Neighbor Indices
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
Neighbor Indices has 4 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
- Data Structure[2]all time · 954ed438 D3a7 48b9 Aa5b 485032720bf2
- Index Array[3]all time · F026078e 8f4c 49fe 81e1 C274e43d2156
Rdfs:labelrdfs:label
- Neighbor Indices[2]all time · 954ed438 D3a7 48b9 Aa5b 485032720bf2
Identifies LocationsidentifiesLocations
- Vector Positions[1]all time · 5b630b30 Be7c 4e71 9257 76d31088943e
Inbound mentions (6)
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.
containsContains(2)
- Indices Array
ex:indices-array - I Variable
ex:I-variable
displaysDisplays(1)
- Code Output
ex:code-output
ex:containsEx:contains(1)
- I
ex:I
representsRepresents(1)
- I
ex:I
returnsReturns(1)
- Faiss Search
ex:faiss-search
Timeline
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.
References (3)
- custom
ctx:claims/beam/5b630b30-be7c-4e71-9257-76d31088943e- full textbeam-chunktext/plain1 KB
doc:beam/5b630b30-be7c-4e71-9257-76d31088943eShow 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…
- custom
ctx:claims/beam/954ed438-d3a7-48b9-aa5b-485032720bf2 - custom
ctx:claims/beam/f026078e-8f4c-49fe-81e1-c274e43d2156- full textbeam-chunktext/plain1006 B
doc:beam/f026078e-8f4c-49fe-81e1-c274e43d2156Show 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 …
See also
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