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

What this post added

Introduces Max-Min Semantic Chunking, a new strategy for RAG pipelines that embeds sentences first and then uses semantic similarity to define chunk boundaries. This method contrasts with traditional fixed-size or recursive chunking by prioritizing semantic coherence over arbitrary length. The post details the six-step process of Max-Min Semantic Chunking, its strengths (dynamic boundaries, practical tuning, low overhead), and its limitations (potential for context fragmentation in long documents). It also references Milvus as a suitable vector database for storing embeddings and discusses the impact of chunking quality on RAG system performance.