Unverified50% confidenceFactExact time
Vectorization is a core step in the RAG pipeline, where raw documents are split into segments and an embedding model converts each text segment into high-dimensional numerical vectors that are stored in a vector database.
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10/7/2026
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VerifiedIn the offline phase of the traditional RAG process, documents are chunked and converted into vectors via an embedding model and stored in a vector database; in the online phase, similar document chunks are retrieved using an Approximate Nearest Neighbor (ANN) algorithm.78% similarVerifiedA standard RAG pipeline typically consists of seven stages: document preprocessing, text chunking, embedding vectorization, vector database storage, approximate nearest neighbor retrieval, optional reranking, and final generation.78% similarUnverifiedA typical RAG pipeline consists of five steps: text chunking, vector embedding, vector retrieval, context injection, and generation, and is often used in conjunction with vector databases such as Pinecone, Chroma, and Weaviate.76% similarVerified典型的RAG流程包括文档切片、向量化嵌入、存入向量数据库、语义检索、上下文拼接和最终生成六个步骤76% similarUnverifiedModern RAG systems widely adopt vector retrieval technology, converting text into high-dimensional vectors via embedding models and computing cosine similarity to find relevant documents.76% similar
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