SemanticChunker is an experimental LangChain splitter that uses embedding similarity to divide text at semantically logical boundaries, unlike fixed-size splitters that cut arbitrarily based on character or token counts.
SemanticChunker is an advanced splitting strategy available in langchain_experimental. Instead of splitting text at arbitrary positions (character count, newlines, spaces), it analyzes the meaning of the content. It computes embeddings for sentences or paragraphs and then splits at points where the semantic similarity between adjacent segments drops below a threshold. This creates chunks that are more semantically coherent, often aligning with topic boundaries or logical sections, which is highly beneficial for retrieval-augmented generation.
The advantage of SemanticChunker is that it can produce chunks that are more meaningful for the LLM, potentially leading to better retrieval and generation results. However, it comes with a computational cost (requires embedding calculations) and is slower than fixed-size splitters. It is particularly useful for long, narrative texts (e.g., articles, books) where semantic boundaries matter, but may be overkill for simple, factual data. It's an experimental feature, so APIs may change.