feat: image generation overhaul (model-aware text, dim clamping, \.30 pricing), event-driven dashboard cache invalidation, SEO insights (AI visibility, GSC, keyword gap), YouTube OAuth/publish, blog writer & content planning improvements, scheduler monitoring updates

backend/services/intelligence/agents/specialized/citation_expert.py

@@ -9,36 +9,97 @@ from services.intelligence.agents.core_agent_framework import TaskProposal
 from services.intelligence.txtai_service import TxtaiIntelligenceService
 
 class CitationExpert(SIFBaseAgent):
-    """Agent for fact-checking and source management."""
-    
+    """Agent for fact-checking and source management using the SIF index."""
+
     def __init__(self, intelligence_service: TxtaiIntelligenceService, user_id: str, **kwargs):
         super().__init__(intelligence_service, user_id, agent_type="citation_expert", **kwargs)
 
     async def verify_citations(self, content: str) -> Dict[str, Any]:
-        """Verify citations in content against trusted sources."""
-        # Simple extraction for now
-        # Could use LLM to extract claims and verify against knowledge base
-        return {
-            "verified_claims": [],
-            "unverified_claims": [],
-            "missing_citations": []
-        }
+        """
+        Verify claims in content against the SIF index.
+        Searches for supporting or refuting evidence for each extracted claim.
+        """
+        if not self.intelligence.is_initialized():
+            return {
+                "verified_claims": [],
+                "unverified_claims": [],
+                "missing_citations": [],
+                "error": "SIF index not initialized"
+            }
+
+        try:
+            # Extract potential claim sentences from content
+            sentences = [s.strip() for s in content.replace("\n", " ").split(".") if len(s.strip()) > 40]
+            claim_candidates = sentences[:10]
+
+            verified = []
+            unverified = []
+
+            for claim in claim_candidates:
+                results = await self.intelligence.search(claim, limit=3)
+                if results and any(r.get("score", 0) > 0.7 for r in results):
+                    verified.append({
+                        "claim": claim[:200],
+                        "supporting_sources": [
+                            {"url": r.get("id", ""), "score": r.get("score", 0)}
+                            for r in results if r.get("score", 0) > 0.7
+                        ]
+                    })
+                else:
+                    unverified.append({"claim": claim[:200], "sources_found": len(results)})
+
+            return {
+                "verified_claims": verified,
+                "unverified_claims": unverified,
+                "missing_citations": [c["claim"] for c in unverified],
+                "analysis_timestamp": datetime.utcnow().isoformat()
+            }
+        except Exception as e:
+            logger.error(f"[{self.__class__.__name__}] Citation verification failed: {e}")
+            return {
+                "verified_claims": [],
+                "unverified_claims": [],
+                "missing_citations": [],
+                "error": str(e)
+            }
 
     async def propose_daily_tasks(self, context: Dict[str, Any]) -> List[TaskProposal]:
-        """Propose fact-checking tasks."""
+        """
+        Propose fact-checking tasks based on SIF index coverage.
+        """
         proposals = []
-        
-        # 1. Fact Check High-Value Content
-        proposals.append(TaskProposal(
-            title="Verify Sources for 'AI Trends 2025'",
-            description="Double-check statistical claims in your latest draft.",
-            pillar_id="create",
-            priority="medium",
-            estimated_time=20,
-            source_agent="CitationExpert",
-            reasoning="Ensures credibility and trust.",
-            action_type="navigate",
-            action_url="/content-planning-dashboard"
-        ))
-        
+        indexed_count = 0
+
+        if self.intelligence.is_initialized():
+            try:
+                results = await self.intelligence.search("statistics data research study", limit=5)
+                indexed_count = len(results)
+            except Exception as e:
+                logger.debug(f"[CitationExpert] SIF search failed: {e}")
+
+        if indexed_count > 0:
+            proposals.append(TaskProposal(
+                title="Verify Data Claims",
+                description=f"SIF found {indexed_count} reference pages. Check recent drafts for unsupported statistics.",
+                pillar_id="create",
+                priority="medium",
+                estimated_time=20,
+                source_agent="CitationExpert",
+                reasoning="Verified sources build audience trust and SEO authority.",
+                action_type="navigate",
+                action_url="/content-planning-dashboard"
+            ))
+        else:
+            proposals.append(TaskProposal(
+                title="Add Source Citations",
+                description="Index authoritative sources in SIF to enable automated fact-checking.",
+                pillar_id="create",
+                priority="low",
+                estimated_time=15,
+                source_agent="CitationExpert",
+                reasoning="Citing authoritative sources improves content credibility.",
+                action_type="navigate",
+                action_url="/content-planning-dashboard"
+            ))
+
         return proposals