Umstellung auf Model Tools
This commit is contained in:
Christoph K.
169
agent/loop.go
169
agent/loop.go
@@ -18,16 +18,10 @@ const (
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maxTurns = 10
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)
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var systemPrompt = `Du bist ein Coding-Agent und programmierst Go.
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Erledige deine Aufgabe mit folgenden Tools:
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TOOL:READ_FILE:pfad
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TOOL:WRITE_FILE:pfad:<<<inhalt>>>
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TOOL:LIST_FILES:pfad
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REGELN:
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- Nutze relative Pfade
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- Kein Markdown in Dateiinhalten
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- Wenn Task erledigt: schreibe nur TASK_COMPLETE`
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var systemPrompt = `Du bist ein autonomer Coding-Agent.
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Erledige den gegebenen Task vollständig mit den bereitgestellten Tools.
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Rufe task_complete auf sobald der Task erledigt ist.
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Nutze ausschließlich relative Pfade.`
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type AgentLoop struct {
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client *openai.Client
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@@ -74,6 +68,7 @@ func (a *AgentLoop) Run() error {
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a.log.TaskStart(task.Title)
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success := false
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var lastErr error
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for attempt := 1; attempt <= maxRetries; attempt++ {
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if attempt > 1 {
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a.log.Info("🔁 Retry %d/%d...", attempt, maxRetries)
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@@ -84,6 +79,7 @@ func (a *AgentLoop) Run() error {
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success = true
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break
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} else {
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lastErr = err
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a.log.Info("⚠️ Fehler: %v", err)
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}
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}
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@@ -93,6 +89,7 @@ func (a *AgentLoop) Run() error {
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a.log.TaskDone(task.Title)
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} else {
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a.log.TaskFailed(task.Title, maxRetries)
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_ = lastErr
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}
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}
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@@ -101,137 +98,116 @@ func (a *AgentLoop) Run() error {
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}
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func (a *AgentLoop) runTask(task prd.Task) error {
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executor := NewToolExecutor(a.workDir)
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// Frischer Kontext pro Task
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messages := []openai.ChatCompletionMessageParamUnion{
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openai.SystemMessage(systemPrompt),
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openai.UserMessage(fmt.Sprintf(
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"Task: %s\nArbeitsverzeichnis: %s",
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task.Title,
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a.workDir,
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task.Title, a.workDir,
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)),
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}
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a.log.ChatMessage("system", systemPrompt)
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a.log.ChatMessage("user", fmt.Sprintf(
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"Task: %s\nArbeitsverzeichnis: %s",
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task.Title,
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a.workDir,
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task.Title, a.workDir,
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))
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for turn := 0; turn < maxTurns; turn++ {
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a.log.Turn(turn + 1)
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// Token-Schätzung für Debugging
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totalChars := 0
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for _, m := range messages {
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totalChars += len(fmt.Sprintf("%v", m))
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}
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start := time.Now()
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a.log.Debug("MODEL REQUEST: model=%s ~%d Zeichen\n%s", a.model, totalChars, formatMessages(messages))
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a.log.Debug("MODEL REQUEST: model=%s ~%d Zeichen\n%s",
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a.model, totalChars, formatMessages(messages))
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ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
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resp, err := a.client.Chat.Completions.New(
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context.Background(),
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ctx,
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openai.ChatCompletionNewParams{
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Model: a.model,
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Messages: messages,
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Tools: Tools, // ← Tool Calling
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},
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)
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cancel()
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elapsed := time.Since(start)
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if resp != nil && len(resp.Choices) > 0 {
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a.log.Debug("MODEL RESPONSE\n%s", formatResponse(resp, elapsed))
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}
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if err != nil {
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return fmt.Errorf("API-Fehler (~%d Zeichen im Kontext): %w", totalChars, err)
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return fmt.Errorf("API-Fehler (~%d Zeichen): %w", totalChars, err)
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}
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response := resp.Choices[0].Message.Content
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a.log.ChatMessage("assistant", response)
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messages = append(messages, openai.AssistantMessage(response))
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choice := resp.Choices[0]
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// Completion Detection
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if isTaskComplete(response) {
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if turn == 0 {
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// LLM hat sofort TASK_COMPLETE ohne Tool-Call → nichts wurde getan
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nudge := "Du hast die Datei noch nicht erstellt! Nutze zuerst WRITE_FILE, dann schreibe TASK_COMPLETE."
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a.log.ChatMessage("user", nudge)
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messages = append(messages, openai.UserMessage(nudge))
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continue // nächster Turn
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}
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return nil
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}
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// Antwort zur History hinzufügen
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messages = append(messages, choice.Message.ToParam())
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// Tool Execution
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toolOutput, hadTools := ExecuteTools(response, a.workDir)
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if hadTools {
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a.log.ChatMessage("tool", toolOutput)
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messages = append(messages, openai.UserMessage(toolOutput))
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// Kein Tool-Call → LLM hat nur Text geantwortet
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if len(choice.Message.ToolCalls) == 0 {
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a.log.ChatMessage("assistant", choice.Message.Content)
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nudge := "Nutze die bereitgestellten Tools. Rufe task_complete auf wenn du fertig bist."
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a.log.ChatMessage("user", nudge)
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messages = append(messages, openai.UserMessage(nudge))
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continue
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}
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// Kein Tool, kein TASK_COMPLETE → anstupsen
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nudge := "Fahre fort. Wenn der Task erledigt ist, schreibe TASK_COMPLETE."
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a.log.ChatMessage("user", nudge)
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messages = append(messages, openai.UserMessage(nudge))
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}
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// Tool-Calls ausführen
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for _, toolCall := range choice.Message.ToolCalls {
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a.log.Info(" 🔧 %s(%s)", toolCall.Function.Name,
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truncate(toolCall.Function.Arguments, 80))
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return fmt.Errorf("maximale Turns (%d) erreicht ohne TASK_COMPLETE", maxTurns)
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}
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result, done := executor.Execute(toolCall)
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a.log.ChatMessage("tool",
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fmt.Sprintf("%s → %s", toolCall.Function.Name, result))
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// isTaskComplete erkennt TASK_COMPLETE auch bei häufigen LLM-Tippfehlern
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func isTaskComplete(response string) bool {
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if strings.Contains(response, "TASK_COMPLETE") {
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return true
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}
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typos := []string{
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"TUTK_COMPLETE",
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"TASK_COMPLET",
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"TASK_COMPLETED",
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"TASK_COMPETE",
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"TAKS_COMPLETE",
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}
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upper := strings.ToUpper(response)
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for _, t := range typos {
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if strings.Contains(upper, t) {
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return true
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// Tool-Ergebnis zurück ans LLM
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messages = append(messages, openai.ToolMessage(result, toolCall.ID))
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if done {
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return nil // task_complete aufgerufen → Erfolg
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}
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}
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}
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return false
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return fmt.Errorf("maximale Turns (%d) erreicht", maxTurns)
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}
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// ─── Hilfsfunktionen ─────────────────────────────────────
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func truncate(s string, max int) string {
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if len(s) <= max {
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return s
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}
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return s[:max] + "..."
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}
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// formatMessages gibt die Chat-History lesbar aus
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func formatMessages(messages []openai.ChatCompletionMessageParamUnion) string {
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var sb strings.Builder
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for i, m := range messages {
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var role, content string
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switch {
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case m.OfSystem != nil:
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role = "system"
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if len(m.OfSystem.Content.OfString.Value) > 0 {
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content = m.OfSystem.Content.OfString.Value
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}
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content = m.OfSystem.Content.OfString.Value
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case m.OfUser != nil:
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role = "user"
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if len(m.OfUser.Content.OfString.Value) > 0 {
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content = m.OfUser.Content.OfString.Value
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}
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content = m.OfUser.Content.OfString.Value
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case m.OfAssistant != nil:
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role = "assistant"
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if len(m.OfAssistant.Content.OfString.Value) > 0 {
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content = m.OfAssistant.Content.OfString.Value
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}
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content = m.OfAssistant.Content.OfString.Value
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default:
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role = "unknown"
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content = fmt.Sprintf("%+v", m)
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role = "other"
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}
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// Inhalt auf 120 Zeichen kürzen für Übersicht
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preview := content
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if len(preview) > 120 {
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preview = preview[:120] + "..."
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}
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// Zeilenumbrüche für einzeilige Darstellung ersetzen
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preview = strings.ReplaceAll(preview, "\n", "↵")
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preview := strings.ReplaceAll(truncate(content, 120), "\n", "↵")
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sb.WriteString(fmt.Sprintf(" [%d] %-10s : %s\n", i, role, preview))
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}
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return sb.String()
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@@ -239,7 +215,6 @@ func formatMessages(messages []openai.ChatCompletionMessageParamUnion) string {
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func formatResponse(resp *openai.ChatCompletion, elapsed time.Duration) string {
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var sb strings.Builder
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sb.WriteString(fmt.Sprintf(" ID : %s\n", resp.ID))
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sb.WriteString(fmt.Sprintf(" Modell : %s\n", resp.Model))
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sb.WriteString(fmt.Sprintf(" Elapsed : %s\n", elapsed.Round(time.Millisecond)))
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@@ -250,17 +225,21 @@ func formatResponse(resp *openai.ChatCompletion, elapsed time.Duration) string {
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resp.Usage.TotalTokens,
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))
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// Tokens/Sekunde aus den Timing-Daten (Ollama-spezifisch)
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if timings, ok := resp.JSON.ExtraFields["timings"]; ok {
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sb.WriteString(fmt.Sprintf(" Timings : %s\n", timings.Raw()))
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// Tool-Calls anzeigen
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if len(resp.Choices[0].Message.ToolCalls) > 0 {
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sb.WriteString(" Tool-Calls :\n")
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for _, tc := range resp.Choices[0].Message.ToolCalls {
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sb.WriteString(fmt.Sprintf(" → %s(%s)\n",
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tc.Function.Name,
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truncate(tc.Function.Arguments, 100),
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))
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}
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} else {
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content := resp.Choices[0].Message.Content
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sb.WriteString(" Content :\n")
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for _, line := range strings.Split(content, "\n") {
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sb.WriteString(fmt.Sprintf(" %s\n", line))
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}
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}
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sb.WriteString(fmt.Sprintf(" Content :\n"))
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// Inhalt eingerückt und vollständig ausgeben
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content := resp.Choices[0].Message.Content
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for _, line := range strings.Split(content, "\n") {
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sb.WriteString(fmt.Sprintf(" %s\n", line))
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}
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return sb.String()
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}
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