Embedding First, Then Chunking: Smarter RAG Retrieval with Max–Min Semantic Chunking

Introduces Max–Min Semantic Chunking, a new strategy for text chunking in RAG pipelines. This method embeds all sentences first and then uses semantic similarity (cosine similarity) to determine chunk boundaries, aiming for more meaningful and coherent chunks than traditional fixed-length or recursive methods. It details the algorithm's steps, including measuring chunk consistency and comparing new sentences to existing chunks, and discusses its strengths (dynamic boundaries, practical tuning, low overhead) and limitations (potential for context fragmentation in long documents).