Expired50% confidenceFactExact time
Embedding models such as OpenAI's text-embedding-ada-002, BGE, and E5 map text into points in a high-dimensional vector space so that semantically similar texts are closer together.
1
Sources
50%
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
The Complete Evolution of RAG: A Six-Generation Journey from Naive Retrieval to Multimodal
bilibili赋范课堂6/17/2026
Related Claims
UnverifiedOpenAI's text-embedding-ada-002 can map text into a high-dimensional vector space, where semantically similar texts are closer together in the vector space.78% similarVerifiedOpenAI's text-embedding-3-small model converts text into a floating-point array of 1,536 dimensions70% similarUnverifiedVector 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.70% 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70% similarUnverifiedMainstream embedding models include OpenAI's text-embedding-ada-002 and the Sentence-BERT series, with cosine similarity being the most commonly used similarity measure.69% similar
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