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
@@ -6,7 +6,7 @@ Provider parity:
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- No direct provider coupling here; Google grounding remains in research only
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"""
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from typing import Any, Dict
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from typing import Any, Dict, List
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from services.llm_providers.main_text_generation import llm_text_gen
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from .source_url_manager import SourceURLManager
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@@ -22,11 +22,12 @@ class EnhancedContentGenerator:
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self.transitioner = TransitionGenerator()
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self.flow = FlowAnalyzer()
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async def generate_section(self, section: Any, research: Any, mode: str = "polished", user_id: str = None) -> Dict[str, Any]:
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async def generate_section(self, section: Any, research: Any = None, mode: str = "polished", user_id: str = None, competitive_advantage: str = "") -> Dict[str, Any]:
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prev_summary = self.memory.build_previous_sections_summary(limit=2)
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urls = self.url_manager.pick_relevant_urls(section, research)
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prompt = self._build_prompt(section, research, prev_summary, urls)
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# Provider-agnostic text generation (respect GPT_PROVIDER & circuit-breaker)
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research_context, section_sources = self._build_research_context(section)
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urls = self.url_manager.pick_relevant_urls(section, research) if not research_context else []
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global_research_context = self._build_global_research_context(research, competitive_advantage)
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prompt = self._build_prompt(section, prev_summary, research_context, urls, global_research_context)
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content_text: str = ""
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try:
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ai_resp = llm_text_gen(
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@@ -40,29 +41,22 @@ class EnhancedContentGenerator:
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elif isinstance(ai_resp, str):
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content_text = ai_resp
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else:
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# Fallback best-effort extraction
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content_text = str(ai_resp or "")
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except Exception as e:
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content_text = ""
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result = {
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"content": content_text,
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"sources": [{"title": u.get("title", ""), "url": u.get("url", "")} for u in urls] if urls else [],
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"sources": section_sources,
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}
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# Generate transition and compute intelligent flow metrics
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previous_text = prev_summary
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current_text = result.get("content", "")
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transition = self.transitioner.generate_transition(previous_text, getattr(section, 'heading', 'This section'), use_llm=True)
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metrics = self.flow.assess_flow(previous_text, current_text, use_llm=True)
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# Update memory for subsequent sections and store continuity snapshot
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if current_text:
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self.memory.update_with_section(getattr(section, 'id', 'unknown'), current_text, use_llm=True)
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# Return enriched result
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result["transition"] = transition
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result["continuity_metrics"] = metrics
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# Persist a lightweight continuity snapshot for API access
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try:
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sid = getattr(section, 'id', 'unknown')
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if not hasattr(self, "_last_continuity"):
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@@ -72,22 +66,188 @@ class EnhancedContentGenerator:
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pass
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return result
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def _build_prompt(self, section: Any, research: Any, prev_summary: str, urls: list) -> str:
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def _build_research_context(self, section: Any) -> tuple:
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"""Build a rich research context block from the section's mapped sources.
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Returns (context_string, sources_list) where context_string is the
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formatted research context for the prompt, and sources_list contains
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{title, url} dicts for downstream use.
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When section.references is empty, returns ("", []) — the caller should
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handle this as a research gap and avoid generating unsupported claims.
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"""
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references = getattr(section, 'references', []) or []
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if not references:
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return ("", [])
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context_parts = []
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sources_out = []
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for i, ref in enumerate(references, 1):
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if isinstance(ref, dict):
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title = ref.get('title', '')
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excerpt = ref.get('excerpt', '')
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highlights = ref.get('highlights', []) or []
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summary = ref.get('summary', '')
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url = ref.get('url', '')
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content = ref.get('content', '') or ''
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author = ref.get('author', '') or ''
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source_type = ref.get('source_type', '') or ''
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credibility_score = ref.get('credibility_score')
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published_at = ref.get('published_at', '') or ''
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else:
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title = getattr(ref, 'title', '')
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excerpt = getattr(ref, 'excerpt', '')
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highlights = getattr(ref, 'highlights', []) or []
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summary = getattr(ref, 'summary', '')
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url = getattr(ref, 'url', '')
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content = getattr(ref, 'content', '') or ''
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author = getattr(ref, 'author', '') or ''
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source_type = getattr(ref, 'source_type', '') or ''
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credibility_score = getattr(ref, 'credibility_score', None)
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published_at = getattr(ref, 'published_at', '') or ''
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sources_out.append({"title": title, "url": url})
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attribution_parts = []
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if author:
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attribution_parts.append(f"by {author}")
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if source_type:
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attribution_parts.append(f"[{source_type}]")
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attribution = " ".join(attribution_parts)
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credibility_tag = ""
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if credibility_score is not None:
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try:
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score = float(credibility_score)
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if score >= 0.9:
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credibility_tag = " (high-credibility)"
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elif score >= 0.75:
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credibility_tag = " (credible)"
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except (ValueError, TypeError):
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pass
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recency_tag = ""
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if published_at:
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recency_tag = f" (published {published_at[:10]})" if len(published_at) >= 10 else f" (published {published_at})"
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header = f"Source {i}: {title}"
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if attribution:
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header += f" {attribution}"
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header += f"{credibility_tag}{recency_tag}"
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part = header + "\n"
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if summary:
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part += f" Summary: {summary[:1000]}\n"
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if excerpt:
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part += f" Key excerpt: {excerpt[:1000]}\n"
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if content and not summary and not excerpt:
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part += f" Content: {content[:800]}\n"
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if highlights:
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part += " Key findings:\n"
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for h in highlights[:3]:
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h_text = h[:500] if h else ''
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if h_text:
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part += f" - {h_text}\n"
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context_parts.append(part)
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return ("\n".join(context_parts), sources_out)
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def _build_global_research_context(self, research: Any, competitive_advantage: str = "") -> str:
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"""Build global research context from the full BlogResearchResponse object.
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Extracts keyword_analysis, competitor_analysis, search_queries,
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and competitive_advantage into a compact context block that provides
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the LLM with strategic direction beyond per-section sources.
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"""
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if research is None:
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return ""
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parts = []
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ka = getattr(research, 'keyword_analysis', None) or {}
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if ka:
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primary = ka.get('primary', [])
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secondary = ka.get('secondary', [])
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search_intent = ka.get('search_intent', '')
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kw_lines = []
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if primary:
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kw_lines.append(f"Primary keywords: {', '.join(primary[:10])}")
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if secondary:
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kw_lines.append(f"Secondary keywords: {', '.join(secondary[:10])}")
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if search_intent:
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kw_lines.append(f"Search intent: {search_intent}")
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if kw_lines:
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parts.append("=== KEYWORD & SEARCH STRATEGY ===\n" + "\n".join(kw_lines))
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ca = getattr(research, 'competitor_analysis', None) or {}
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if ca:
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ca_lines = []
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content_gaps = ca.get('content_gaps', [])
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if content_gaps:
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ca_lines.append(f"Content gaps (address these): {', '.join(content_gaps[:5])}")
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industry_leaders = ca.get('industry_leaders', [])
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if industry_leaders:
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ca_lines.append(f"Industry leaders: {', '.join(industry_leaders[:5])}")
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opportunities = ca.get('opportunities', [])
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if opportunities:
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ca_lines.append(f"Opportunities: {', '.join(opportunities[:5])}")
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if ca_lines:
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parts.append("=== COMPETITIVE LANDSCAPE ===\n" + "\n".join(ca_lines))
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sq = getattr(research, 'search_queries', None) or []
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if sq:
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parts.append(f"=== SEARCH INTENT SIGNALS ===\nOriginal search queries: {', '.join(sq[:8])}")
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if competitive_advantage:
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parts.append(f"=== COMPETITIVE ADVANTAGE ===\nEmphasize this differentiator: {competitive_advantage}")
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return "\n\n".join(parts) if parts else ""
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def _build_prompt(self, section: Any, prev_summary: str, research_context: str, urls: list, global_research_context: str = "") -> str:
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heading = getattr(section, 'heading', 'Section')
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key_points = getattr(section, 'key_points', [])
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keywords = getattr(section, 'keywords', [])
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subheadings = getattr(section, 'subheadings', []) or []
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target_words = getattr(section, 'target_words', 300)
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url_block = "\n".join([f"- {u.get('title','')} ({u.get('url','')})" for u in urls]) if urls else "(no specific URLs provided)"
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return (
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prompt = (
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f"You are writing the blog section '{heading}'.\n\n"
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f"Context summary (previous sections): {prev_summary}\n\n"
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f"Authoring requirements:\n"
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f"- Target word count: ~{target_words}\n"
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f"- Use the following key points: {', '.join(key_points)}\n"
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f"- Include these keywords naturally: {', '.join(keywords)}\n"
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f"- Cite insights from these sources when relevant (do not output raw URLs):\n{url_block}\n\n"
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"Write engaging, well-structured markdown with clear paragraphs (2-4 sentences each) separated by double line breaks."
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)
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if subheadings:
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prompt += f"- Cover these subtopics: {', '.join(subheadings)}\n"
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if global_research_context:
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prompt += f"\n{global_research_context}\n\n"
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if research_context:
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prompt += (
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f"\nResearch sources for this section (use these facts, statistics, "
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f"and insights to support your writing):\n{research_context}\n\n"
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"IMPORTANT: Base your writing on the research sources above. "
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"Use specific facts, statistics, and data from these sources. "
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"Do not invent numbers, statistics, or claims not supported by the research.\n"
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)
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elif urls:
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import logging
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logging.getLogger('content_generator').warning(
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f"No research context for section '{heading}' — falling back to bare URLs"
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)
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url_lines = []
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for u in urls:
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if isinstance(u, dict):
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url_lines.append(f"- {u.get('title','')} ({u.get('url','')})")
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else:
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url_lines.append(f"- {u}")
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prompt += f"\nReference URLs (consult for additional context):\n" + "\n".join(url_lines) + "\n"
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prompt += (
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"\nWrite engaging, well-structured markdown with clear paragraphs "
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"(2-4 sentences each) separated by double line breaks."
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)
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return prompt
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