FAISS index initialization
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
FAISS index initialization has 6 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Mostly:rdf:type(2), uses(1), assumes dimension(1)
Maturity scale
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| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Initialization Step | [1] |
| Rdf:type | Index Configuration | [2] |
| Uses | Use Gpu Variable | [1] |
| Assumes Dimension | 128 | [2] |
| Has Comment | Assuming 128-dimensional vectors | [2] |
Timeline
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References (2)
ctx:claims/beam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12- full textbeam-chunktext/plain1 KB
doc:beam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12Show excerpt
use_gpu = False # Set to True if you want to use GPU acceleration index = initialize_faiss_index(dim, use_gpu) # Generate random document embeddings and a query embedding document_embeddings = np.random.rand(200000, dim).astype('float32')…
ctx:claims/beam/149dec1b-3c49-4cff-a826-bc9175d778ec- full textbeam-chunktext/plain1 KB
doc:beam/149dec1b-3c49-4cff-a826-bc9175d778ecShow excerpt
[Turn 4940] User: I'm trying to assess Weaviate 1.20.0 for its search time on 300K vectors, but I'm having trouble understanding how it compares to other alternatives like FAISS 1.7.4, which I've also been testing for its 180ms search time …
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