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awsopensearch add normalize for cosine similarity when indexing data #614

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awsopensearch add normalize for cosine similarity when indexing data #614

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awsopensearch add normalize for cosine similarity, solved isuue of re…
norrishuang Oct 15, 2025
7328bdb
fix format issue
norrishuang Oct 15, 2025
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6 changes: 6 additions & 0 deletions vectordb_bench/backend/clients/aws_opensearch/aws_opensearch.py
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Expand Up @@ -64,6 +64,12 @@ def __init__(
self._update_ef_search_before_search(client)
self._load_graphs_to_memory(client)

def need_normalize_cosine(self) -> bool:
if self.case_config.metric_type.upper() == "COSINE":
log.info("cosine dataset need normalize.")
return True
return False
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The need_normalize_cosine function is designed for databases that do NOT natively support cosine similarity metrics. When testing cosine-based datasets, this function normalizes all training and query vectors before tests, to make the metric equivalent to L2 (Euclidean) or IP (Inner Product), ensuring accurate recall calculations. This is a workaround solution when cosine similarity is not directly supported.

For databases that already support cosine similarity, we do not recommend enabling this feature. The goal is to maintain consistent benchmarking across different databases and ensure fair testing conditions.


def _create_index(self, client: OpenSearch) -> None:
ef_search_value = self.case_config.ef_search
log.info(f"Creating index with ef_search: {ef_search_value}")
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