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Search Return

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

Search Return has 5 facts recorded in Dontopedia across 4 references.

5 facts·5 predicates·4 sources

Mostly:unpacking(1), variable count(1), returns collection(1)

Maturity scale raw canonical shape-checked rule-derived certified

Unpackingunpacking

  • true[3]sourceall time · 08b0d2a8 8bf2 4d6b A17c 63c766133348

Variable CountvariableCount

  • 2[4]sourceall time · F026078e 8f4c 49fe 81e1 C274e43d2156

Returns CollectionreturnsCollection

Returns Last K ElementsreturnsLastKElements

  • true[2]sourceall time · 3c5f5c5b 6881 4f14 9961 C13194b540b4

Uses Array SlicingusesArraySlicing

Inbound mentions (1)

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(1)

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.

5 undated
—
returnsCollectionbeam/e7d51436-3ca5-4efa-9aae-3966f2e3f857
ex:filtered-results
—
returnsLastKElementsbeam/3c5f5c5b-6881-4f14-9961-c13194b540b4
true
—
unpackingbeam/08b0d2a8-8bf2-4d6b-a17c-63c766133348
true
—
usesArraySlicingbeam/3c5f5c5b-6881-4f14-9961-c13194b540b4
ex:negative-slicing
—
variableCountbeam/f026078e-8f4c-49fe-81e1-c274e43d2156
2

References (4)

4 references
  1. [1]beam-chunk1 fact
    customctx:claims/beam/e7d51436-3ca5-4efa-9aae-3966f2e3f857
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e7d51436-3ca5-4efa-9aae-3966f2e3f857
      Show excerpt
      artifact.update(**kwargs) else: raise KeyError(f"No artifact found with ID {artifact_id}") def remove_artifact(self, artifact_id): if artifact_id in self.artifacts: del self.artifacts…
  2. [2]beam-chunk2 facts
    customctx:claims/beam/3c5f5c5b-6881-4f14-9961-c13194b540b4
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3c5f5c5b-6881-4f14-9961-c13194b540b4
      Show excerpt
      # Define the vector database class VectorDatabase: def __init__(self): self.vectors = [] def add_vector(self, vector): self.vectors.append(vector) def search(self, query_vector, top_k=10): # Calculate t…
  3. [3]beam-chunk1 fact
    customctx:claims/beam/08b0d2a8-8bf2-4d6b-a17c-63c766133348
    • full textbeam-chunk
      text/plain1 KBdoc:beam/08b0d2a8-8bf2-4d6b-a17c-63c766133348
      Show excerpt
      # Example query vector with different dimensions query_vector = np.random.rand(120) # Query vector with 120 dimensions # Pad query vector to the target dimension padded_query_vector = pad_vectors(query_vector.reshape(1, -1), dimension) #…
  4. [4]beam-chunk1 fact
    customctx:claims/beam/f026078e-8f4c-49fe-81e1-c274e43d2156
    • full textbeam-chunk
      text/plain1006 Bdoc:beam/f026078e-8f4c-49fe-81e1-c274e43d2156
      Show 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 …

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