Verified75% confidenceFactExact time
In RAG implementations, text is converted into high-dimensional vectors by embedding models and stored in vector databases such as Pinecone, Weaviate, and Milvus; at query time, the closest segments are found via cosine similarity.
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8/31/2026
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VerifiedRAG通过将文档切分为语义块后经嵌入模型转化为高维向量存入向量数据库，查询时通过近似最近邻（ANN）搜索找到语义最相近的文档片段75% similarVerifiedVector databases (such as Pinecone, Weaviate, Milvus, etc.) convert text into high-dimensional vectors via embedding models and use cosine similarity or Euclidean distance for approximate nearest neighbor (ANN) search.75% similarVerifiedThe RAG architecture is divided into a retrieval stage and a generation stage; the retrieval stage uses vector databases (such as Chroma, Pinecone, and Milvus) to recall relevant text segments via cosine similarity.73% similar
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