D

as donto believed on

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

D has 42 facts recorded in Dontopedia across 15 references, with 6 live disagreements.

42 facts·14 predicates·15 sources·6 in dispute

Mostly:rdf:type(15), represents(6), contains(3)

Rdf:typein dispute`rdf:type`

Output Variable^[1]all time · Af536fe5 Aae4 407e Ad16 72341fd39f7f
Distance Array^[2]sourceall time · 9f354551 A9f5 474b A587 082e952c4a41
Distances Array^[3]all time · 49101dfd 4fc4 460c 9cd9 8e0457730c83
Search Result^[4]all time · F262ba02 38a8 487c Ac31 F121b18f4323
Distance Array^[5]all time · F5f66e1a 01a9 4eb3 81b7 Fc768e5be38a
Distances^[5]all time · F5f66e1a 01a9 4eb3 81b7 Fc768e5be38a
Distances Array^[6]all time · C024e566 7bde 4344 Ad2d Cef3f5639007
Distances Array^[7]all time · F026078e 8f4c 49fe 81e1 C274e43d2156
Distances Array^[9]all time · F1d44342 2a97 4d27 8633 2b8cdeffb413
Search Result Array^[10]all time · F9316ee6 847e 4064 80dd 6097ca97e0d6

Inbound mentions (26)

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.

`returns`Returns(7)

Code Snippetex:code-snippet
Index.searchex:index.search
Index.searchex:index.search
Searchex:search
Search Executionex:search-execution
Search Operationex:search-operation
Vector Searchex:vector search

`returnsDistances`Returns Distances(4)

Dense Retrieval Serviceex:dense-retrieval-service
Faiss Index Ivf Pqex:faiss-index-ivf-pq
Nearest Neighbor Searchex:nearest-neighbor-search
Search Operationex:search-operation

`produces`Produces(2)

Searchex:search
Search Operationex:search_operation

`assignedTo`Assigned to(1)

Search Resultex:search-result

`contains`Contains(1)

Resultsex:results

`ex:output`Ex:output(1)

Search Operationex:search-operation

`ex:outputDistance`Ex:output Distance(1)

Search Operationex:search-operation

`ex:prints`Ex:prints(1)

Print Resultsex:print-results

`ex:returnValues`Ex:return Values(1)

Search Operationex:search-operation

`inverseOf`Inverse of(1)

Iex:I

`outputVariables`Output Variables(1)

Search Operationex:search-operation

`prints`Prints(1)

Print Dex:print-D

`returnsMultipleValues`Returns Multiple Values(1)

Faiss Searchex:faiss-search

`returnsPair`Returns Pair(1)

Nearest Neighbor Searchex:nearest-neighbor-search

`returnValues`Return Values(1)

Search Vector Functionex:search-vector-function

`unpacksSearchResult`Unpacks Search Result(1)

Dense Retrieval Serviceex:dense-retrieval-service

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.

22 facts

Predicate	Value	Ref
Represents		[7]
Represents	Distances	[8]
Represents	distances	[9]
Represents	distances	[12]
Represents	Distances	[13]
Represents	Distances	[15]
Contains	Distance Values	[1]
Contains	nearest neighbor distances	[6]
Contains	distances	[9]
Stores	Distances	[4]
Stores	distances	[12]
Is Returned by	Index.search	[6]
Is Returned by	index.search	[11]
Position	0	[1]
Ex:contains	Distance Values	[2]
Not Printed	true	[5]
Result Type	Distances	[5]
Data Structure	Array	[8]
Returned by	Search	[9]
Inverse of	I	[12]
Data Type	matrix	[13]
Result of	Search	[13]

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.

42 undated

—

typebeam/af536fe5-aae4-407e-ad16-72341fd39f7f

ex:OutputVariable

—

containsbeam/af536fe5-aae4-407e-ad16-72341fd39f7f

ex:distance-values

—

positionbeam/af536fe5-aae4-407e-ad16-72341fd39f7f

—

typebeam/9f354551-a9f5-474b-a587-082e952c4a41

ex:DistanceArray

—

containsbeam/9f354551-a9f5-474b-a587-082e952c4a41

ex:distance-values

—

typebeam/49101dfd-4fc4-460c-9cd9-8e0457730c83

ex:DistancesArray

—

typebeam/f262ba02-38a8-487c-ac31-f121b18f4323

ex:SearchResult

—

storesbeam/f262ba02-38a8-487c-ac31-f121b18f4323

ex:distances

—

typebeam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a

ex:DistanceArray

—

typebeam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a

ex:Distances

—

notPrintedbeam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a

true

—

resultTypebeam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a

Distances

—

isReturnedBybeam/c024e566-7bde-4344-ad2d-cef3f5639007

ex:index.search

—

typebeam/c024e566-7bde-4344-ad2d-cef3f5639007

ex:DistancesArray

—

labelbeam/c024e566-7bde-4344-ad2d-cef3f5639007

distances to nearest neighbors

—

containsbeam/c024e566-7bde-4344-ad2d-cef3f5639007

nearest neighbor distances

—

typebeam/f026078e-8f4c-49fe-81e1-c274e43d2156

ex:DistancesArray

—

labelbeam/f026078e-8f4c-49fe-81e1-c274e43d2156

—

representsbeam/f026078e-8f4c-49fe-81e1-c274e43d2156

ex:

—

representsbeam/8c21f541-c703-4998-aae0-19638ef54326

ex:distances

—

dataStructurebeam/8c21f541-c703-4998-aae0-19638ef54326

ex:Array

—

typebeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

ex:DistancesArray

—

labelbeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

Distances

—

returnedBybeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

ex:search

—

representsbeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

distances

—

containsbeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

distances

—

typebeam/f9316ee6-847e-4064-80dd-6097ca97e0d6

ex:SearchResultArray

—

labelbeam/f9316ee6-847e-4064-80dd-6097ca97e0d6

Distances array

—

isReturnedBybeam/4efeeb64-8572-49af-812f-e5accd46c4ad

index.search

—

typebeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

ex:Array

—

labelbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

—

representsbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

distances

—

storesbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

distances

—

inverseOfbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

ex:I

—

typebeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

ex:Variable

—

representsbeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

Distances

—

dataTypebeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

matrix

—

resultOfbeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

ex:search

—

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

ex:Distances-array

—

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

ex:DistancesArray

—

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

ex:distances

—

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

ex:Array

References (15)

15 references

[1]Af536fe5 Aae4 407e Ad16 72341fd39f7f3 facts
ctx:claims/beam/af536fe5-aae4-407e-ad16-72341fd39f7f
[2]9f354551 A9f5 474b A587 082e952c4a412 facts
ctx:claims/beam/9f354551-a9f5-474b-a587-082e952c4a41
- full textbeam-chunk
  text/plain1 KBdoc:beam/9f354551-a9f5-474b-a587-082e952c4a41
  Show excerpt
  faiss.omp_set_num_threads(4) # Adjust based on your system's capabilities # Create an IVFFlat index quantizer = faiss.IndexFlatL2(128) index = faiss.IndexIVFFlat(quantizer, 128, nlist, faiss.METRIC_L2) # Train the index index.train(vecto…
[3]49101dfd 4fc4 460c 9cd9 8e0457730c831 fact
ctx:claims/beam/49101dfd-4fc4-460c-9cd9-8e0457730c83
- full textbeam-chunk
  text/plain1 KBdoc:beam/49101dfd-4fc4-460c-9cd9-8e0457730c83
  Show excerpt
  - Adjust the search parameters like `efSearch` for `IndexHNSW` to balance between speed and accuracy. ### Example Implementation Here's an optimized version of your code using `IndexIVFPQ` and enabling multi-threading: ```python impor…
[4]F262ba02 38a8 487c Ac31 F121b18f43232 facts
ctx:claims/beam/f262ba02-38a8-487c-ac31-f121b18f4323
[5]F5f66e1a 01a9 4eb3 81b7 Fc768e5be38a4 facts
ctx:claims/beam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a
- full textbeam-chunk
  text/plain1 KBdoc:beam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a
  Show excerpt
  M = 8 # Number of sub-quantizers nbits = 8 # Number of bits per sub-quantizer index = faiss.IndexIVFPQ(quantizer, 128, nlist, M, nbits) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Search for n…
[6]C024e566 7bde 4344 Ad2d Cef3f56390074 facts
ctx:claims/beam/c024e566-7bde-4344-ad2d-cef3f5639007
- full textbeam-chunk
  text/plain1 KBdoc:beam/c024e566-7bde-4344-ad2d-cef3f5639007
  Show excerpt
  vectors = np.random.rand(100000, 128).astype('float32') # Set the number of threads for parallel processing faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create a…
[7]F026078e 8f4c 49fe 81e1 C274e43d21563 facts
ctx:claims/beam/f026078e-8f4c-49fe-81e1-c274e43d2156
- full textbeam-chunk
  text/plain1006 Bdoc:beam/f026078e-8f4c-49fe-81e1-c274e43d2156
  Show excerpt
  By implementing these optimizations, you should be able to achieve a significant improvement in your dense search goals. [Turn 6398] User: I'm trying to map 3 dense search hurdles with Kathryn for future iterations, and I was wondering if …
[8]8c21f541 C703 4998 Aae0 19638ef543262 facts
ctx:claims/beam/8c21f541-c703-4998-aae0-19638ef54326
- full textbeam-chunk
  text/plain1 KBdoc:beam/8c21f541-c703-4998-aae0-19638ef54326
  Show excerpt
  faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create an IVFPQ index nlist = 100 # Number of clusters M = 8 # Number of sub-quantizers nbits = 8 # Number of bits…
[9]F1d44342 2a97 4d27 8633 2b8cdeffb4135 facts
ctx:claims/beam/f1d44342-2a97-4d27-8633-2b8cdeffb413
- full textbeam-chunk
  text/plain1 KBdoc:beam/f1d44342-2a97-4d27-8633-2b8cdeffb413
  Show excerpt
  M = 8 # Number of sub-quantizers nbits = 8 # Number of bits per sub-quantizer index = faiss.IndexIVFPQ(quantizer, 128, nlist, M, nbits) try: # Train the index index.train(vectors) except Exception as e: logging.error(f"Error …
[10]F9316ee6 847e 4064 80dd 6097ca97e0d62 facts
ctx:claims/beam/f9316ee6-847e-4064-80dd-6097ca97e0d6
- full textbeam-chunk
  text/plain1 KBdoc:beam/f9316ee6-847e-4064-80dd-6097ca97e0d6
  Show excerpt
  - **Logging**: Use structured logging (e.g., JSON) and forward logs to a centralized logging system like ELK Stack or Grafana Cloud. ### Step 3: Implementation Details #### Load Balancer Configuration - **Nginx Example**: ```nginx h…
[11]4efeeb64 8572 49af 812f E5accd46c4ad1 fact
ctx:claims/beam/4efeeb64-8572-49af-812f-e5accd46c4ad
- full textbeam-chunk
  text/plain1 KBdoc:beam/4efeeb64-8572-49af-812f-e5accd46c4ad
  Show excerpt
  query_vector = np.random.rand(1, 128).astype("float32") # Search for nearest neighbors k = 10 # number of nearest neighbors to retrieve D, I = index.search(query_vector, k) # Print the results print("Distances:", D) print("Indices:", I) …
[12]C5e65b2e 6289 4399 808e 64fe4e0eddce5 facts
ctx:claims/beam/c5e65b2e-6289-4399-808e-64fe4e0eddce
- full textbeam-chunk
  text/plain1 KBdoc:beam/c5e65b2e-6289-4399-808e-64fe4e0eddce
  Show excerpt
  m = 8 # number of subquantizers index = faiss.IndexIVFPQ(faiss.MetricType.L2, d, nlist, m, 8) # Train the index index.train(embeddings) # Add the embeddings to the index index.add(embeddings) # Generate a query embedding in a different …
[13]3aa97b5d 2401 4a53 A5d0 4cd1d9b8e0424 facts
ctx:claims/beam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042
[14]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…
[15]2fcc4e7a D497 4bfa B889 84fb8a9dfe403 facts
ctx:claims/beam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

Keep researching

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D

Rdf:typein disputerdf:type

Inbound mentions (26)

returnsReturns(7)

returnsDistancesReturns Distances(4)

producesProduces(2)

assignedToAssigned to(1)

containsContains(1)

ex:outputEx:output(1)

ex:outputDistanceEx:output Distance(1)

ex:printsEx:prints(1)

ex:returnValuesEx:return Values(1)

inverseOfInverse of(1)

outputVariablesOutput Variables(1)

printsPrints(1)

returnsMultipleValuesReturns Multiple Values(1)

returnsPairReturns Pair(1)

returnValuesReturn Values(1)

unpacksSearchResultUnpacks Search Result(1)

Other facts (22)

Timeline

References (15)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`returns`Returns(7)

`returnsDistances`Returns Distances(4)

`produces`Produces(2)

`assignedTo`Assigned to(1)

`contains`Contains(1)

`ex:output`Ex:output(1)

`ex:outputDistance`Ex:output Distance(1)

`ex:prints`Ex:prints(1)

`ex:returnValues`Ex:return Values(1)

`inverseOf`Inverse of(1)

`outputVariables`Output Variables(1)

`prints`Prints(1)

`returnsMultipleValues`Returns Multiple Values(1)

`returnsPair`Returns Pair(1)

`returnValues`Return Values(1)

`unpacksSearchResult`Unpacks Search Result(1)