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Distances and Indices

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Distances and Indices has 12 facts recorded in Dontopedia across 6 references, with 3 live disagreements.

12 facts·5 predicates·6 sources·3 in dispute

Mostly:rdf:type(6), rdfs:label(2), contains(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Rdfs:labelin disputerdfs:label

  • distances and indices tuple[3]all time · 9080e26c 2d73 4ed8 801c D290a10ff5c0
  • Distances and Indices[4]all time · 954ed438 D3a7 48b9 Aa5b 485032720bf2

Containsin disputecontains

  • D Array[1]sourceall time · F9316ee6 847e 4064 80dd 6097ca97e0d6
  • I Array[1]sourceall time · F9316ee6 847e 4064 80dd 6097ca97e0d6

Describesdescribes

Is Returned byisReturnedBy

Inbound mentions (16)

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(9)

returnsMultipleValuesReturns Multiple Values(2)

consistsOfConsists of(1)

displaysDisplays(1)

returnsJSONReturns Json(1)

returnsMultipleReturns Multiple(1)

returnsTupleReturns Tuple(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.

12 undated
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containsbeam/f9316ee6-847e-4064-80dd-6097ca97e0d6
ex:D-array
—
containsbeam/f9316ee6-847e-4064-80dd-6097ca97e0d6
ex:I-array
—
describesbeam/dec68f27-fa07-4dd3-9e72-4e86e758bea4
ex:10-nearest-neighbors
—
isReturnedBybeam/dec68f27-fa07-4dd3-9e72-4e86e758bea4
ex:search-method
—
labelbeam/9080e26c-2d73-4ed8-801c-d290a10ff5c0
distances and indices tuple
—
labelbeam/954ed438-d3a7-48b9-aa5b-485032720bf2
Distances and Indices
—
typebeam/9080e26c-2d73-4ed8-801c-d290a10ff5c0
ex:ResultTuple
—
typebeam/dec68f27-fa07-4dd3-9e72-4e86e758bea4
ex:ReturnData
—
typebeam/02a7ad2c-cb05-4e89-b0b4-a0cfec772912
ex:SearchOutput
—
typebeam/f9316ee6-847e-4064-80dd-6097ca97e0d6
ex:SearchResult
—
typebeam/1ff09d58-969c-42dc-bcbe-4edd4781d196
ex:SearchResults
—
typebeam/954ed438-d3a7-48b9-aa5b-485032720bf2
ex:SearchResults

References (6)

6 references
  1. [1]beam-chunk3 facts
    customctx:claims/beam/f9316ee6-847e-4064-80dd-6097ca97e0d6
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f9316ee6-847e-4064-80dd-6097ca97e0d6
      Show excerpt
      - **Logging**: Use structured logging (e.g., JSON) and forward logs to a centralized logging system like ELK Stack or Grafana Cloud. ### Step 3: Implementation Details #### Load Balancer Configuration - **Nginx Example**: ```nginx h…
  2. [2]beam-chunk3 facts
    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…
  3. [3]9080e26c 2d73 4ed8 801c D290a10ff5c02 facts
    customctx:claims/beam/9080e26c-2d73-4ed8-801c-d290a10ff5c0
  4. [4]954ed438 D3a7 48b9 Aa5b 485032720bf22 facts
    customctx:claims/beam/954ed438-d3a7-48b9-aa5b-485032720bf2
  5. [5]beam-chunk1 fact
    customctx:claims/beam/02a7ad2c-cb05-4e89-b0b4-a0cfec772912
    • full textbeam-chunk
      text/plain1 KBdoc:beam/02a7ad2c-cb05-4e89-b0b4-a0cfec772912
      Show excerpt
      [Turn 4754] User: I'm trying to optimize the search time for my 100K vectors using FAISS 1.7.4, but I'm seeing a search time of 180ms, which seems a bit high. Can you help me improve this? I've heard that indexing tools can make a big diffe…
  6. [6]beam-chunk1 fact
    customctx:claims/beam/1ff09d58-969c-42dc-bcbe-4edd4781d196
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1ff09d58-969c-42dc-bcbe-4edd4781d196
      Show excerpt
      k = 1 # Number of nearest neighbors to retrieve distances, indices = index.search(query_vector.reshape(1, -1), k) print("Distances:", distances) print("Indices:", indices) ``` ### Explanation 1. **Dimensionality**: - Ensure the dimen…

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