Expired60% confidenceFactExact time
Dify's RAG module splits private documents into text chunks during the indexing stage, converts them into high-dimensional vectors via an embedding model, and stores them in a vector database (such as Milvus, Weaviate, or pgvector).
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Dify + MCP in Practice: Build a Multi-Function AI Agent Workflow in Four Steps
bilibili大模型教程6/25/2026
Related Claims
UnverifiedA typical RAG system uses a vector database (such as Pinecone, Milvus, or Weaviate) to store document embeddings, paired with an embedding model (such as OpenAI's text-embedding series) for semantic matching.81% similarUnverifiedIn 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.80% similarUnverifiedDuring the RAG indexing phase, documents are chunked and converted into vectors via an embedding model, then stored in vector databases such as Faiss, Pinecone, and Weaviate.76% similarUnverifiedVector databases, by converting text into high-dimensional vectors and building approximate nearest neighbor (ANN) indexes, form the core infrastructure of the RAG (retrieval-augmented generation) architecture.76% similarUnverifiedRAG (Retrieval-Augmented Generation) systems can be built on top of vector databases such as Milvus, Weaviate, and Qdrant to supplement large models with private domain knowledge.75% similar
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