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+# Semantic Chunking
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 <!-- SEO: Explore semantic chunking for Retrieval Augmented Generation (RAG) in this comprehensive guide. Learn about embedding similarity, hierarchical clustering, and LLM-based methods for optimal text chunking. Discover how semantic chunking improves RAG performance compared to traditional rule-based approaches. Includes code examples, evaluation metrics, and comparisons using HotpotQA and SQUAD datasets with BAAI/bge-small-en-v1.5 embeddings.
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-# Semantic Chunking
-
 Chunking in Natural Language Processing is simply dividing large bodies of text into smaller pieces that computers can manage more easily. Splitting large datasets into chunks enables your Retrieval Augmented Generation (RAG) system to embed, index, and store even very large datasets optimally. But *how* you chunk your data is crucial in determining whether you can efficiently return only the most relevant results to your user queries. 
 
 To get your RAG system to handle user queries better, you need a chunking method that's a good fit for your data. Some widely used chunking algorithms are **rule-based** - e.g., fixed character splitter, recursive character splitter, document-specific splitter, among others. But in some real-world applications, rule-based methods have trouble. If, for example, your dataset has multi-topic documents, rule-based splitting algorithms can result in incomplete contexts or noise-filled chunks. **Semantic chunking**, on the other hand - because it divides text on the basis of meaning rather than rules - creates chunks that are semantically independent and cohesive, and therefore results in more effective text processing and information retrieval.