search times

as donto believed on

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

search times has 26 facts recorded in Dontopedia across 18 references, with 2 live disagreements.

26 facts·7 predicates·18 sources·2 in dispute

Mostly:rdf:type(15), part of(1), inverse of(1)

Rdf:typein dispute`rdf:type`

Performance Metric^[1]sourceall time · 32c1e7e5 4ce5 48df A04d Ccdefa61e55d
Performance Metric^[2]all time · 2779d4a3 4771 4c6d B19e Dd8fd2a610e7
Performance Metric^[3]all time · 6ec3a2c8 A4c5 4d8f B39a C00b8aac8e2c
Metric^[4]all time · A4f328d2 64d4 4628 9ccd E5fcf0511f60
Performance Metric^[5]all time · 0f35b798 8b35 4770 Abf4 3d1bc1caf195
Performance Metric^[6]sourceall time · 401284ac 4b49 4678 A3e2 Aa44c5ceacbb
Performance Metric^[7]all time · 0e56e8f7 6bb5 47d4 Bd16 A0b896835d01
Performance Metric^[8]all time · B5dd457b 4a88 464d 9e56 Df15d7316326
Performance Metric^[9]all time · 30cfcb2d 27af 4962 B51a 166d7c86b3a4
Performance Metric^[11]all time · Af536fe5 Aae4 407e Ad16 72341fd39f7f

Inbound mentions (36)

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.

`affects`Affects(8)

High Nprobe Effectex:high-nprobe-effect
Hnsw Ef Searchex:hnsw-efSearch
Low Nprobe Effectex:low-nprobe-effect
M Parameterex:m-parameter
Nlistex:nlist
Nprobeex:nprobe
Nprobeex:nprobe
Parameter Nprobeex:parameter-nprobe

`balances`Balances(2)

Hns W Indexex:hnsW-index
Optimal Configurationex:optimal-configuration

`inverseOf`Inverse of(2)

Memory Usageex:memory-usage
Search Timeex:search-time

`balance`Balance(1)

Index Parametersex:index-parameters

`benefit`Benefit(1)

Parallel Processingex:parallel-processing

`between`Between(1)

Balanceex:balance

`contrastsWith`Contrasts With(1)

Accuracyex:accuracy

`enables`Enables(1)

Parallel Processingex:parallel-processing

`enablesTradeOff`Enables Trade Off(1)

Search Parametersex:search-parameters

`ex:affects`Ex:affects(1)

Nprobeex:nprobe

`existsBetween`Exists Between(1)

Trade Offex:trade-off

`factor2`Factor2(1)

Recall Speed Tradeoffex:recall-speed-tradeoff

`hasEffectOn`Has Effect on(1)

Number of Treesex:number-of-trees

`improves`Improves(1)

Toolingex:tooling

`includes`Includes(1)

Performance Benefitsex:performance-benefits

`inverseEffectOn`Inverse Effect on(1)

M Parameterex:M-parameter

`involves`Involves(1)

Trade Offtrade-off

`mayImpact`May Impact(1)

Aes 256 Encryptionex:aes-256-encryption

`mentioned`Mentioned(1)

Assistantex:assistant

`optimizationTarget`Optimization Target(1)

Optimize Basic Search Queryex:optimize-basic-search-query

`optimizes`Optimizes(1)

Optimization Quantizationex:optimization-quantization

`prioritizes`Prioritizes(1)

Real Time Constraintex:real-time-constraint

`provides`Provides(1)

Optimization Quantizationex:optimization-quantization

`purpose`Purpose(1)

Quantizationex:quantization

`relates`Relates(1)

Trade Off Relationshipex:trade-off-relationship

`resultsIn`Results in(1)

Parallel Processingex:parallel-processing

`tradeOffWith`Trade Off With(1)

Accuracyex:accuracy

Other facts (6)

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.

6 facts

Predicate	Value	Ref
Part of	Trade Offs	[1]
Inverse of	Memory Usage	[1]
Contrasts With	Accuracy	[4]
Trade Off With	Accuracy	[10]
Inversely Correlated With	Nlist Value	[12]
Qualitative Assessment	Impressive	[16]

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.

26 undated

—

typebeam/32c1e7e5-4ce5-48df-a04d-ccdefa61e55d

ex:PerformanceMetric

—

partOfbeam/32c1e7e5-4ce5-48df-a04d-ccdefa61e55d

ex:trade-offs

—

inverseOfbeam/32c1e7e5-4ce5-48df-a04d-ccdefa61e55d

ex:memory-usage

—

typebeam/2779d4a3-4771-4c6d-b19e-dd8fd2a610e7

ex:PerformanceMetric

—

typebeam/6ec3a2c8-a4c5-4d8f-b39a-c00b8aac8e2c

ex:PerformanceMetric

—

labelbeam/6ec3a2c8-a4c5-4d8f-b39a-c00b8aac8e2c

search times

—

typebeam/a4f328d2-64d4-4628-9ccd-e5fcf0511f60

ex:Metric

—

contrastsWithbeam/a4f328d2-64d4-4628-9ccd-e5fcf0511f60

ex:accuracy

—

typebeam/0f35b798-8b35-4770-abf4-3d1bc1caf195

ex:PerformanceMetric

—

typebeam/401284ac-4b49-4678-a3e2-aa44c5ceacbb

ex:PerformanceMetric

—

typebeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01

ex:PerformanceMetric

—

labelbeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01

Search Speed

—

typebeam/b5dd457b-4a88-464d-9e56-df15d7316326

ex:PerformanceMetric

—

typebeam/30cfcb2d-27af-4962-b51a-166d7c86b3a4

ex:PerformanceMetric

—

tradeOffWithbeam/8c2a3b82-efd0-4f8b-ac35-4f5154e36e3a

ex:accuracy

—

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

ex:PerformanceMetric

—

inverselyCorrelatedWithbeam/5b630b30-be7c-4e71-9257-76d31088943e

ex:nlist-value

—

typebeam/281cbbcd-971c-4f22-9941-258f26a50c16

ex:PerformanceMetric

—

labelbeam/281cbbcd-971c-4f22-9941-258f26a50c16

Search Speed

—

typebeam/a02cf99c-1e1e-40c4-8dae-5d9c0cadac18

ex:PerformanceMetric

—

labelbeam/a02cf99c-1e1e-40c4-8dae-5d9c0cadac18

Search Speed

—

typebeam/954ed438-d3a7-48b9-aa5b-485032720bf2

ex:PerformanceMetric

—

labelbeam/954ed438-d3a7-48b9-aa5b-485032720bf2

Search Speed

—

qualitativeAssessmentbeam/56ee2108-aa51-4d60-a5b9-7c895e8b18ef

ex:impressive

—

typebeam/cf0ed255-8ae0-4772-bb7f-346329f56249

ex:PerformanceBenefit

—

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

ex:PerformanceBenefit

References (18)

18 references

[1]32c1e7e5 4ce5 48df A04d Ccdefa61e55d3 facts
ctx:claims/beam/32c1e7e5-4ce5-48df-a04d-ccdefa61e55d
- full textbeam-chunk
  text/plain1 KBdoc:beam/32c1e7e5-4ce5-48df-a04d-ccdefa61e55d
  Show excerpt
  - **Choosing the Right Index Type**: Different index types (e.g., IVF_FLAT, HNSW, ANNOY) have different trade-offs between search speed, memory usage, and accuracy. Choose an index type that best fits your use case. - **Parameter Tuning**: …
[2]2779d4a3 4771 4c6d B19e Dd8fd2a610e71 fact
ctx:claims/beam/2779d4a3-4771-4c6d-b19e-dd8fd2a610e7
- full textbeam-chunk
  text/plain1 KBdoc:beam/2779d4a3-4771-4c6d-b19e-dd8fd2a610e7
  Show excerpt
  [Turn 1967] Assistant: To optimize the search time in FAISS, especially for a large number of vectors, you can leverage more advanced indexing techniques that reduce the computational complexity compared to the brute-force approach used by …
[3]6ec3a2c8 A4c5 4d8f B39a C00b8aac8e2c2 facts
ctx:claims/beam/6ec3a2c8-a4c5-4d8f-b39a-c00b8aac8e2c
[4]A4f328d2 64d4 4628 9ccd E5fcf0511f602 facts
ctx:claims/beam/a4f328d2-64d4-4628-9ccd-e5fcf0511f60
- full textbeam-chunk
  text/plain1 KBdoc:beam/a4f328d2-64d4-4628-9ccd-e5fcf0511f60
  Show excerpt
  [Turn 1968] User: hmm, which indexing method would you say is more suitable for real-time search applications? [Turn 1969] Assistant: For real-time search applications, the choice of indexing method in FAISS depends on the specific require…
[5]0f35b798 8b35 4770 Abf4 3d1bc1caf1951 fact
ctx:claims/beam/0f35b798-8b35-4770-abf4-3d1bc1caf195
- full textbeam-chunk
  text/plain1 KBdoc:beam/0f35b798-8b35-4770-abf4-3d1bc1caf195
  Show excerpt
  [Turn 1977] Assistant: To improve the efficiency of your vector similarity search using FAISS, you can leverage more advanced indexing techniques that reduce the computational complexity compared to the brute-force approach used by `IndexFl…
[6]401284ac 4b49 4678 A3e2 Aa44c5ceacbb1 fact
ctx:claims/beam/401284ac-4b49-4678-a3e2-aa44c5ceacbb
- full textbeam-chunk
  text/plain1 KBdoc:beam/401284ac-4b49-4678-a3e2-aa44c5ceacbb
  Show excerpt
  print(f"Adjusted nprobe search time: {end_time - start_time:.2f} seconds") ``` By systematically adjusting these parameters, you can find the optimal configuration that balances search speed and accuracy for your application. [Turn 1978] …
[7]0e56e8f7 6bb5 47d4 Bd16 A0b896835d012 facts
ctx:claims/beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
- full textbeam-chunk
  text/plain1 KBdoc:beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
  Show excerpt
  matrix.loc['Faiss 1.7.3', 'search_time'] = 200 matrix.loc['Annoy 1.18.0', 'search_time'] = 250 matrix.loc['Hnswlib 0.9.2', 'search_time'] = 220 matrix.loc['Qdrant 0.8.1', 'search_time'] = 190 matrix.loc['Weaviate 1.14.0', 'search_time'] = 2…
[8]B5dd457b 4a88 464d 9e56 Df15d73163261 fact
ctx:claims/beam/b5dd457b-4a88-464d-9e56-df15d7316326
[9]30cfcb2d 27af 4962 B51a 166d7c86b3a41 fact
ctx:claims/beam/30cfcb2d-27af-4962-b51a-166d7c86b3a4
[10]8c2a3b82 Efd0 4f8b Ac35 4f5154e36e3a1 fact
ctx:claims/beam/8c2a3b82-efd0-4f8b-ac35-4f5154e36e3a
- full textbeam-chunk
  text/plain1 KBdoc:beam/8c2a3b82-efd0-4f8b-ac35-4f5154e36e3a
  Show excerpt
  Approximate nearest neighbor search methods can significantly reduce search time while maintaining reasonable accuracy. One popular choice is the `IndexIVFFlat` index, which combines inverted file indexing with flat indexing. ### 2. Optimi…
[11]Af536fe5 Aae4 407e Ad16 72341fd39f7f1 fact
ctx:claims/beam/af536fe5-aae4-407e-ad16-72341fd39f7f
[12]5b630b30 Be7c 4e71 9257 76d31088943e1 fact
ctx:claims/beam/5b630b30-be7c-4e71-9257-76d31088943e
- full textbeam-chunk
  text/plain1 KBdoc:beam/5b630b30-be7c-4e71-9257-76d31088943e
  Show excerpt
  index = faiss.IndexIVFPQ(quantizer, 128, nlist, m, nbits) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Set the number of probes index.nprobe = nprobe # Search for the nearest neighbors D, I = in…
[13]281cbbcd 971c 4f22 9941 258f26a50c162 facts
ctx:claims/beam/281cbbcd-971c-4f22-9941-258f26a50c16
- full textbeam-chunk
  text/plain1 KBdoc:beam/281cbbcd-971c-4f22-9941-258f26a50c16
  Show excerpt
  - Test different configurations of `nlist`, `nprobe`, and the number of threads to find the optimal settings for your use case. ### Example Code Here's an example of how you can use `IndexIVFFlat` with multi-threading and precompute table…
[14]A02cf99c 1e1e 40c4 8dae 5d9c0cadac182 facts
ctx:claims/beam/a02cf99c-1e1e-40c4-8dae-5d9c0cadac18
- full textbeam-chunk
  text/plain1 KBdoc:beam/a02cf99c-1e1e-40c4-8dae-5d9c0cadac18
  Show excerpt
  5. **Save the Index**: - We save the index to disk. We wrap this in a try-except block to handle any errors. 6. **Load the Index**: - We load the index from disk. We wrap this in a try-except block to handle any errors. 7. **Generat…
[15]954ed438 D3a7 48b9 Aa5b 485032720bf22 facts
ctx:claims/beam/954ed438-d3a7-48b9-aa5b-485032720bf2
[16]56ee2108 Aa51 4d60 A5b9 7c895e8b18ef1 fact
ctx:claims/beam/56ee2108-aa51-4d60-a5b9-7c895e8b18ef
- full textbeam-chunk
  text/plain1 KBdoc:beam/56ee2108-aa51-4d60-a5b9-7c895e8b18ef
  Show excerpt
  - Use load balancers to distribute the load between sparse and dense query processors. - Consider using container orchestration tools like Kubernetes to manage and scale your services. 4. **Health Checks and Monitoring:** - Implem…
[17]Cf0ed255 8ae0 4772 Bb7f 346329f562491 fact
ctx:claims/beam/cf0ed255-8ae0-4772-bb7f-346329f56249
- full textbeam-chunk
  text/plain1 KBdoc:beam/cf0ed255-8ae0-4772-bb7f-346329f56249
  Show excerpt
  [Turn 7203] Assistant: Integrating vector search with approximate nearest neighbors (ANN) for a hybrid retrieval prototype can significantly enhance the performance and scalability of your search functionality. Here are some key strategies …
[18]88bd05bd F58b 4516 Adae Bf469048d9801 fact
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 times

Rdf:typein disputerdf:type

Inbound mentions (36)

affectsAffects(8)

balancesBalances(2)

inverseOfInverse of(2)

balanceBalance(1)

benefitBenefit(1)

betweenBetween(1)

contrastsWithContrasts With(1)

enablesEnables(1)

enablesTradeOffEnables Trade Off(1)

ex:affectsEx:affects(1)

existsBetweenExists Between(1)

factor2Factor2(1)

hasEffectOnHas Effect on(1)

improvesImproves(1)

includesIncludes(1)

inverseEffectOnInverse Effect on(1)

involvesInvolves(1)

mayImpactMay Impact(1)

mentionedMentioned(1)

optimizationTargetOptimization Target(1)

optimizesOptimizes(1)

prioritizesPrioritizes(1)

providesProvides(1)

purposePurpose(1)

relatesRelates(1)

resultsInResults in(1)

tradeOffWithTrade Off With(1)

Other facts (6)

Timeline

References (18)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`affects`Affects(8)

`balances`Balances(2)

`inverseOf`Inverse of(2)

`balance`Balance(1)

`benefit`Benefit(1)

`between`Between(1)

`contrastsWith`Contrasts With(1)

`enables`Enables(1)

`enablesTradeOff`Enables Trade Off(1)

`ex:affects`Ex:affects(1)

`existsBetween`Exists Between(1)

`factor2`Factor2(1)

`hasEffectOn`Has Effect on(1)

`improves`Improves(1)

`includes`Includes(1)

`inverseEffectOn`Inverse Effect on(1)

`involves`Involves(1)

`mayImpact`May Impact(1)

`mentioned`Mentioned(1)

`optimizationTarget`Optimization Target(1)

`optimizes`Optimizes(1)

`prioritizes`Prioritizes(1)

`provides`Provides(1)

`purpose`Purpose(1)

`relates`Relates(1)

`resultsIn`Results in(1)

`tradeOffWith`Trade Off With(1)