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Semantic chunking is a chunking strategy that groups text based on meaning rather than fixed size. Instead of splitting text after a certain number of tokens, semantic chunking tries to identify: topic boundaries, semantic transitions, and coherent conceptual units. The goal is simple: Keep semantically related information together.

Chunking is the process of splitting documents into smaller retrievable units before embedding and indexing them. In a RAG pipeline: Documents are split into chunks. Each chunk is converted into embeddings. The embeddings are stored in a vector database. User queries retrieve the most relevant chunks. The retrieved chunks are passed to the LLM as context. This means retrieval quality depends heavily on chunk quality.

At a high level, Vectorless RAG is exactly what the name suggests: A Retrieval-Augmented Generation system that avoids using vector embeddings and vector databases for retrieval.