feat: 放大 AI 规格候选上限 (#284)

admin/service/ai_specmatch_test.go

@@ -3,7 +3,9 @@ package service
 import (
 	"context"
 	"database/sql"
+	"encoding/json"
 	"errors"
+	"fmt"
 	"testing"
 
 	"cmautobuy/admin/model"
@@ -71,6 +73,27 @@ func TestResolveExtraDimensionSignals_额外维度必须有确定信号(t *testi
 }
 
 func TestMatchSybSpecWithAI_候选白名单低置信度和人工优先(t *testing.T) {
+	t.Run("100个合格候选允许调用模型", func(t *testing.T) {
+		db := newTestDB(t)
+		actor := seedAIMatchContextWithData(t, db, "SYB-AI-100", "黑色,M", collectedAIChoicesWithCount(t, 100))
+		fake := &fakeAIModelClient{response: AIModelMatchResponse{Conclusion: "match", CandidateID: "C01", ConfidenceBPS: 9200, Reason: "候选之一规格一致"}}
+		result, err := MatchSybSpecWithAI(context.Background(), db, &actor, testAIMatchSnapshot(fake), "SYB-AI-100", "")
+		if err != nil || result.Outcome != "ai_saved" || fake.calls != 1 || len(fake.last.Candidates) != 100 {
+			t.Fatalf("100 条候选应调用模型: result=%+v calls=%d candidate_count=%d err=%v", result, fake.calls, len(fake.last.Candidates), err)
+		}
+	})
+
+	t.Run("101个合格候选不调用模型也不保存映射", func(t *testing.T) {
+		db := newTestDB(t)
+		actor := seedAIMatchContextWithData(t, db, "SYB-AI-101", "黑色,M", collectedAIChoicesWithCount(t, 101))
+		fake := &fakeAIModelClient{err: errors.New("候选超过上限时不应调用模型")}
+		result, err := MatchSybSpecWithAI(context.Background(), db, &actor, testAIMatchSnapshot(fake), "SYB-AI-101", "")
+		if err != nil || result.Outcome != "rejected" || result.Message != "可购买候选超过 100 条，请先人工核对" || fake.calls != 0 {
+			t.Fatalf("101 条候选应在模型调用前拒绝: result=%+v calls=%d err=%v", result, fake.calls, err)
+		}
+		assertNoAIMapping(t, db)
+	})
+
 	t.Run("合格AI结果直接保存", func(t *testing.T) {
 		db := newTestDB(t)
 		actor := seedAIMatchContext(t, db, "SYB-AI-SAVE")
@@ -165,6 +188,37 @@ func TestMatchSybSpecWithAI_候选白名单低置信度和人工优先(t *testin
 const collectedAIChoices = `{"goods_id":"737116531267","price_granularity":"sku","dimensions":[{"key":"color","name":"颜色"},{"key":"size","name":"尺码"}],"skus":[{"options":{"color":"黑色","size":"M"},"price_cent":1180,"available":true},{"options":{"color":"白色","size":"L"},"price_cent":1280,"available":true}]}`
 const collectedRuleChoices = `{"goods_id":"737116531267","price_granularity":"sku","dimensions":[{"key":"color","name":"颜色"},{"key":"size","name":"尺码"}],"skus":[{"options":{"color":"灰色中长款","size":"L（106-114斤）"},"price_cent":1180,"available":true},{"options":{"color":"黑色","size":"L（106-114斤）"},"price_cent":1280,"available":true}]}`
 
+func collectedAIChoicesWithCount(t *testing.T, count int) string {
+	t.Helper()
+	type collectedSKU struct {
+		Options   map[string]string `json:"options"`
+		PriceCent int64             `json:"price_cent"`
+		Available bool              `json:"available"`
+	}
+	payload := struct {
+		GoodsID    string              `json:"goods_id"`
+		Dimensions []map[string]string `json:"dimensions"`
+		SKUs       []collectedSKU      `json:"skus"`
+	}{
+		GoodsID: "737116531267",
+		Dimensions: []map[string]string{
+			{"key": "color", "name": "颜色"},
+			{"key": "size", "name": "尺码"},
+		},
+		SKUs: make([]collectedSKU, 0, count),
+	}
+	for index := 1; index <= count; index++ {
+		payload.SKUs = append(payload.SKUs, collectedSKU{
+			Options: map[string]string{"color": fmt.Sprintf("黑色款%03d", index), "size": "M"}, PriceCent: 1180, Available: true,
+		})
+	}
+	raw, err := json.Marshal(payload)
+	if err != nil {
+		t.Fatal(err)
+	}
+	return string(raw)
+}
+
 func seedAIMatchContext(t *testing.T, db *sql.DB, sybID string) model.User {
 	return seedAIMatchContextWithData(t, db, sybID, "黑色,M", collectedAIChoices)
 }