fix: include linkedin_post in GET /api/blog response for SLL matching
This commit is contained in:
Rene Fichtmueller
parent
d9f5fc253f
commit
15f3ff5bef
@ -16,8 +16,8 @@ import { pool } from "../db/client";
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const pipelineProgress = new Map<string, { step: number; total: number; label: string; pct: number }>();
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function setProgress(draftId: string, step: number, label: string): void {
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const pct = Math.round((step / 14) * 92) + 2; // 2%..94% during run, 100% on complete
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pipelineProgress.set(draftId, { step, total: 14, label, pct });
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const pct = Math.round((step / 17) * 92) + 2; // 2%..94% during run, 100% on complete
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pipelineProgress.set(draftId, { step, total: 17, label, pct });
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}
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function clearProgress(draftId: string): void {
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@ -1001,20 +1001,24 @@ async function runLlmPipeline(
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STEP5_REALITY_INJECTION,
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STEP6_TECHNICAL_DEEPENING,
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STEP7_OPINION_LAYER,
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STEP_AFE,
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STEP8_KILL_AI_TONE,
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STEP8b_REDUCTION,
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STEP_AEM,
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STEP8c_STYLE_LOCK,
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STEP9_QA_CHECK,
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STEP10_QUALITY_SCORE,
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STEP_APM,
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STEP_LINKEDIN_POST,
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BLOG_TYPES,
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buildFeedbackContext,
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buildSLLContext,
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withCalibration,
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} = await import("../llm/fo-blog-pipeline");
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const LLM_OPTS = { temperature: 0.7, maxTokens: 8192, timeoutMs: 480000 };
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const LLM_REFINE = { temperature: 0.4, maxTokens: 6144, timeoutMs: 480000 };
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const TOTAL_STEPS = 14; // 10 original + 4b Narrative Control + 8b Reduction + 8c Style Lock + LinkedIn
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const TOTAL_STEPS = 17; // 16-step pipeline + APM final cut
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let stepsCompleted = 0;
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try {
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@ -1034,7 +1038,14 @@ async function runLlmPipeline(
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})));
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} catch { /* no feedback yet, that's fine */ }
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const systemPrompt = withCalibration(FO_BLOG_SYSTEM_PROMPT + feedbackContext);
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// Load SLL learned patterns (safe-fails if no data yet)
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let sllContext = "";
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try {
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sllContext = await buildSLLContext();
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if (sllContext) console.log(" SLL: Learned patterns injected into system prompt");
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} catch { /* no SLL data yet, fine */ }
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const systemPrompt = withCalibration(FO_BLOG_SYSTEM_PROMPT + feedbackContext + sllContext);
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// Warmup
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await generate("Test", "OK", { temperature: 0.1, maxTokens: 8, timeoutMs: 60000 }).catch(() => {});
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@ -1147,66 +1158,88 @@ async function runLlmPipeline(
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stepsCompleted = 6;
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// ═══ STEP 6: Technical Deepening ═══
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console.log(" Step 7/13: Technical Deepening...");
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setProgress(draftId, 7, "Step 7/13: Technical Deepening");
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console.log(" Step 7/16: Technical Deepening...");
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setProgress(draftId, 7, "Step 7/16: Technical Deepening");
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const step6 = await generate(systemPrompt,
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STEP6_TECHNICAL_DEEPENING.replace("{{ARTICLE}}", step5.text),
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LLM_REFINE
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);
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stepsCompleted = 6;
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stepsCompleted = 7;
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// ═══ STEP 7: Opinion Layer ═══
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console.log(" Step 8/13: Opinion Layer...");
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setProgress(draftId, 8, "Step 8/13: Opinion Layer");
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console.log(" Step 8/16: Opinion Layer...");
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setProgress(draftId, 8, "Step 8/16: Opinion Layer");
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const step7 = await generate(systemPrompt,
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STEP7_OPINION_LAYER.replace("{{ARTICLE}}", step6.text),
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LLM_REFINE
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);
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stepsCompleted = 8;
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// ═══ STEP 8: Kill AI Tone ═══
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console.log(" Step 9/13: Kill AI Tone...");
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setProgress(draftId, 9, "Step 9/13: Kill AI Tone");
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const step8 = await generate(systemPrompt,
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STEP8_KILL_AI_TONE.replace("{{ARTICLE}}", step7.text),
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// ═══ STEP AFE: Auto-Focus Enforcer (ONE idea, ONE scenario, kill drift) ═══
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console.log(" Step 9/16: Auto-Focus Enforcer (kill multi-topic drift)...");
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setProgress(draftId, 9, "Step 9/16: Auto-Focus Enforcer");
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const stepAFE = await generate(systemPrompt,
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STEP_AFE.replace("{{ARTICLE}}", step7.text),
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LLM_REFINE
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);
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stepsCompleted = 9;
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const wordsAFE = stepAFE.text.split(/\s+/).length;
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const wordsBeforeAFE = step7.text.split(/\s+/).length;
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const pctAFE = Math.round((1 - wordsAFE / wordsBeforeAFE) * 100);
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if (pctAFE > 5) console.log(` AFE cut: ${wordsBeforeAFE} → ${wordsAFE} words (−${pctAFE}%) — drift removed`);
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// ═══ STEP 8: Kill AI Tone ═══
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console.log(" Step 10/16: Kill AI Tone...");
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setProgress(draftId, 10, "Step 10/16: Kill AI Tone");
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const step8 = await generate(systemPrompt,
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STEP8_KILL_AI_TONE.replace("{{ARTICLE}}", stepAFE.text),
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LLM_REFINE
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);
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stepsCompleted = 10;
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// ═══ STEP 8b: Reduction Engine (5-pass: Repetition Kill → Tech Prune → Flow Rebuild → Weight Correction → Humanization) ═══
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console.log(" Step 10/14: Reduction Engine (5-pass, target 700-1000 words)...");
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setProgress(draftId, 10, "Step 10/14: Reduction Engine");
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console.log(" Step 11/16: Reduction Engine (5-pass, target 700-1000 words)...");
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setProgress(draftId, 11, "Step 11/16: Reduction Engine");
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const step8b = await generate(systemPrompt,
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STEP8b_REDUCTION.replace("{{ARTICLE}}", step8.text),
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LLM_REFINE
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);
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stepsCompleted = 10;
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stepsCompleted = 11;
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const wordsAfter = step8b.text.split(/\s+/).length;
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const wordsBefore = step8.text.split(/\s+/).length;
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const pctChange = Math.round((1 - wordsAfter / wordsBefore) * 100);
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console.log(` After reduction: ${wordsAfter} words (was ${wordsBefore}, −${pctChange}%) ${wordsAfter > 1300 ? "⚠ WARNING: >1300 words" : wordsAfter < 600 ? "⚠ WARNING: <600 words" : "✓ in target range"}`);
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// ═══ STEP 8c: Style Lock ═══
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console.log(" Step 11/14: Style Lock (tone consistency + scope/SKU fixes)...");
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setProgress(draftId, 11, "Step 11/14: Style Lock");
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const step8c = await generate(systemPrompt,
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STEP8c_STYLE_LOCK.replace("{{ARTICLE}}", step8b.text),
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LLM_REFINE
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);
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stepsCompleted = 11;
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// ═══ STEP 9: QA Check ═══
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console.log(" Step 12/14: QA Check...");
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setProgress(draftId, 12, "Step 12/14: QA Check");
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const step9 = await generate(systemPrompt,
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STEP9_QA_CHECK.replace("{{ARTICLE}}", step8c.text),
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// ═══ STEP AEM: Auto-Editor Mode (Senior Engineer voice polish) ═══
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console.log(" Step 12/16: Auto-Editor Mode (senior engineer voice polish)...");
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setProgress(draftId, 12, "Step 12/16: Auto-Editor Mode");
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const stepAEM = await generate(systemPrompt,
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STEP_AEM.replace("{{ARTICLE}}", step8b.text),
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LLM_REFINE
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);
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stepsCompleted = 12;
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// ═══ STEP 8c: Style Lock ═══
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console.log(" Step 13/16: Style Lock (tone consistency + scope/SKU fixes)...");
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setProgress(draftId, 13, "Step 13/16: Style Lock");
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const step8c = await generate(systemPrompt,
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STEP8c_STYLE_LOCK.replace("{{ARTICLE}}", stepAEM.text),
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LLM_REFINE
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);
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stepsCompleted = 13;
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// ═══ STEP 9: QA Check ═══
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console.log(" Step 14/16: QA Check...");
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setProgress(draftId, 14, "Step 14/16: QA Check");
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const step9 = await generate(systemPrompt,
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STEP9_QA_CHECK.replace("{{ARTICLE}}", step8c.text),
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LLM_REFINE
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);
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stepsCompleted = 14;
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// ═══ STEP 10: Quality Score ═══
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console.log(" Step 13/14: Quality Score...");
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setProgress(draftId, 13, "Step 13/14: Quality Score");
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console.log(" Step 15/16: Quality Score...");
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setProgress(draftId, 15, "Step 15/16: Quality Score");
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let autoQaScore: Record<string, unknown> | null = null;
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try {
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const step10 = await generate(systemPrompt,
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@ -1222,16 +1255,29 @@ async function runLlmPipeline(
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} catch {
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console.log(" Quality scoring skipped (parse error)");
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}
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stepsCompleted = 13;
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stepsCompleted = 15;
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// ═══ STEP APM: Auto-Precision Mode (Final Cut — last filter before publish) ═══
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console.log(" Step 16/17: Auto-Precision Mode (final cut — if a word can go, it must go)...");
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setProgress(draftId, 16, "Step 16/17: Auto-Precision Mode");
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const stepAPM = await generate(systemPrompt,
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STEP_APM.replace("{{ARTICLE}}", step9.text),
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LLM_REFINE
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);
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stepsCompleted = 16;
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const wordsAPM = stepAPM.text.split(/\s+/).length;
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const wordsBeforeAPM = step9.text.split(/\s+/).length;
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const pctAPM = Math.round((1 - wordsAPM / wordsBeforeAPM) * 100);
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console.log(` APM: ${wordsBeforeAPM} → ${wordsAPM} words (−${pctAPM}%) — precision cut done`);
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// ═══ LinkedIn Post ═══
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console.log(" Step 14/14: LinkedIn Post (max 2,800 chars)...");
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setProgress(draftId, 14, "Step 14/14: LinkedIn Post");
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console.log(" Step 17/17: LinkedIn Post (max 2,800 chars)...");
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setProgress(draftId, 17, "Step 17/17: LinkedIn Post");
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let linkedinPost: string | null = null;
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let linkedinCharCount: number | null = null;
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try {
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const stepLinkedIn = await generate(systemPrompt,
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STEP_LINKEDIN_POST.replace("{{ARTICLE}}", step9.text),
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STEP_LINKEDIN_POST.replace("{{ARTICLE}}", stepAPM.text),
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{ temperature: 0.6, maxTokens: 1024, timeoutMs: 120000 }
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);
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linkedinPost = stepLinkedIn.text.trim();
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@ -1247,11 +1293,11 @@ async function runLlmPipeline(
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} catch {
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console.log(" LinkedIn post generation skipped");
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}
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stepsCompleted = 14;
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stepsCompleted = 17;
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// Extract only the article from STEP9 output (QA returns review + fixed article)
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// Look for "COMPLETE FIXED ARTICLE" marker and take everything after it
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let finalArticleText = step9.text;
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// Extract only the article from APM output (APM returns clean article only)
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// Fall back to step9.text if APM output looks too short or empty
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let finalArticleText = stepAPM.text.trim().length > 200 ? stepAPM.text : step9.text;
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const articleMarkers = [
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"### COMPLETE FIXED ARTICLE",
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"## COMPLETE FIXED ARTICLE",
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@ -1259,13 +1305,16 @@ async function runLlmPipeline(
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"---\n\n**You're",
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"---\n\nYou're",
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];
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// Also check step9 for QA markers (APM may have stripped them already)
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for (const marker of articleMarkers) {
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const idx = step9.text.indexOf(marker);
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if (idx !== -1) {
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// Skip past the marker line itself
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const afterMarker = step9.text.slice(idx + marker.length).trimStart();
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// Strip leading --- separator if present
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finalArticleText = afterMarker.replace(/^---\s*\n/, "").trimStart();
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const extractedFromQA = afterMarker.replace(/^---\s*\n/, "").trimStart();
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// Only use QA extraction if it's meaningfully longer than APM output
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if (extractedFromQA.split(/\s+/).length > finalArticleText.split(/\s+/).length * 0.8) {
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finalArticleText = extractedFromQA;
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}
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break;
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}
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}
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@ -1280,18 +1329,29 @@ async function runLlmPipeline(
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const wordCount = draftContent.split(/\s+/).length;
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const finalIssues = validateArticle(draftContent);
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// Update the draft in DB
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// Hard minimum word count gate (1200 for LLM pipeline)
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if (wordCount < 1200) {
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const shortMsg = `⚠ WORD COUNT FAIL: ${wordCount} words — minimum 1200 for LLM pipeline`;
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console.log(` ${shortMsg}`);
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if (!finalIssues.includes(`Too short: ${wordCount} words`)) {
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finalIssues.push(`Too short: ${wordCount} words (minimum 1200 for LLM pipeline — article needs expansion)`);
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}
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} else {
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console.log(` ✓ Word count: ${wordCount} words (≥1200 — OK)`);
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}
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// Update the draft in DB — promote to 'ready' on full pipeline completion
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await pool.query(
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`UPDATE blog_drafts
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SET draft_content = $1, word_count = $2,
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generated_by = 'fo-blog-engine-v5',
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pipeline_version = 'v5-narrative-control',
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generated_by = 'fo-blog-engine-v6',
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pipeline_version = 'v6-precision-mode',
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pipeline_steps_completed = $3,
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auto_qa_score = $4,
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outline = $5,
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linkedin_post = $6,
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linkedin_char_count = $7,
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status = 'draft',
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status = 'review',
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updated_at = NOW()
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WHERE id = $8::uuid`,
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[
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@ -1329,10 +1389,10 @@ async function runLlmPipeline(
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}
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clearProgress(draftId);
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console.log(`Blog FO Pipeline: ${draftId} complete — ${wordCount} words, ${stepsCompleted}/14 steps, QA: ${(autoQaScore as any)?.overall || "N/A"}/10, LinkedIn: ${linkedinCharCount ?? "n/a"} chars`);
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console.log(`Blog FO Pipeline: ${draftId} complete — ${wordCount} words, ${stepsCompleted}/17 steps, QA: ${(autoQaScore as any)?.overall || "N/A"}/10, LinkedIn: ${linkedinCharCount ?? "n/a"} chars`);
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} catch (llmErr) {
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clearProgress(draftId);
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console.warn(`Blog FO Pipeline failed at step ${stepsCompleted + 1}/14 for ${draftId}: ${(llmErr as Error).message}`);
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console.warn(`Blog FO Pipeline failed at step ${stepsCompleted + 1}/16 for ${draftId}: ${(llmErr as Error).message}`);
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// Update with partial progress
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await pool.query(
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`UPDATE blog_drafts SET pipeline_steps_completed = $1, pipeline_version = 'v5-narrative-control',
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@ -1382,6 +1442,19 @@ blogRouter.post("/generate", async (req: Request, res: Response) => {
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const data = await gatherBlogData(keywords, selectedTopic);
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// Clean up stale template drafts for the same title (idempotent regeneration)
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// If a template draft already exists for this title, remove it before creating a fresh one
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await pool.query(
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`DELETE FROM blog_feedback WHERE blog_id IN (
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SELECT id FROM blog_drafts WHERE title = $1 AND generated_by = 'tip-blog-engine-template'
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)`,
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[title]
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).catch(() => {});
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await pool.query(
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`DELETE FROM blog_drafts WHERE title = $1 AND generated_by = 'tip-blog-engine-template'`,
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[title]
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).catch(() => {});
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// Always create a template draft first (instant response)
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const draftContent = generateTemplateDraft(title, selectedTopic, data);
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const wordCount = draftContent.split(/\s+/).length;
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@ -1459,7 +1532,7 @@ blogRouter.post("/generate", async (req: Request, res: Response) => {
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blogRouter.get("/", async (_req: Request, res: Response) => {
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try {
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const result = await pool.query(
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`SELECT id, title, topic, target_audience, status, word_count, seo_keywords, generated_by, created_at
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`SELECT id, title, topic, target_audience, status, word_count, seo_keywords, generated_by, created_at, linkedin_post
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FROM blog_drafts
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ORDER BY created_at DESC
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LIMIT 50`,
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