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Add rag-ingestion module: PDF, Word and HTML readers and chunking strategies compared by retrieval hit rate; metadata in the embedding, content-hash idempotent ingestion, re-embedding cost of an edit

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Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_0146opDKBYtADXvkc39zdiyw
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2026-10-22 18:04:49 +00:00
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# rag-ingestion
Companion code for [Spring AI RAG Ingestion Pipeline: PDF, Word and HTML Readers with Chunking Strategies Compared](https://ankurm.com/spring-ai-rag-ingestion-pipeline-pdf-word-html-chunking/).
PDF, Word and HTML versions of one 12-section runbook go through `PagePdfDocumentReader`, `TikaDocumentReader` and `JsoupDocumentReader`, then through several chunkers, and retrieval hit rate is measured on 37 fixed questions. Also: metadata in the embedding, content-hash ingestion, and what one edit costs to re-embed. Spring Boot 4.1.2, Spring AI 2.0.2, Java 25.
Run `scripts/run-all.sh` (needs PostgreSQL 16 with pgvector; see the script header). Every number in the article is a line in `output/`.