Faiss Query
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Faiss Query has 5 facts recorded in Dontopedia across 1 reference.
Mostly:performs conversion(1), constructed using(1), data format(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedPerforms ConversionperformsConversion
- float32[1]sourceall time · Cd9b13af 512f 4087 B34b 2124116b3091
Constructed UsingconstructedUsing
- List Comprehension[1]sourceall time · Cd9b13af 512f 4087 B34b 2124116b3091
Data FormatdataFormat
- float32[1]sourceall time · Cd9b13af 512f 4087 B34b 2124116b3091
Constructed FromconstructedFrom
Rdf:typerdf:type
- Faiss Query Object[1]sourceall time · Cd9b13af 512f 4087 B34b 2124116b3091
Inbound mentions (2)
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.
createsCreates(1)
- Search Vectors Function
ex:search-vectors-function
usesUses(1)
- Search Vectors Function
ex:search-vectors-function
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 (1)
- custom
ctx:claims/beam/cd9b13af-512f-4087-b34b-2124116b3091- full textbeam-chunktext/plain1 KB
doc:beam/cd9b13af-512f-4087-b34b-2124116b3091Show excerpt
# Define the vector search function. def search_vectors(tokens): # Create a FAISS query. query = np.array([vector for vector in tokens]).astype('float32') # Search for similar vectors. distances, indices = index.search(quer…
See also
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