implementing-document-indexing

What problem does it solve?

This Skill enables efficient, structured document search by converting long documents into a hierarchy-aware set of chunks, embedding them, storing them in a vector index, and enabling hybrid retrieval that combines semantic ranking with structural filtering.

Core Features & Use Cases

• Heading-boundary chunking preserves document structure for coherent, context-rich retrieval
• Embedding with sentence-transformers and FAISS vector store for fast similarity search
• Hybrid retrieval that combines vector search with PageIndex-style tree filtering for precise results
• Use cases include building RAG pipelines, document search, and memory layers in knowledge-heavy applications

Quick Start

Ingest a Markdown document and run the hybrid retrieval pipeline to fetch the most relevant sections.