Add rag module: Spring AI 2.0 RAG with pgvector, chunking, reranking and a faithfulness check

Co-Authored-By: Claude Sonnet 5 <[email protected]> Claude-Session: https://claude.ai/code/session_01B38FGKKam5SCGgwgduVAh3
2026-09-21 19:09:05 +00:00
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+# LLM reranking: calls, order, latency and failure
+
+
+--- one model call per candidate; only topN survive (topN = 2) ---
+candidates in: 4, model calls made: 4, chunks out: 2
+order from the vector search, best first:
+    similarity 0.5891  page 2
+    similarity 0.4077  page 3
+    similarity 0.2535  page 1
+    similarity 0.2023  page 4
+order after reranking, best first:
+    rerank_score 8  page 2
+    rerank_score 6  page 3
+the rating prompt for the page 2 candidate (calls run concurrently, so pick it by content):
+Rate how well the PASSAGE helps answer the QUESTION, from 0 (irrelevant) to 10 (answers it).
+Reply with a single integer and nothing else.
+QUESTION: How many days of annual leave do employees get?
+PASSAGE: 4.1 Annual Leave Entitlement. Full-time employees are entitled to 20 working days
+
+--- latency: 20 candidates, each rating call takes 200 ms (a Thread.sleep in the fake model) ---
+the 20 calls one after another take 4000 ms or more: true
+LlmReranker, one virtual thread per candidate, takes under 1000 ms: true
+a real API adds its own rate limits, which this test cannot show
+
+--- failure: the model does not reply with a bare integer ---
+reply "Score: 8" for every candidate -> failures counted: 4 of 4
+scores assigned: [0, 0]
+pages kept, in order: [2, 3] (the vector-search order, because every score is 0)
+reply " 9\n" (padded) -> score 9