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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Handling Complex Document Structures

Implement strategies for effectively chunking and retrieving information from intricate documents, such as tables or nested sections.

Beyond Simple Text: Complex Documents

When building RAG systems, we often deal with documents that aren't just plain, flowing text. Think about financial reports, scientific papers, or legal contracts.

These documents frequently contain tables, nested sections (like chapters and sub-chapters), and other intricate structures. Standard text chunking methods often struggle with these, breaking context and making retrieval less effective.

Tables: A Challenge for RAG

Tables are a prime example of complex structures. They present data in a structured, grid-like format where relationships between rows and columns are crucial.

  • Lost Context: A simple character-based chunker might split a table row, separating a value from its header, making the chunk meaningless.
  • Poor Embeddings: Without proper context, the generated embeddings for table fragments might not accurately represent the data.

All lessons in this course

  1. Query Rewriting and Reranking
  2. Multi-stage and Agentic RAG Patterns
  3. Handling Complex Document Structures
  4. Self-Querying & Citations
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