Semantic chunking is a method of splitting text into meaningful pieces ("chunks") based on semantic meaning (context and ideas) rather than just fixed size (like number of tokens or words).

Instead of blindly cutting every 500 words, semantic chunking ensures that each chunk preserves context and represents a complete thought or unit of meaning.

🔹 Why Do We Need It?

• In RAG (Retrieval-Augmented Generation), large texts are stored in vector databases.

• If you chunk text incorrectly (cut mid-sentence/paragraph), retrieval will return incomplete or meaningless fragments.

• Semantic chunking solves this by aligning chunks with natural language meaning, improving search, retrieval, and embeddings quality.

🔹 How Does Semantic Chunking Work?

1. Input text → (e.g., a long document, article, or transcript).

2. Sentence splitting → Break into sentences or paragraphs first.

3. Semantic similarity grouping → Merge sentences that are closely related in meaning.

   • Use embedding similarity (cosine similarity).

   • Or use rules (keep paragraph boundaries intact).

4. Chunk creation → Form chunks of text that preserve cohesive ideas.

5. Store in vector DB → Each chunk gets its embedding.

🔹 Example

🔸 Normal (fixed-size) chunking:

"Machine learning is great. It powers AI systems like ChatGPT."
Chunk 1: "Machine learning is great. It po"
Chunk 2: "wers AI systems like ChatGPT."

⚠️ Context broken!

🔸 Semantic chunking:

Chunk 1: "Machine learning is great. It powers AI systems like ChatGPT."

✅ Full meaning preserved.

🔹 Methods of Semantic Chunking

• Rule-based: Keep sentences/paragraphs intact until a token limit is reached.

• Embedding-based: Use vector similarity to decide where to cut.

• Hybrid: Combine rules + embeddings for balance.

🔹 Benefits

✅ Higher retrieval accuracy
✅ Better embeddings quality
✅ Improved context for LLMs
✅ Natural alignment with human thought

👉 In short: Semantic Chunking = Meaning-aware text splitting for smarter AI retrieval.