Still struggling to get correct answer in RAG pipeline? Here is Semantic Chunking with Javascript

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Semantic chunking in a Retrieval-Augmented Generation (RAG) pipeline involves dividing large text corpora into smaller, meaningful chunks based on semantic content rather than arbitrary lengths. This technique ensures that each chunk is a coherent unit of information, which improves the quality of the retrieved content during the generation process. By enhancing the relevance and coherence of the retrieved documents, semantic chunking enables the RAG model to generate more accurate and contextually appropriate responses.

Advantages of Semantic Chunking:

📈 Improved Accuracy: Enhances the relevance of retrieved content, leading to more precise responses.
🧠 Better Context Understanding: Ensures chunks are semantically coherent, improving context retention in generated outputs.
⚡ Efficient Retrieval: Speeds up the retrieval process by working with smaller, more relevant chunks.
📚 Enhanced Readability: Results in more readable and logically structured output.
🛠️ Flexible Adaptation: Easily adaptable to various domains and content types for diverse applications.