RAG Database Tooling
A RAG pipeline is only as good as the database that feeds it. Raw documents are messy — PDFs, HTML, Word docs, and KB exports all come in different shapes and sizes. I build tooling that normalizes, chunks, and enriches this data so it’s ready for embeddings, fast retrieval, and clean citation.
My Approach
Parsing Layer – Converts PDFs, HTML, Docx, and other formats into normalized Markdown or JSON with metadata.
Chunking Strategies – Fixed token windows, semantic boundary detection, and heading-aware splits to preserve context.
Metadata Schema – Every chunk is tagged with source, section, headings, version, and type, ensuring traceability.
Auto-Tagging & Section Titles – Generates semantic labels and hierarchy for better retrieval alignment.
Doc Store + JSONL Export – Outputs both a human-navigable doc store and machine-ready JSONL for embedding pipelines.
Advancing Further
I expand this framework toward:
Adaptive Chunking – dynamic strategies tuned per document type and use case.
Entity Extraction – auto-identification of key technical terms, commands, or parameters for richer metadata.
Knowledge Graph Integration – connecting chunks into graph structures for multi-hop retrieval.
Why It Matters
Most RAG failures trace back to poorly prepared data. By controlling the parse, chunk, organize workflow, I deliver clean, contextualized knowledge bases that increase retrieval accuracy, reduce noise, and provide verifiable source citations.
