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https://github.com/Memo-2023/mana-monorepo.git
synced 2026-05-14 19:21:10 +02:00
make auth working
This commit is contained in:
Wuesteon
parent
7a1f1e9aef
commit
25824ed0ac
73 changed files with 9093 additions and 3877 deletions
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@ -1,19 +1,20 @@
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// Chat Service - AI API Aufrufe werden über das Backend gehandhabt
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/**
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* Chat Service - AI API calls via Backend
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* This service wraps the backend API for AI completions
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*/
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import { availableModels } from '../config/azure';
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import { sendChatRequest as sendChatRequestApi } from '../utils/api';
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import { supabase } from '../utils/supabase';
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import { chatApi, modelApi, usageApi, type ChatMessage, type TokenUsage } from './api';
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// Typdefinition für eine Nachricht
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export type ChatMessage = {
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role: 'system' | 'user' | 'assistant';
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content: string;
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};
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// Re-export types for backward compatibility
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export type { ChatMessage };
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// Typdefinition für die Chat-Antwort vom o3-mini-Modell
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// Re-export TokenUsage
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export type { TokenUsage };
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// Chat response type (kept for compatibility)
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export type ChatResponse = {
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id: string;
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choices: {
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// Für o3-mini-Modell
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content_filter_results?: any;
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finish_reason: string;
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index: number;
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@ -38,34 +39,20 @@ export type ChatResponse = {
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};
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};
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// Token-Nutzungsinformationen
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export type TokenUsage = {
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prompt_tokens: number;
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completion_tokens: number;
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total_tokens: number;
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};
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// Rückgabetyp für die Chat-Anfrage
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// Return type for chat request
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export type ChatRequestResult = {
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content: string;
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usage: TokenUsage;
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};
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// Backend-URL für sichere API-Aufrufe
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const BACKEND_URL = process.env.EXPO_PUBLIC_BACKEND_URL || 'http://localhost:3001';
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// Logging der Konfiguration
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// Logging configuration
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console.log('Chat Service Konfiguration:', {
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backendUrl: BACKEND_URL,
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backendUrl: process.env.EXPO_PUBLIC_BACKEND_URL || 'http://localhost:3001',
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availableModels: availableModels.length,
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});
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/**
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* Berechnet die geschätzten Kosten einer LLM-Anfrage
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* @param promptTokens Anzahl der Eingabe-Tokens
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* @param completionTokens Anzahl der Ausgabe-Tokens
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* @param modelId ID des verwendeten Modells
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* @returns Geschätzte Kosten in der kleinsten Währungseinheit (z.B. Cent)
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* Calculates estimated cost for an LLM request
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*/
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export async function calculateTokenCost(
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promptTokens: number,
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@ -73,45 +60,31 @@ export async function calculateTokenCost(
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modelId: string
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): Promise<number> {
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try {
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// Hole die Kosteninformationen aus dem Modell
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const { data: modelData, error } = await supabase
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.from('models')
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.select('cost_settings')
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.eq('id', modelId)
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.single();
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if (error || !modelData || !modelData.cost_settings) {
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console.warn('Fehler beim Laden der Kosteninformationen, verwende Standardwerte:', error);
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// Standardwerte verwenden
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const promptCost = 0.0001; // pro 1K Tokens
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const completionCost = 0.0002; // pro 1K Tokens
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// Berechne die Kosten
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// Get cost settings from model
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const modelData = await modelApi.getModel(modelId);
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if (!modelData || !modelData.costSettings) {
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console.warn('Fehler beim Laden der Kosteninformationen, verwende Standardwerte');
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const promptCost = 0.0001;
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const completionCost = 0.0002;
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const cost = (promptTokens * promptCost + completionTokens * completionCost) / 1000;
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return Number(cost.toFixed(6));
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}
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// Extrahiere die Kostensätze
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const promptCost = parseFloat(modelData.cost_settings.prompt_per_1k_tokens) || 0.0001;
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const completionCost = parseFloat(modelData.cost_settings.completion_per_1k_tokens) || 0.0002;
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// Berechne die Kosten
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const promptCost = modelData.costSettings.prompt_per_1k_tokens || 0.0001;
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const completionCost = modelData.costSettings.completion_per_1k_tokens || 0.0002;
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const cost = (promptTokens * promptCost + completionTokens * completionCost) / 1000;
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return Number(cost.toFixed(6));
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} catch (error) {
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console.error('Fehler bei der Kostenberechnung:', error);
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// Fallback: vereinfachte Berechnung
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return Number(((promptTokens * 0.0001 + completionTokens * 0.0002) / 1000).toFixed(6));
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}
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}
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/**
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* Speichert die Token-Nutzung in der Datenbank
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* @param usage Token-Nutzungsinformationen
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* @param conversationId ID der Konversation
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* @param messageId ID der Nachricht
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* @param userId ID des Benutzers
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* @param modelId ID des verwendeten Modells
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* Logs token usage to the database
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*/
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export async function logTokenUsage(
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usage: TokenUsage,
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@ -121,36 +94,31 @@ export async function logTokenUsage(
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modelId: string
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): Promise<void> {
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try {
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// Berechne die geschätzten Kosten
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const estimatedCost = await calculateTokenCost(
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usage.prompt_tokens,
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usage.completion_tokens,
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modelId
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);
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// Speichere die Nutzungsinformationen
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const { error } = await supabase
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.from('usage_logs')
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.insert({
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conversation_id: conversationId,
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message_id: messageId,
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user_id: userId,
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model_id: modelId,
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prompt_tokens: usage.prompt_tokens,
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completion_tokens: usage.completion_tokens,
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total_tokens: usage.total_tokens,
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estimated_cost: estimatedCost
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});
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if (error) {
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console.error('Fehler beim Speichern der Token-Nutzung:', error);
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} else {
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const success = await usageApi.logTokenUsage({
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conversationId,
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messageId,
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modelId,
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promptTokens: usage.prompt_tokens,
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completionTokens: usage.completion_tokens,
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totalTokens: usage.total_tokens,
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estimatedCost,
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});
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if (success) {
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console.log('Token-Nutzung erfolgreich gespeichert:', {
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conversationId,
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messageId,
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totalTokens: usage.total_tokens,
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estimatedCost
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estimatedCost,
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});
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} else {
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console.error('Fehler beim Speichern der Token-Nutzung');
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}
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} catch (error) {
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console.error('Fehler beim Loggen der Token-Nutzung:', error);
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@ -158,12 +126,7 @@ export async function logTokenUsage(
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}
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/**
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* Sendet eine Chat-Anfrage über das Backend
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* Das Backend handhabt die Azure OpenAI API Aufrufe sicher
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* @param messages Array von Nachrichten im Chat
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* @param temperature Kreativität der Antwort (0.0 - 1.0)
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* @param maxTokens Maximale Anzahl der Tokens in der Antwort
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* @returns Die Antwort des LLM-Modells und Tokeninformationen
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* Sends a chat request via the backend
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*/
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export async function sendChatRequest(
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messages: ChatMessage[],
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@ -172,57 +135,66 @@ export async function sendChatRequest(
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): Promise<string | ChatRequestResult> {
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console.log('sendChatRequest gestartet mit:', {
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messagesCount: messages.length,
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maxTokens
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maxTokens,
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});
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try {
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// Hole aktuelle Modellparameter aus der Konversation (für Modellwechsel)
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let deployment = '';
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// Find model deployment from system message
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let modelId = '550e8400-e29b-41d4-a716-446655440000'; // Default to GPT-O3-Mini
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// System-Nachricht mit Modell-Präfix suchen
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const systemMessage = messages.find(msg => msg.role === 'system' && msg.content.startsWith('MODEL:'));
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const systemMessage = messages.find(
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(msg) => msg.role === 'system' && msg.content.startsWith('MODEL:')
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);
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if (systemMessage) {
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deployment = systemMessage.content.split(':')[1].trim();
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const deployment = systemMessage.content.split(':')[1].trim();
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console.log('Modell in system Nachricht erkannt:', deployment);
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// Map deployment to model ID
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const deploymentToModelId: Record<string, string> = {
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'gpt-o3-mini-se': '550e8400-e29b-41d4-a716-446655440000',
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'gpt-4o-mini-se': '550e8400-e29b-41d4-a716-446655440004',
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'gpt-4o-se': '550e8400-e29b-41d4-a716-446655440005',
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};
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modelId = deploymentToModelId[deployment] || modelId;
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} else {
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console.warn('Keine System-Nachricht mit MODEL-Präfix gefunden!');
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}
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// Falls kein Modell angegeben wurde, setze auf Fallback gpt-o3-mini-se
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const deploymentToUse = deployment || 'gpt-o3-mini-se';
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console.log('Verwende Deployment:', deploymentToUse);
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console.log('Verwende Model ID:', modelId);
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// Konfiguration für den API-Wrapper (Backend benötigt nur deployment)
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const config = {
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deployment: deploymentToUse
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};
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// Filter out MODEL: system messages before sending to API
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const filteredMessages = messages.filter(
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(msg) => !(msg.role === 'system' && msg.content.startsWith('MODEL:'))
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);
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// Verwende den zentralen API-Wrapper - dieser ruft das Backend auf
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const result = await sendChatRequestApi(messages, temperature, maxTokens, config);
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// Send request to backend
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const result = await chatApi.createCompletion({
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messages: filteredMessages,
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modelId,
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temperature,
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maxTokens,
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});
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// Wenn es ein einfacher String ist (Fehlerfall), diesen zurückgeben
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if (typeof result === 'string') {
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return result;
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if (!result) {
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return 'Es tut mir leid, aber ich konnte keine Antwort generieren. Bitte stelle sicher, dass das Backend läuft.';
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}
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// Ansonsten die vollständige Antwort mit Token-Nutzung zurückgeben
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return {
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content: result.content,
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usage: result.usage
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usage: result.usage,
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};
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} catch (error) {
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console.error('Fehler bei der Chat-Anfrage:', error);
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// Versuche, mehr Informationen über den Fehler zu erhalten
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if (error instanceof Error) {
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console.error('Fehlerdetails:', {
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name: error.name,
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message: error.message,
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stack: error.stack
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stack: error.stack,
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});
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}
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// Gib eine benutzerfreundliche Fehlermeldung zurück, anstatt den Fehler zu werfen
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return `Es tut mir leid, aber ich konnte keine Antwort generieren. Bitte stelle sicher, dass das Backend läuft. Fehlerdetails: ${error instanceof Error ? error.message : 'Unbekannter Fehler'}`;
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}
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}
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