astype('float32')
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
astype('float32') has 14 facts recorded in Dontopedia across 5 references, with 4 live disagreements.
Mostly:rdf:type(5), converts to(4), applied to(2)
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.
appliesConversionApplies Conversion(1)
- Document Embeddings Generation
ex:document-embeddings-generation
Other facts (12)
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 | Type Conversion | [1] |
| Rdf:type | Data Type Conversion | [2] |
| Rdf:type | Type Conversion | [3] |
| Rdf:type | Type Conversion | [4] |
| Rdf:type | Type Cast Operation | [5] |
| Converts to | Float32 | [1] |
| Converts to | float32 | [3] |
| Converts to | np.float32 | [4] |
| Converts to | Float32 Dtype | [5] |
| Applied to | Document Embeddings | [2] |
| Applied to | Query Embedding | [2] |
| Applies to | Vectors Variable | [4] |
Timeline
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References (5)
ctx:claims/beam/632c2d87-a215-40e6-b5e2-7665e190379f- full textbeam-chunktext/plain1 KB
doc:beam/632c2d87-a215-40e6-b5e2-7665e190379fShow excerpt
This example demonstrates how to use FAISS for efficient similarity search on a large dataset of document embeddings. By leveraging FAISS, you can achieve significant improvements in both memory usage and search performance. [Turn 4860] Us…
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/a8f9767f-e515-4c18-876d-5a6237129dbe- full textbeam-chunktext/plain1 KB
doc:beam/a8f9767f-e515-4c18-876d-5a6237129dbeShow excerpt
query_embedding = np.random.rand(1, 512).astype('float32') # Search the index distances, indices = index.search(query_embedding, k=10) print(distances) print(indices) ``` ->-> 4,22 [Turn 4869] Assistant: Certainly! FAISS is a powerful li…
ctx:claims/beam/d708c4e2-67ca-4cca-9507-831d3241e3aactx:claims/beam/9d96f8cb-54e9-48bd-a699-50a1796601b9
See also
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