Expired80% confidenceFactExact time
Embedding models (such as OpenAI's text-embedding-ada-002 or the open-source BGE series) convert text into high-dimensional vectors, typically 768 or 1536 dimensions.
11
Sources
80%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Related Claims
UnverifiedVector databases use embedding models (such as OpenAI's text-embedding-3-large or the open-source BGE-M3) to convert text into floating-point vectors of hundreds to thousands of dimensions, where vector distance corresponds to semantic similarity.77% similarVerifiedOpenAI's text-embedding-3-small model converts text into a floating-point array of 1,536 dimensions77% similarUnverified嵌入模型可将文本压缩为1536维或1024维的浮点向量，OpenAI的text-embedding-ada-002和Cohere的embed-multilingual是常见示例77% similar
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