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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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Claude
2026-10-22 18:04:49 +00:00
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@@ -25,5 +25,6 @@ Runnable companion code for the Spring AI articles on [ankurm.com](https://ankur
| [`semantic-cache/`](semantic-cache) | A `CallAdvisor` that serves a stored answer when a new question embeds close to an old one, over a Redis Stack vector index and a real in-process embedding model (all-MiniLM-L6-v2). A labelled set of 30 intents x 4 phrasings plus 20 off-topic questions swept across thresholds (hit rate against wrong answers), 600 requests replayed with an illustrative cost model, and the traps reproduced: Spring AI's score is `(1 + cosine) / 2`, reset-password and reset-2FA are close enough to answer for each other, a tenant filter, expiry, and an index built for another embedding model. The chat model is a script that counts calls; one test uses a real local model. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Semantic Caching for LLM Calls in Spring Boot with Redis Vector Search](https://ankurm.com/semantic-caching-for-llm-calls-in-spring-boot-with-redis-vector-search/) |
| [`langchain4j-interop/`](langchain4j-interop) | The same order assistant in Spring AI 2.0.1 and LangChain4j 1.22.0 in one Spring Boot 4.1.1 app, against a local fake OpenAI server: the bean collision when both OpenAI starters are present, the two requests compared on the wire, a throwing tool and a looping model (what each framework bounds), the meters each records, one MCP tool server used from both, and which lines of the 2025 version of the article still compile. Java 25. | [Spring AI vs LangChain4j in 2026: Is the Pairing Still Worth It?](https://ankurm.com/spring-ai-langchain4j-production-ai-microservices-java/) |
| [`agent-skills/`](agent-skills) | `SKILL.md` folders loaded on demand by the `Skill` tool from `spring-ai-agent-utils` 0.13.0: what the model sees before it loads anything (name and description only), the two-call load round trip, unknown, duplicate and description-less skills, and listing size against inlining every body (character counts). Scripted chat model, no live model. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Agent Skills in Spring AI: Packaging Reusable Capabilities for Your Agents](https://ankurm.com/agent-skills-spring-ai-packaging-reusable-capabilities/) |
| [`rag-ingestion/`](rag-ingestion) | PDF, Word and HTML versions of one 12-section runbook through `PagePdfDocumentReader`, `TikaDocumentReader` and `JsoupDocumentReader`, then `TokenTextSplitter` at six sizes, a paragraph chunker, a sentence splitter and a section chunker; retrieval hit@1/3/5 on 37 fixed questions, metadata in the embedding, content-hash idempotent ingestion into pgvector and the re-embedding cost of one edit. Local all-MiniLM-L6-v2; a synthetic corpus, so differences of two or three questions are noise. Spring Boot 4.1.2, Spring AI 2.0.2, Java 25. | [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/) |
Upgrading from Spring AI 1.x: [migration guide](https://ankurm.com/spring-ai-1-to-2-migration-guide/).