vector search logic
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
vector search logic has 9 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(3), location(1), status(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
performsActionPerforms Action(2)
- Get Method
ex:get-method - Post Method
ex:post-method
containsPlaceholderContains Placeholder(1)
- Get Method
ex:get-method
isImplementationOfIs Implementation of(1)
- Perform Vector Search Function
ex:perform-vector-search-function
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Implementation Placeholder | [1] |
| Rdf:type | Algorithm | [2] |
| Rdf:type | Operation | [3] |
| Location | Get Method | [1] |
| Status | Unimplemented | [1] |
| Called Function | Perform Vector Search | [3] |
| Called by | Get Method | [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.
References (3)
ctx:claims/beam/fdf8898b-efa0-4bd1-8940-8157d32e6ff0- full textbeam-chunktext/plain1 KB
doc:beam/fdf8898b-efa0-4bd1-8940-8157d32e6ff0Show excerpt
# For demonstration, let's assume we have a function `perform_vector_search` results = perform_vector_search(query_vector, top_k) return jsonify(results) api.add_resource(VectorSearch, '/vector-search') ```…
ctx:claims/beam/bd212467-5fca-46eb-a028-99f3f2a293ba- full textbeam-chunktext/plain1 KB
doc:beam/bd212467-5fca-46eb-a028-99f3f2a293baShow excerpt
top_k = data.get('top_k', 10) # Perform vector search logic here results = perform_vector_search(query_vector, top_k) return jsonify(results) api.add_resource(VectorSearch, '/vector-search'…
ctx:claims/beam/a8f42853-2865-4e3c-a260-ec8d3de4712d- full textbeam-chunktext/plain935 B
doc:beam/a8f42853-2865-4e3c-a260-ec8d3de4712dShow excerpt
# Perform vector search logic here results = perform_vector_search(query_vector, top_k) return jsonify(results) def post(self): data = request.get_json() query_vector = data.…
See also
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