utility inference layer 3

packages/memory-service/src/summarization/project.js

@@ -1,4 +1,4 @@
-const { SERVICES, getEnv, SUMMARIES } = require('@nexusai/shared');
+const { SERVICES, getEnv, SUMMARIES, utilityInference } = require('@nexusai/shared');
 const { 
     getSessionSummariesForProject,
     getProjectOverviewSummary,
@@ -9,9 +9,6 @@ const {
  const { getEpisodesByProject } = require('../episodic');
  const { getProject } = require('../db/projects');
 
-const EXTRACTION_URL   = getEnv('EXTRACTION_URL', 'http://localhost:11434');
-const EXTRACTION_MODEL = getEnv('EXTRACTION_MODEL', 'qwen2.5:3b');
-
 const MAX_SUMMARY_CHARS = SUMMARIES.MAX_SUMMARY_CHARS; // generous ceiling before we truncate input
 
 function buildProjectSummaryPrompt(projectName, sessionSummaries) {
@@ -24,8 +21,9 @@ function buildProjectSummaryPrompt(projectName, sessionSummaries) {
         summaryBlock = summaryBlock.slice(-MAX_SUMMARY_CHARS);
     }
 
+    // No ChatML wrapper — the model's own prompt template is applied server-side
+    // by the inference service's /utility/complete route (Ollama /api/chat).
     return [
-        '<|im_start|>user',
         `The following are session summaries from a project called "${projectName}".`,
         'Write a project overview covering: goals, progress, key decisions, and current state.',
         'Scale the length to the material — use multiple paragraphs for complex projects, a few sentences for simple ones.',
@@ -33,8 +31,6 @@ function buildProjectSummaryPrompt(projectName, sessionSummaries) {
         'Write in third person. Output only the overview text, no headings or labels.',
         '',
         summaryBlock,
-        '<|im_end|>',
-        '<|im_start|>assistant',
     ].join('\n');
 }
 
@@ -49,8 +45,9 @@ function buildProjectSummaryFromEpisodesPrompt(projectName, episodes) {
         episodeBlock = episodeBlock.slice(-MAX_SUMMARY_CHARS);
     }
 
+    // No ChatML wrapper — the model's own prompt template is applied server-side
+    // by the inference service's /utility/complete route (Ollama /api/chat).
     return [
-        '<|im_start|>user',
         `The following are conversations from a project called "${projectName}".`,
         'Write a project overview covering: goals, progress, key decisions, and current state.',
         'Scale the length to the material — use multiple paragraphs for complex projects, a few sentences for simple ones.',
@@ -58,58 +55,17 @@ function buildProjectSummaryFromEpisodesPrompt(projectName, episodes) {
         'Write in third person. Output only the overview text, no headings or labels.',
         '',
         episodeBlock,
-        '<|im_end|>',
-        '<|im_start|>assistant',
     ].join('\n');
 }
 
 async function generateProjectSummaryFromEpisodes(projectName, episodes) {
-    const prompt = buildProjectSummaryFromEpisodesPrompt(projectName, episodes);
+    const user = buildProjectSummaryFromEpisodesPrompt(projectName, episodes);
-
+    return utilityInference({ user, temperature: 0.2, maxTokens: 1200 });
-    const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-            model: EXTRACTION_MODEL,
-            prompt,
-            stream: false,
-            options: { temperature: 0.2, num_predict: 1200 },
-        }),
-    });
-
-    if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
-    const data = await res.json();
-
-    const raw = data.response?.trim() ?? '';
-    return raw
-        .replace(/<\|im_start\|>.*?<\|im_end\|>/gs, '')
-        .replace(/<\|im_start\|>|<\|im_end\|>|<\|im_sep\|>/g, '')
-        .trim();
 }
 
 async function generateProjectSummary(projectName, sessionSummaries) {
-    const prompt = buildProjectSummaryPrompt(projectName, sessionSummaries);
+    const user = buildProjectSummaryPrompt(projectName, sessionSummaries);
-
+    return utilityInference({ user, temperature: 0.2, maxTokens: 1200 });
-    const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-            model: EXTRACTION_MODEL,
-            prompt,
-            stream: false,
-            // No format: 'json' — we want free-text narrative, same as session summarization
-            options: { temperature: 0.2, num_predict: 1200 },
-        }),
-    });
-
-    if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
-    const data = await res.json();
-
-    const raw = data.response?.trim() ?? '';
-    return raw
-        .replace(/<\|im_start\|>.*?<\|im_end\|>/gs, '')
-        .replace(/<\|im_start\|>|<\|im_end\|>|<\|im_sep\|>/g, '')
-        .trim();
 }
 
 // Main entry point — called by the route handler

packages/orchestration-service/src/services/summarization.js

@@ -1,7 +1,5 @@
-const { getEnv, SERVICES, SUMMARIES, logger } = require('@nexusai/shared');
+const { getEnv, SERVICES, SUMMARIES, logger, utilityInference } = require('@nexusai/shared');
 
-const EXTRACTION_URL  = getEnv('EXTRACTION_URL', 'http://localhost:11434');
-const EXTRACTION_MODEL = getEnv('EXTRACTION_MODEL', 'qwen2.5:3b');
 const MEMORY_URL      = getEnv('MEMORY_SERVICE_URL', SERVICES.MEMORY_URL);
 
 const THRESHOLD_TOKENS  = parseInt(getEnv('SUMMARY_THRESHOLD_TOKENS', SUMMARIES.THRESHOLD_TOKENS));
@@ -35,41 +33,20 @@ Do not include greetings, sign-offs, or filler. Output only the summary text.
 Conversation:
 ${context}`;
 
-    return [
+    // No ChatML wrapper — the model's own prompt template is applied server-side
-        '<|im_start|>user',   // ChatML for qwen2.5
+    // by the inference service's /utility/complete route (Ollama /api/chat).
-        instruction,
+    return instruction;
-        '<|im_end|>',
-        '<|im_start|>assistant',
-    ].join('\n');
 }
 
 async function generateSummary(episodes, existingSummary = null) {
-    const prompt = buildSummaryPrompt(episodes, existingSummary);
+    const user = buildSummaryPrompt(episodes, existingSummary);
 
-    const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
+    const content = await utilityInference({
-        method: 'POST',
+        user,
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-            model: EXTRACTION_MODEL,
-            prompt,
-            stream: false,
-            options: {
         temperature: 0.2,   // slightly higher than entities — summaries benefit from some fluency
-                num_predict: 500,   // generous but bounded — keeps summaries from running long
+        maxTokens: 500,     // generous but bounded — keeps summaries from running long
-            },
-        }),
     });
 
-    if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
-    const data = await res.json();
-
-
-    const raw = data.response?.trim() ?? '';
-    // Strip any leaked ChatML tokens Qwen echoes back
-    const content = raw
-        .replace(/<\|im_start\|>.*?<\|im_end\|>/gs, '')
-        .replace(/<\|im_start\|>|<\|im_end\|>|<\|im_sep\|>/g, '')
-        .trim();
     return content;
 }