Text Chunking for RAG: Optimizing Vector Search

When building a RAG application, you can't just dump a whole PDF into a vector database. You need to split it into smaller, semantically meaningful "chunks."

Why Chunking Matters

Embedding models have token limits (e.g., 8192 tokens). More importantly, smaller chunks often yield more precise search results. If a chunk is too large, it might contain multiple topics, diluting the vector's meaning.

Strategies

• Fixed Size: Split every 500 characters. Simple but can cut sentences in half.
• Recursive: Split by paragraphs, then sentences. Preserves structure.

Experiment with different strategies using our Text Chunker tool.