feat(ai-sdk): citation-graph assessment + opt-in graph expansion (Phase 2)
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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>
This commit is contained in:
@@ -75,9 +75,10 @@ func (h *RAGHandlers) Search(c *gin.Context) {
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}
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c.JSON(http.StatusOK, gin.H{
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"query": req.Query,
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"results": results,
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"count": len(results),
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"query": req.Query,
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"results": results,
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"count": len(results),
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"assessment": ucca.Assess(results),
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})
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}
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@@ -0,0 +1,134 @@
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package ucca
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import (
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"fmt"
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"strings"
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)
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const (
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assessConnectedCap = 12 // cap connected norms surfaced in the assessment
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assessCrossRegimeTopN = 5 // window over which "cross regime" is judged
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assessReviewMargin = 0.05 // a tighter winner gap → recommend human review
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)
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// Assess builds the auditable explanation layer over a ranked result set:
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// primary norm, the norms it connects to (citation graph), cross-regime, a
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// human-review flag, the winner margin and a short reasoning string. Pure →
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// unit-testable. It EXPLAINS the ranking, it does not change it. Returns nil for
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// an empty result set.
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func Assess(results []LegalSearchResult) *LegalAssessment {
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if len(results) == 0 {
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return nil
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}
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// Norm-level view: collapse multiple chunks of the same article/annex so the
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// margin and cross-regime are judged between DISTINCT norms, not near-identical
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// chunks of one norm (which would make every winner margin ~0).
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norms := distinctNorms(results)
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p := norms[0]
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primary := primaryLabel(p)
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connected := dedupStrings(p.ReferencesOut, p.ReferencesIn, p.CitationUnit)
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if len(connected) > assessConnectedCap {
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connected = connected[:assessConnectedCap]
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}
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window := norms
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if len(window) > assessCrossRegimeTopN {
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window = window[:assessCrossRegimeTopN]
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}
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regimes := make(map[string]bool)
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for _, r := range window {
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if r.RegulationShort != "" {
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regimes[r.RegulationShort] = true
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}
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}
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crossRegime := len(regimes) > 1
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margin := 0.0
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if len(norms) > 1 {
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margin = norms[0].Score - norms[1].Score
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}
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primaryBinding := p.SourceClass == "binding_law"
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humanReview := margin < assessReviewMargin || crossRegime || !primaryBinding
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return &LegalAssessment{
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PrimaryNorm: primary,
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PrimaryRegulation: p.RegulationShort,
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ConnectedNorms: connected,
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CrossRegime: crossRegime,
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HumanReviewFlag: humanReview,
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WinnerMargin: margin,
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ScoreReasoning: assessReasoning(p, margin, crossRegime, primaryBinding),
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}
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}
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func primaryLabel(p LegalSearchResult) string {
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if p.CitationUnit != "" {
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return p.CitationUnit
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}
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if p.ArticleLabel != "" {
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return p.ArticleLabel
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}
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return strings.TrimSpace(p.RegulationShort + " " + p.Article)
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}
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// assessReasoning renders a short, human-readable justification (German).
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func assessReasoning(p LegalSearchResult, margin float64, crossRegime, primaryBinding bool) string {
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label := primaryLabel(p)
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parts := make([]string, 0, 4)
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if primaryBinding {
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parts = append(parts, fmt.Sprintf("Primärtreffer %s: bindendes Recht (Autorität %d).", label, p.AuthorityWeight))
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} else {
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parts = append(parts, fmt.Sprintf("Primärtreffer %s ist keine bindende Norm (Leitlinie/Standard) — Quelle prüfen.", label))
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}
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if margin > 0 {
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parts = append(parts, fmt.Sprintf("Vorsprung %.2f vor #2.", margin))
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}
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if margin < assessReviewMargin {
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parts = append(parts, "Knapper Vorsprung — Alternativtreffer prüfen.")
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}
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if crossRegime {
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parts = append(parts, "Mehrere Regime betroffen — Querbezug prüfen.")
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}
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return strings.Join(parts, " ")
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}
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// distinctNorms collapses results that share a citation (multiple chunks of the
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// same article/annex) to the first — i.e. highest-ranked — occurrence. Results
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// without any citation identity are each kept, since they cannot be matched.
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func distinctNorms(results []LegalSearchResult) []LegalSearchResult {
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seen := make(map[string]bool, len(results))
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out := make([]LegalSearchResult, 0, len(results))
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for _, r := range results {
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key := r.CitationUnit
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if key == "" {
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key = r.ArticleLabel
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}
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if key != "" {
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if seen[key] {
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continue
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}
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seen[key] = true
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}
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out = append(out, r)
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}
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return out
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}
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// dedupStrings concatenates out+in, drops empties and the excluded value, and
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// returns a stable de-duplicated slice (insertion order preserved).
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func dedupStrings(out, in []string, exclude string) []string {
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seen := map[string]bool{exclude: true}
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res := make([]string, 0, len(out)+len(in))
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for _, list := range [][]string{out, in} {
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for _, s := range list {
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if s == "" || seen[s] {
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continue
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}
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seen[s] = true
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res = append(res, s)
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}
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}
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return res
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}
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@@ -0,0 +1,112 @@
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package ucca
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import "testing"
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func ares(reg, cu, sc string, score float64, weight int, out, in []string) LegalSearchResult {
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return LegalSearchResult{
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RegulationShort: reg, CitationUnit: cu, SourceClass: sc, Score: score,
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AuthorityWeight: weight, ReferencesOut: out, ReferencesIn: in,
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}
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}
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func TestAssess_Empty(t *testing.T) {
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if Assess(nil) != nil {
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t.Error("empty results → nil assessment")
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}
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}
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func TestAssess_BindingPrimary_NoReview(t *testing.T) {
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results := []LegalSearchResult{
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ares("CRA", "Art. 13 CRA", "binding_law", 1.05, 100,
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[]string{"CRA Anhang I", "Art. 14 CRA"}, []string{"Art. 12 CRA"}),
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ares("CRA", "Art. 14 CRA", "binding_law", 0.80, 100, nil, nil),
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}
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a := Assess(results)
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if a == nil {
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t.Fatal("nil assessment")
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}
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if a.PrimaryNorm != "Art. 13 CRA" || a.PrimaryRegulation != "CRA" {
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t.Errorf("primary wrong: %+v", a)
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}
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if len(a.ConnectedNorms) != 3 { // out(2) + in(1), self excluded, deduped
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t.Errorf("connected norms: %v", a.ConnectedNorms)
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}
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if a.CrossRegime {
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t.Error("single regime must not be cross-regime")
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}
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if a.WinnerMargin < 0.24 || a.WinnerMargin > 0.26 {
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t.Errorf("margin = %v, want ~0.25", a.WinnerMargin)
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}
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if a.HumanReviewFlag {
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t.Error("clean binding + healthy margin + single regime → no review")
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}
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}
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func TestAssess_CrossRegimeFlagsReview(t *testing.T) {
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a := Assess([]LegalSearchResult{
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ares("CRA", "Art. 13 CRA", "binding_law", 1.05, 100, nil, nil),
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ares("DORA", "Art. 6 DORA", "binding_law", 0.70, 100, nil, nil),
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})
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if !a.CrossRegime || !a.HumanReviewFlag {
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t.Errorf("cross-regime must flag review: %+v", a)
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}
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}
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func TestAssess_NonBindingFlagsReview(t *testing.T) {
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a := Assess([]LegalSearchResult{
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ares("ENISA", "ENISA SBOM", "supervisory_guidance", 0.90, 70, nil, nil),
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ares("ENISA", "ENISA X", "supervisory_guidance", 0.40, 70, nil, nil),
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})
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if !a.HumanReviewFlag {
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t.Error("non-binding primary → review")
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}
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}
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func TestAssess_TightMarginFlagsReview(t *testing.T) {
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a := Assess([]LegalSearchResult{
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ares("CRA", "Art. 13 CRA", "binding_law", 1.00, 100, nil, nil),
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ares("CRA", "Art. 14 CRA", "binding_law", 0.98, 100, nil, nil),
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})
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if a.WinnerMargin >= 0.05 || !a.HumanReviewFlag {
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t.Errorf("tight margin → review: %+v", a)
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}
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}
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func TestAssess_MarginIsNormLevelNotChunkLevel(t *testing.T) {
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// Two near-identical chunks of the SAME norm at the top, then a distinct norm.
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results := []LegalSearchResult{
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ares("CRA", "Art. 13 CRA", "binding_law", 1.050, 100, []string{"CRA Anhang I"}, nil),
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ares("CRA", "Art. 13 CRA", "binding_law", 1.049, 100, nil, nil), // same norm
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ares("CRA", "Art. 14 CRA", "binding_law", 0.800, 100, nil, nil),
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}
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a := Assess(results)
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if a.WinnerMargin < 0.24 || a.WinnerMargin > 0.26 { // Art.13 vs Art.14, not chunk vs chunk
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t.Errorf("margin must be norm-level (~0.25), got %v", a.WinnerMargin)
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}
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if a.HumanReviewFlag {
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t.Error("healthy norm-level margin → no review")
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}
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}
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func TestDistinctNorms(t *testing.T) {
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got := distinctNorms([]LegalSearchResult{
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{CitationUnit: "Art. 13 CRA"},
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{CitationUnit: "Art. 13 CRA"}, // duplicate norm → collapsed
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{CitationUnit: "Art. 14 CRA"},
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{CitationUnit: ""}, // no identity → kept
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{CitationUnit: ""}, // no identity → kept
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})
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if len(got) != 4 {
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t.Errorf("want 4 (2 distinct + 2 unidentified), got %d", len(got))
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}
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}
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func TestDedupStrings(t *testing.T) {
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got := dedupStrings([]string{"a", "b", "", "a"}, []string{"b", "c"}, "self")
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if len(got) != 3 || got[0] != "a" || got[1] != "b" || got[2] != "c" {
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t.Errorf("dedup: %v", got)
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}
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if len(dedupStrings([]string{"self"}, nil, "self")) != 0 {
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t.Error("excluded value must be dropped")
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}
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}
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@@ -20,6 +20,7 @@ type LegalRAGClient struct {
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httpClient *http.Client
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textIndexEnsured map[string]bool
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hybridEnabled bool
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graphEnabled bool
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}
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// NewLegalRAGClient creates a new Legal RAG client using Ollama bge-m3 embeddings.
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@@ -38,6 +39,11 @@ func NewLegalRAGClient() *LegalRAGClient {
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}
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hybridEnabled := os.Getenv("RAG_HYBRID_SEARCH") != "false"
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// Graph-Expansion ist OPT-IN: kein gemessener Rang-Nutzen ggue. der Binding-Augmentation,
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// +1 Qdrant-Call/Suche, Flutungsrisiko ueber Reverse-Kanten. Bleibt als Recall-Sicherheitsnetz
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// fuer spaetere Luecken (RAG_GRAPH_EXPANSION=true). Die Graph-Kanten werden in der Response
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// zur Begruendung/Vollstaendigkeit genutzt, nicht zur Pool-Expansion (Default).
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graphEnabled := os.Getenv("RAG_GRAPH_EXPANSION") == "true"
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return &LegalRAGClient{
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qdrantURL: qdrantURL,
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@@ -47,6 +53,7 @@ func NewLegalRAGClient() *LegalRAGClient {
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collection: "bp_compliance_ce",
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textIndexEnsured: make(map[string]bool),
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hybridEnabled: hybridEnabled,
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graphEnabled: graphEnabled,
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httpClient: &http.Client{
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Timeout: 60 * time.Second,
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},
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@@ -100,6 +107,13 @@ func (c *LegalRAGClient) searchInternal(ctx context.Context, collection string,
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hits = mergeDedupHits(hits, bindingHits)
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}
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// Graph-Augmentation: verbundene Normen (references_out/in) der Top-Hits ueber die
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// praezise Zitations-Kante in den Pool ziehen — z.B. Art. 13 CRA zieht Anhang I (die
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// eigentliche Pflichtquelle). Pool-Augmentation only; Re-Rank + topK bleiben.
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if c.graphEnabled {
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hits = c.expandViaGraph(ctx, collection, hits)
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}
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results := make([]LegalSearchResult, len(hits))
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for i, hit := range hits {
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// Legal-Metadaten nach rag_reingest_spec.md §2: bevorzugt die normalisierten Felder
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@@ -131,6 +145,9 @@ func (c *LegalRAGClient) searchInternal(ctx context.Context, collection string,
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AuthorityWeight: getInt(hit.Payload, "authority_weight"),
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SourceClass: getString(hit.Payload, "source_class"),
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Jurisdiction: getString(hit.Payload, "jurisdiction"),
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CitationUnit: getString(hit.Payload, "citation_unit"),
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ReferencesOut: getStringSlice(hit.Payload, "references_out"),
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ReferencesIn: getStringSlice(hit.Payload, "references_in"),
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}
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}
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@@ -0,0 +1,162 @@
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package ucca
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import (
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"bytes"
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"context"
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"encoding/json"
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"fmt"
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"io"
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"net/http"
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"sort"
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)
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// Graph-augmented retrieval: when a top hit cites an annex/article (references_out)
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// or is cited by one (references_in), pull that connected norm into the candidate
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// pool via the PRECISE citation graph instead of hoping semantic search surfaces
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// it. E.g. a hit on CRA Art. 13 pulls in CRA Anhang I (the actual requirement).
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// Pool-augmentation only — authority re-rank + topK slice still apply, so the
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// response schema is unchanged.
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const (
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graphSeedCount = 5 // only the top hits seed the expansion
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graphMaxExpand = 15 // cap connected norms pulled in (avoid pool explosion)
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graphHopPenalty = 0.05 // a one-hop neighbour ranks just below its seed
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)
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// expandViaGraph augments hits with the norms they cite and the norms that cite
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// them. Best-effort: on any error (or nothing to expand) the original hits are
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// returned unchanged.
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func (c *LegalRAGClient) expandViaGraph(ctx context.Context, collection string, hits []qdrantSearchHit) []qdrantSearchHit {
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if len(hits) == 0 {
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return hits
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}
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present := make(map[string]bool, len(hits))
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for _, h := range hits {
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if cu := getString(h.Payload, "citation_unit"); cu != "" {
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present[cu] = true
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}
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}
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seeds := hits
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if len(seeds) > graphSeedCount {
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seeds = seeds[:graphSeedCount]
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}
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// Forward edges only (references_out = the detail a hit explicitly points to,
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// e.g. Art. 13 → Anhang I). Reverse (references_in) has high fan-out for popular
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// annexes (Anhang I is cited by 23 articles) → pool flooding; it is surfaced as
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// connected-norm metadata in the Phase 2 response instead of expanding the pool.
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want := make(map[string]float64) // connected citation_unit -> best seeding score
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for _, h := range seeds {
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for _, cu := range getStringSlice(h.Payload, "references_out") {
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if cu == "" || present[cu] {
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continue
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}
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if s, ok := want[cu]; !ok || h.Score > s {
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want[cu] = h.Score
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}
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}
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}
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if len(want) == 0 {
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return hits
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}
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units := topByScore(want, graphMaxExpand)
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fetched, err := c.fetchByCitationUnits(ctx, collection, units)
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if err != nil || len(fetched) == 0 {
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return hits
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}
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neighbours := make([]qdrantSearchHit, 0, len(fetched))
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for cu, pt := range fetched {
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neighbours = append(neighbours, qdrantSearchHit{ID: pt.ID, Score: want[cu] - graphHopPenalty, Payload: pt.Payload})
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}
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return mergeDedupHits(hits, neighbours)
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}
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|
||||
// topByScore returns up to n keys with the highest values. Deterministic: ties
|
||||
// broken by the key string so the cap is stable across runs.
|
||||
func topByScore(m map[string]float64, n int) []string {
|
||||
keys := make([]string, 0, len(m))
|
||||
for k := range m {
|
||||
keys = append(keys, k)
|
||||
}
|
||||
sort.Slice(keys, func(i, j int) bool {
|
||||
if m[keys[i]] != m[keys[j]] {
|
||||
return m[keys[i]] > m[keys[j]]
|
||||
}
|
||||
return keys[i] < keys[j]
|
||||
})
|
||||
if len(keys) > n {
|
||||
keys = keys[:n]
|
||||
}
|
||||
return keys
|
||||
}
|
||||
|
||||
// fetchByCitationUnits loads one representative point (the first chunk) per
|
||||
// citation_unit from the given collection.
|
||||
func (c *LegalRAGClient) fetchByCitationUnits(ctx context.Context, collection string, units []string) (map[string]qdrantScrollPoint, error) {
|
||||
should := make([]map[string]interface{}, 0, len(units))
|
||||
for _, cu := range units {
|
||||
should = append(should, map[string]interface{}{"key": "citation_unit", "match": map[string]interface{}{"value": cu}})
|
||||
}
|
||||
reqBody := map[string]interface{}{
|
||||
"limit": len(units) * 4,
|
||||
"with_payload": true,
|
||||
"with_vectors": false,
|
||||
"filter": map[string]interface{}{"should": should},
|
||||
}
|
||||
jsonBody, err := json.Marshal(reqBody)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
url := fmt.Sprintf("%s/collections/%s/points/scroll", c.qdrantURL, collection)
|
||||
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewReader(jsonBody))
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
req.Header.Set("Content-Type", "application/json")
|
||||
if c.qdrantAPIKey != "" {
|
||||
req.Header.Set("api-key", c.qdrantAPIKey)
|
||||
}
|
||||
resp, err := c.httpClient.Do(req)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
defer func() { _ = resp.Body.Close() }()
|
||||
if resp.StatusCode != http.StatusOK {
|
||||
body, _ := io.ReadAll(resp.Body)
|
||||
return nil, fmt.Errorf("qdrant scroll returned %d: %s", resp.StatusCode, string(body))
|
||||
}
|
||||
var scrollResp qdrantScrollResponse
|
||||
if err := json.NewDecoder(resp.Body).Decode(&scrollResp); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
out := make(map[string]qdrantScrollPoint, len(units))
|
||||
for _, pt := range scrollResp.Result.Points {
|
||||
cu := getString(pt.Payload, "citation_unit")
|
||||
if cu != "" {
|
||||
if _, seen := out[cu]; !seen {
|
||||
out[cu] = pt
|
||||
}
|
||||
}
|
||||
}
|
||||
return out, nil
|
||||
}
|
||||
|
||||
// getStringSlice extracts a []string from a Qdrant payload list field
|
||||
// (references_out / references_in are stored as JSON arrays of strings).
|
||||
func getStringSlice(m map[string]interface{}, key string) []string {
|
||||
v, ok := m[key]
|
||||
if !ok {
|
||||
return nil
|
||||
}
|
||||
arr, ok := v.([]interface{})
|
||||
if !ok {
|
||||
return nil
|
||||
}
|
||||
out := make([]string, 0, len(arr))
|
||||
for _, item := range arr {
|
||||
if s, ok := item.(string); ok {
|
||||
out = append(out, s)
|
||||
}
|
||||
}
|
||||
return out
|
||||
}
|
||||
@@ -0,0 +1,89 @@
|
||||
package ucca
|
||||
|
||||
import (
|
||||
"context"
|
||||
"encoding/json"
|
||||
"net/http"
|
||||
"net/http/httptest"
|
||||
"testing"
|
||||
)
|
||||
|
||||
func TestGetStringSlice(t *testing.T) {
|
||||
m := map[string]interface{}{
|
||||
"refs": []interface{}{"a", "b", 3, "c"}, // non-strings are skipped
|
||||
"str": "not-a-list",
|
||||
}
|
||||
got := getStringSlice(m, "refs")
|
||||
if len(got) != 3 || got[0] != "a" || got[2] != "c" {
|
||||
t.Errorf("refs: %v", got)
|
||||
}
|
||||
if getStringSlice(m, "missing") != nil {
|
||||
t.Error("missing key should be nil")
|
||||
}
|
||||
if getStringSlice(m, "str") != nil {
|
||||
t.Error("non-list should be nil")
|
||||
}
|
||||
}
|
||||
|
||||
func TestTopByScore_DeterministicCap(t *testing.T) {
|
||||
m := map[string]float64{"x": 0.5, "y": 0.9, "z": 0.5, "w": 0.7}
|
||||
got := topByScore(m, 2)
|
||||
if len(got) != 2 || got[0] != "y" || got[1] != "w" {
|
||||
t.Errorf("want [y w], got %v", got)
|
||||
}
|
||||
all := topByScore(m, 10)
|
||||
if all[2] != "x" || all[3] != "z" { // tie 0.5 broken by key string
|
||||
t.Errorf("tie-break not deterministic: %v", all)
|
||||
}
|
||||
}
|
||||
|
||||
func TestExpandViaGraph_NoSeedsOrRefs(t *testing.T) {
|
||||
c := &LegalRAGClient{} // nil httpClient → must not be called on these paths
|
||||
if out := c.expandViaGraph(context.Background(), "x", nil); out != nil {
|
||||
t.Error("empty hits should return nil")
|
||||
}
|
||||
hits := []qdrantSearchHit{{ID: 1, Score: 0.8, Payload: map[string]interface{}{"citation_unit": "Art. 1 CRA"}}}
|
||||
if out := c.expandViaGraph(context.Background(), "x", hits); len(out) != 1 {
|
||||
t.Errorf("no references → unchanged, got %d", len(out))
|
||||
}
|
||||
}
|
||||
|
||||
func TestExpandViaGraph_PullsConnectedNorm(t *testing.T) {
|
||||
srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, _ *http.Request) {
|
||||
_ = json.NewEncoder(w).Encode(map[string]interface{}{
|
||||
"result": map[string]interface{}{
|
||||
"points": []map[string]interface{}{
|
||||
{"id": 99, "payload": map[string]interface{}{
|
||||
"citation_unit": "CRA Anhang I", "chunk_text": "Sicherheitsanforderungen",
|
||||
"source_class": "binding_law", "authority_weight": 100, "regulation_short": "CRA",
|
||||
}},
|
||||
},
|
||||
"next_page_offset": nil,
|
||||
},
|
||||
})
|
||||
}))
|
||||
defer srv.Close()
|
||||
|
||||
c := &LegalRAGClient{qdrantURL: srv.URL, httpClient: srv.Client()}
|
||||
hits := []qdrantSearchHit{
|
||||
{ID: 1, Score: 0.70, Payload: map[string]interface{}{
|
||||
"citation_unit": "Art. 13 CRA", "references_out": []interface{}{"CRA Anhang I"},
|
||||
}},
|
||||
}
|
||||
out := c.expandViaGraph(context.Background(), "bp_compliance_ce", hits)
|
||||
if len(out) != 2 {
|
||||
t.Fatalf("want 2 hits (seed + connected annex), got %d", len(out))
|
||||
}
|
||||
var found *qdrantSearchHit
|
||||
for i := range out {
|
||||
if getString(out[i].Payload, "citation_unit") == "CRA Anhang I" {
|
||||
found = &out[i]
|
||||
}
|
||||
}
|
||||
if found == nil {
|
||||
t.Fatal("connected norm CRA Anhang I was not pulled into the pool")
|
||||
}
|
||||
if found.Score < 0.64 || found.Score > 0.66 { // 0.70 seed − 0.05 hop penalty
|
||||
t.Errorf("connected score = %v, want ~0.65", found.Score)
|
||||
}
|
||||
}
|
||||
@@ -27,6 +27,27 @@ type LegalSearchResult struct {
|
||||
AuthorityWeight int `json:"-"`
|
||||
SourceClass string `json:"-"`
|
||||
Jurisdiction string `json:"-"`
|
||||
|
||||
// Zitations-Graph (Phase 2) — intern, speist nur die Assessment-Berechnung
|
||||
// (verbundene Normen, Begruendung). Pro-Result-Schema bleibt eingefroren.
|
||||
CitationUnit string `json:"-"`
|
||||
ReferencesOut []string `json:"-"`
|
||||
ReferencesIn []string `json:"-"`
|
||||
}
|
||||
|
||||
// LegalAssessment is the auditable explanation layer over a ranked result set:
|
||||
// which norm is primary, which norms connect to it via the citation graph,
|
||||
// whether the answer crosses regulatory regimes, and whether a human should
|
||||
// review. Computed from the already-ranked results — it EXPLAINS retrieval, it
|
||||
// does not change it (graph edges for reasoning/completeness, not pool-expansion).
|
||||
type LegalAssessment struct {
|
||||
PrimaryNorm string `json:"primary_norm"`
|
||||
PrimaryRegulation string `json:"primary_regulation"`
|
||||
ConnectedNorms []string `json:"connected_norms"`
|
||||
CrossRegime bool `json:"cross_regime"`
|
||||
HumanReviewFlag bool `json:"human_review_flag"`
|
||||
WinnerMargin float64 `json:"winner_margin"`
|
||||
ScoreReasoning string `json:"score_reasoning"`
|
||||
}
|
||||
|
||||
// LegalContext represents aggregated legal context for an assessment.
|
||||
|
||||
Reference in New Issue
Block a user