v3: Self-learning loop — nightly /api/learn, keyword-based scoring boost, performance feedback into topic selection

core/orchestrator.py

@@ -358,14 +358,21 @@ Respond with a JSON array of strings, each a compelling article title."""
 
 
 def _score_and_assign(raw_topics: list[str]) -> list[dict]:
-    """Score topics and assign to verticals using LLM."""
+    """Score topics and assign to verticals using LLM, boosted by learning data."""
     if not raw_topics:
         return []
     
+    # Phase 0: Get learning insights from live sites
+    learning_insights = _get_learning_insights()
+    
     # Deduplicate first
     unique = list(dict.fromkeys(raw_topics))[:50]
     
-    prompt = f"""You are a content strategist. Score and categorize these {len(unique)} topics.
+    insights_text = ""
+    if learning_insights:
+        insights_text = f"\n\nLEARNING DATA — content that performs well on our sites:\n{json.dumps(learning_insights, indent=2)}\n\nUse this to boost composite_score for topics similar to what our audience already reads. Topics matching high-performing patterns get +10 to composite_score."
+    
+    prompt = f"""You are a content strategist. Score and categorize these {len(unique)} topics.{insights_text}
 
 Topics:
 {json.dumps(unique)}
@@ -378,7 +385,7 @@ For each topic, return:
 - "competition_score": 0-100 (how many competing articles exist)
 - "freshness_score": 0-100 (how new/urgent)
 - "evergreen_score": 0-100 (will this be relevant in 5 years)
-- "composite_score": overall value score 0-100 (higher = publish now)
+- "composite_score": overall value score 0-100 (higher = publish now) — apply learning boosts here
 
 Vertical assignment rules:
 - AI/ML topics → ai
@@ -395,13 +402,100 @@ Respond with a JSON array of objects. No markdown, no explanation."""
     try:
         result = ollama_json(prompt, model="qwen3.5:4b", temperature=0.3)
         if isinstance(result, list):
-            return result
+            # Apply algorithmic boost on top of LLM scores
+            return _apply_learning_boost(result, learning_insights)
         return []
     except Exception as e:
         log.warning(f"Topic scoring failed: {e}")
         return []
 
 
+def _get_learning_insights() -> dict:
+    """Query all 8 live sites for their top-performing content patterns."""
+    insights = {}
+    for vertical, vinfo in VERTICALS.items():
+        ct_ip = vinfo.get("ip")
+        if not ct_ip:
+            continue
+        try:
+            r = requests.get(f"http://{ct_ip}:5000/api/stats", timeout=5)
+            if r.status_code == 200:
+                data = r.json()
+                popular = data.get("popular", [])
+                if popular:
+                    # Extract keyword patterns from popular articles
+                    all_keywords = []
+                    for art in popular:
+                        kw_str = art.get("keywords", "[]")
+                        try:
+                            kws = json.loads(kw_str) if isinstance(kw_str, str) else kw_str
+                            all_keywords.extend(kws)
+                        except (json.JSONDecodeError, TypeError):
+                            pass
+                    
+                    # Most frequent keywords = winning topics
+                    from collections import Counter
+                    kw_counts = Counter(all_keywords)
+                    
+                    insights[vertical] = {
+                        "top_keywords": [kw for kw, _ in kw_counts.most_common(8)],
+                        "top_articles": [a.get("title", "")[:80] for a in popular[:3]],
+                        "avg_word_count": sum(a.get("word_count", 0) for a in popular) // max(len(popular), 1),
+                        "total_articles": data.get("total_articles", 0),
+                    }
+        except Exception:
+            pass
+    
+    # Also check orchestrator's own performance_learning DB
+    try:
+        db = sqlite3.connect(str(DB_PATH))
+        db.row_factory = sqlite3.Row
+        for vertical in VERTICALS:
+            row = db.execute(
+                "SELECT * FROM performance_learning WHERE vertical=? ORDER BY updated_at DESC LIMIT 1",
+                (vertical,)
+            ).fetchone()
+            if row:
+                if vertical not in insights:
+                    insights[vertical] = {}
+                try:
+                    insights[vertical]["stored_patterns"] = json.loads(row["top_patterns"])
+                except (json.JSONDecodeError, TypeError):
+                    pass
+        db.close()
+    except Exception:
+        pass
+    
+    return insights if insights else {}
+
+
+def _apply_learning_boost(scored: list[dict], insights: dict) -> list[dict]:
+    """Boost composite scores for topics matching winning patterns."""
+    if not insights:
+        return scored
+    
+    for topic in scored:
+        vertical = topic.get("vertical", "")
+        title = topic.get("title", "").lower()
+        vin = insights.get(vertical, {})
+        top_kws = vin.get("top_keywords", [])
+        
+        # Count keyword matches between topic title and winning keywords
+        matches = sum(1 for kw in top_kws if kw.lower() in title)
+        boost = min(matches * 8, 25)  # Up to 25-point boost
+        
+        # Boost for matching the optimal word count range (signals topic depth fits)
+        if vin.get("avg_word_count", 0) > 0:
+            boost += 3  # Minor boost for having any data
+        
+        if boost > 0:
+            old_score = topic.get("composite_score", 50)
+            topic["composite_score"] = min(old_score + boost, 100)
+            topic["learning_boost"] = boost
+    
+    return scored
+
+
 def _deduplicate_and_rank(scored: list[dict]) -> list[dict]:
     """Remove near-duplicates and rank by composite score."""
     seen_titles = set()