Index.search
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Index.search has 39 facts recorded in Dontopedia across 9 references, with 6 live disagreements.
Mostly:returns(9), rdf:type(7), has argument(2)
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Index.search has 39 facts recorded in Dontopedia across 9 references, with 6 live disagreements.
Mostly:returns(9), rdf:type(7), has argument(2)
returnshasArgumentisCalledWithtakesParametersparameteralgorithmmethod_signatureparameter2parameter1stepOrderOther 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.
returnedByReturned by(4)callsCalls(3)ex:search-operationex:search_similar_vectorsex:search_vectorsassignedByAssigned by(2)ex:variable-distancesex:variable-indicesfollowedByFollowed by(1)ex:index.addinput_toInput to(1)ex:normalized_query_vectormeasuresMeasures(1)ex:time measurementusedAsParameterForUsed As Parameter for(1)ex:kusedInUsed in(1)ex:query_embeddingusesFunctionUses Function(1)ex:code-snippetThe 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.
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doc:beam/c5e65b2e-6289-4399-808e-64fe4e0eddcem = 8 # number of subquantizers index = faiss.IndexIVFPQ(faiss.MetricType.L2, d, nlist, m, 8) # Train the index index.train(embeddings) # Add the embeddings to the index index.add(embeddings) # Generate a query embedding in a different …
doc:beam/c024e566-7bde-4344-ad2d-cef3f5639007vectors = np.random.rand(100000, 128).astype('float32') # Set the number of threads for parallel processing faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create a…
doc:beam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38aM = 8 # Number of sub-quantizers nbits = 8 # Number of bits per sub-quantizer index = faiss.IndexIVFPQ(quantizer, 128, nlist, M, nbits) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Search for n…
doc:beam/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7index = faiss.IndexFlatL2(embedding_dim) # Add the document embeddings to the index index.add(document_embeddings) # Generate a random query embedding query_embedding = np.random.rand(1, embedding_dim).astype('float32') # Search the inde…
doc:beam/2543d3b9-8f0f-47ad-b540-af23d84524d6# Configure logging logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s') # Load the SpaCy model try: nlp = spacy.load("en_core_web_sm") except OSError as e: logging.error(f"Failed to load Spa…
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