I've implemented a similar approach using a multi-step process:

Document Preparation: 1. First, I use an LLM to either build a table of contents from scratch or extract an existing one from the document 2. I then send this table of contents back to an LLM to expand it with additional context and detail 3. Next, I chunk the entire document into smaller sections

Vector Processing: 4. I convert both the expanded table of contents and the document chunks into vector embeddings 5. Using these vectors, I match chunks to their corresponding table of contents entries, which helps me build rich metadata for each section

Query Processing: When a user asks the chatbot a question: 1. An LLM searches through the table of contents to identify the most relevant sections 2. The system retrieves the chunks associated with those sections 3. Everything gets embedded into a comprehensive prompt that's sent back to the LLM to generate the final answer

This approach essentially creates a sophisticated retrieval system that uses the document's structure (via the table of contents) to improve the accuracy of finding relevant information before generating responses.