feat(deepdive): integrate Phase 0 fleet context into synthesis (#830)

intelligence/deepdive/pipeline.py

@@ -52,6 +52,15 @@ except ImportError:
     np = None
     SentenceTransformer = None
 
+# Phase 0: Fleet context grounding
+try:
+    from fleet_context import build_fleet_context, FleetContext
+    HAS_FLEET_CONTEXT = True
+except ImportError:
+    HAS_FLEET_CONTEXT = False
+    build_fleet_context = None
+    FleetContext = None
+
 # Setup logging
 logging.basicConfig(
     level=logging.INFO,
@@ -371,7 +380,9 @@ Guidelines:
 - Keep tone professional but urgent
 - Structure: Headlines → Deep Dive → Implications
 
-Context: Hermes agents run locally with Gemma 4, sovereign infrastructure."""
+Context: Hermes agents run locally with Gemma 4, sovereign infrastructure.
+If Fleet Context is provided above, use it to explain how external developments
+impact our live repos, open issues, and current architecture."""
 
     def _call_llm(self, prompt: str) -> str:
         if not HAS_HTTPX or not httpx:
@@ -411,7 +422,8 @@ Context: Hermes agents run locally with Gemma 4, sovereign infrastructure."""
         lines.append("*Generated by Deep Dive pipeline*")
         return "\n".join(lines)
     
-    def generate_structured(self, items: List[tuple]) -> Dict[str, Any]:
+    def generate_structured(self, items: List[tuple],
+                            fleet_context: Optional[FleetContext] = None) -> Dict[str, Any]:
         if not items:
             return {
                 'headline': 'No relevant intelligence today',
@@ -419,7 +431,14 @@ Context: Hermes agents run locally with Gemma 4, sovereign infrastructure."""
                 'sources': []
             }
         
-        lines = ["Generate an intelligence briefing from these research items:", ""]
+        lines = []
+        if fleet_context:
+            lines.append("FLEET CONTEXT:")
+            lines.append(fleet_context.to_prompt_text(max_items_per_section=5))
+            lines.append("")
+        
+        lines.append("Generate an intelligence briefing from these research items:")
+        lines.append("")
         for i, (item, score) in enumerate(items, 1):
             lines.append(f"{i}. [{item.source}] {item.title}")
             lines.append(f"   Score: {score}")
@@ -632,9 +651,22 @@ class DeepDivePipeline:
         if not ranked and not force:
             return {'status': 'filtered', 'items_count': len(items), 'ranked_count': 0}
         
+        # Phase 0 — injected before Phase 3
+        logger.info("Phase 0: Fleet Context Grounding")
+        fleet_ctx = None
+        if HAS_FLEET_CONTEXT:
+            try:
+                fleet_ctx = build_fleet_context(self.cfg)
+                if fleet_ctx:
+                    logger.info(f"Fleet context built: {len(fleet_ctx.repos)} repos, "
+                                f"{len(fleet_ctx.open_issues)} issues/PRs, "
+                                f"{len(fleet_ctx.recent_commits)} recent commits")
+            except Exception as e:
+                logger.warning(f"Fleet context build failed: {e}")
+        
         # Phase 3
         logger.info("Phase 3: Synthesis")
-        briefing = self.synthesizer.generate_structured(ranked)
+        briefing = self.synthesizer.generate_structured(ranked, fleet_context=fleet_ctx)
         
         timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
         briefing_path = self.cache_dir / f"briefing_{timestamp}.json"