Effective Chunking Strategies
Don't just split text. Learn to respect document structure with header-aware splitting and implement Contextual Enrichment to give every chunk global awareness.
In the previous tutorial, we converted a PDF into structured Markdown. Now, we must prepare that text for the actual retrieval system. Why? Passing a 300+ page document to an LLM is a bad idea. You'll hit context window limits, waste tokens on irrelevant information, and overwhelm the model with too much data at once.
If you simply split text every 500 characters, you will break tables, sever sentences, and isolate statements from their headers. A chunk describing "Risk Factors" is useless if it doesn't contain the header saying it belongs to "Risk Factors". You need a strategy that preserves context and structure.
Tutorial Goals
- Implement MarkdownHeaderTextSplitter to preserve document hierarchy
- Build a Recursive Splitter fallback for long sections
- Create a Contextual Enrichment pipeline using a local LLM
- Visualize chunk statistics and token counts
- Analyze the cost of contextual enrichment