A Visual Exploration of Semantic Text Chunking

Semantic text chunking is a technique for dividing text into meaningful segments based on their semantic similarity. This can be useful for various NLP applications, including Retrieval Augmented Generation (RAG). To perform semantic chunking, text is first split into smaller chunks using a method like recursive chunking. Then, embeddings are created for each chunk using a transformer-based bi-encoder or other model. Cosine distances between the embeddings of sequential chunks are calculated, and breakpoints are chosen where the distances are large, indicating a semantic shift. This process helps create chunks that are both coherent and semantically distinct. To visualize the breakpoints, a graph can be generated showing the cosine distance between subsequent chunks over the length of the text. By adjusting the breakpoint threshold, the granularity of the chunks can be controlled. Recursive breakpoint generation can be used to create smaller, more refined chunks. Clustering techniques can also be applied to further group similar chunks together. Additionally, LLMs can be used to summarize the chunks, providing a quick overview of their content. By experimenting with different parameters and visualization tools, optimal chunking can be achieved for a specific application.