Added enhanced linguistic analyzer and persona quality improver

backend/services/blog_writer/content/enhanced_content_generator.py

@@ -0,0 +1,74 @@
+"""
+EnhancedContentGenerator - thin orchestrator combining URL selection and Gemini provider.
+
+Provides Draft vs Polished modes and optional URL Context usage.
+"""
+
+from typing import Any, Dict
+
+from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
+from .source_url_manager import SourceURLManager
+from .context_memory import ContextMemory
+from .transition_generator import TransitionGenerator
+from .flow_analyzer import FlowAnalyzer
+
+
+class EnhancedContentGenerator:
+    def __init__(self):
+        self.provider = GeminiGroundedProvider()
+        self.url_manager = SourceURLManager()
+        self.memory = ContextMemory(max_entries=12)
+        self.transitioner = TransitionGenerator()
+        self.flow = FlowAnalyzer()
+
+    async def generate_section(self, section: Any, research: Any, mode: str = "polished") -> Dict[str, Any]:
+        urls = self.url_manager.pick_relevant_urls(section, research)
+        prev_summary = self.memory.build_previous_sections_summary(limit=2)
+        prompt = self._build_prompt(section, research, prev_summary)
+        result = await self.provider.generate_grounded_content(
+            prompt=prompt,
+            content_type="linkedin_article",
+            temperature=0.6 if mode == "polished" else 0.8,
+            max_tokens=2048,
+            urls=urls,
+            mode=mode,
+        )
+        # Generate transition and compute intelligent flow metrics
+        previous_text = prev_summary
+        current_text = result.get("content", "")
+        transition = self.transitioner.generate_transition(previous_text, getattr(section, 'heading', 'This section'), use_llm=True)
+        metrics = self.flow.assess_flow(previous_text, current_text, use_llm=True)
+
+        # Update memory for subsequent sections and store continuity snapshot
+        if current_text:
+            self.memory.update_with_section(getattr(section, 'id', 'unknown'), current_text, use_llm=True)
+
+        # Return enriched result
+        result["transition"] = transition
+        result["continuity_metrics"] = metrics
+        # Persist a lightweight continuity snapshot for API access
+        try:
+            sid = getattr(section, 'id', 'unknown')
+            if not hasattr(self, "_last_continuity"):
+                self._last_continuity = {}
+            self._last_continuity[sid] = metrics
+        except Exception:
+            pass
+        return result
+
+    def _build_prompt(self, section: Any, research: Any, prev_summary: str) -> str:
+        heading = getattr(section, 'heading', 'Section')
+        key_points = getattr(section, 'key_points', [])
+        keywords = getattr(section, 'keywords', [])
+        target_words = getattr(section, 'target_words', 300)
+
+        return (
+            f"You are writing the blog section '{heading}'.\n\n"
+            f"Context summary: {prev_summary}\n"
+            f"Key points: {', '.join(key_points)}\n"
+            f"Keywords: {', '.join(keywords)}\n"
+            f"Target word count: {target_words}.\n"
+            "Use only factual info from provided sources; add short transition, then body."
+        )
+
+