Dontopedia

get_embeddings

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

get_embeddings has 9 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

9 facts·5 predicates·3 sources·1 in dispute

Mostly:rdf:type(3), called by(1), is called by(1)

Maturity scale raw canonical shape-checked rule-derived certified

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.

callsInSequenceCalls in Sequence(1)

computedFromComputed From(1)

Other facts (7)

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.

7 facts
PredicateValueRef
Rdf:typeFunction[1]
Rdf:typeFunction[2]
Rdf:typeFunction[3]
Called byHybrid Ranking Function[2]
Is Called byHybrid Ranking Function[2]
Has ParameterSegments[3]
ReturnsEmbeddings List[3]

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.

typebeam/b4174542-e9f5-41d0-809f-ec6511b667bb
ex:Function
labelbeam/b4174542-e9f5-41d0-809f-ec6511b667bb
get_embeddings
typebeam/7780940c-0855-4439-b672-6739b7459e87
ex:Function
calledBybeam/7780940c-0855-4439-b672-6739b7459e87
ex:hybrid-ranking-function
isCalledBybeam/7780940c-0855-4439-b672-6739b7459e87
ex:hybrid-ranking-function
typebeam/1f03a14c-2fd6-4e99-ad8a-4f5c5bc5218d
ex:Function
labelbeam/1f03a14c-2fd6-4e99-ad8a-4f5c5bc5218d
get_embeddings
hasParameterbeam/1f03a14c-2fd6-4e99-ad8a-4f5c5bc5218d
ex:segments
returnsbeam/1f03a14c-2fd6-4e99-ad8a-4f5c5bc5218d
ex:embeddings-list

References (3)

3 references
  1. ctx:claims/beam/b4174542-e9f5-41d0-809f-ec6511b667bb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b4174542-e9f5-41d0-809f-ec6511b667bb
      Show excerpt
      dense_scores = get_embeddings([query]).dot(embeddings.T) combined_scores = 0.5 * sparse_scores + 0.5 * dense_scores return combined_scores # Example usage documents = ["This is a sample document.", "Este es un documento de mues
  2. ctx:claims/beam/7780940c-0855-4439-b672-6739b7459e87
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7780940c-0855-4439-b672-6739b7459e87
      Show excerpt
      url = 'https://api-free.deepl.com/v2/translate' data = { 'auth_key': api_key, 'text': text, 'target_lang': target_lang } response = requests.post(url, data=data) return response.js
  3. ctx:claims/beam/1f03a14c-2fd6-4e99-ad8a-4f5c5bc5218d

See also

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