Faiss

as donto believed on

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

Faiss has 43 facts recorded in Dontopedia across 10 references, with 5 live disagreements.

43 facts·23 predicates·10 sources·5 in dispute

Mostly:rdf:type(10), f1 score lower than(3), requires(2)

Rdf:typein dispute`rdf:type`

Software Library^[1]all time · 924a6db5 B2b0 42d4 9e5c Bd5a7a159a3a
Vector Database^[2]sourceall time · A69de95e 31c3 4093 B05b Cb7f043a2ae1
Library^[3]sourceall time · 3827376e 4bbb 46c4 Bfcf F6a1df85aa1b
Vector Database^[4]all time · 9f797393 50e3 41f0 A90a Ffaea027f129
Retrieval Engine^[5]all time · 475e93cf 7217 4357 9d01 D4dc6e10f13a
Retrieval System^[6]all time · D26a5287 Fb4f 4619 B610 Ba0ca857b51f
Information Retrieval System^[7]all time · 63063c97 1ded 45a2 9117 C21c3bcc4f66
Vector Database^[8]all time · Af788904 68c3 46da Af19 38caaa62c0ca
Python Library^[9]all time · C024e566 7bde 4344 Ad2d Cef3f5639007
Software Library^[10]all time · 88bd05bd F58b 4516 Adae Bf469048d980

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.

`comparedWith`Compared With(2)

Pineconeex:Pinecone
Pineconeex:Pinecone

`f1ScoreExceeds`F1 Score Exceeds(2)

Hnswlibex:Hnswlib
Sparse Retrievalex:Sparse-Retrieval

`mentions`Mentions(2)

Assistantex:assistant
Assistantex:assistant

`queryLatencyHigherThan`Query Latency Higher Than(2)

Dense Passage Retrieverex:Dense-Passage-Retriever
Dprex:DPR

`comparesEntities`Compares Entities(1)

Comparison Sectionex:comparison-section

`containsMember`Contains Member(1)

Enginesex:engines

`exampleLibrariesInclude`Example Libraries Include(1)

Library Comparisonex:library-comparison

`hasEngines`Has Engines(1)

Comparison Matrixex:comparison-matrix

`hasLibrary`Has Library(1)

Code Snippetex:code-snippet

`hasMember`Has Member(1)

Three Databasesex:three-databases

`indexingTimeHigherThan`Indexing Time Higher Than(1)

Dense Passage Retrieverex:Dense-Passage-Retriever

`indexingTimeLowerThan`Indexing Time Lower Than(1)

Sparse Retrievalex:Sparse-Retrieval

`isMeasuredFor`Is Measured for(1)

Recallex:recall

`memoryUsageHigherThan`Memory Usage Higher Than(1)

Dense Passage Retrieverex:Dense-Passage-Retriever

`memoryUsageLowerThan`Memory Usage Lower Than(1)

Sparse Retrievalex:Sparse-Retrieval

`queryLatencyLowerThan`Query Latency Lower Than(1)

Sparse Retrievalex:Sparse-Retrieval

`usesLibrary`Uses Library(1)

Codeex:code

Other facts (26)

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.

26 facts

Predicate	Value	Ref
F1 Score Lower Than	Dpr	[6]
F1 Score Lower Than	Qdrant	[6]
F1 Score Lower Than	Hnswlib	[6]
Requires	Flat Index Configuration	[4]
Requires	Num Py	[9]
Query Latency Lower Than	Dpr	[6]
Query Latency Lower Than	Dense Passage Retriever	[6]
Manufacturer	Meta	[1]
Mentioned in	Comparison Section	[2]
Not Detailed in	Key Differences Highlighted	[2]
Lacks Detailed Comparison	true	[2]
Has Initialization Method	Flat Index	[4]
Compared With	Milvus	[4]
Has Recall	0.6	[5]
Has Lowest Recall	0.6	[5]
F1 Score	0.62	[6]
Query Latency	220	[6]
Indexing Time	320	[6]
Memory Usage	520	[6]
Indexing Time Higher Than	Sparse Retrieval	[6]
Memory Usage Higher Than	Sparse Retrieval	[6]
Has Community Support	0.8	[7]
Has Cost	110	[7]
Has Moderate Performance	true	[7]
May Offer Other Advantages	true	[7]
Supports	Multi Threading	[10]

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.

43 undated

—

typebeam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a

ex:software-library

—

manufacturerbeam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a

ex:Meta

—

typebeam/a69de95e-31c3-4093-b05b-cb7f043a2ae1

ex:VectorDatabase

—

labelbeam/a69de95e-31c3-4093-b05b-cb7f043a2ae1

Faiss

—

mentionedInbeam/a69de95e-31c3-4093-b05b-cb7f043a2ae1

ex:comparison-section

—

notDetailedInbeam/a69de95e-31c3-4093-b05b-cb7f043a2ae1

ex:Key-Differences-Highlighted

—

lacksDetailedComparisonbeam/a69de95e-31c3-4093-b05b-cb7f043a2ae1

true

—

typebeam/3827376e-4bbb-46c4-bfcf-f6a1df85aa1b

ex:Library

—

hasInitializationMethodbeam/9f797393-50e3-41f0-a90a-ffaea027f129

ex:flat_index

—

typebeam/9f797393-50e3-41f0-a90a-ffaea027f129

ex:VectorDatabase

—

labelbeam/9f797393-50e3-41f0-a90a-ffaea027f129

Faiss

—

comparedWithbeam/9f797393-50e3-41f0-a90a-ffaea027f129

ex:Milvus

—

requiresbeam/9f797393-50e3-41f0-a90a-ffaea027f129

ex:flat_index_configuration

—

typebeam/475e93cf-7217-4357-9d01-d4dc6e10f13a

ex:RetrievalEngine

—

hasRecallbeam/475e93cf-7217-4357-9d01-d4dc6e10f13a

0.6

—

labelbeam/475e93cf-7217-4357-9d01-d4dc6e10f13a

Faiss

—

hasLowestRecallbeam/475e93cf-7217-4357-9d01-d4dc6e10f13a

0.6

—

typebeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:RetrievalSystem

—

labelbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

Faiss

—

f1_scorebeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

0.62

—

query_latencybeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

220

—

indexing_timebeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

320

—

memory_usagebeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

520

—

f1ScoreLowerThanbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:DPR

—

f1ScoreLowerThanbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:Qdrant

—

f1ScoreLowerThanbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:Hnswlib

—

queryLatencyLowerThanbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:DPR

—

queryLatencyLowerThanbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:Dense-Passage-Retriever

—

indexingTimeHigherThanbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:Sparse-Retrieval

—

memoryUsageHigherThanbeam/d26a5287-fb4f-4619-b610-ba0ca857b51f

ex:Sparse-Retrieval

—

hasCommunitySupportbeam/63063c97-1ded-45a2-9117-c21c3bcc4f66

0.8

—

hasCostbeam/63063c97-1ded-45a2-9117-c21c3bcc4f66

110

—

hasModeratePerformancebeam/63063c97-1ded-45a2-9117-c21c3bcc4f66

true

—

mayOfferOtherAdvantagesbeam/63063c97-1ded-45a2-9117-c21c3bcc4f66

true

—

typebeam/63063c97-1ded-45a2-9117-c21c3bcc4f66

ex:InformationRetrievalSystem

—

labelbeam/63063c97-1ded-45a2-9117-c21c3bcc4f66

Faiss

—

typebeam/af788904-68c3-46da-af19-38caaa62c0ca

ex:VectorDatabase

—

labelbeam/af788904-68c3-46da-af19-38caaa62c0ca

Faiss

—

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

ex:PythonLibrary

—

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

Faiss library for dense vector similarity search

—

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

ex:NumPy

—

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

ex:SoftwareLibrary

—

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

ex:multi-threading

References (10)

10 references

[1]924a6db5 B2b0 42d4 9e5c Bd5a7a159a3a2 facts
ctx:claims/beam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a
- full textbeam-chunk
  text/plain1 KBdoc:beam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a
  Show excerpt
  6. **Build Index**: Use Faiss to build an index of the document vectors. 7. **Search and Retrieve**: Encode the query into a vector, normalize it, and search the index to find the most similar documents based on cosine similarity. ### Conc…
[2]A69de95e 31c3 4093 B05b Cb7f043a2ae15 facts
ctx:claims/beam/a69de95e-31c3-4093-b05b-cb7f043a2ae1
- full textbeam-chunk
  text/plain979 Bdoc:beam/a69de95e-31c3-4093-b05b-cb7f043a2ae1
  Show excerpt
  - **Ease of Use**: Subjective evaluation based on documentation and API simplicity. - **Cost**: Depends on the pricing model of the library. 3. **Comparison**: - Compare the metrics for Pinecone, Faiss, and Milvus. ### Key Differ…
[3]3827376e 4bbb 46c4 Bfcf F6a1df85aa1b1 fact
ctx:claims/beam/3827376e-4bbb-46c4-bfcf-f6a1df85aa1b
- full textbeam-chunk
  text/plain1 KBdoc:beam/3827376e-4bbb-46c4-bfcf-f6a1df85aa1b
  Show excerpt
  evaluator = VectorDBEvaluator(library) search_time = evaluator.evaluate() print(search_time) ``` I'm using a simple evaluation metric to compare libraries, but I'm not sure if this is the best approach. Can you review my code and suggest im…
[4]9f797393 50e3 41f0 A90a Ffaea027f1295 facts
ctx:claims/beam/9f797393-50e3-41f0-a90a-ffaea027f129
- full textbeam-chunk
  text/plain1 KBdoc:beam/9f797393-50e3-41f0-a90a-ffaea027f129
  Show excerpt
  'storage_efficiency': storage_efficiency, 'scalability': scalability, 'ease_of_use': ease_of_use, 'cost': cost } for library, metrics in results.items(): print(f"Library: {library}") print(f"Sear…
[5]475e93cf 7217 4357 9d01 D4dc6e10f13a4 facts
ctx:claims/beam/475e93cf-7217-4357-9d01-d4dc6e10f13a
- full textbeam-chunk
  text/plain1 KBdoc:beam/475e93cf-7217-4357-9d01-d4dc6e10f13a
  Show excerpt
  This enhanced report provides a more comprehensive analysis and helps you make a more informed decision about which vector database to use for your RAG system. [Turn 2210] User: I'm trying to evaluate the performance of different sparse re…
[6]D26a5287 Fb4f 4619 B610 Ba0ca857b51f13 facts
ctx:claims/beam/d26a5287-fb4f-4619-b610-ba0ca857b51f
- full textbeam-chunk
  text/plain1 KBdoc:beam/d26a5287-fb4f-4619-b610-ba0ca857b51f
  Show excerpt
  matrix.loc['Dense Passage Retriever', 'f1_score'] = .72 matrix.loc['Sparse Retrieval', 'f1_score'] = 0.92 matrix.loc['Faiss', 'f1_score'] = 0.62 matrix.loc['Hnswlib', 'f1_score'] = 0.82 matrix.loc['Qdrant', 'f1_score'] = 0.72 matrix.loc['D…
[7]63063c97 1ded 45a2 9117 C21c3bcc4f666 facts
ctx:claims/beam/63063c97-1ded-45a2-9117-c21c3bcc4f66
- full textbeam-chunk
  text/plain1 KBdoc:beam/63063c97-1ded-45a2-9117-c21c3bcc4f66
  Show excerpt
  matrix.loc['Dense Passage Retriever', 'community_support'] = 0.85 matrix.loc['Sparse Retrieval', 'community_support'] = 0.95 matrix.loc['Faiss', 'community_support'] = 0.8 matrix.loc['Hnswlib', 'community_support'] = 0.88 matrix.loc['Qdrant…
[8]Af788904 68c3 46da Af19 38caaa62c0ca2 facts
ctx:claims/beam/af788904-68c3-46da-af19-38caaa62c0ca
[9]C024e566 7bde 4344 Ad2d Cef3f56390073 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…
[10]88bd05bd F58b 4516 Adae Bf469048d9802 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…

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.

Faiss

Rdf:typein disputerdf:type

Inbound mentions (21)

comparedWithCompared With(2)

f1ScoreExceedsF1 Score Exceeds(2)

mentionsMentions(2)

queryLatencyHigherThanQuery Latency Higher Than(2)

comparesEntitiesCompares Entities(1)

containsMemberContains Member(1)

exampleLibrariesIncludeExample Libraries Include(1)

hasEnginesHas Engines(1)

hasLibraryHas Library(1)

hasMemberHas Member(1)

indexingTimeHigherThanIndexing Time Higher Than(1)

indexingTimeLowerThanIndexing Time Lower Than(1)

isMeasuredForIs Measured for(1)

memoryUsageHigherThanMemory Usage Higher Than(1)

memoryUsageLowerThanMemory Usage Lower Than(1)

queryLatencyLowerThanQuery Latency Lower Than(1)

usesLibraryUses Library(1)

Other facts (26)

Timeline

References (10)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`comparedWith`Compared With(2)

`f1ScoreExceeds`F1 Score Exceeds(2)

`mentions`Mentions(2)

`queryLatencyHigherThan`Query Latency Higher Than(2)

`comparesEntities`Compares Entities(1)

`containsMember`Contains Member(1)

`exampleLibrariesInclude`Example Libraries Include(1)

`hasEngines`Has Engines(1)

`hasLibrary`Has Library(1)

`hasMember`Has Member(1)

`indexingTimeHigherThan`Indexing Time Higher Than(1)

`indexingTimeLowerThan`Indexing Time Lower Than(1)

`isMeasuredFor`Is Measured for(1)

`memoryUsageHigherThan`Memory Usage Higher Than(1)

`memoryUsageLowerThan`Memory Usage Lower Than(1)

`queryLatencyLowerThan`Query Latency Lower Than(1)

`usesLibrary`Uses Library(1)