{
  "id": 717797,
  "title": "Why I Chose PDF RAG Chunking and Metadata for Catalog Semantic Search",
  "url": "https://urgent.news/2026/08/13/why-i-chose-pdf-rag-chunking-and-metadata-for-catalog-semantic-search",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-13T00:52:33.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/zylahmorn61835/why-i-chose-pdf-rag-chunking-and-metadata-for-catalog-semantic-search-4f90"
  },
  "original_language": "en",
  "account": null,
  "summary": "Short answer: for semantic search over messy B2B catalog PDFs, I would spend the latency budget during ingestion, preserve page-level evidence, and keep the query path to one embedding plus one vector search; if a catalog must become searchable immediately after every upload, I would choose simpler deterministic chunks and defer enrichment. The decisive constraint is not the PDF parser or the…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}