Meine Kollegen und ich können nicht mehr auf den Flur-Kopierern drucken.
Die Bürodrucker laufen noch.
`}, model.Category{ID: 67, Name: "Arbeitsplatzdrucker"}, model.ContextSnapshot{})
+ if err != nil {
+ t.Fatal(err)
+ }
+ if attempts != 1 {
+ t.Fatalf("attempts=%d want 1", attempts)
+ }
+ if d.AffectedScope != "multiple_users" || !model.HasReasonCode(d.ReasonCodes, "multiple_users_affected") || !model.HasReasonCode(d.ReasonCodes, "workaround_available") || model.HasReasonCode(d.ReasonCodes, "insufficient_information") || d.RecommendedPriority != 3 {
+ t.Fatalf("unexpected reconciled decision: %+v", d)
+ }
+ if d.Confidence != 0.2 {
+ t.Fatalf("confidence was increased: %v", d.Confidence)
+ }
+}
+
+func TestAnalyseEscalationNormalizesNegativeDecision(t *testing.T) {
+ srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+ var body map[string]any
+ if err := json.NewDecoder(r.Body).Decode(&body); err != nil {
+ t.Fatal(err)
+ }
+ formatJSON, _ := json.Marshal(body["format"])
+ if !strings.Contains(string(formatJSON), "recommended_action") || !strings.Contains(string(formatJSON), "no_human_response") {
+ t.Fatalf("unexpected escalation schema: %s", formatJSON)
+ }
+ _ = json.NewEncoder(w).Encode(map[string]any{"message": map[string]any{"content": `{"escalate":false,"level":3,"recommended_action":"raise_priority","reason_codes":["already_being_handled"],"confidence":0.91,"reason":"Ein Techniker arbeitet bereits am Ticket."}`}})
+ }))
+ defer srv.Close()
+ c := New(srv.URL, "m", "e", "de-DE", "formal", time.Second, 768, time.Minute, false, 1, 0)
+ d, err := c.AnalyseEscalation(context.Background(), model.Ticket{ID: 1}, []model.Followup{{ID: 4, UserID: 7}}, model.ContextSnapshot{})
+ if err != nil {
+ t.Fatal(err)
+ }
+ if d.Escalate || d.Level != 0 || d.RecommendedAction != "none" {
+ t.Fatalf("negative escalation was not normalized: %+v", d)
+ }
+}
diff --git a/internal/prioritysignals/signals.go b/internal/prioritysignals/signals.go
new file mode 100644
index 0000000..eec33bd
--- /dev/null
+++ b/internal/prioritysignals/signals.go
@@ -0,0 +1,239 @@
+package prioritysignals
+
+import (
+ "html"
+ "regexp"
+ "strings"
+ "unicode/utf8"
+
+ "github.com/example/glpi-ai-agent/internal/model"
+)
+
+// Signal is deterministic evidence extracted from the user-provided ticket.
+// It does not decide the priority. It only makes explicit statements available
+// to the dedicated AI run and to consistency checks around its output.
+type Signal struct {
+ Code string `json:"code"`
+ Source string `json:"source"`
+ Excerpt string `json:"excerpt"`
+}
+
+// Evidence contains only signals that are stated explicitly enough to be used
+// as a consistency guard. Absence of a signal is never interpreted as proof of
+// the opposite condition.
+type Evidence struct {
+ Signals []Signal `json:"signals,omitempty"`
+}
+
+type rule struct {
+ code string
+ re *regexp.Regexp
+}
+
+var rules = []rule{
+ {code: "multiple_users_affected", re: regexp.MustCompile(`(?i)\b(?:meine|unsere)\s+(?:kolleg(?:e|en|innen)|mitarbeiter(?:innen)?|teammitglieder)\s+und\s+ich\b`)},
+ {code: "multiple_users_affected", re: regexp.MustCompile(`(?i)\b(?:kolleg(?:e|en|innen)|mitarbeiter(?:innen)?|benutzer|nutzer|personen)\s+und\s+ich\b`)},
+ {code: "multiple_users_affected", re: regexp.MustCompile(`(?i)\b(?:mehrere|viele|alle|sämtliche)\s+(?:kolleg(?:en|innen)?|mitarbeiter(?:innen)?|benutzer|nutzer|personen|arbeitsplätze|rechner|pcs|geräte)\b`)},
+ {code: "multiple_users_affected", re: regexp.MustCompile(`(?i)\bwir\s+alle\b`)},
+ {code: "site_affected", re: regexp.MustCompile(`(?i)\b(?:gesamter|ganzer|kompletter)\s+(?:standort|gebäude|liegenschaft|campus)\b`)},
+ {code: "organization_affected", re: regexp.MustCompile(`(?i)\b(?:gesamte|ganze|komplette)\s+(?:organisation|verwaltung|behörde|firma|unternehmen)\b`)},
+ {code: "workaround_available", re: regexp.MustCompile(`(?i)\b(?:workaround|ausweichmöglichkeit|alternative(?:r|n)?\s+weg|ersatzlösung)\b`)},
+ {code: "workaround_available", re: regexp.MustCompile(`(?i)\b(?:andere|weitere|alternative)\s+(?:drucker|geräte|systeme|anwendungen?)\b.{0,60}\b(?:funktionier(?:t|en)|lauf(?:en|t)|geh(?:t|en))\b`)},
+ {code: "workaround_available", re: regexp.MustCompile(`(?i)\b(?:büro|arbeitsplatz)[- ]?drucker\b.{0,50}\b(?:funktionier(?:t|en)|lauf(?:en|t)|geh(?:t|en)|noch\s+verfügbar)\b`)},
+ {code: "no_workaround", re: regexp.MustCompile(`(?i)\b(?:kein(?:e|en)?\s+(?:workaround|alternative|ausweichmöglichkeit)|ohne\s+ausweichmöglichkeit|überhaupt\s+nicht\s+weiterarbeiten)\b`)},
+ {code: "business_deadline", re: regexp.MustCompile(`(?i)\b(?:frist|deadline|abgabetermin|termin)\b.{0,60}\b(?:heute|morgen|bis\s+\d{1,2}[.\/-]\d{1,2}|dringend|läuft\s+ab)\b`)},
+ {code: "security_incident_suspected", re: regexp.MustCompile(`(?i)\b(?:phishing|ransomware|schadsoftware|malware|konto\s+übernommen|unbefugter\s+zugriff|datenabfluss)\b`)},
+ {code: "data_loss_possible", re: regexp.MustCompile(`(?i)\b(?:datenverlust|daten\s+verloren|dateien\s+gelöscht|nicht\s+gespeichert|überschrieben)\b`)},
+}
+
+var tagRE = regexp.MustCompile(`(?s)<[^>]*>`)
+var whitespaceRE = regexp.MustCompile(`\s+`)
+
+// Extract returns deterministic evidence from subject and body. It intentionally
+// uses conservative patterns: false negatives are acceptable; false positives
+// must not become automatic priority changes.
+func Extract(t model.Ticket) Evidence {
+ text := normalize(t.Name + "\n" + t.Content)
+ seen := map[string]struct{}{}
+ out := Evidence{}
+ for _, r := range rules {
+ loc := r.re.FindStringIndex(text)
+ if loc == nil {
+ continue
+ }
+ if _, ok := seen[r.code]; ok {
+ continue
+ }
+ seen[r.code] = struct{}{}
+ out.Signals = append(out.Signals, Signal{Code: r.code, Source: "ticket_text", Excerpt: excerpt(text, loc[0], loc[1], 90)})
+ }
+ return out
+}
+
+func (e Evidence) Has(code string) bool {
+ code = strings.TrimSpace(strings.ToLower(code))
+ for _, s := range e.Signals {
+ if strings.EqualFold(strings.TrimSpace(s.Code), code) {
+ return true
+ }
+ }
+ return false
+}
+
+func (e Evidence) Codes() []string {
+ out := make([]string, 0, len(e.Signals))
+ for _, s := range e.Signals {
+ if strings.TrimSpace(s.Code) != "" {
+ out = append(out, s.Code)
+ }
+ }
+ return out
+}
+
+func normalize(v string) string {
+ v = strings.NewReplacer("Meine Kollegen und ich können nicht mehr auf den Flur-Kopierern drucken.
Die Bürodrucker laufen noch.
`})
+ if !e.Has("multiple_users_affected") {
+ t.Fatalf("missing multiple user signal: %+v", e.Signals)
+ }
+ if !e.Has("workaround_available") {
+ t.Fatalf("missing workaround signal: %+v", e.Signals)
+ }
+ if e.Has("insufficient_information") {
+ t.Fatalf("extractor must not invent insufficient_information")
+ }
+}
+
+func TestExtractDoesNotTreatGenericSingularAsSingleUserProof(t *testing.T) {
+ e := Extract(model.Ticket{Content: `Ich kann nicht drucken.`})
+ if len(e.Signals) != 0 {
+ t.Fatalf("unexpected signals: %+v", e.Signals)
+ }
+}
+
+func TestReconcileDoesNotRetryOrRaiseConfidence(t *testing.T) {
+ ticket := model.Ticket{Priority: 3, Content: `Meine Kollegen und ich können nicht drucken. Die Bürodrucker laufen noch.`}
+ e := Extract(ticket)
+ out := Reconcile(ticket, e, model.PriorityDecision{
+ RecommendedPriority: 3,
+ AffectedScope: "unknown",
+ ReasonCodes: []string{"insufficient_information"},
+ Confidence: 0.2,
+ Reason: "insufficient_information",
+ })
+ if out.AffectedScope != "multiple_users" {
+ t.Fatalf("scope=%q", out.AffectedScope)
+ }
+ if !model.HasReasonCode(out.ReasonCodes, "multiple_users_affected") || !model.HasReasonCode(out.ReasonCodes, "workaround_available") {
+ t.Fatalf("reason codes=%v", out.ReasonCodes)
+ }
+ if model.HasReasonCode(out.ReasonCodes, "insufficient_information") {
+ t.Fatalf("contradictory reason retained: %v", out.ReasonCodes)
+ }
+ if out.Confidence != 0.2 {
+ t.Fatalf("confidence=%v", out.Confidence)
+ }
+ if out.Reason == "" || out.Reason == "insufficient_information" {
+ t.Fatalf("reason=%q", out.Reason)
+ }
+}
diff --git a/internal/queue/queue.go b/internal/queue/queue.go
index 7e27070..9d0dca1 100644
--- a/internal/queue/queue.go
+++ b/internal/queue/queue.go
@@ -1,47 +1,159 @@
package queue
import (
+ "container/heap"
"context"
+ "fmt"
"sync"
+ "time"
)
+const (
+ PriorityScheduled = 20
+ PriorityPoll = 50
+ PriorityWebhook = 80
+ PriorityManual = 100
+)
+
+type WorkItem struct {
+ TicketID int64 `json:"ticket_id"`
+ Trigger string `json:"trigger"`
+ Priority int `json:"priority"`
+ Enqueued time.Time `json:"enqueued_at"`
+ Attempt int `json:"attempt,omitempty"`
+ Force bool `json:"force,omitempty"`
+ sequence uint64
+}
+
+func (w WorkItem) Key() string {
+ trigger := w.Trigger
+ if trigger == "" {
+ trigger = "poll"
+ }
+ return fmt.Sprintf("%d:%s", w.TicketID, trigger)
+}
+
+type itemHeap []WorkItem
+
+func (h itemHeap) Len() int { return len(h) }
+func (h itemHeap) Less(i, j int) bool {
+ if h[i].Priority != h[j].Priority {
+ return h[i].Priority > h[j].Priority
+ }
+ return h[i].sequence < h[j].sequence
+}
+func (h itemHeap) Swap(i, j int) { h[i], h[j] = h[j], h[i] }
+func (h *itemHeap) Push(x any) { *h = append(*h, x.(WorkItem)) }
+func (h *itemHeap) Pop() any {
+ old := *h
+ n := len(old)
+ item := old[n-1]
+ *h = old[:n-1]
+ return item
+}
+
type Queue struct {
- ch chan int64
mu sync.Mutex
- pending map[int64]struct{}
+ items itemHeap
+ pending map[string]struct{}
+ notify chan struct{}
+ limit int
+ seq uint64
}
func New(size int) *Queue {
- return &Queue{ch: make(chan int64, size), pending: make(map[int64]struct{})}
+ if size < 1 {
+ size = 1
+ }
+ q := &Queue{pending: make(map[string]struct{}), notify: make(chan struct{}, 1), limit: size}
+ heap.Init(&q.items)
+ return q
}
+
func (q *Queue) Enqueue(id int64) bool {
- if id <= 0 {
+ return q.EnqueueWork(WorkItem{TicketID: id, Trigger: "poll", Priority: PriorityPoll})
+}
+
+func (q *Queue) EnqueueWork(item WorkItem) bool {
+ if item.TicketID <= 0 {
return false
}
+ if item.Trigger == "" {
+ item.Trigger = "poll"
+ }
+ if item.Priority == 0 {
+ item.Priority = PriorityPoll
+ }
+ if item.Enqueued.IsZero() {
+ item.Enqueued = time.Now()
+ }
+ key := item.Key()
q.mu.Lock()
- if _, ok := q.pending[id]; ok {
+ if _, ok := q.pending[key]; ok || len(q.items) >= q.limit {
q.mu.Unlock()
return false
}
- q.pending[id] = struct{}{}
+ q.seq++
+ item.sequence = q.seq
+ q.pending[key] = struct{}{}
+ heap.Push(&q.items, item)
q.mu.Unlock()
+ q.signal()
+ return true
+}
+
+func (q *Queue) signal() {
select {
- case q.ch <- id:
- return true
+ case q.notify <- struct{}{}:
default:
- q.mu.Lock()
- delete(q.pending, id)
- q.mu.Unlock()
- return false
}
}
+
func (q *Queue) Next(ctx context.Context) (int64, bool) {
- select {
- case <-ctx.Done():
- return 0, false
- case id := <-q.ch:
- return id, true
+ item, ok := q.NextWork(ctx)
+ return item.TicketID, ok
+}
+
+func (q *Queue) NextWork(ctx context.Context) (WorkItem, bool) {
+ for {
+ q.mu.Lock()
+ if len(q.items) > 0 {
+ item := heap.Pop(&q.items).(WorkItem)
+ more := len(q.items) > 0
+ q.mu.Unlock()
+ if more {
+ q.signal()
+ }
+ return item, true
+ }
+ q.mu.Unlock()
+ select {
+ case <-ctx.Done():
+ return WorkItem{}, false
+ case <-q.notify:
+ }
}
}
-func (q *Queue) Done(id int64) { q.mu.Lock(); delete(q.pending, id); q.mu.Unlock() }
-func (q *Queue) Len() int { return len(q.ch) }
+
+func (q *Queue) Done(id int64) {
+ q.mu.Lock()
+ for key := range q.pending {
+ prefix := fmt.Sprintf("%d:", id)
+ if len(key) >= len(prefix) && key[:len(prefix)] == prefix {
+ delete(q.pending, key)
+ }
+ }
+ q.mu.Unlock()
+}
+
+func (q *Queue) DoneWork(item WorkItem) {
+ q.mu.Lock()
+ delete(q.pending, item.Key())
+ q.mu.Unlock()
+}
+
+func (q *Queue) Len() int {
+ q.mu.Lock()
+ defer q.mu.Unlock()
+ return len(q.items)
+}
diff --git a/internal/queue/queue_test.go b/internal/queue/queue_test.go
new file mode 100644
index 0000000..bf6ad0f
--- /dev/null
+++ b/internal/queue/queue_test.go
@@ -0,0 +1,36 @@
+package queue
+
+import (
+ "context"
+ "testing"
+ "time"
+)
+
+func TestPriorityOrderAndFIFO(t *testing.T) {
+ q := New(10)
+ q.EnqueueWork(WorkItem{TicketID: 1, Trigger: "poll", Priority: 10})
+ q.EnqueueWork(WorkItem{TicketID: 2, Trigger: "webhook", Priority: 80})
+ q.EnqueueWork(WorkItem{TicketID: 3, Trigger: "manual", Priority: 80})
+ ctx, cancel := context.WithTimeout(context.Background(), time.Second)
+ defer cancel()
+ for i, want := range []int64{2, 3, 1} {
+ got, ok := q.NextWork(ctx)
+ if !ok || got.TicketID != want {
+ t.Fatalf("item %d = %#v, want ticket %d", i, got, want)
+ }
+ q.DoneWork(got)
+ }
+}
+
+func TestSameTicketDifferentTriggers(t *testing.T) {
+ q := New(3)
+ if !q.EnqueueWork(WorkItem{TicketID: 7, Trigger: "poll"}) {
+ t.Fatal("poll enqueue failed")
+ }
+ if !q.EnqueueWork(WorkItem{TicketID: 7, Trigger: "scheduled_escalation"}) {
+ t.Fatal("separate trigger should be accepted")
+ }
+ if q.EnqueueWork(WorkItem{TicketID: 7, Trigger: "poll"}) {
+ t.Fatal("duplicate trigger should be rejected")
+ }
+}
diff --git a/internal/state/store.go b/internal/state/store.go
index 895ffaa..061f4c1 100644
--- a/internal/state/store.go
+++ b/internal/state/store.go
@@ -8,24 +8,43 @@ import (
"os"
"path/filepath"
"sort"
+ "strconv"
+ "strings"
"sync"
"github.com/example/glpi-ai-agent/internal/model"
)
type Store struct {
- mu sync.RWMutex
- path string
- processed map[string]struct{}
- runs []model.RunRecord
- maxRuns int
+ mu sync.RWMutex
+ path string
+ indexPath string
+ processedVersions map[int64]string
+ escalationKeys map[string]struct{}
+ runs []model.RunRecord
+ runIndex map[string]int
+ analysisIndex map[string]model.AnalysisRun
+ maxRuns int
+}
+
+type durableIndex struct {
+ ProcessedVersions map[string]string `json:"processed_versions"`
+ EscalationKeys []string `json:"escalation_keys"`
}
func Open(dir string, maxRuns int) (*Store, error) {
if err := os.MkdirAll(dir, 0o750); err != nil {
return nil, err
}
- s := &Store{path: filepath.Join(dir, "runs.jsonl"), processed: map[string]struct{}{}, maxRuns: maxRuns}
+ s := &Store{
+ path: filepath.Join(dir, "runs.jsonl"),
+ indexPath: filepath.Join(dir, "state-index.json"),
+ processedVersions: map[int64]string{},
+ escalationKeys: map[string]struct{}{},
+ runIndex: map[string]int{},
+ analysisIndex: map[string]model.AnalysisRun{},
+ maxRuns: maxRuns,
+ }
// Fail fast during startup if the persistent data path is not writable.
// A read-only/root-owned Docker volume must not be discovered only after the first ticket.
f, err := os.OpenFile(s.path, os.O_CREATE|os.O_APPEND|os.O_WRONLY, 0o640)
@@ -35,22 +54,25 @@ func Open(dir string, maxRuns int) (*Store, error) {
if err := f.Close(); err != nil {
return nil, fmt.Errorf("close state write probe: %w", err)
}
+ if err := s.loadDurableIndex(); err != nil {
+ return nil, err
+ }
if err := s.load(); err != nil {
return nil, err
}
return s, nil
}
-func key(id int64, version string) string { return fmtKey(id, version) }
-func fmtKey(id int64, version string) string {
- b, _ := json.Marshal([]any{id, version})
- return string(b)
-}
func (s *Store) Seen(id int64, version string) bool {
s.mu.RLock()
defer s.mu.RUnlock()
- _, ok := s.processed[key(id, version)]
- return ok
+ return version != "" && s.processedVersions[id] == version
}
+func (s *Store) ProcessedVersionCount() int {
+ s.mu.RLock()
+ defer s.mu.RUnlock()
+ return len(s.processedVersions)
+}
+
func (s *Store) Append(r model.RunRecord) error {
s.mu.Lock()
defer s.mu.Unlock()
@@ -69,13 +91,20 @@ func (s *Store) Append(r model.RunRecord) error {
if err != nil {
return err
}
- if r.Outcome != "error" && r.Error == "" {
- s.processed[key(r.TicketID, r.SourceVersion)] = struct{}{}
- }
+ s.absorbDurableStateLocked(r)
s.runs = append(s.runs, r)
if len(s.runs) > s.maxRuns {
s.runs = s.runs[len(s.runs)-s.maxRuns:]
}
+ s.rebuildIndexesLocked()
+ if err := s.persistDurableIndexLocked(); err != nil {
+ return fmt.Errorf("persist durable state index: %w", err)
+ }
+ if info, statErr := os.Stat(s.path); statErr == nil && info.Size() > 64<<20 {
+ if compactErr := s.compactLocked(); compactErr != nil {
+ return fmt.Errorf("compact state: %w", compactErr)
+ }
+ }
return nil
}
func (s *Store) Recent(limit int) []model.RunRecord {
@@ -94,12 +123,32 @@ func (s *Store) Recent(limit int) []model.RunRecord {
func (s *Store) FindRun(runID string) (model.RunRecord, bool) {
s.mu.RLock()
defer s.mu.RUnlock()
- for i := len(s.runs) - 1; i >= 0; i-- {
- if s.runs[i].RunID == runID {
- return s.runs[i], true
- }
+ i, ok := s.runIndex[runID]
+ if !ok || i < 0 || i >= len(s.runs) {
+ return model.RunRecord{}, false
}
- return model.RunRecord{}, false
+ return s.runs[i], true
+}
+
+func marksTicketVersionProcessed(r model.RunRecord) bool {
+ if r.Outcome == "error" || r.Error != "" || strings.EqualFold(r.Trigger, "scheduled_escalation") {
+ return false
+ }
+ return r.SourceVersion != ""
+}
+
+func (s *Store) FindAnalysis(analysisID string) (model.AnalysisRun, bool) {
+ s.mu.RLock()
+ defer s.mu.RUnlock()
+ a, ok := s.analysisIndex[analysisID]
+ return a, ok
+}
+
+func (s *Store) HasEscalationKey(key string) bool {
+ s.mu.RLock()
+ defer s.mu.RUnlock()
+ _, ok := s.escalationKeys[strings.TrimSpace(key)]
+ return ok
}
func (s *Store) load() error {
@@ -117,15 +166,168 @@ func (s *Store) load() error {
for sc.Scan() {
var r model.RunRecord
if json.Unmarshal(sc.Bytes(), &r) == nil {
+ s.absorbDurableStateLocked(r)
s.runs = append(s.runs, r)
- if r.Outcome != "error" && r.Error == "" {
- s.processed[key(r.TicketID, r.SourceVersion)] = struct{}{}
- }
}
}
if len(s.runs) > s.maxRuns {
s.runs = s.runs[len(s.runs)-s.maxRuns:]
}
sort.SliceStable(s.runs, func(i, j int) bool { return s.runs[i].FinishedAt.Before(s.runs[j].FinishedAt) })
+ s.rebuildIndexesLocked()
return sc.Err()
}
+
+func (s *Store) rebuildIndexesLocked() {
+ s.runIndex = make(map[string]int, len(s.runs))
+ s.analysisIndex = make(map[string]model.AnalysisRun)
+ for i, r := range s.runs {
+ s.runIndex[r.RunID] = i
+ for _, a := range r.Analyses {
+ s.analysisIndex[a.AnalysisID] = a
+ }
+ }
+}
+
+func (s *Store) LatestTicketRun(ticketID int64, excludedTriggers ...string) (model.RunRecord, bool) {
+ s.mu.RLock()
+ defer s.mu.RUnlock()
+ excluded := make(map[string]struct{}, len(excludedTriggers))
+ for _, trigger := range excludedTriggers {
+ excluded[strings.ToLower(strings.TrimSpace(trigger))] = struct{}{}
+ }
+ for i := len(s.runs) - 1; i >= 0; i-- {
+ r := s.runs[i]
+ if r.TicketID != ticketID {
+ continue
+ }
+ if _, skip := excluded[strings.ToLower(strings.TrimSpace(r.Trigger))]; skip {
+ continue
+ }
+ return r, true
+ }
+ return model.RunRecord{}, false
+}
+
+func (s *Store) absorbDurableStateLocked(r model.RunRecord) {
+ if marksTicketVersionProcessed(r) {
+ s.processedVersions[r.TicketID] = r.SourceVersion
+ }
+ for _, a := range r.Analyses {
+ if a.AnalysisType != "escalation" || !a.Action.Executed {
+ continue
+ }
+ if key := escalationKeyFromResult(a.Action.Result); key != "" {
+ s.escalationKeys[key] = struct{}{}
+ }
+ }
+}
+
+func escalationKeyFromResult(result string) string {
+ var ticketPart, levelPart string
+ for _, part := range strings.Split(result, ";") {
+ part = strings.TrimSpace(part)
+ switch {
+ case strings.HasPrefix(part, "ticket="):
+ ticketPart = part
+ case strings.HasPrefix(part, "level="):
+ levelPart = part
+ }
+ }
+ if ticketPart != "" && levelPart != "" {
+ return ticketPart + ";" + levelPart
+ }
+ return ""
+}
+
+func (s *Store) loadDurableIndex() error {
+ b, err := os.ReadFile(s.indexPath)
+ if errors.Is(err, os.ErrNotExist) {
+ return nil
+ }
+ if err != nil {
+ return fmt.Errorf("read durable state index: %w", err)
+ }
+ var idx durableIndex
+ if err := json.Unmarshal(b, &idx); err != nil {
+ return fmt.Errorf("decode durable state index: %w", err)
+ }
+ for rawID, version := range idx.ProcessedVersions {
+ id, err := strconv.ParseInt(rawID, 10, 64)
+ if err == nil && id > 0 && version != "" {
+ s.processedVersions[id] = version
+ }
+ }
+ for _, key := range idx.EscalationKeys {
+ key = strings.TrimSpace(key)
+ if key != "" {
+ s.escalationKeys[key] = struct{}{}
+ }
+ }
+ return nil
+}
+
+func (s *Store) persistDurableIndexLocked() error {
+ idx := durableIndex{ProcessedVersions: make(map[string]string, len(s.processedVersions)), EscalationKeys: make([]string, 0, len(s.escalationKeys))}
+ for id, version := range s.processedVersions {
+ if id > 0 && version != "" {
+ idx.ProcessedVersions[strconv.FormatInt(id, 10)] = version
+ }
+ }
+ for key := range s.escalationKeys {
+ idx.EscalationKeys = append(idx.EscalationKeys, key)
+ }
+ sort.Strings(idx.EscalationKeys)
+ b, err := json.MarshalIndent(idx, "", " ")
+ if err != nil {
+ return err
+ }
+ tmp := s.indexPath + ".tmp"
+ f, err := os.OpenFile(tmp, os.O_CREATE|os.O_TRUNC|os.O_WRONLY, 0o640)
+ if err != nil {
+ return err
+ }
+ cleanup := func() {
+ _ = f.Close()
+ _ = os.Remove(tmp)
+ }
+ if _, err := f.Write(append(b, '\n')); err != nil {
+ cleanup()
+ return err
+ }
+ if err := f.Sync(); err != nil {
+ cleanup()
+ return err
+ }
+ if err := f.Close(); err != nil {
+ _ = os.Remove(tmp)
+ return err
+ }
+ return os.Rename(tmp, s.indexPath)
+}
+
+func (s *Store) compactLocked() error {
+ tmp := s.path + ".tmp"
+ f, err := os.OpenFile(tmp, os.O_CREATE|os.O_TRUNC|os.O_WRONLY, 0o640)
+ if err != nil {
+ return err
+ }
+ enc := json.NewEncoder(f)
+ for _, r := range s.runs {
+ if err := enc.Encode(r); err != nil {
+ f.Close()
+ _ = os.Remove(tmp)
+ return err
+ }
+ }
+ if err := f.Sync(); err != nil {
+ f.Close()
+ _ = os.Remove(tmp)
+ return err
+ }
+ if err := f.Close(); err != nil {
+ _ = os.Remove(tmp)
+ return err
+ }
+ return os.Rename(tmp, s.path)
+}
diff --git a/internal/state/store_test.go b/internal/state/store_test.go
index 00bdac6..2e1a815 100644
--- a/internal/state/store_test.go
+++ b/internal/state/store_test.go
@@ -49,3 +49,68 @@ func TestErrorRunIsRetriedAfterRestart(t *testing.T) {
t.Fatal("failed run became seen after restart")
}
}
+
+func TestAnalysisIndexAndEscalationIdempotencySurviveRestart(t *testing.T) {
+ dir := t.TempDir()
+ s, err := Open(dir, 10)
+ if err != nil {
+ t.Fatal(err)
+ }
+ a := model.AnalysisRun{
+ AnalysisID: "analysis-1",
+ ParentRunID: "run-1",
+ TicketID: 17,
+ AnalysisType: "escalation",
+ Action: model.ActionAudit{
+ Type: "raise_priority",
+ Executed: true,
+ Result: "ticket=17;level=2; priority_written",
+ },
+ }
+ r := model.RunRecord{RunID: "run-1", TicketID: 17, Trigger: "scheduled_escalation", SourceVersion: "v1", FinishedAt: time.Now(), Outcome: "processed", Analyses: []model.AnalysisRun{a}}
+ if err := s.Append(r); err != nil {
+ t.Fatal(err)
+ }
+ if s.Seen(17, "v1") {
+ t.Fatal("scheduled escalation must not consume the normal ticket version")
+ }
+ got, ok := s.FindAnalysis("analysis-1")
+ if !ok || got.ParentRunID != "run-1" {
+ t.Fatalf("analysis index result=%+v ok=%v", got, ok)
+ }
+ if !s.HasEscalationKey("ticket=17;level=2") {
+ t.Fatal("executed escalation key not found")
+ }
+
+ s2, err := Open(dir, 10)
+ if err != nil {
+ t.Fatal(err)
+ }
+ if _, ok := s2.FindAnalysis("analysis-1"); !ok {
+ t.Fatal("analysis index was not rebuilt after restart")
+ }
+ if !s2.HasEscalationKey("ticket=17;level=2") {
+ t.Fatal("escalation idempotency was not rebuilt after restart")
+ }
+}
+
+func TestLatestTicketRunCanExcludeScheduledRuns(t *testing.T) {
+ dir := t.TempDir()
+ s, err := Open(dir, 10)
+ if err != nil {
+ t.Fatal(err)
+ }
+ base := time.Now()
+ for _, r := range []model.RunRecord{
+ {RunID: "normal", TicketID: 5, Trigger: "poll", SourceVersion: "v1", FinishedAt: base, Outcome: "processed"},
+ {RunID: "scheduled", TicketID: 5, Trigger: "scheduled_escalation", SourceVersion: "v1", FinishedAt: base.Add(time.Second), Outcome: "processed"},
+ } {
+ if err := s.Append(r); err != nil {
+ t.Fatal(err)
+ }
+ }
+ got, ok := s.LatestTicketRun(5, "scheduled_escalation")
+ if !ok || got.RunID != "normal" {
+ t.Fatalf("latest non-scheduled run=%+v ok=%v", got, ok)
+ }
+}
diff --git a/internal/web/server.go b/internal/web/server.go
index cd4c915..ff6f914 100644
--- a/internal/web/server.go
+++ b/internal/web/server.go
@@ -86,6 +86,7 @@ func (s *Server) Handler() http.Handler {
mux.Handle("GET /diagnostics", s.auth(http.HandlerFunc(s.diagnosticsPage)))
mux.Handle("GET /category-mappings", s.auth(http.HandlerFunc(s.categoryMappingsPage)))
mux.Handle("GET /api/diagnostics/run/{id}", s.auth(http.HandlerFunc(s.diagnosticRun)))
+ mux.Handle("GET /api/diagnostics/analysis/{id}", s.auth(http.HandlerFunc(s.diagnosticAnalysis)))
mux.Handle("GET /api/diagnostics/run/{id}/knowledge", s.auth(http.HandlerFunc(s.diagnosticKnowledge)))
mux.Handle("GET /api/diagnostics/knowledge", s.auth(http.HandlerFunc(s.diagnosticKnowledgeSearch)))
mux.Handle("GET /api/status", s.auth(http.HandlerFunc(s.status)))
@@ -101,6 +102,7 @@ func (s *Server) Handler() http.Handler {
mux.Handle("GET /api/learning", s.auth(http.HandlerFunc(s.learningList)))
mux.Handle("POST /api/learning", s.auth(s.mutation(http.HandlerFunc(s.learningAdd))))
mux.Handle("DELETE /api/learning/{id}", s.auth(s.mutation(http.HandlerFunc(s.learningDelete))))
+ mux.Handle("POST /api/tickets/{id}/reprocess", s.auth(s.mutation(http.HandlerFunc(s.reprocessTicket))))
mux.HandleFunc("POST /webhook/glpi", s.webhook)
return securityHeaders(requestLog(mux))
}
@@ -260,6 +262,19 @@ func (s *Server) diagnosticRun(w http.ResponseWriter, r *http.Request) {
respondJSON(w, run)
}
+func (s *Server) diagnosticAnalysis(w http.ResponseWriter, r *http.Request) {
+ if s.state == nil {
+ http.Error(w, "analysis diagnostics unavailable", http.StatusNotImplemented)
+ return
+ }
+ analysis, ok := s.state.FindAnalysis(strings.TrimSpace(r.PathValue("id")))
+ if !ok {
+ http.Error(w, "analysis not found", http.StatusNotFound)
+ return
+ }
+ respondJSON(w, analysis)
+}
+
func (s *Server) diagnosticKnowledge(w http.ResponseWriter, r *http.Request) {
if s.diagnostics == nil {
http.Error(w, "diagnostics unavailable", http.StatusNotImplemented)
@@ -315,13 +330,16 @@ func (s *Server) diagnosticKnowledgeSearch(w http.ResponseWriter, r *http.Reques
func (s *Server) status(w http.ResponseWriter, r *http.Request) {
g, o := s.metrics.Health()
+ poll := s.metrics.PollStatus()
kbOK, kbDocs, kbLastSync, kbLastErr := s.metrics.GLPIKBStatus()
loadStats := s.knowledge.LoadStats()
initStatus := s.knowledge.InitStatus()
respondJSON(w, map[string]any{
"uptime_seconds": int(time.Since(s.metrics.Started).Seconds()), "dry_run": s.cfg.DryRun, "auto_reply": s.cfg.AutoReply, "auto_category": s.cfg.AutoCategory,
- "processed": s.metrics.Processed.Load(), "skipped": s.metrics.Skipped.Load(), "errors": s.metrics.Errors.Load(), "category_changes": s.metrics.CategoryChanged.Load(), "replies": s.metrics.Replies.Load(), "queue_depth": s.q.Len(),
- "glpi_ok": g, "ollama_ok": o, "knowledge_docs": s.metrics.KnowledgeDocs(), "last_poll": s.metrics.LastPoll(),
+ "priority_enabled": s.cfg.PriorityEnabled, "auto_priority": s.cfg.AutoPriority, "priority_confidence": s.cfg.PriorityConfidence, "priority_analysis_timeout": s.cfg.PriorityAnalysisTimeout.String(), "priority_max_increase": s.cfg.PriorityMaxIncrease, "priority_allowed_reason_codes": s.cfg.PriorityAllowedReasonCodes,
+ "escalation_enabled": s.cfg.EscalationEnabled, "auto_escalation": s.cfg.AutoEscalation, "escalation_scan_interval": s.cfg.EscalationScanInterval.String(), "escalation_min_age": s.cfg.EscalationMinAge.String(), "escalation_confidence": s.cfg.EscalationConfidence, "escalation_max_level": s.cfg.EscalationMaxLevel, "escalation_allowed_reason_codes": s.cfg.EscalationAllowedReasonCodes, "escalation_allowed_actions": s.cfg.EscalationAllowedActions, "glpi_escalation_filter_configured": strings.TrimSpace(s.cfg.GLPIEscalationFilter) != "", "glpi_escalation_limit": s.cfg.GLPIEscalationLimit,
+ "processed": s.metrics.Processed.Load(), "skipped": s.metrics.Skipped.Load(), "errors": s.metrics.Errors.Load(), "category_changes": s.metrics.CategoryChanged.Load(), "replies": s.metrics.Replies.Load(), "priority_recommendations": s.metrics.PriorityRecommendations.Load(), "priority_changes": s.metrics.PriorityChanges.Load(), "escalation_runs": s.metrics.EscalationRuns.Load(), "escalations": s.metrics.Escalations.Load(), "queue_depth": s.q.Len(),
+ "glpi_ok": g, "ollama_ok": o, "knowledge_docs": s.metrics.KnowledgeDocs(), "last_poll": poll.At, "polls_total": s.metrics.Polls.Load(), "poll_last_fetched": poll.Fetched, "poll_last_seen": poll.Seen, "poll_last_unseen": poll.Unseen, "poll_last_enqueued": poll.Enqueued, "poll_last_rejected": poll.Rejected, "poll_last_error": poll.Error, "processed_version_count": s.state.ProcessedVersionCount(),
"knowledge_ready": s.knowledge.Ready(), "knowledge_init_state": initStatus.State, "knowledge_init_phase": initStatus.Phase, "knowledge_init_total_files": initStatus.TotalFiles, "knowledge_init_processed_files": initStatus.ProcessedFiles, "knowledge_init_loaded_docs": initStatus.LoadedDocs, "knowledge_init_indexed_docs": initStatus.IndexedDocs, "knowledge_init_cache_hits": initStatus.CacheHits, "knowledge_init_pending_embeddings": initStatus.PendingEmbeddings, "knowledge_init_started_at": initStatus.StartedAt, "knowledge_init_finished_at": initStatus.FinishedAt, "knowledge_init_error": initStatus.LastError,
"knowledge_index_mode": s.cfg.KnowledgeIndexMode, "knowledge_embed_batch_size": s.cfg.KnowledgeEmbedBatchSize, "knowledge_index_scan_interval": s.cfg.KnowledgeIndexScanInterval.String(), "knowledge_snapshot_loaded": initStatus.SnapshotLoaded, "knowledge_snapshot_path": initStatus.SnapshotPath, "knowledge_snapshot_saved_at": initStatus.SnapshotSavedAt, "knowledge_last_scan_at": initStatus.LastScanAt, "knowledge_last_scan_error": initStatus.LastScanError, "knowledge_changed_files": initStatus.ChangedFiles, "knowledge_deleted_files": initStatus.DeletedFiles, "knowledge_reused_files": initStatus.ReusedFiles,
"communication_language": s.cfg.CommunicationLanguage, "communication_style": s.cfg.CommunicationStyle, "ai_content_label_enabled": s.cfg.AIContentLabelEnabled, "knowledge_allowed_sources": s.cfg.KnowledgeAllowedSources, "knowledge_category_sources": s.cfg.KnowledgeCategorySources, "knowledge_auto_reply_sources": s.cfg.KnowledgeAutoReplySources, "knowledge_category_mode": s.cfg.KnowledgeCategoryMode, "knowledge_category_map_configured": strings.TrimSpace(s.cfg.KnowledgeCategoryMapFile) != "", "knowledge_ignore_globs": s.cfg.KnowledgeIgnoreGlobs, "knowledge_ignored_files": loadStats.IgnoredFiles, "knowledge_unmapped_category_files": loadStats.UnmappedCategoryFiles, "knowledge_unmapped_categories": loadStats.UnmappedCategories,
@@ -560,6 +578,24 @@ func (s *Server) mutation(next http.Handler) http.Handler {
})
}
+func (s *Server) reprocessTicket(w http.ResponseWriter, r *http.Request) {
+ id, err := strconv.ParseInt(strings.TrimSpace(r.PathValue("id")), 10, 64)
+ if err != nil || id <= 0 {
+ http.Error(w, "invalid ticket id", http.StatusBadRequest)
+ return
+ }
+ if !s.knowledge.Ready() {
+ http.Error(w, "knowledge index is not ready", http.StatusServiceUnavailable)
+ return
+ }
+ if !s.q.EnqueueWork(queue.WorkItem{TicketID: id, Trigger: "manual_recheck", Priority: queue.PriorityManual, Force: true}) {
+ http.Error(w, "ticket is already queued or the queue is full", http.StatusConflict)
+ return
+ }
+ s.metrics.QueueDepth.Store(int64(s.q.Len()))
+ respondJSON(w, map[string]any{"accepted": true, "ticket_id": id, "trigger": "manual_recheck", "dry_run": s.cfg.DryRun})
+}
+
var ticketRE = regexp.MustCompile(`(?i)/Ticket/(\d+)`)
func (s *Server) webhook(w http.ResponseWriter, r *http.Request) {
@@ -583,7 +619,7 @@ func (s *Server) webhook(w http.ResponseWriter, r *http.Request) {
return
}
s.metrics.WebhookEvents.Add(1)
- if !s.q.Enqueue(id) {
+ if !s.q.EnqueueWork(queue.WorkItem{TicketID: id, Trigger: "webhook", Priority: queue.PriorityWebhook}) {
w.WriteHeader(http.StatusAccepted)
return
}
diff --git a/internal/web/server_test.go b/internal/web/server_test.go
index fb28032..f5947cd 100644
--- a/internal/web/server_test.go
+++ b/internal/web/server_test.go
@@ -10,12 +10,14 @@ import (
"os"
"path/filepath"
"testing"
+ "time"
"github.com/example/glpi-ai-agent/internal/config"
"github.com/example/glpi-ai-agent/internal/knowledge"
"github.com/example/glpi-ai-agent/internal/metrics"
"github.com/example/glpi-ai-agent/internal/model"
"github.com/example/glpi-ai-agent/internal/queue"
+ "github.com/example/glpi-ai-agent/internal/state"
)
func TestExtractTicketID(t *testing.T) {
@@ -165,7 +167,7 @@ func TestDashboardContainsControlCenterSections(t *testing.T) {
t.Fatalf("dashboard = %d: %s", rr.Code, rr.Body.String())
}
body := rr.Body.String()
- for _, want := range []string{"Control Center", "Verarbeitungen", "Knowledge Base", "Effektive Konfiguration", "Knowledge-Ranking"} {
+ for _, want := range []string{"Control Center", "Verarbeitungen", "Knowledge Base", "Effektive Konfiguration", "Knowledge-Ranking", "Priorität & Eskalation"} {
if !bytes.Contains([]byte(body), []byte(want)) {
t.Fatalf("dashboard missing %q", want)
}
@@ -179,8 +181,10 @@ func TestDiagnosticsTemplateAndKnowledgeSearch(t *testing.T) {
if rr.Code != http.StatusOK {
t.Fatalf("diagnostics = %d: %s", rr.Code, rr.Body.String())
}
- if !bytes.Contains(rr.Body.Bytes(), []byte("Entscheidungsdiagnose")) {
- t.Fatal("diagnostics page missing title")
+ for _, want := range []string{"Entscheidungsdiagnose", "Prioritätsentscheidung", "priority_checks", "Begründung Prioritätsanalyse"} {
+ if !bytes.Contains(rr.Body.Bytes(), []byte(want)) {
+ t.Fatalf("diagnostics page missing %q", want)
+ }
}
}
@@ -257,3 +261,56 @@ func TestCategoryMappingEditorFlow(t *testing.T) {
t.Fatalf("mapping not applied to knowledge doc: %+v, ok=%v", updated, ok)
}
}
+
+func TestAnalysisDiagnosticEndpointReturnsIndependentRun(t *testing.T) {
+ root := t.TempDir()
+ staticDir := filepath.Join(root, "knowledge")
+ dataDir := filepath.Join(root, "data")
+ if err := os.MkdirAll(staticDir, 0o750); err != nil {
+ t.Fatal(err)
+ }
+ knowledgeStore, err := knowledge.Load(context.Background(), staticDir, dataDir, nil, false, []string{"internal-kb"})
+ if err != nil {
+ t.Fatal(err)
+ }
+ stateStore, err := state.Open(dataDir, 10)
+ if err != nil {
+ t.Fatal(err)
+ }
+ analysis := model.AnalysisRun{AnalysisID: "analysis-priority-1", ParentRunID: "run-1", TicketID: 7, AnalysisType: "priority", PromptVersion: "priority-v1", InputHash: "abc", Outcome: "processed"}
+ if err := stateStore.Append(model.RunRecord{RunID: "run-1", TicketID: 7, SourceVersion: "v1", Outcome: "processed", FinishedAt: time.Now(), Analyses: []model.AnalysisRun{analysis}}); err != nil {
+ t.Fatal(err)
+ }
+ cfg := config.Config{WebAllowAnonymous: true, CommunicationLanguage: "de-DE", CommunicationStyle: "formal", KnowledgeAllowedSources: []string{"internal-kb"}}
+ srv, err := New(cfg, metrics.New(), stateStore, queue.New(8), knowledgeStore, fakeFeedback{})
+ if err != nil {
+ t.Fatal(err)
+ }
+ rr := httptest.NewRecorder()
+ srv.Handler().ServeHTTP(rr, httptest.NewRequest(http.MethodGet, "/api/diagnostics/analysis/analysis-priority-1", nil))
+ if rr.Code != http.StatusOK {
+ t.Fatalf("analysis endpoint = %d: %s", rr.Code, rr.Body.String())
+ }
+ var got model.AnalysisRun
+ if err := json.Unmarshal(rr.Body.Bytes(), &got); err != nil {
+ t.Fatal(err)
+ }
+ if got.AnalysisID != analysis.AnalysisID || got.AnalysisType != "priority" || got.ParentRunID != "run-1" {
+ t.Fatalf("unexpected analysis response: %+v", got)
+ }
+}
+
+func TestManualReprocessQueuesForcedWorkItem(t *testing.T) {
+ h, _ := newKnowledgeTestServer(t)
+ rr := mutationRequest(t, h, http.MethodPost, "/api/tickets/42/reprocess", map[string]any{})
+ if rr.Code != http.StatusOK {
+ t.Fatalf("reprocess = %d: %s", rr.Code, rr.Body.String())
+ }
+ var body map[string]any
+ if err := json.Unmarshal(rr.Body.Bytes(), &body); err != nil {
+ t.Fatal(err)
+ }
+ if body["accepted"] != true || int64(body["ticket_id"].(float64)) != 42 {
+ t.Fatalf("unexpected response: %#v", body)
+ }
+}
diff --git a/internal/web/templates/dashboard.html b/internal/web/templates/dashboard.html
index d0321a8..fc1fc80 100644
--- a/internal/web/templates/dashboard.html
+++ b/internal/web/templates/dashboard.html
@@ -47,7 +47,7 @@ button,input,textarea,select{font:inherit}button{color:inherit}.app{display:grid
Integrationen
Aktueller Zustand der Kontextquellen.
@@ -117,10 +117,10 @@ function progress(label,value){const c=scoreClass(value);return `
${text}
`}
-function renderStatusChrome(){const g=!!statusData.glpi_ok,o=!!statusData.ollama_ok,k=!!statusData.knowledge_ready,ks=statusData.knowledge_init_state||'waiting';$('#glpiChip').innerHTML=`
GLPI ${g?'OK':'Fehler'}`;$('#ollamaChip').innerHTML=`
Ollama ${o?'OK':'Fehler'}`;$('#knowledgeChip').innerHTML=`
Knowledge ${k?'bereit':ks==='error'?'Fehler':'lädt'}`;$('#sideMode').innerHTML=`${statusData.dry_run?badge('DRY RUN','warn'):badge('LIVE','good')} ${statusData.auto_reply?badge('Auto-Reply','good'):badge('Auto-Reply aus','warn')}
${esc(statusData.ollama_model||'–')} · ${esc(statusData.communication_language||'–')} / ${esc(statusData.communication_style||'–')}
`;$('#lastRefresh').textContent=new Date().toLocaleTimeString('de-DE')}
-function renderOverview(){const stats=[['Verarbeitet',fmtNum(statusData.processed),'seit Start'],['Fehler',fmtNum(statusData.errors),statusData.errors?'prüfen':'keine'],['Queue',fmtNum(statusData.queue_depth),`von ${fmtNum(statusData.queue_size)}`],['Knowledge',fmtNum(statusData.knowledge_docs),`${fmtNum(statusData.glpi_kb_documents)} aus GLPI`],['Lernbeispiele',fmtNum(statusData.learning_examples),`${fmtNum(statusData.learning_examples_per_category)} je Kategorie im Prompt`],['Auto-Aktionen',`${fmtNum(statusData.category_changes)} / ${fmtNum(statusData.replies)}`,'Kategorie / Antwort']];$('#overviewStats').innerHTML=stats.map(x=>`
`).join('');
+function renderStatusChrome(){const g=!!statusData.glpi_ok,o=!!statusData.ollama_ok,k=!!statusData.knowledge_ready,ks=statusData.knowledge_init_state||'waiting';$('#glpiChip').innerHTML=`
GLPI ${g?'OK':'Fehler'}`;$('#ollamaChip').innerHTML=`
Ollama ${o?'OK':'Fehler'}`;$('#knowledgeChip').innerHTML=`
Knowledge ${k?'bereit':ks==='error'?'Fehler':'lädt'}`;$('#sideMode').innerHTML=`${statusData.dry_run?badge('DRY RUN','warn'):badge('LIVE','good')} ${statusData.auto_reply?badge('Auto-Reply','good'):badge('Auto-Reply aus','warn')} ${statusData.priority_enabled?badge(statusData.auto_priority?'Auto-Priorität':'Priorität Shadow',statusData.auto_priority?'good':'info'):badge('Priorität aus','warn')} ${statusData.escalation_enabled?badge(statusData.auto_escalation?'Auto-Eskalation':'Eskalation Shadow',statusData.auto_escalation?'good':'info'):badge('Eskalation aus','warn')}
${esc(statusData.ollama_model||'–')} · ${esc(statusData.communication_language||'–')} / ${esc(statusData.communication_style||'–')}
`;$('#lastRefresh').textContent=new Date().toLocaleTimeString('de-DE')}
+function renderOverview(){const stats=[['Verarbeitet',fmtNum(statusData.processed),'seit Start'],['Fehler',fmtNum(statusData.errors),statusData.errors?'prüfen':'keine'],['Queue',fmtNum(statusData.queue_depth),`von ${fmtNum(statusData.queue_size)}`],['Knowledge',fmtNum(statusData.knowledge_docs),`${fmtNum(statusData.glpi_kb_documents)} aus GLPI`],['KI-Triage',`${fmtNum(statusData.priority_recommendations)} / ${fmtNum(statusData.escalation_runs)}`,'Priorität / Eskalationsläufe'],['Auto-Aktionen',`${fmtNum(statusData.category_changes)} / ${fmtNum(statusData.priority_changes)} / ${fmtNum(statusData.replies)} / ${fmtNum(statusData.escalations)}`,'Kat. / Prio / Reply / Esk.']];$('#overviewStats').innerHTML=stats.map(x=>`
`).join('');
const recent=runsData.slice(0,6);$('#recentRuns').innerHTML=recent.length?recent.map(x=>{const score=x.knowledge_score?` · KB ${pct(x.knowledge_score)}`:'';return `
#${esc(x.ticket_id)} ${esc(x.ticket_name||'')}
${esc(fmtDate(x.finished_at))}${esc(score)}
${outcomeBadge(x.outcome)}
`}).join(''):'
Noch keine Verarbeitungen.
';
- const notices=[];if(!statusData.knowledge_ready&&statusData.knowledge_init_state!=='error'){const total=Number(statusData.knowledge_init_total_files||0),done=Number(statusData.knowledge_init_processed_files||0),idx=Number(statusData.knowledge_init_indexed_docs||0),docs=Number(statusData.knowledge_init_loaded_docs||0);notices.push(configNotice(`
Knowledge-Index wird aufgebaut. Phase: ${esc(statusData.knowledge_init_phase||'–')} · Dateien ${fmtNum(done)} / ${fmtNum(total)} · Dokumente ${fmtNum(docs)} · indexiert ${fmtNum(idx)}. Ticketverarbeitung bleibt bis zur Bereitschaft pausiert.`,'warn'))}if(statusData.knowledge_snapshot_loaded)notices.push(configNotice(`
Persistenter Knowledge-Index geladen. ${fmtNum(statusData.knowledge_docs)} Artikel waren sofort verfügbar; Quelldateien werden inkrementell im Hintergrund geprüft.`,'good'));if(statusData.knowledge_last_scan_error)notices.push(configNotice(`
Letzter inkrementeller Knowledge-Scan fehlgeschlagen. Der vorherige Index bleibt aktiv. ${esc(statusData.knowledge_last_scan_error)}`,'warn'));if(statusData.knowledge_init_state==='error')notices.push(configNotice(`
Knowledge-Initialisierung fehlgeschlagen. ${esc(statusData.knowledge_init_error||'Kein Fehlertext verfügbar.')}`,'bad'));if(statusData.dry_run)notices.push(configNotice('
Dry Run aktiv. Änderungen und Antworten werden nur simuliert.','warn'));if(!statusData.auto_reply)notices.push(configNotice('
Auto-Reply global deaktiviert. KB-Treffer werden bewertet, aber nicht gesendet.','warn'));if(statusData.knowledge_min_score>=.8)notices.push(configNotice(`
Hoher Evidenz-Schwellwert: ${pct(statusData.knowledge_min_score)}. Dieser gilt erst nach der KI-Auswahl.`,'warn'));if(statusData.knowledge_retrieval_floor>=.5)notices.push(configNotice(`
Hoher Retrieval-Floor: ${pct(statusData.knowledge_retrieval_floor)}. Kurze Tickets könnten bereits vor dem Evidenz-Reranking blockiert werden.`,'warn'));if(statusData.glpi_kb_enabled&&!statusData.glpi_kb_ok)notices.push(configNotice(`
GLPI-KB-Sync gestört. ${esc(statusData.glpi_kb_last_error||'Kein Fehlertext verfügbar.')}`,'bad'));if(statusData.knowledge_unmapped_category_files>0)notices.push(configNotice(`
${esc(statusData.knowledge_unmapped_category_files)} KB-Datei(en) mit nicht zugeordneten Fremdkategorien. Diese Artikel bleiben suchbar, Auto-Reply ist dafür fail-closed deaktiviert. Nicht zugeordnet: ${esc((statusData.knowledge_unmapped_categories||[]).join(', ')||'–')}`,'warn'));if(statusData.knowledge_ignored_files>0)notices.push(configNotice(`
${esc(statusData.knowledge_ignored_files)} KB-Datei(en) durch Kompatibilitäts-/Ignore-Regeln übersprungen. `,'warn'));if(statusData.rag_enabled&&!statusData.knowledge_docs)notices.push(configNotice('
RAG aktiv, aber keine Knowledge-Dokumente geladen. ','bad'));if(statusData.context_fail_closed)notices.push(configNotice('Kontextquellen arbeiten
fail-closed : Fehler können Auto-Replies blockieren.'));if(!notices.length)notices.push(configNotice('Keine auffälligen Konfigurationshinweise erkannt.','good'));$('#diagnosticNotices').innerHTML=notices.join('');
+ const notices=[];const pollAt=statusData.last_poll?fmtDate(statusData.last_poll):'–';if(statusData.poll_last_error){notices.push(configNotice(`
Letzter GLPI-Poll fehlgeschlagen. ${esc(statusData.poll_last_error)}`,'bad'))}else if(!statusData.last_poll){notices.push(configNotice('
Noch kein GLPI-Ticket-Poll abgeschlossen. Die Ticketverarbeitung wurde noch nicht aktiv oder der erste Abruf läuft.','warn'))}else if(Number(statusData.poll_last_fetched||0)===0){notices.push(configNotice(`
GLPI-Poll aktiv, aber ohne Treffer. Letzter Abruf ${esc(pollAt)} · 0 Tickets. Prüfen Sie insbesondere GLPI_TICKET_FILTER und die API-Berechtigung auf /Assistance/Ticket.`,'warn'))}else if(Number(statusData.poll_last_unseen||0)===0){notices.push(configNotice(`
GLPI-Poll funktioniert. ${fmtNum(statusData.poll_last_fetched)} Ticket(s) abgerufen, davon ${fmtNum(statusData.poll_last_seen)} bereits in derselben Version verarbeitet; deshalb 0 neue Queue-Einträge. Dauerhaft bekannte Ticketversionen: ${fmtNum(statusData.processed_version_count||0)}.`,'good'))}else{notices.push(configNotice(`
GLPI-Poll funktioniert. ${fmtNum(statusData.poll_last_fetched)} abgerufen · ${fmtNum(statusData.poll_last_unseen)} neu · ${fmtNum(statusData.poll_last_enqueued)} eingereiht · ${fmtNum(statusData.poll_last_rejected)} abgewiesen.`,'good'))}if(!statusData.knowledge_ready&&statusData.knowledge_init_state!=='error'){const total=Number(statusData.knowledge_init_total_files||0),done=Number(statusData.knowledge_init_processed_files||0),idx=Number(statusData.knowledge_init_indexed_docs||0),docs=Number(statusData.knowledge_init_loaded_docs||0);notices.push(configNotice(`
Knowledge-Index wird aufgebaut. Phase: ${esc(statusData.knowledge_init_phase||'–')} · Dateien ${fmtNum(done)} / ${fmtNum(total)} · Dokumente ${fmtNum(docs)} · indexiert ${fmtNum(idx)}. Ticketverarbeitung bleibt bis zur Bereitschaft pausiert.`,'warn'))}if(statusData.knowledge_snapshot_loaded)notices.push(configNotice(`
Persistenter Knowledge-Index geladen. ${fmtNum(statusData.knowledge_docs)} Artikel waren sofort verfügbar; Quelldateien werden inkrementell im Hintergrund geprüft.`,'good'));if(statusData.knowledge_last_scan_error)notices.push(configNotice(`
Letzter inkrementeller Knowledge-Scan fehlgeschlagen. Der vorherige Index bleibt aktiv. ${esc(statusData.knowledge_last_scan_error)}`,'warn'));if(statusData.knowledge_init_state==='error')notices.push(configNotice(`
Knowledge-Initialisierung fehlgeschlagen. ${esc(statusData.knowledge_init_error||'Kein Fehlertext verfügbar.')}`,'bad'));if(statusData.dry_run)notices.push(configNotice('
Dry Run aktiv. Änderungen und Antworten werden nur simuliert.','warn'));if(!statusData.auto_reply)notices.push(configNotice('
Auto-Reply global deaktiviert. KB-Treffer werden bewertet, aber nicht gesendet.','warn'));if(statusData.priority_enabled&&!statusData.auto_priority)notices.push(configNotice('
Prioritätsanalyse im Shadow Mode. Empfehlungen werden als eigener KI-Lauf protokolliert, GLPI bleibt unverändert.'));if(statusData.auto_priority)notices.push(configNotice(`
Automatische Priorisierung aktiv. Erhöhungen sind auf ${esc(statusData.priority_max_increase)} Stufe(n) je Lauf begrenzt.${statusData.dry_run?' Dry Run verhindert Schreibzugriffe.':''}`,statusData.dry_run?'warn':'bad'));if(statusData.escalation_enabled&&!statusData.auto_escalation)notices.push(configNotice('
Eskalationsanalyse im Shadow Mode. Zeittrigger erzeugen eigene Diagnose-Läufe, führen aber keine Aktion aus.'));if(statusData.auto_escalation)notices.push(configNotice(`
Automatische Eskalation aktiv. Der Scheduler darf freigegebene Aktionen bis Stufe ${esc(statusData.escalation_max_level)} ausführen.${statusData.dry_run?' Dry Run verhindert Schreibzugriffe.':''}`,statusData.dry_run?'warn':'bad'));if(statusData.knowledge_min_score>=.8)notices.push(configNotice(`
Hoher Evidenz-Schwellwert: ${pct(statusData.knowledge_min_score)}. Dieser gilt erst nach der KI-Auswahl.`,'warn'));if(statusData.knowledge_retrieval_floor>=.5)notices.push(configNotice(`
Hoher Retrieval-Floor: ${pct(statusData.knowledge_retrieval_floor)}. Kurze Tickets könnten bereits vor dem Evidenz-Reranking blockiert werden.`,'warn'));if(statusData.glpi_kb_enabled&&!statusData.glpi_kb_ok)notices.push(configNotice(`
GLPI-KB-Sync gestört. ${esc(statusData.glpi_kb_last_error||'Kein Fehlertext verfügbar.')}`,'bad'));if(statusData.knowledge_unmapped_category_files>0)notices.push(configNotice(`
${esc(statusData.knowledge_unmapped_category_files)} KB-Datei(en) mit nicht zugeordneten Fremdkategorien. Diese Artikel bleiben suchbar, Auto-Reply ist dafür fail-closed deaktiviert. Nicht zugeordnet: ${esc((statusData.knowledge_unmapped_categories||[]).join(', ')||'–')}`,'warn'));if(statusData.knowledge_ignored_files>0)notices.push(configNotice(`
${esc(statusData.knowledge_ignored_files)} KB-Datei(en) durch Kompatibilitäts-/Ignore-Regeln übersprungen. `,'warn'));if(statusData.rag_enabled&&!statusData.knowledge_docs)notices.push(configNotice('
RAG aktiv, aber keine Knowledge-Dokumente geladen. ','bad'));if(statusData.context_fail_closed)notices.push(configNotice('Kontextquellen arbeiten
fail-closed : Fehler können Auto-Replies blockieren.'));if(!notices.length)notices.push(configNotice('Keine auffälligen Konfigurationshinweise erkannt.','good'));$('#diagnosticNotices').innerHTML=notices.join('');
const kTotal=Number(statusData.knowledge_init_loaded_docs||0),kIndexed=Number(statusData.knowledge_init_indexed_docs||0),kPct=kTotal?Math.round((kIndexed/kTotal)*100):0;const health=[['Lokale Knowledge Base',!!statusData.knowledge_ready,statusData.knowledge_ready?`${fmtNum(statusData.knowledge_docs)} Artikel bereit`:`${esc(statusData.knowledge_init_phase||'waiting')} · ${fmtNum(kIndexed)}/${fmtNum(kTotal)} indexiert (${kPct}%)`],['GLPI API',statusData.glpi_ok,statusData.glpi_api_version||''],['Ollama',statusData.ollama_ok,statusData.ollama_model||''],['GLPI Knowledge Base',!statusData.glpi_kb_enabled||statusData.glpi_kb_ok,statusData.glpi_kb_enabled?`${statusData.glpi_kb_documents||0} Artikel · Sync ${fmtDate(statusData.glpi_kb_last_sync)}`:'deaktiviert'],['Uptime Kuma',true,statusData.uptime_kuma_enabled?`aktiv · ${statusData.uptime_kuma_mode}`:'deaktiviert'],['Change Calendar',true,statusData.change_calendar_enabled?'aktiv':'deaktiviert'],['Major Incidents',true,statusData.major_incidents_enabled?'aktiv':'deaktiviert'],['Benutzer ↔ Geräte',true,statusData.user_device_context_enabled?'aktiv':'deaktiviert']];$('#integrationHealth').innerHTML=health.map(x=>`
${x[1]?badge('OK','good'):badge('Fehler','bad')}
`).join('');
$('#scoringOverview').innerHTML=`${progress('Retrieval: Semantik',statusData.knowledge_weight_semantic)}${progress('Retrieval: Titel',statusData.knowledge_weight_title)}${progress('Retrieval: Lexikalisch',statusData.knowledge_weight_lexical)}${progress('Retrieval: Keywords',statusData.knowledge_weight_keywords)}${progress('Retrieval: Kategorie/Lernen',statusData.knowledge_weight_category)}
Retrieval-Floor ${esc(pct(statusData.knowledge_retrieval_floor))}
Finaler Evidenz-Schwellwert ${esc(pct(statusData.knowledge_min_score))}
Evidenz: Retrieval / KI / Kategorie ${esc(pct(statusData.knowledge_evidence_weight_retrieval))} / ${esc(pct(statusData.knowledge_evidence_weight_ai))} / ${esc(pct(statusData.knowledge_evidence_weight_category))}
Kategorie-Confidence ${esc(pct(statusData.category_confidence))}
Reply-Confidence ${esc(pct(statusData.reply_confidence))}
`}
function categoryMini(x){if(!x.ai_recommended_category_id)return `
${badge('Keine Empfehlung','warn')}
KI-Sicherheit ${pct(x.ai_category_confidence)}
`;const p=policyLabel(x.category_decision);return `
${esc(x.ai_recommended_category_name||`#${x.ai_recommended_category_id}`)} ${esc(pct(x.ai_category_confidence))}
${badge(p[0],p[1])}
Schwellwert ${esc(pct(x.category_threshold))}
`}
@@ -145,7 +145,7 @@ function renderKB(){const st=kbStatsData();$('#kbStats').innerHTML=st.map(x=>`
!q||[x.ticket_id,x.subject,x.text,x.category_name,x.category_id].join(' ').toLowerCase().includes(q));const corrections=learningRows.filter(x=>x.correction).length;$('#learningStats').innerHTML=[['Gesamt',learningRows.length,'bestätigte Beispiele'],['Korrekturen',corrections,'KI lag anders'],['Bestätigungen',learningRows.length-corrections,'KI wurde bestätigt']].map(x=>`${esc(x[0])}
${fmtNum(x[1])}
#${esc(x.ticket_id)} ${esc(x.subject)}
${esc((x.text||'').slice(0,220))}
${esc(x.category_name)} (#${esc(x.category_id)})${x.ai_recommended_category_id?`KI: #${esc(x.ai_recommended_category_id)} · ${esc(pct(x.ai_confidence))}
`:''}${x.correction?badge('Korrektur','warn'):badge('Bestätigung','good')} ${esc(fmtDate(x.created_at))} Löschen Keine Lernbeispiele. ${esc(title)}
${esc(subtitle)}
${rows.map(([k,v])=>`
`).join('')}
${pct(s.knowledge_min_score)} `],['Retrieval-Floor',`${pct(s.knowledge_retrieval_floor)} `],['Evidenzgewicht Retrieval',pct(s.knowledge_evidence_weight_retrieval)],['Evidenzgewicht KI',pct(s.knowledge_evidence_weight_ai)],['Evidenzgewicht Kategorie',pct(s.knowledge_evidence_weight_category)],['Retrieval: Semantik',pct(s.knowledge_weight_semantic)],['Retrieval: Titel',pct(s.knowledge_weight_title)],['Retrieval: Lexikalisch',pct(s.knowledge_weight_lexical)],['Retrieval: Keywords',pct(s.knowledge_weight_keywords)],['Retrieval: Kategorie/Lernen',pct(s.knowledge_weight_category)],['Chunk-Wörter',val(s.knowledge_chunk_words)],['Overlap-Wörter',val(s.knowledge_chunk_overlap_words)],['Max. KB-Chunks',val(s.knowledge_max_chunks_per_doc)],['Max. Ticket-Chunks',val(s.knowledge_max_query_chunks)],['Antwort-/Retrieval-Quellen',val(s.knowledge_allowed_sources)],['Kategorisierungsquellen',val(s.knowledge_category_sources)],['Auto-Reply-Quellen',val(s.knowledge_auto_reply_sources)],['Fremdkategorie-Modus',val(s.knowledge_category_mode)],['Kategorie-Mapping',val(s.knowledge_category_map_configured?'konfiguriert':'–')],['Ignore-Globs',val(s.knowledge_ignore_globs)],['Ignorierte Dateien',val(s.knowledge_ignored_files)],['KBs mit ungemappten Kategorien',val(s.knowledge_unmapped_category_files)],['Ungemappte Kategorien',val(s.knowledge_unmapped_categories)]]),configCard('GLPI Knowledge Base','Synchronisation der GLPI-Wissensdatenbank',[['Aktiv',val(s.glpi_kb_enabled)],['Sync OK',val(s.glpi_kb_ok)],['Dokumente',val(s.glpi_kb_documents)],['Letzter Sync',val(fmtDate(s.glpi_kb_last_sync))],['Intervall',val(s.glpi_kb_sync_interval)],['Pfad',val(s.glpi_kb_path)],['Filter gesetzt',val(s.glpi_kb_filter_configured)],['Limit',val(s.glpi_kb_limit)],['Auto-Reply',val(s.glpi_kb_auto_reply)],['Auto-Reply-Kategorien',val(s.glpi_kb_auto_reply_category_ids)],['Letzter Fehler',val(s.glpi_kb_last_error||'–')]]),configCard('Policy & Kommunikation','Entscheidungsschwellen und Sprache',[['Kategorie-Confidence',`${pct(s.category_confidence)} `],['Reply-Confidence',`${pct(s.reply_confidence)} `],['Sprache',val(s.communication_language)],['Stil',val(s.communication_style)],['KI-Kennzeichnung',val(s.ai_content_label_enabled)],['KB-Webeditor',val(s.knowledge_edit_enabled)],['Lernen',val(s.learning_enabled)],['Max. Lernbeispiele',val(s.learning_max_examples)],['Beispiele/Kategorie',val(s.learning_examples_per_category)]]),configCard('Kontextquellen','Störungen, Changes, Incidents und Geräte',[['Kontext aktiv',val(s.context_enabled)],['Timeout',val(s.context_timeout)],['Relevanz-Minimum',pct(s.context_relevance_min_score)],['Fail-closed',val(s.context_fail_closed)],['Incident blockiert normalen Reply',val(s.context_incident_block)],['Vordefinierte Statusantwort',val(s.context_status_reply_enabled)],['Status: Relevanz-Minimum',pct(s.context_status_reply_min_relevance)],['Status: KI-Minimum',pct(s.context_status_reply_min_ai_confidence)],['Status: Final-Minimum',pct(s.context_status_reply_min_final_score)],['Störungstext konfiguriert',val(s.context_incident_reply_text_configured)],['Wartungstext konfiguriert',val(s.context_maintenance_reply_text_configured)],['Change Calendar',val(s.change_calendar_enabled)],['Lookback',val(s.change_lookback)],['Lookahead',val(s.change_lookahead)],['Major Incidents',val(s.major_incidents_enabled)],['Benutzer-Geräte',val(s.user_device_context_enabled)],['Uptime Kuma',val(s.uptime_kuma_enabled)],['Uptime-Modus',val(s.uptime_kuma_mode)]] )];$('#configGroups').innerHTML=groups.join('')}
+function renderConfig(){const s=statusData;const groups=[configCard('Agent & GLPI','Polling, Worker und Schreibmodus',[['Dry Run',val(s.dry_run)],['Auto-Kategorie',val(s.auto_category)],['Auto-Reply',val(s.auto_reply)],['Worker',val(s.workers)],['Queue-Größe',val(s.queue_size)],['API-Version',val(s.glpi_api_version)],['Poll-Intervall',val(s.glpi_poll_interval)],['Poll-Limit',val(s.glpi_poll_limit)],['Letzter Poll',val(fmtDate(s.last_poll))],['Poll: abgerufen / bekannt / neu / Queue',val(`${s.poll_last_fetched||0} / ${s.poll_last_seen||0} / ${s.poll_last_unseen||0} / ${s.poll_last_enqueued||0}`)],['Poll-Fehler',val(s.poll_last_error||'–')],['Bekannte Ticketversionen',val(s.processed_version_count||0)],['Ticket-Filter gesetzt',val(s.glpi_ticket_filter_configured)],['Erlaubte Status',val(s.glpi_allowed_status_ids)],['GLPI-Timeout',val(s.glpi_timeout)]]),configCard('Priorität & Eskalation','Separate KI-Läufe mit deterministischen Schreibregeln',[['Prioritätsanalyse',val(s.priority_enabled)],['Auto-Priorität',val(s.auto_priority)],['Prioritäts-Confidence',`${pct(s.priority_confidence)} `],['Prioritäts-Timeout',val(s.priority_analysis_timeout)],['Max. Erhöhung/Lauf',val(s.priority_max_increase)],['Erlaubte Prioritätsgründe',val(s.priority_allowed_reason_codes)],['Eskalationsanalyse',val(s.escalation_enabled)],['Auto-Eskalation',val(s.auto_escalation)],['Scan-Intervall',val(s.escalation_scan_interval)],['Mindestalter',val(s.escalation_min_age)],['Eskalations-Confidence',`${pct(s.escalation_confidence)} `],['Max. Eskalationsstufe',val(s.escalation_max_level)],['Erlaubte Eskalationsgründe',val(s.escalation_allowed_reason_codes)],['Erlaubte Eskalationsaktionen',val(s.escalation_allowed_actions)],['GLPI-Eskalationsfilter gesetzt',val(s.glpi_escalation_filter_configured)],['Kandidatenlimit',val(s.glpi_escalation_limit)]]),configCard('Ollama','Modelle und Inferenzbudget',[['Chat-Modell',val(s.ollama_model)],['Embedding-Modell',val(s.ollama_embedding_model)],['Embedding-Profil',val(s.knowledge_embedding_profile)],['Timeout',val(s.ollama_timeout)],['Num Predict',val(s.ollama_num_predict)],['Keep Alive',val(s.ollama_keep_alive)],['Thinking',val(s.ollama_think)],['Max. parallel',val(s.ollama_max_concurrent)],['JSON-Retries',val(s.ollama_json_retries)]]),configCard('Knowledge / RAG','Retrieval, Chunking und Ranking',[['RAG',val(s.rag_enabled)],['Index-Modus',val(s.knowledge_index_mode)],['Snapshot geladen',val(s.knowledge_snapshot_loaded)],['Snapshot gespeichert',val(fmtDate(s.knowledge_snapshot_saved_at))],['Letzter Delta-Scan',val(fmtDate(s.knowledge_last_scan_at))],['Letzter Scanfehler',val(s.knowledge_last_scan_error||'–')],['Geänderte Dateien',val(s.knowledge_changed_files)],['Gelöschte Dateien',val(s.knowledge_deleted_files)],['Wiederverwendete Vektoren',val(s.knowledge_reused_files)],['Embedding-Batch',val(s.knowledge_embed_batch_size)],['Scan-Intervall',val(s.knowledge_index_scan_interval)],['Knowledge bereit',val(s.knowledge_ready)],['Startup-Status',val(s.knowledge_init_state)],['Startup-Phase',val(s.knowledge_init_phase)],['Dateien verarbeitet',val(`${s.knowledge_init_processed_files||0} / ${s.knowledge_init_total_files||0}`)],['Dokumente geladen',val(s.knowledge_init_loaded_docs)],['Dokumente indexiert',val(s.knowledge_init_indexed_docs)],['Embedding-Cache-Treffer',val(s.knowledge_init_cache_hits)],['Offene Embeddings',val(s.knowledge_init_pending_embeddings)],['Startup-Fehler',val(s.knowledge_init_error||'–')],['Max. Kandidaten an KI',val(s.knowledge_top_k)],['Audit Top K',val(s.knowledge_audit_top_k)],['Max. Abstand zum Top-Treffer',pct(s.knowledge_candidate_max_gap)],['Finaler Evidenz-Schwellwert',`${pct(s.knowledge_min_score)} `],['Retrieval-Floor',`${pct(s.knowledge_retrieval_floor)} `],['Evidenzgewicht Retrieval',pct(s.knowledge_evidence_weight_retrieval)],['Evidenzgewicht KI',pct(s.knowledge_evidence_weight_ai)],['Evidenzgewicht Kategorie',pct(s.knowledge_evidence_weight_category)],['Retrieval: Semantik',pct(s.knowledge_weight_semantic)],['Retrieval: Titel',pct(s.knowledge_weight_title)],['Retrieval: Lexikalisch',pct(s.knowledge_weight_lexical)],['Retrieval: Keywords',pct(s.knowledge_weight_keywords)],['Retrieval: Kategorie/Lernen',pct(s.knowledge_weight_category)],['Chunk-Wörter',val(s.knowledge_chunk_words)],['Overlap-Wörter',val(s.knowledge_chunk_overlap_words)],['Max. KB-Chunks',val(s.knowledge_max_chunks_per_doc)],['Max. Ticket-Chunks',val(s.knowledge_max_query_chunks)],['Antwort-/Retrieval-Quellen',val(s.knowledge_allowed_sources)],['Kategorisierungsquellen',val(s.knowledge_category_sources)],['Auto-Reply-Quellen',val(s.knowledge_auto_reply_sources)],['Fremdkategorie-Modus',val(s.knowledge_category_mode)],['Kategorie-Mapping',val(s.knowledge_category_map_configured?'konfiguriert':'–')],['Ignore-Globs',val(s.knowledge_ignore_globs)],['Ignorierte Dateien',val(s.knowledge_ignored_files)],['KBs mit ungemappten Kategorien',val(s.knowledge_unmapped_category_files)],['Ungemappte Kategorien',val(s.knowledge_unmapped_categories)]]),configCard('GLPI Knowledge Base','Synchronisation der GLPI-Wissensdatenbank',[['Aktiv',val(s.glpi_kb_enabled)],['Sync OK',val(s.glpi_kb_ok)],['Dokumente',val(s.glpi_kb_documents)],['Letzter Sync',val(fmtDate(s.glpi_kb_last_sync))],['Intervall',val(s.glpi_kb_sync_interval)],['Pfad',val(s.glpi_kb_path)],['Filter gesetzt',val(s.glpi_kb_filter_configured)],['Limit',val(s.glpi_kb_limit)],['Auto-Reply',val(s.glpi_kb_auto_reply)],['Auto-Reply-Kategorien',val(s.glpi_kb_auto_reply_category_ids)],['Letzter Fehler',val(s.glpi_kb_last_error||'–')]]),configCard('Policy & Kommunikation','Entscheidungsschwellen und Sprache',[['Kategorie-Confidence',`${pct(s.category_confidence)} `],['Reply-Confidence',`${pct(s.reply_confidence)} `],['Sprache',val(s.communication_language)],['Stil',val(s.communication_style)],['KI-Kennzeichnung',val(s.ai_content_label_enabled)],['KB-Webeditor',val(s.knowledge_edit_enabled)],['Lernen',val(s.learning_enabled)],['Max. Lernbeispiele',val(s.learning_max_examples)],['Beispiele/Kategorie',val(s.learning_examples_per_category)]]),configCard('Kontextquellen','Störungen, Changes, Incidents und Geräte',[['Kontext aktiv',val(s.context_enabled)],['Timeout',val(s.context_timeout)],['Relevanz-Minimum',pct(s.context_relevance_min_score)],['Fail-closed',val(s.context_fail_closed)],['Incident blockiert normalen Reply',val(s.context_incident_block)],['Vordefinierte Statusantwort',val(s.context_status_reply_enabled)],['Status: Relevanz-Minimum',pct(s.context_status_reply_min_relevance)],['Status: KI-Minimum',pct(s.context_status_reply_min_ai_confidence)],['Status: Final-Minimum',pct(s.context_status_reply_min_final_score)],['Störungstext konfiguriert',val(s.context_incident_reply_text_configured)],['Wartungstext konfiguriert',val(s.context_maintenance_reply_text_configured)],['Change Calendar',val(s.change_calendar_enabled)],['Lookback',val(s.change_lookback)],['Lookahead',val(s.change_lookahead)],['Major Incidents',val(s.major_incidents_enabled)],['Benutzer-Geräte',val(s.user_device_context_enabled)],['Uptime Kuma',val(s.uptime_kuma_enabled)],['Uptime-Modus',val(s.uptime_kuma_mode)]] )];$('#configGroups').innerHTML=groups.join('')}
function renderSourceOptions(){const filterOld=$('#kbSourceFilter').value,sourceOld=$('#kbSource').value;const sources=[...new Set(kbDocs.map(x=>x.source).filter(Boolean))].sort();$('#kbSourceFilter').innerHTML='Alle Quellen '+sources.map(x=>`${esc(x)} `).join('');if([...$('#kbSourceFilter').options].some(o=>o.value===filterOld))$('#kbSourceFilter').value=filterOld;const allowed=statusData.knowledge_allowed_sources||[];$('#kbSource').innerHTML=allowed.map(x=>`${esc(x)} `).join('');if([...$('#kbSource').options].some(o=>o.value===sourceOld))$('#kbSource').value=sourceOld;else if([...$('#kbSource').options].some(o=>o.value==='internal-kb'))$('#kbSource').value='internal-kb'}
function renderCategoryPicker(filter=''){const q=filter.toLowerCase();$('#kbCategoryList').innerHTML=categories.filter(c=>!q||(c.completename||c.name||'').toLowerCase().includes(q)).map(c=>`${esc(c.completename||c.name)} #${Number(c.id)} Keine Kategorie gefunden.
'}
function clearKbForm(){currentKbId='';kbCategorySelection=new Set();$('#kbForm').reset();$('#kbId').disabled=false;$('#kbId').value='';$('#kbLanguage').value=statusData.communication_language||'de-DE';$('#kbStyle').value=statusData.communication_style||'formal';$('#kbScore').value=Number(statusData.knowledge_min_score||.70).toFixed(2);if([...$('#kbSource').options].some(x=>x.value==='internal-kb'))$('#kbSource').value='internal-kb';$('#kbModalTitle').textContent='Neuen Artikel anlegen';$('#kbModalEyebrow').textContent='Interne Knowledge Base';$('#kbEditState').textContent='Neuer Artikel';$('#kbFormMessage').className='form-message';$('#kbCategorySearch').value='';renderCategoryPicker();updateCounts()}
@@ -159,6 +159,7 @@ function openLearn(runID,preferred){currentLearnRun=runID;const r=runsData.find(
function closeLearn(){$('#learnBackdrop').classList.remove('show');currentLearnRun=''}
async function saveLearning(){const b=$('#saveLearnBtn');b.disabled=true;try{await api('/api/learning',{method:'POST',body:JSON.stringify({run_id:currentLearnRun,category_id:Number($('#learnCategory').value)})});toast('Kategorie als Lernbeispiel gespeichert.');closeLearn();await loadLearning();await loadRuns()}catch(e){toast(e.message,'error')}finally{b.disabled=false}}
async function deleteLearning(id){if(!confirm('Lernbeispiel wirklich löschen?'))return;try{await api(`/api/learning/${encodeURIComponent(id)}`,{method:'DELETE'});toast('Lernbeispiel gelöscht.');await loadLearning()}catch(e){toast(e.message,'error')}}
+async function reprocessTicket(){const id=Number($('#reprocessTicketID').value||0);if(!Number.isInteger(id)||id<=0){toast('Bitte eine gültige Ticket-ID eingeben.','error');return}const live=!statusData.dry_run;if(live&&!confirm(`Ticket #${id} im LIVE-Modus neu analysieren? Konfigurierte Auto-Aktionen können ausgeführt werden.`))return;const b=$('#reprocessTicketBtn');b.disabled=true;try{await api(`/api/tickets/${id}/reprocess`,{method:'POST',body:'{}'});toast(`Ticket #${id} wurde zur Neuanalyse eingereiht.`);setTimeout(()=>refreshAll(),800)}catch(e){toast(e.message,'error')}finally{b.disabled=false}}
async function loadStatus(){statusData=await api('/api/status');renderStatusChrome();renderOverview();renderConfig();renderSourceOptions()}
async function loadRuns(){const limit=Number($('#runLimit').value||50);runsData=await api(`/api/runs?limit=${limit}`);renderRuns();renderOverview()}
async function loadKnowledge(){kbDocs=await api('/api/knowledge');renderSourceOptions();renderKB();renderOverview()}
@@ -167,7 +168,7 @@ async function loadCategories(){categories=await api('/api/categories');renderCa
async function refreshAll(showToast=false){$('#refreshBtn').disabled=true;try{await Promise.all([loadStatus(),loadRuns(),loadKnowledge(),loadLearning(),loadCategories()]);renderSourceOptions();renderOverview();renderRuns();renderKB();renderLearning();renderConfig();if(showToast)toast('Dashboard aktualisiert.')}catch(e){toast(e.message,'error')}finally{$('#refreshBtn').disabled=false;$('#lastRefresh').textContent=new Date().toLocaleTimeString('de-DE')}}
$('#nav').addEventListener('click',e=>{const b=e.target.closest('[data-view]');if(b)setView(b.dataset.view)});document.addEventListener('click',e=>{const g=e.target.closest('[data-goto]');if(g)setView(g.dataset.goto);const row=e.target.closest('[data-run-id]');if(row){const x=runsData.find(r=>r.run_id===row.dataset.runId);if(x)renderRunDrawer(x)}const ed=e.target.closest('[data-kb-edit]');if(ed)editKB(ed.dataset.kbEdit);const del=e.target.closest('[data-kb-delete]');if(del)deleteKB(del.dataset.kbDelete);const ld=e.target.closest('[data-learning-delete]');if(ld)deleteLearning(ld.dataset.learningDelete);const lr=e.target.closest('[data-learn-run]');if(lr)openLearn(lr.dataset.learnRun,Number(lr.dataset.learnCat||0))});
-$('#refreshBtn').addEventListener('click',()=>refreshAll(true));$('#runSearch').addEventListener('input',renderRuns);$('#runOutcome').addEventListener('change',renderRuns);$('#runLimit').addEventListener('change',loadRuns);$('#kbSearch').addEventListener('input',renderKB);$('#kbSourceFilter').addEventListener('change',renderKB);$('#kbManageFilter').addEventListener('change',renderKB);$('#learningSearch').addEventListener('input',renderLearning);$('#newKbBtn').addEventListener('click',()=>{clearKbForm();openKbModal()});$('#closeKbModal').addEventListener('click',closeKbModal);$('#cancelKbBtn').addEventListener('click',closeKbModal);$('#kbModalBackdrop').addEventListener('click',e=>{if(e.target===e.currentTarget)closeKbModal()});$('#kbForm').addEventListener('submit',saveKB);$('#kbCategorySearch').addEventListener('input',e=>renderCategoryPicker(e.target.value));$('#kbCategoryList').addEventListener('change',e=>{if(e.target.type==='checkbox'){const id=Number(e.target.value);e.target.checked?kbCategorySelection.add(id):kbCategorySelection.delete(id)}});$('#kbText').addEventListener('input',updateCounts);$('#kbAnswer').addEventListener('input',updateCounts);$('#runBackdrop').addEventListener('click',closeRunDrawer);$('#closeRunDrawer').addEventListener('click',closeRunDrawer);$('#closeLearnModal').addEventListener('click',closeLearn);$('#cancelLearnBtn').addEventListener('click',closeLearn);$('#saveLearnBtn').addEventListener('click',saveLearning);$('#learnBackdrop').addEventListener('click',e=>{if(e.target===e.currentTarget)closeLearn()});document.addEventListener('keydown',e=>{if(e.key==='Escape'){closeRunDrawer();closeKbModal();closeLearn()}});
+$('#refreshBtn').addEventListener('click',()=>refreshAll(true));$('#reprocessTicketBtn').addEventListener('click',reprocessTicket);$('#reprocessTicketID').addEventListener('keydown',e=>{if(e.key==='Enter')reprocessTicket()});$('#runSearch').addEventListener('input',renderRuns);$('#runOutcome').addEventListener('change',renderRuns);$('#runLimit').addEventListener('change',loadRuns);$('#kbSearch').addEventListener('input',renderKB);$('#kbSourceFilter').addEventListener('change',renderKB);$('#kbManageFilter').addEventListener('change',renderKB);$('#learningSearch').addEventListener('input',renderLearning);$('#newKbBtn').addEventListener('click',()=>{clearKbForm();openKbModal()});$('#closeKbModal').addEventListener('click',closeKbModal);$('#cancelKbBtn').addEventListener('click',closeKbModal);$('#kbModalBackdrop').addEventListener('click',e=>{if(e.target===e.currentTarget)closeKbModal()});$('#kbForm').addEventListener('submit',saveKB);$('#kbCategorySearch').addEventListener('input',e=>renderCategoryPicker(e.target.value));$('#kbCategoryList').addEventListener('change',e=>{if(e.target.type==='checkbox'){const id=Number(e.target.value);e.target.checked?kbCategorySelection.add(id):kbCategorySelection.delete(id)}});$('#kbText').addEventListener('input',updateCounts);$('#kbAnswer').addEventListener('input',updateCounts);$('#runBackdrop').addEventListener('click',closeRunDrawer);$('#closeRunDrawer').addEventListener('click',closeRunDrawer);$('#closeLearnModal').addEventListener('click',closeLearn);$('#cancelLearnBtn').addEventListener('click',closeLearn);$('#saveLearnBtn').addEventListener('click',saveLearning);$('#learnBackdrop').addEventListener('click',e=>{if(e.target===e.currentTarget)closeLearn()});document.addEventListener('keydown',e=>{if(e.key==='Escape'){closeRunDrawer();closeKbModal();closeLearn()}});
setView(location.hash.replace('#','')||'overview');refreshAll();setInterval(async()=>{try{await Promise.all([loadStatus(),loadRuns()])}catch{}},10000);