search call

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

search call has 60 facts recorded in Dontopedia across 14 references, with 10 live disagreements.

60 facts·23 predicates·14 sources·10 in dispute

Mostly:rdf:type(13), has argument(6), argument(6)

Rdf:typein dispute`rdf:type`

Function Call^[1]all time · Efd9e47b 8b3a 4eab A817 A886c4565864
Function Call^[2]sourceall time · 3c5f5c5b 6881 4f14 9961 C13194b540b4
Code Construct^[3]all time · A62e0ed1 9011 4f17 B311 Aa52982c8569
Function Call^[4]all time · Adbf517e 1335 405d 8a65 Aca63a92c7f3
Method Invocation^[5]sourceall time · 7f086001 95b5 4788 B203 Dee071ab04fa
Function Call^[6]all time · B5d9ecaf E81d 404e B6ba 4ff3bc636acc
Python Method Call^[7]sourceall time · 2e6d9029 C016 4f7e 8cb4 E4aceb2e6845
Function Call^[8]all time · 9aef4a43 C110 4730 Bed6 18e6312b77ad
Function Call^[10]all time · 3aa97b5d 2401 4a53 A5d0 4cd1d9b8e042
Function Call^[11]all time · 46073acc 6b04 4701 Bd7b E0db2b09431d

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.

`is-assigned-from`Is Assigned From(2)

D Variableex:D-variable
I Variableex:I-variable

`awaits`Awaits(1)

Search Async Functionex:search-async-function

`contains`Contains(1)

Code Exampleex:code-example

`containsStatement`Contains Statement(1)

Search Function Bodyex:search-function-body

`exampleUsage`Example Usage(1)

Elasticsearch Exampleex:elasticsearch-example

`executesInOrder`Executes in Order(1)

Search Vectors Functionex:search-vectors-function

`isAssignedBy`Is Assigned by(1)

Response Variableex:response-variable

Other facts (41)

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.

41 facts

Predicate	Value	Ref
Has Argument	Cpu Numpy Conversion	[1]
Has Argument	K Variable	[1]
Has Argument	Index Argument	[12]
Has Argument	Body Argument	[12]
Has Argument	index="my_index"	[13]
Has Argument	body={...}	[13]
Argument	Query Vector	[9]
Argument	10	[9]
Argument	query_vectors	[10]
Argument	k	[10]
Argument	Index Parameter	[14]
Argument	Body Parameter	[14]
Uses Argument	Collection Name	[4]
Uses Argument	Query Vector	[4]
Uses Argument	Top K	[4]
Keyword Argument	K Argument	[5]
Keyword Argument	index	[7]
Keyword Argument	body	[7]
Receiver	Index Object	[1]
Receiver	Variable Es	[14]
Contains	Query Vector	[3]
Contains	10	[3]
Object	es	[7]
Object	Elasticsearch Client	[12]
Returns	Distances Variable	[9]
Returns	Indices Variable	[9]
Method Name	search	[12]
Method Name	search	[14]
Returns Multiple Values	true	[1]
First Return Value	Distances Variable	[1]
Second Return Value	Indices Variable	[1]
Assigns to	Results Variable	[2]
Takes Argument	vectors[:10]	[8]
Takes Parameter	k=10	[8]
Returns Multiple Values	true	[8]
Method of	Faiss Index	[9]
Function	index.search	[10]
Targets Index	my_index	[13]
Has Parameter	Index Parameter	[13]
Calls Api	Elasticsearch Search Api	[13]
Invokes Api	Elasticsearch Api	[14]

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.

60 undated

—

typebeam/efd9e47b-8b3a-4eab-a817-a886c4565864

ex:FunctionCall

—

receiverbeam/efd9e47b-8b3a-4eab-a817-a886c4565864

ex:index-object

—

hasArgumentbeam/efd9e47b-8b3a-4eab-a817-a886c4565864

ex:cpu-numpy-conversion

—

hasArgumentbeam/efd9e47b-8b3a-4eab-a817-a886c4565864

ex:k-variable

—

returnsMultipleValuesbeam/efd9e47b-8b3a-4eab-a817-a886c4565864

true

—

firstReturnValuebeam/efd9e47b-8b3a-4eab-a817-a886c4565864

ex:distances-variable

—

secondReturnValuebeam/efd9e47b-8b3a-4eab-a817-a886c4565864

ex:indices-variable

—

typebeam/3c5f5c5b-6881-4f14-9961-c13194b540b4

ex:function-call

—

labelbeam/3c5f5c5b-6881-4f14-9961-c13194b540b4

results = db.search(query_vector)

—

assignsTobeam/3c5f5c5b-6881-4f14-9961-c13194b540b4

ex:results-variable

—

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

ex:CodeConstruct

—

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

search method call

—

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

ex:query-vector

—

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

—

typebeam/adbf517e-1335-405d-8a65-aca63a92c7f3

ex:FunctionCall

—

usesArgumentbeam/adbf517e-1335-405d-8a65-aca63a92c7f3

ex:COLLECTION_NAME

—

usesArgumentbeam/adbf517e-1335-405d-8a65-aca63a92c7f3

ex:query-vector

—

usesArgumentbeam/adbf517e-1335-405d-8a65-aca63a92c7f3

ex:TOP_K

—

typebeam/7f086001-95b5-4788-b203-dee071ab04fa

ex:MethodInvocation

—

keywordArgumentbeam/7f086001-95b5-4788-b203-dee071ab04fa

ex:k-argument

—

typebeam/b5d9ecaf-e81d-404e-b6ba-4ff3bc636acc

ex:FunctionCall

—

typebeam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845

ex:PythonMethodCall

—

objectbeam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845

—

keywordArgumentbeam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845

index

—

keywordArgumentbeam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845

body

—

typebeam/9aef4a43-c110-4730-bed6-18e6312b77ad

ex:FunctionCall

—

labelbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

index.search

—

takes-argumentbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

vectors[:10]

—

takes-parameterbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

k=10

—

returns-multiple-valuesbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

true

—

methodOfbeam/daafd359-0fc9-4026-9a83-26b7334abfe5

ex:faiss-index

—

argumentbeam/daafd359-0fc9-4026-9a83-26b7334abfe5

ex:query-vector

—

argumentbeam/daafd359-0fc9-4026-9a83-26b7334abfe5

—

returnsbeam/daafd359-0fc9-4026-9a83-26b7334abfe5

ex:distances-variable

—

returnsbeam/daafd359-0fc9-4026-9a83-26b7334abfe5

ex:indices-variable

—

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

ex:FunctionCall

—

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

index.search

—

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

query_vectors

—

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

—

typebeam/46073acc-6b04-4701-bd7b-e0db2b09431d

ex:FunctionCall

—

typebeam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce

ex:MethodCall

—

labelbeam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce

es.search call

—

methodNamebeam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce

—

objectbeam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce

ex:elasticsearch-client

—

hasArgumentbeam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce

ex:index-argument

—

hasArgumentbeam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce

ex:body-argument

—

typebeam/21515cc8-a152-4441-9529-eb4062fb2226

ex:FunctionCall

—

labelbeam/21515cc8-a152-4441-9529-eb4062fb2226

async_es.search call

—

hasArgumentbeam/21515cc8-a152-4441-9529-eb4062fb2226

index="my_index"

—

hasArgumentbeam/21515cc8-a152-4441-9529-eb4062fb2226

body={...}

—

targetsIndexbeam/21515cc8-a152-4441-9529-eb4062fb2226

my_index

—

hasParameterbeam/21515cc8-a152-4441-9529-eb4062fb2226

ex:index-parameter

—

callsAPIbeam/21515cc8-a152-4441-9529-eb4062fb2226

ex:elasticsearch-search-api

—

typebeam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

ex:MethodCall

—

labelbeam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

search call

—

receiverbeam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

ex:variable-es

—

methodNamebeam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

—

argumentbeam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

ex:index-parameter

—

argumentbeam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

ex:body-parameter

—

invokesAPIbeam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

ex:elasticsearch-api

References (14)

14 references

[1]Efd9e47b 8b3a 4eab A817 A886c45658647 facts
ctx:claims/beam/efd9e47b-8b3a-4eab-a817-a886c4565864
- full textbeam-chunk
  text/plain1 KBdoc:beam/efd9e47b-8b3a-4eab-a817-a886c4565864
  Show excerpt
  #### Step 7: Search and Retrieve ```python query = "Query in a rare language" query_language = detect_language(query) if query_language == 'rare_language': query_embedding = language_specific_model.encode(query, convert_to_tensor=True…
[2]3c5f5c5b 6881 4f14 9961 C13194b540b43 facts
ctx:claims/beam/3c5f5c5b-6881-4f14-9961-c13194b540b4
- full textbeam-chunk
  text/plain1 KBdoc:beam/3c5f5c5b-6881-4f14-9961-c13194b540b4
  Show excerpt
  # Define the vector database class VectorDatabase: def __init__(self): self.vectors = [] def add_vector(self, vector): self.vectors.append(vector) def search(self, query_vector, top_k=10): # Calculate t…
[3]A62e0ed1 9011 4f17 B311 Aa52982c85694 facts
ctx:claims/beam/a62e0ed1-9011-4f17-b311-aa52982c8569
[4]Adbf517e 1335 405d 8a65 Aca63a92c7f34 facts
ctx:claims/beam/adbf517e-1335-405d-8a65-aca63a92c7f3
- full textbeam-chunk
  text/plain1 KBdoc:beam/adbf517e-1335-405d-8a65-aca63a92c7f3
  Show excerpt
  # Perform search results = search(COLLECTION_NAME, query_vector, TOP_K) print(results) ``` ### Explanation 1. **Collection Creation**: - `create_collection`: Creates a collection with specified parameters, including dimensi…
[5]7f086001 95b5 4788 B203 Dee071ab04fa2 facts
ctx:claims/beam/7f086001-95b5-4788-b203-dee071ab04fa
- full textbeam-chunk
  text/plain1 KBdoc:beam/7f086001-95b5-4788-b203-dee071ab04fa
  Show excerpt
  Returns: tuple: Tuple containing distances and indices of the nearest neighbors. """ return self.index.search(query_embedding, k) # Example usage if __name__ == "__main__": # Create instances of the modu…
[6]B5d9ecaf E81d 404e B6ba 4ff3bc636acc1 fact
ctx:claims/beam/b5d9ecaf-e81d-404e-b6ba-4ff3bc636acc
[7]2e6d9029 C016 4f7e 8cb4 E4aceb2e68454 facts
ctx:claims/beam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845
- full textbeam-chunk
  text/plain1 KBdoc:beam/2e6d9029-c016-4f7e-8cb4-e4aceb2e6845
  Show excerpt
  - Batch documents into groups of 500-1000 for optimal performance. #### Example Code ```python from elasticsearch import Elasticsearch es = Elasticsearch(["http://localhost:9200"]) actions = [ { "_index": "my_index", …
[8]9aef4a43 C110 4730 Bed6 18e6312b77ad5 facts
ctx:claims/beam/9aef4a43-c110-4730-bed6-18e6312b77ad
[9]Daafd359 0fc9 4026 9a83 26b7334abfe55 facts
ctx:claims/beam/daafd359-0fc9-4026-9a83-26b7334abfe5
- full textbeam-chunk
  text/plain1 KBdoc:beam/daafd359-0fc9-4026-9a83-26b7334abfe5
  Show excerpt
  By following these steps, you should be able to reduce the dense search latency under 180ms for 90% of your daily requests while maintaining efficient caching. [Turn 6434] User: I'm experiencing "MemoryAllocationError" impacting 12% of vec…
[10]3aa97b5d 2401 4a53 A5d0 4cd1d9b8e0424 facts
ctx:claims/beam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042
[11]46073acc 6b04 4701 Bd7b E0db2b09431d1 fact
ctx:claims/beam/46073acc-6b04-4701-bd7b-e0db2b09431d
- full textbeam-chunk
  text/plain1 KBdoc:beam/46073acc-6b04-4701-bd7b-e0db2b09431d
  Show excerpt
  # Search the vectors using a vector search algorithm results = search_algorithm(query) # Log memory usage after the search mem_after = psutil.virtual_memory().used logging.debug(f"Memory usage after …
[12]558a52b6 49be 4e52 B9cd Bd0ff2f5adce6 facts
ctx:claims/beam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce
- full textbeam-chunk
  text/plain1 KBdoc:beam/558a52b6-49be-4e52-b9cd-bd0ff2f5adce
  Show excerpt
```sh curl -X PUT "http://localhost:9200/_cluster/settings" -H 'Content-Type: application/json' -d' { "persistent": { "cluster.routing.allocation.enable": "all" } } ' curl -X POST "http://localhost:9200/_cluster/nodes/join" -H 'Con…
[13]21515cc8 A152 4441 9529 Eb4062fb22267 facts
ctx:claims/beam/21515cc8-a152-4441-9529-eb4062fb2226
[14]958b21c1 Ac2f 492c 9ace Ddc56b7f93f67 facts
ctx:claims/beam/958b21c1-ac2f-492c-9ace-ddc56b7f93f6

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search call

Rdf:typein disputerdf:type

Inbound mentions (8)

is-assigned-fromIs Assigned From(2)

awaitsAwaits(1)

containsContains(1)

containsStatementContains Statement(1)

exampleUsageExample Usage(1)

executesInOrderExecutes in Order(1)

isAssignedByIs Assigned by(1)

Other facts (41)

Timeline

References (14)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`is-assigned-from`Is Assigned From(2)

`awaits`Awaits(1)

`contains`Contains(1)

`containsStatement`Contains Statement(1)

`exampleUsage`Example Usage(1)

`executesInOrder`Executes in Order(1)

`isAssignedBy`Is Assigned by(1)