Add an `assessment` object to the legal RAG search response: primary norm,
connected norms (from the citation graph references_out/in of the primary),
cross_regime, human_review_flag, a norm-level winner_margin and a short
reasoning string. The margin is computed over DISTINCT norms, so a long
article split into several chunks no longer fabricates uncertainty. The
per-result schema stays frozen — graph fields are internal (json:"-").
Also wire optional citation-graph expansion (RAG_GRAPH_EXPANSION=true,
default off): top hits pull their referenced norms into the candidate pool
via the precise edge (e.g. Art. 13 CRA -> Anhang I). Measured to add no
rank gain over the existing binding-law augmentation, with +1 Qdrant call
per search and reverse-edge fan-out risk, so it ships off-by-default as a
recall safety net. The graph EXPLAINS retrieval (assessment), it does not
expand it by default.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ai-sdk (legal_rag_client/scroll/types) liest die gepinnten Spec-Felder
article_label/regulation_code/article/paragraph/sub/citation_style/is_recital
mit Fallback auf alt-ingestierte Chunks (regulation_id, section); neuer getBool-Helfer.
Advisor + Drafting-Engine bilden die Quellenzeile primaer aus article_label
("BDSG § 38 Abs. 1"), sonst aus den strukturierten Feldern. 17 Tests gruen, tsc sauber.
Vertrag: docs-src/development/rag_reingest_spec.md (§2/§7). Deploy an den Re-Ingest
gekoppelt — neue Felder sind bis dahin leer (graceful Fallback).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Benjamin Admin
Benjamin_Boenisch