Unverified50% confidenceFactExact time
In RAG architecture, original pages are split into chunks (usually a few hundred tokens), mapped into high-dimensional vectors via embedding models (such as OpenAI's text-embedding series), stored in a vector database, and when a user asks a question the most relevant passages are recalled by cosine similarity.
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Long-term
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10/13/2026
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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79% similarVerifiedRAG addresses the limitation of large models' knowledge cutoff dates by splitting private documents into text chunks, converting them into vector representations stored in a vector database, and injecting retrieved relevant content into the Prompt.78% similarUnverifiedThe RAG architecture splits historical information into semantic chunks, converts them into vectors via an embedding model, and stores them in vector databases such as Faiss, Pinecone, and Weaviate.77% similar
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