Distances Indices Tuple
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-07.)
Distances Indices Tuple is tuple of distances and indices arrays.
Mostly:rdf:type(4), contains(2), consists of(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound 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.
returnsReturns(5)
- Index Search Method
ex:index-search-method - Index Search Operation
ex:index-search-operation - Return Statement Success
ex:return-statement-success - Search Similar Vectors
ex:search-similar-vectors - Search Similar Vectors Function
ex:search-similar-vectors-function
hasReturnValueHas Return Value(1)
- Refine Indexing Logic
ex:refine-indexing-logic
returnsValueReturns Value(1)
- Refine Indexing Logic Function
ex:refine-indexing-logic-function
returnTypeReturn Type(1)
- Refine Function
ex:refine-function
Other facts (9)
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Timeline
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References (4)
ctx:claims/beam/9080e26c-2d73-4ed8-801c-d290a10ff5c0ctx:claims/beam/4acac4d0-910b-4fa1-96b2-afff0416f947- full textbeam-chunktext/plain1 KB
doc:beam/4acac4d0-910b-4fa1-96b2-afff0416f947Show excerpt
# Normalize the vectors for cosine similarity faiss.normalize_L2(vectors) # Create an HNSW index M = 16 # Number of links per node efConstruction = 200 # Number of neighbors to consider during construction efSearch = 64 # Number of neig…
ctx:claims/beam/632c2d87-a215-40e6-b5e2-7665e190379f- full textbeam-chunktext/plain1 KB
doc:beam/632c2d87-a215-40e6-b5e2-7665e190379fShow excerpt
This example demonstrates how to use FAISS for efficient similarity search on a large dataset of document embeddings. By leveraging FAISS, you can achieve significant improvements in both memory usage and search performance. [Turn 4860] Us…
ctx:claims/beam/c93f21b2-5d63-4700-acd2-ac16decca67b
See also
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