Training and Adding
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Training and Adding has 5 facts recorded in Dontopedia across 2 references.
Mostly:uses same dataset(1), rdf:type(1), step number(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedOther facts (5)
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References (2)
ctx:claims/beam/2b210dd9-dd14-4daf-ba9f-ea7913237b0a- full textbeam-chunktext/plain1 KB
doc:beam/2b210dd9-dd14-4daf-ba9f-ea7913237b0aShow excerpt
Here's an optimized version of your code using `IndexIVFFlat` and enabling multi-threading: ```python import faiss import numpy as np # Assume we have a dataset of 100,000 vectors vectors = np.random.rand(100000, 128).astype('float32') #…
ctx:claims/beam/c7655ab4-acea-456f-a24c-7535c6e9c644- full textbeam-chunktext/plain1 KB
doc:beam/c7655ab4-acea-456f-a24c-7535c6e9c644Show excerpt
print(f"Query time: {query_time * 1000:.2f} ms") ``` By following these steps and adjusting the parameters, you should be able to achieve a query time of around 120ms for 50,000 embeddings using the FAISS library. [Turn 6452] User: hmm, w…
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