Faiss Branch
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-05.)
Faiss Branch has 11 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Mostly:accesses attribute(2), creates variable(1), calls method(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound 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.
hasConditionalBranchHas Conditional Branch(1)
- Evaluate Storage Efficiency
ex:evaluate-storage-efficiency
Other facts (11)
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 |
|---|---|---|
| Accesses Attribute | Self Index Ntotal | [1] |
| Accesses Attribute | Self Index D | [1] |
| Creates Variable | Vectors | [1] |
| Calls Method | Self Index Add | [1] |
| Returns | Storage Calculation | [1] |
| Calls Method on Attribute | Self.index Add | [1] |
| Uses Numpy Array | true | [1] |
| Converts to | Numpy Array | [1] |
| Calculates Bytes | Total Bytes | [1] |
| Uses Faiss Library | true | [2] |
| Calls Index Add | Index Add | [2] |
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 (2)
ctx:claims/beam/6deee081-c9a8-4ef0-b743-a35ef9816a7d- full textbeam-chunktext/plain1 KB
doc:beam/6deee081-c9a8-4ef0-b743-a35ef9816a7dShow excerpt
vectors = np.random.rand(num_vectors, 128).astype('float32').tolist() ids = [str(i) for i in range(num_vectors)] start_time = time.time() self.collection.insert(vectors, ids) end_t…
ctx:claims/beam/7da0d616-0de7-4880-bacb-4a0a15c5a9c9- full textbeam-chunktext/plain1 KB
doc:beam/7da0d616-0de7-4880-bacb-4a0a15c5a9c9Show excerpt
vectors = np.random.rand(num_vectors, 128).astype('float32').tolist() ids = [str(i) for i in range(num_vectors)] self.collection.insert(vectors, ids) query_vector = np.random.rand(1, 128).asty…
See also
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