feat: Zusammenfassung der endgültigen Antwort in zwei Schritten umgestaltet

- Zerlegung von `summarizeFinalAnswer` in zwei separate LLM-Aufrufe:
  1. Zusammenfassung, Quellen und vorgeschlagene Suchen
  2. Anreicherung mit Emojis und HTML-Tags
- Verbesserte Prompt-Formulierung für detailliertere Zusammenfassungen
- Verwendung eines günstigeren Modells für den Formatierungsschritt
- Hinzufügen separater Kostenprotokollierung pro Schritt
- Aktualisierung der JSON-Schema-Antwortstruktur

src/services/openRouterService.js

@@ -206,30 +206,33 @@ export async function rephraseQuestion({ question, previousClarification, origin
 }
 
 export async function summarizeFinalAnswer({ openrouter, text, question }) {
-  const prompt = `
+  const apiKey = process.env.OPENROUTER_API_KEY;
+  
+  // Step 1: Get summary, sources, and suggested searches
+  const summaryPrompt = `
     You are a search result analyst. Today is the date of ${new Date().toLocaleDateString()}.
     Based on the following search results for the query "${question}",
-    Summarize the search results to answer the original query. Use Emoji and HTML. Tags allowed: <b>, <i>, <u>, <pre>, <ul>, <li>, <span style="color:...">, <p> <div> <hr/>
+    Summarize the search results to answer the original query in a detailed manner. 
-    Also provide the most relevant sources. Answer in the language of the question. You may suggest followup searched to the user.
+    Also provide the most relevant sources. Answer in the language of the question. You may suggest 2 followup searches to the user.
   `;
 
-  const params = {
+  const summaryParams = {
     model: 'openai/gpt-oss-120b:nitro',
     messages: [
-      { role: 'system', content: prompt },
+      { role: 'system', content: summaryPrompt },
       { role: 'user', content: text },
     ],
     reasoning: { effort: 'low' },
     response_format: {
       type: 'json_schema',
       json_schema: {
-        name: 'response',
+        name: 'summary',
         strict: true,
         schema: {
           type: 'object',
-          required: ['fullAnswerHTMLSnippet', 'mostRelevantSources', 'suggestedSearches'],
+          required: ['summary', 'mostRelevantSources', 'suggestedSearches'],
           properties: {
-            fullAnswerHTMLSnippet: { type: 'string' },
+            summary: { type: 'string' },
             mostRelevantSources: { type: 'array', items: { type: 'string' } },
             suggestedSearches: { type: 'array', items: { type: 'string' } },
           },
@@ -239,19 +242,88 @@ export async function summarizeFinalAnswer({ openrouter, text, question }) {
     stream: false,
   };
 
-  // Using direct fetch API instead of OpenRouter SDK
+  const summaryFetchResponse = await fetch('https://openrouter.ai/api/v1/chat/completions', {
-  const apiKey = process.env.OPENROUTER_API_KEY;
-  const fetchResponse = await fetch('https://openrouter.ai/api/v1/chat/completions', {
     method: 'POST',
     headers: {
       Authorization: `Bearer ${apiKey}`,
       'Content-Type': 'application/json',
     },
-    body: JSON.stringify(params),
+    body: JSON.stringify(summaryParams),
   });
-  const response = await fetchResponse.json();
+  const summaryResponse = await summaryFetchResponse.json();
+  await logOpenRouterCall('summarizeFinalAnswer-step1', text, summaryParams, summaryResponse);
+  const summaryData = parseResponse(summaryResponse);
   
-  await logOpenRouterCall('summarizeFinalAnswer', text, params, response);
+  // Step 2: Enrich the summary with HTML tags and emojis
+  const enrichmentPrompt = `
+    You are a content formatter. Take the following summary and enrich it with emojis and HTML tags.
+    Allowed tags: <b>, <i>, <u>, <pre>, <ul>, <li>, <span style="color:...">, <p>, <div>, <hr/>
+    Make it visually appealing and easy to read. Keep the same language as the original summary.
+    Summary to enrich:
+    ${summaryData.data.summary}
+  `;
 
-  return parseResponse(response);
+  const enrichmentParams = {
+    model: 'kwaipilot/kat-coder-pro-v2',
+    messages: [
+      { role: 'system', content: enrichmentPrompt },
+      { role: 'user', content: 'Please enrich the summary with HTML tags and emojis.' },
+    ],
+    reasoning: { effort: 'low' },
+    response_format: {
+      type: 'json_schema',
+      json_schema: {
+        name: 'enrichedSummary',
+        strict: true,
+        schema: {
+          type: 'object',
+          required: ['fullAnswerHTMLSnippet'],
+          properties: {
+            fullAnswerHTMLSnippet: { type: 'string' },
+          },
+        },
+      },
+    },
+    stream: false,
+  };
+
+  const enrichmentFetchResponse = await fetch('https://openrouter.ai/api/v1/chat/completions', {
+    method: 'POST',
+    headers: {
+      Authorization: `Bearer ${apiKey}`,
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify(enrichmentParams),
+  });
+  const enrichmentResponse = await enrichmentFetchResponse.json();
+  await logOpenRouterCall('summarizeFinalAnswer-step2', summaryData.data.summary, enrichmentParams, enrichmentResponse);
+  const enrichmentData = parseResponse(enrichmentResponse);
+  
+  // Combine results with separate cost breakdowns
+  return {
+    totalCost: summaryData.cost + enrichmentData.cost,
+    totalPromptTokens: summaryData.prompt_tokens + enrichmentData.prompt_tokens,
+    totalCompletionTokens: summaryData.completion_tokens + enrichmentData.completion_tokens,
+    steps: [
+      {
+        name: 'summary',
+        model: 'openai/gpt-oss-120b:nitro',
+        cost: summaryData.cost,
+        promptTokens: summaryData.prompt_tokens,
+        completionTokens: summaryData.completion_tokens,
+      },
+      {
+        name: 'enrichment',
+        model: 'kwaipilot/kat-coder-pro-v2',
+        cost: enrichmentData.cost,
+        promptTokens: enrichmentData.prompt_tokens,
+        completionTokens: enrichmentData.completion_tokens,
+      },
+    ],
+    data: {
+      fullAnswerHTMLSnippet: enrichmentData.data.fullAnswerHTMLSnippet,
+      mostRelevantSources: summaryData.data.mostRelevantSources,
+      suggestedSearches: summaryData.data.suggestedSearches,
+    },
+  };
 }

src/services/searchService.js

@@ -1,6 +1,6 @@
 import { extractContent } from './extractContent.js';
 import { logExtraction } from './extractionLogger.js';
-import { summarizeFinalAnswer, summarizeSources, rephraseQuestion } from './openRouterService.js';
+import { summarizeFinalAnswer, summarizeSources, summarizeDetail, rephraseQuestion } from './openRouterService.js';
 import { formatSummarySources } from './searchFormatter.js';
 import { get_encoding } from 'tiktoken';
 
@@ -58,11 +58,22 @@ async function fetchDetailedContents({ exa, question, sources, broadcast }) {
 
         const content = await exa.getContents([source.url], EXA_CONTENT_OPTIONS(question));
 
-        console.log(content.results.highlights.join('\n'));
+        console.log(content.results[0].highlights);
+        return { url: source.url, content , cost: content.costDollars.total };
 
-        const summary = await summarizeDetail({text: content.results.highlights.join('\n'), url: source.url, question})
+        /*const summary = await summarizeDetail({text: content.results, url: source.url, question})
 
-        return { url: source.url, content:summary, cost: content.costDollars.total };
+        const cost2 = {
+          type:'openrouter_detail_summary', 
+          amount: summary.cost,
+          prompt_tokens: summary.prompt_tokens,
+          completion_tokens: summary.completion_tokens,
+          model: 'openai/gpt-oss-120b:nitro'
+        };
+
+        const summaryIsLonger = JSON.stringify(summary.data).length > JSON.stringify(content.results).length;
+
+        return { url: source.url, content: summaryIsLonger ? content.results : source.summary, cost: content.costDollars.total, cost2 };*/
       } catch (error) {
         broadcast(`⚠️ Could not fetch content for ${source.url}: ${error.message}`, 'warning');
         return { url: source.url, content: null };
@@ -256,6 +267,9 @@ export function createSearchService({ exa, openrouter, broadcast }) {
             completion_tokens: outputTokens
           });
         }
+        //if (detailed.cost2) {
+        //  cost.push(detailed.cost2);
+        //}
       }
 
       broadcast('📊 Generating final summary...', 'info');
@@ -268,13 +282,28 @@ export function createSearchService({ exa, openrouter, broadcast }) {
           question,
         });
         finalSummary = finalSummaryResult.data;
+        
+        // Push each step separately for detailed cost tracking
+        if (finalSummaryResult.steps && Array.isArray(finalSummaryResult.steps)) {
+          for (const step of finalSummaryResult.steps) {
+            cost.push({
+              type: `openrouter_${step.name}`,
+              amount: step.cost,
+              prompt_tokens: step.promptTokens,
+              completion_tokens: step.completionTokens,
+              model: step.model,
+            });
+          }
+        } else {
+          // Fallback for backward compatibility
           cost.push({
             type: 'openrouter_final_summary',
-          amount: finalSummaryResult.cost,
+            amount: finalSummaryResult.totalCost,
-          prompt_tokens: finalSummaryResult.prompt_tokens,
+            prompt_tokens: finalSummaryResult.totalPromptTokens,
-          completion_tokens: finalSummaryResult.completion_tokens,
+            completion_tokens: finalSummaryResult.totalCompletionTokens,
-          model: 'openai/gpt-oss-120b:nitro'
+            model: 'openai/gpt-oss-120b:nitro',
           });
+        }
       } catch (error) {
         throw new SearchServiceError('Failed to generate final summary', 500, error);
       }