[DEEP-DIVE] Scaffold component — #830

scaffold/deep-dive/synthesis/synthesis_engine.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""
+Synthesis Engine for Deep Dive
+Generates intelligence briefings from filtered content
+"""
+
+import json
+from datetime import datetime
+from typing import List, Any
+from dataclasses import dataclass
+
+# Load prompt template
+with open("synthesis_prompt.txt") as f:
+    SYSTEM_PROMPT = f.read()
+
+@dataclass
+class Briefing:
+    date: str
+    headlines: List[dict]
+    deep_dives: List[dict]
+    implications: str
+    reading_list: List[dict]
+    raw_text: str
+
+def generate_briefing(
+    papers: List[Any],
+    blogs: List[Any],
+    model_client=None,  # Hermes AIAgent or similar
+    date: str = None
+) -> Briefing:
+    """Generate a briefing from ranked papers and blog posts."""
+    
+    date = date or datetime.now().strftime("%Y-%m-%d")
+    
+    # Build input for LLM
+    input_data = {
+        "date": date,
+        "papers": [
+            {
+                "title": p.title,
+                "authors": p.authors,
+                "abstract": p.abstract[:500] + "..." if len(p.abstract) > 500 else p.abstract,
+                "url": p.url,
+                "arxiv_id": p.arxiv_id,
+                "relevance_score": score
+            }
+            for p, score in papers[:10]  # Top 10 papers
+        ],
+        "blogs": [
+            {
+                "title": b.title,
+                "source": b.source,
+                "url": b.url,
+                "summary": b.summary[:300] if b.summary else ""
+            }
+            for b in blogs[:5]  # Top 5 blog posts
+        ]
+    }
+    
+    # Call LLM for synthesis (placeholder - integrate with Hermes routing)
+    if model_client:
+        response = model_client.chat(
+            system_message=SYSTEM_PROMPT,
+            message=f"Generate briefing from this data:\n```json\n{json.dumps(input_data, indent=2)}\n```"
+        )
+        raw_text = response
+    else:
+        # Mock output for testing
+        raw_text = f"# Deep Dive Briefing — {date}\n\n(Mock output - integrate LLM)"
+    
+    # Parse structured data from raw_text
+    # (In production, use structured output or regex parsing)
+    
+    return Briefing(
+        date=date,
+        headlines=[],
+        deep_dives=[],
+        implications="",
+        reading_list=[],
+        raw_text=raw_text
+    )
+
+if __name__ == "__main__":
+    print("Synthesis engine loaded")
+    print(f"Prompt length: {len(SYSTEM_PROMPT)} chars")