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Find Nearest Neighbors

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

Find Nearest Neighbors has 3 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

3 facts·2 predicates·2 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

  • Function[1]all time · 3c7c96d1 549b 4085 8bd9 152174bddc1f
  • Operation[2]sourceall time · Dec68f27 Fa07 4dd3 9e72 4e86e758bea4

Rdfs:labelrdfs:label

  • find the nearest neighbors[1]sourceall time · 3c7c96d1 549b 4085 8bd9 152174bddc1f

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.

functionFunction(1)

purposePurpose(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.

labelbeam/3c7c96d1-549b-4085-8bd9-152174bddc1f
find the nearest neighbors
typebeam/3c7c96d1-549b-4085-8bd9-152174bddc1f
ex:Function
typebeam/dec68f27-fa07-4dd3-9e72-4e86e758bea4
ex:Operation

References (2)

2 references
  1. [1]beam-chunk2 facts
    customctx:claims/beam/3c7c96d1-549b-4085-8bd9-152174bddc1f
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3c7c96d1-549b-4085-8bd9-152174bddc1f
      Show excerpt
      - `efConstruction`: Construction parameter. - `efSearch`: Search parameter. 3. **Multi-threading**: - `faiss.omp_set_num_threads(8)` enables multi-threading to take advantage of multiple CPU cores. 4. **Adding Vectors**: - Vec
  2. [2]beam-chunk1 fact
    customctx:claims/beam/dec68f27-fa07-4dd3-9e72-4e86e758bea4
    • full textbeam-chunk
      text/plain1 KBdoc:beam/dec68f27-fa07-4dd3-9e72-4e86e758bea4
      Show excerpt
      - We use the `search` method to find the 10 nearest neighbors to the query embedding. The method returns the distances and indices of the nearest neighbors. ### Benefits of FAISS - **Reduced Memory Usage**: FAISS can store large number

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