Unverified90% confidenceFactTime unknown
RAG管线的关键组件包括文档解析与分块、向量嵌入、向量数据库存储（Milvus、Pinecone、Weaviate等）、检索策略（混合检索、重排序）以及上下文注入与Prompt组装
1
Sources
90%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
低代码Agent平台的免费陷阱：平台锁定风险与技术自主策略
bilibili朱老师AI课堂
Related Entities
Related Claims
Partially VerifiedThe core RAG pipeline includes three main steps — document loading, text splitting, and vectorized storage — with support for vector databases such as FAISS.79% similarVerifiedRAG (Retrieval-Augmented Generation) typically comprises three core components: a vector database, a retriever (which recalls relevant document chunks based on cosine similarity), and a generator.78% similarUnverifiedBuilding a knowledge base with RAG involves three steps: data collection, text chunking, and vectorized storage.77% similarVerifiedRAG技术栈由向量数据库（如Pinecone、Weaviate、Chroma）、嵌入模型、重排序等技术构成76% similar
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