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docs: update yellowbrick.ipynb (#30729)

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retreival -> retrieval
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Ikko Eltociear Ashimine 2025-04-09 00:56:35 +09:00 committed by GitHub
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docs/docs/integrations/vectorstores/yellowbrick.ipynb
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@ -413,7 +413,7 @@
"Yellowbrick also supports indexing using the Locality-Sensitive Hashing approach. This is an approximate nearest-neighbor search technique, and allows one to trade off similarity search time at the expense of accuracy. The index introduces two new tunable parameters:\n", "Yellowbrick also supports indexing using the Locality-Sensitive Hashing approach. This is an approximate nearest-neighbor search technique, and allows one to trade off similarity search time at the expense of accuracy. The index introduces two new tunable parameters:\n",
"\n", "\n",
"- The number of hyperplanes, which is provided as an argument to `create_lsh_index(num_hyperplanes)`. The more documents, the more hyperplanes are needed. LSH is a form of dimensionality reduction. The original embeddings are transformed into lower dimensional vectors where the number of components is the same as the number of hyperplanes.\n", "- The number of hyperplanes, which is provided as an argument to `create_lsh_index(num_hyperplanes)`. The more documents, the more hyperplanes are needed. LSH is a form of dimensionality reduction. The original embeddings are transformed into lower dimensional vectors where the number of components is the same as the number of hyperplanes.\n",
"- The Hamming distance, an integer representing the breadth of the search. Smaller Hamming distances result in faster retreival but lower accuracy.\n", "- The Hamming distance, an integer representing the breadth of the search. Smaller Hamming distances result in faster retrieval but lower accuracy.\n",
"\n", "\n",
"Here's how you can create an index on the embeddings we loaded into Yellowbrick. We'll also re-run the previous chat session, but this time the retrieval will use the index. Note that for such a small number of documents, you won't see the benefit of indexing in terms of performance." "Here's how you can create an index on the embeddings we loaded into Yellowbrick. We'll also re-run the previous chat session, but this time the retrieval will use the index. Note that for such a small number of documents, you won't see the benefit of indexing in terms of performance."
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