:claims/beam/9170f193-72c4-43d3-9c09-87f869d91b8b
- full textbeam-chunk
  text/plain1 KBdoc:beam/9170f193-72c4-43d3-9c09-87f869d91b8b
  Show 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…
[8]6260578c Fa34 4b5f 871e 0d090a2956db1 fact
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…

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Inbound mentions (21)

assignedByAssigned by(4)

callsCalls(4)

containsContains(3)

returnedByReturned by(2)

appliedBeforeApplied Before(1)

calledBeforeCalled Before(1)

delegatesToDelegates to(1)

hasMethodHas Method(1)

methodMethod(1)

usedByUsed by(1)

usesMethodUses Method(1)

wrapsWraps(1)

Other facts (28)

Timeline

References (8)

See also

Keep researching

`assignedBy`Assigned by(4)

`calls`Calls(4)

`contains`Contains(3)

`returnedBy`Returned by(2)

`appliedBefore`Applied Before(1)

`calledBefore`Called Before(1)

`delegatesTo`Delegates to(1)

`hasMethod`Has Method(1)

`method`Method(1)

`usedBy`Used by(1)

`usesMethod`Uses Method(1)

`wraps`Wraps(1)