chore: push all remaining changes
- Blog writer enhancements and bug fixes - Wix integration improvements - Frontend UI updates - GSC dashboard docs cleanup - Image studio assets - LinkedIn requirements file - Various dependency updates
This commit is contained in:
ajaysi
@@ -2,6 +2,13 @@
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Applies actionable SEO recommendations to existing blog content using the
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provider-agnostic `llm_text_gen` dispatcher. Ensures GPT_PROVIDER parity.
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Key design principles:
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- Make TARGETED edits, not full rewrites
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- Preserve existing content structure and factual claims
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- Only modify sections that have applicable recommendations
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- Never fabricate statistics, case studies, or citations
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- Ground changes in research sources when available
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"""
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import asyncio
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@@ -15,7 +22,7 @@ logger = get_service_logger("blog_seo_recommendation_applier")
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class BlogSEORecommendationApplier:
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"""Apply actionable SEO recommendations to blog content."""
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"""Apply actionable SEO recommendations to blog content with targeted edits."""
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def __init__(self):
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logger.debug("Initialized BlogSEORecommendationApplier")
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@@ -35,6 +42,7 @@ class BlogSEORecommendationApplier:
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persona = payload.get("persona", {})
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tone = payload.get("tone")
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audience = payload.get("audience")
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competitive_advantage = payload.get("competitive_advantage", "")
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if not sections:
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return {"success": False, "error": "No sections provided for recommendation application"}
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@@ -43,16 +51,21 @@ class BlogSEORecommendationApplier:
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logger.warning("apply_recommendations called without recommendations")
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return {"success": True, "title": title, "sections": sections, "applied": []}
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# Determine which sections actually need changes based on recommendations
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sections_to_edit = self._identify_affected_sections(sections, recommendations)
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prompt = self._build_prompt(
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title=title,
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introduction=introduction,
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sections=sections,
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sections_to_edit=sections_to_edit,
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outline=outline,
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research=research,
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recommendations=recommendations,
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persona=persona,
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tone=tone,
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audience=audience,
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competitive_advantage=competitive_advantage,
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)
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schema = {
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@@ -87,14 +100,14 @@ class BlogSEORecommendationApplier:
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"required": ["sections"],
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}
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logger.info("Applying SEO recommendations via llm_text_gen")
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logger.info("Applying SEO recommendations via llm_text_gen (targeted edit mode)")
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result = await asyncio.to_thread(
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llm_text_gen,
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prompt,
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None,
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schema,
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user_id, # Pass user_id for subscription checking
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user_id,
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max_tokens=8192,
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)
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@@ -106,14 +119,12 @@ class BlogSEORecommendationApplier:
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raw_sections = result.get("sections", []) or []
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normalized_sections: List[Dict[str, Any]] = []
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# Warn if LLM returned different number of sections (may miss intro/conclusion added as new sections)
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if len(raw_sections) != len(sections):
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logger.warning(
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f"LLM returned {len(raw_sections)} sections but {len(sections)} were sent. "
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"Extra sections will be ignored; missing sections fall back to original content."
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)
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# Build lookup table from updated sections using their identifiers
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updated_map: Dict[str, Dict[str, Any]] = {}
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for updated in raw_sections:
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section_id = str(
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@@ -156,7 +167,6 @@ class BlogSEORecommendationApplier:
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mapped = updated_map.get(fallback_id)
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if not mapped and raw_sections:
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# Fall back to positional match if identifier lookup failed
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candidate = raw_sections[index] if index < len(raw_sections) else {}
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heading = (
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candidate.get("heading")
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@@ -176,7 +186,6 @@ class BlogSEORecommendationApplier:
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}
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if not mapped:
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# Fallback to original content if nothing else available
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mapped = {
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"id": fallback_id,
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"heading": original.get("heading") or original.get("title") or f"Section {index + 1}",
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@@ -190,12 +199,11 @@ class BlogSEORecommendationApplier:
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logger.info("SEO recommendations applied successfully")
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# Extract updated introduction from LLM response if available
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updated_introduction = result.get("introduction") or ""
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if updated_introduction and updated_introduction != introduction:
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logger.info(f"Introduction updated: {len(updated_introduction)} chars")
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elif not updated_introduction:
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updated_introduction = introduction # fall back to original
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updated_introduction = introduction
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return {
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"success": True,
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@@ -205,37 +213,133 @@ class BlogSEORecommendationApplier:
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"applied": applied,
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}
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def _identify_affected_sections(self, sections: List[Dict[str, Any]], recommendations: List[Dict[str, Any]]) -> List[str]:
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"""Identify which section IDs are likely affected by the recommendations.
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Maps recommendation categories to section headings for targeted editing.
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Returns a list of section IDs that should be edited.
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"""
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affected_ids = set()
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for rec in recommendations:
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category = (rec.get("category") or "").lower()
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rec_text = (rec.get("recommendation") or "").lower()
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# Structure recommendations affect first/last sections or all sections
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if category == "structure":
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if sections:
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affected_ids.add(str(sections[0].get("id", "section_1")))
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affected_ids.add(str(sections[-1].get("id", f"section_{len(sections)}")))
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# "Add more sections" or "too many sections" affects all
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if "more section" in rec_text or "combine" in rec_text or "flow" in rec_text:
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for s in sections:
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affected_ids.add(str(s.get("id", "")))
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continue
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# Keyword recommendations affect all sections (keywords should be spread)
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if category == "keywords":
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for s in sections:
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affected_ids.add(str(s.get("id", "")))
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continue
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# Readability affects all sections
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if category == "readability":
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for s in sections:
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affected_ids.add(str(s.get("id", "")))
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continue
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# Content quality — try to match recommendation to specific section headings
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if category in ("content quality", "content", "seo"):
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heading_keywords = {
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s.get("heading", "").lower(): str(s.get("id", ""))
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for s in sections
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}
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matched = False
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for heading_lower, section_id in heading_keywords.items():
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rec_words = rec_text.split()
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if any(word in heading_lower for word in rec_words if len(word) > 3):
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affected_ids.add(section_id)
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matched = True
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if not matched:
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# Affect first and last sections (intro/conclusion) as common targets
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if sections:
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affected_ids.add(str(sections[0].get("id", "section_1")))
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affected_ids.add(str(sections[-1].get("id", f"section_{len(sections)}")))
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# Filter out empty IDs and return
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return [sid for sid in affected_ids if sid]
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def _build_prompt(
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self,
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*,
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title: str,
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introduction: str,
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sections: List[Dict[str, Any]],
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sections_to_edit: List[str],
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outline: List[Dict[str, Any]],
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research: Dict[str, Any],
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recommendations: List[Dict[str, Any]],
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persona: Dict[str, Any],
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tone: str | None,
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audience: str | None,
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competitive_advantage: str = "",
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) -> str:
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"""Construct prompt for applying recommendations."""
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"""Construct prompt for applying targeted recommendations."""
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sections_str = []
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# Build research context block
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research_block = ""
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keyword_analysis = research.get("keyword_analysis", {}) if research else {}
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primary_keywords = ", ".join(keyword_analysis.get("primary", [])[:8]) or "None"
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competitor_analysis = research.get("competitor_analysis", {}) if research else {}
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search_queries = research.get("search_queries", []) if research else []
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suggested_angles = research.get("suggested_angles", []) if research else []
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content_gaps = competitor_analysis.get("content_gaps", []) if competitor_analysis else []
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competitive_advantages = competitor_analysis.get("competitive_advantages", []) if competitor_analysis else []
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research_block += f"\nPRIMARY KEYWORDS: {primary_keywords}"
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if content_gaps:
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research_block += f"\nCONTENT GAPS (address these in your edits): {', '.join(content_gaps[:5])}"
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if competitive_advantages:
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research_block += f"\nKEY DIFFERENTIATORS (emphasize these): {', '.join(competitive_advantages[:3])}"
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if competitive_advantage:
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research_block += f"\nPRIMARY ADVANTAGE: {competitive_advantage}"
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if search_queries:
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research_block += f"\nTARGET SEARCH QUERIES: {', '.join(search_queries[:5])}"
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if suggested_angles:
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research_block += f"\nCONTENT ANGLES: {', '.join(suggested_angles[:3])}"
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# Build per-section content with edit markers
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sections_content = []
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for section in sections:
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sections_str.append(
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f"ID: {section.get('id', 'section')}, Heading: {section.get('heading', 'Untitled')}\n"
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f"Current Content:\n{section.get('content', '')}\n"
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)
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section_id = str(section.get("id", "section"))
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heading = section.get("heading", "Untitled")
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content = section.get("content", "")
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needs_edit = section_id in sections_to_edit
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section_text = f"--- SECTION (ID: {section_id}, Heading: \"{heading}\")"
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if needs_edit:
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section_text += " [NEEDS EDITS based on recommendations]"
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else:
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section_text += " [KEEP AS-IS - no changes needed]"
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section_text += f" ---\n{content}\n"
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sections_content.append(section_text)
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sections_str = "\n\n".join(sections_content)
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outline_str = "\n".join(
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[
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f"- {item.get('heading', 'Section')} (Target words: {item.get('target_words', 'N/A')})"
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for item in outline
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]
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)
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research_summary = research.get("keyword_analysis", {}) if research else {}
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primary_keywords = ", ".join(research_summary.get("primary", [])[:10]) or "None"
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# Build outline with subheadings and key points
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outline_parts = []
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for item in outline:
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heading = item.get("heading", "Section")
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target_words = item.get("target_words", "N/A")
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subheadings = item.get("subheadings", [])
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key_points = item.get("key_points", [])
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line = f"- {heading} (Target: {target_words} words)"
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if subheadings:
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line += f" | Subheadings: {', '.join(subheadings[:4])}"
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if key_points:
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line += f" | Key points: {', '.join(key_points[:4])}"
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outline_parts.append(line)
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outline_str = "\n".join(outline_parts) if outline_parts else "No outline supplied"
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recommendations_str = []
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for rec in recommendations:
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@@ -248,7 +352,7 @@ class BlogSEORecommendationApplier:
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persona_str = (
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f"Persona: {persona}\n"
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if persona
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else "Persona: (not provided)\n"
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else ""
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)
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style_guidance = []
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@@ -258,44 +362,47 @@ class BlogSEORecommendationApplier:
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style_guidance.append(f"Target audience: {audience}")
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style_str = "\n".join(style_guidance) if style_guidance else "Maintain current tone and audience alignment."
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prompt = f"""
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You are an expert SEO content strategist. Update the blog content to apply the actionable recommendations.
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intro_text = introduction if introduction else "(No introduction currently — write one ONLY if a recommendation specifically asks for it)"
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Current Title: {title}
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prompt = f"""You are a careful SEO content editor making TARGETED edits to an existing blog post. Your job is to apply specific SEO recommendations with PRECISION — not to rewrite the entire post.
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Current Introduction:
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{introduction if introduction else '(No introduction exists — write a compelling one if the recommendations require it)'}
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CRITICAL RULES — YOU MUST FOLLOW THESE:
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1. PRESERVE existing content. Only make MINIMAL, targeted changes to address specific recommendations. Do NOT rewrite sections that are working well.
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2. NEVER fabricate statistics, case studies, expert quotes, research data, or specific numbers unless they are explicitly stated in the research context below.
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3. NEVER add content that contradicts or goes beyond what the research sources support.
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4. KEEP the same emotional tone and writing style as the original content.
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5. Return EXACTLY the same number of sections with EXACTLY the same IDs. Do NOT add, remove, or rename sections.
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6. For sections marked [KEEP AS-IS], return the content UNCHANGED — copy it verbatim.
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7. For sections marked [NEEDS EDITS], make ONLY the specific changes needed to address the applicable recommendations.
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8. Do NOT add introductions, conclusions, or case studies unless a recommendation EXPLICITLY asks for one.
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Primary Keywords (for context): {primary_keywords}
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{research_block}
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Outline Overview:
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{outline_str or 'No outline supplied'}
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PLANNED OUTLINE STRUCTURE:
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{outline_str}
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Existing Sections:
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{''.join(sections_str)}
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CURRENT TITLE: {title}
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Actionable Recommendations to Apply:
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CURRENT INTRODUCTION:
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{intro_text}
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CURRENT SECTIONS:
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{sections_str}
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RECOMMENDATIONS TO APPLY:
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{''.join(recommendations_str)}
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{persona_str}{style_str}
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{persona_str}
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{style_str}
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Instructions:
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1. Carefully apply the recommendations while preserving factual accuracy and research alignment.
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2. You MUST return EXACTLY the same number of sections, with EXACTLY the same IDs as provided above. Do NOT add or remove sections.
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3. If a recommendation says content is MISSING (e.g. missing introduction or conclusion), incorporate that missing content into the MOST APPROPRIATE existing section:
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- Missing introduction → PREPEND introductory content to the FIRST section's existing content.
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- Missing conclusion → APPEND concluding content to the LAST section's existing content.
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- For other missing content, add it to the section whose heading best matches the recommendation.
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4. Additionally, if an introduction is missing or weak, write a compelling introduction in the "introduction" field of your response. If the current introduction is adequate, return it unchanged.
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5. Improve clarity, flow, and SEO optimization per the guidance.
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6. Return updated sections in the requested JSON format.
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7. Provide a short summary of which recommendations were addressed.
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INSTRUCTIONS:
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- For sections marked [KEEP AS-IS]: Copy the content EXACTLY as provided. Do not change a single word.
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- For sections marked [NEEDS EDITS]: Make the MINIMUM changes needed to address the recommendations. If a recommendation says "add transition words", add 2-3 transitions — do not rewrite the paragraph. If it says "use more varied vocabulary", replace 2-3 repetitive words — do not rewrite the section.
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- If a recommendation asks for an introduction and none exists, write a brief 2-3 sentence introduction that naturally leads into the first section. Do NOT fabricate hooks or statistics.
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- If a recommendation asks for a conclusion, append 2-3 sentences summarizing key takeaways to the LAST section. Do NOT fabricate conclusions that don't follow from the actual content.
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- Return ALL sections, including the ones you did NOT change.
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- Provide a summary of which recommendations you addressed and what specific changes you made.
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"""
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return prompt
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__all__ = ["BlogSEORecommendationApplier"]
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__all__ = ["BlogSEORecommendationApplier"]
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Reference in New Issue
Block a user