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							@ -85,4 +85,3 @@ temp/
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.astro/data-store.json
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					.astro/data-store.json
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.astro/content.d.ts
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					.astro/content.d.ts
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prompt.md
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					prompt.md
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data/embeddings.json
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							@ -16,7 +16,6 @@
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    "dotenv": "^16.4.5",
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					    "dotenv": "^16.4.5",
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    "jose": "^5.2.0",
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					    "jose": "^5.2.0",
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    "js-yaml": "^4.1.0",
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					    "js-yaml": "^4.1.0",
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    "json-stable-stringify": "^1.3.0",
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    "jsonwebtoken": "^9.0.2",
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					    "jsonwebtoken": "^9.0.2",
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    "zod": "^3.25.76"
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					    "zod": "^3.25.76"
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  },
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					  },
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@ -54,7 +54,7 @@ const sortedTags = Object.entries(tagFrequency)
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          <!-- Semantic Search Toggle - Inline -->
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					          <!-- Semantic Search Toggle - Inline -->
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          <div id="semantic-search-container" class="semantic-search-inline hidden">
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					          <div id="semantic-search-container" class="semantic-search-inline hidden">
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            <label class="semantic-toggle-wrapper" title="Semantische Suche verwendet KI-basierte Bedeutungserkennung statt nur Stichwortvergleich. Findet ähnliche Tools auch wenn andere Begriffe verwendet werden.">
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					            <label class="semantic-toggle-wrapper" title="Semantische Suche verwendet Embeddings. Dadurch kann mit natürlicher Sprache/Begriffen gesucht werden, die Ergebnisse richten sich nach der euklidischen Distanz.">
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              <input type="checkbox" id="semantic-search-enabled" />
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					              <input type="checkbox" id="semantic-search-enabled" />
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              <div class="semantic-checkbox-custom"></div>
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					              <div class="semantic-checkbox-custom"></div>
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              <span class="semantic-toggle-label">
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					              <span class="semantic-toggle-label">
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@ -1,42 +0,0 @@
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// src/pages/api/debug-env.ts
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import type { APIRoute } from 'astro';
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import { debugEmbeddings } from '../../utils/embeddings.js';
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export const GET: APIRoute = async () => {
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  const embeddingVars = Object.keys(process.env)
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    .filter(key => key.includes('EMBEDDINGS'))
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    .reduce((obj: Record<string, string>, key) => {
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      obj[key] = process.env[key] || 'undefined';
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      return obj;
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    }, {});
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  const aiVars = Object.keys(process.env)
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    .filter(key => key.includes('AI_'))
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    .reduce((obj: Record<string, string>, key) => {
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      // Mask sensitive values
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      const value = process.env[key] || 'undefined';
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      obj[key] = key.includes('KEY') || key.includes('SECRET') ? 
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        (value.length > 10 ? `${value.slice(0, 6)}...${value.slice(-4)}` : value) : 
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        value;
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      return obj;
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    }, {});
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  // Force recheck embeddings environment
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  await debugEmbeddings.recheckEnvironment();
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  const embeddingsStatus = debugEmbeddings.getStatus();
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  return new Response(JSON.stringify({
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    timestamp: new Date().toISOString(),
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    embeddingVars,
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    allAiVars: aiVars,
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    totalEnvVars: Object.keys(process.env).length,
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    embeddingsStatus,
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    nodeEnv: process.env.NODE_ENV,
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    platform: process.platform
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  }, null, 2), {
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    status: 200,
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    headers: {
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      'Content-Type': 'application/json'
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    }
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  });
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};
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@ -4,7 +4,6 @@ import path from 'path';
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import { getCompressedToolsDataForAI } from './dataService.js';
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					import { getCompressedToolsDataForAI } from './dataService.js';
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import 'dotenv/config';
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					import 'dotenv/config';
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import crypto from 'crypto';
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					import crypto from 'crypto';
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import stringify from 'json-stable-stringify';
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interface EmbeddingData {
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					interface EmbeddingData {
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  id: string;
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					  id: string;
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