ALwrity AI Blog Writer - Added Google Grounding UI Implementation
This commit is contained in:
ajaysi
@@ -4,7 +4,7 @@ Outline Generator - AI-powered outline generation from research data.
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Generates comprehensive, SEO-optimized outlines using research intelligence.
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"""
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from typing import Dict, Any, List
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from typing import Dict, Any, List, Tuple
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import asyncio
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from loguru import logger
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@@ -14,10 +14,34 @@ from models.blog_models import (
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BlogOutlineSection,
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)
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from .source_mapper import SourceToSectionMapper
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from .section_enhancer import SectionEnhancer
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from .outline_optimizer import OutlineOptimizer
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from .grounding_engine import GroundingContextEngine
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from .title_generator import TitleGenerator
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from .metadata_collector import MetadataCollector
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from .prompt_builder import PromptBuilder
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from .response_processor import ResponseProcessor
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from .parallel_processor import ParallelProcessor
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class OutlineGenerator:
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"""Generates AI-powered outlines from research data."""
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def __init__(self):
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"""Initialize the outline generator with all enhancement modules."""
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self.source_mapper = SourceToSectionMapper()
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self.section_enhancer = SectionEnhancer()
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self.outline_optimizer = OutlineOptimizer()
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self.grounding_engine = GroundingContextEngine()
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# Initialize extracted classes
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self.title_generator = TitleGenerator()
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self.metadata_collector = MetadataCollector()
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self.prompt_builder = PromptBuilder()
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self.response_processor = ResponseProcessor()
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self.parallel_processor = ParallelProcessor(self.source_mapper, self.grounding_engine)
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async def generate(self, request: BlogOutlineRequest) -> BlogOutlineResponse:
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"""
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Generate AI-powered outline using research results
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@@ -34,7 +58,7 @@ class OutlineGenerator:
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custom_instructions = getattr(request, 'custom_instructions', None)
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# Build comprehensive outline generation prompt with rich research data
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outline_prompt = self._build_outline_prompt(
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outline_prompt = self.prompt_builder.build_outline_prompt(
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primary_keywords, secondary_keywords, content_angles, sources,
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search_intent, request, custom_instructions
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)
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@@ -42,32 +66,63 @@ class OutlineGenerator:
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logger.info("Generating AI-powered outline using research results")
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# Define schema with proper property ordering (critical for Gemini API)
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outline_schema = self._get_outline_schema()
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outline_schema = self.prompt_builder.get_outline_schema()
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# Generate outline using structured JSON response with retry logic
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outline_data = await self._generate_with_retry(outline_prompt, outline_schema)
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outline_data = await self.response_processor.generate_with_retry(outline_prompt, outline_schema)
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# Convert to BlogOutlineSection objects
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outline_sections = self._convert_to_sections(outline_data, sources)
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outline_sections = self.response_processor.convert_to_sections(outline_data, sources)
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# Extract title options
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title_options = outline_data.get('title_options', [])
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if not title_options:
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title_options = self._generate_fallback_titles(primary_keywords)
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# Run parallel processing for speed optimization
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mapped_sections, grounding_insights = await self.parallel_processor.run_parallel_processing_async(
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outline_sections, research
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)
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logger.info(f"Generated outline with {len(outline_sections)} sections and {len(title_options)} title options")
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# Enhance sections with grounding insights
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logger.info("Enhancing sections with grounding insights...")
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grounding_enhanced_sections = self.grounding_engine.enhance_sections_with_grounding(
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mapped_sections, research.grounding_metadata, grounding_insights
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)
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# Optimize outline for better flow, SEO, and engagement
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logger.info("Optimizing outline for better flow and engagement...")
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optimized_sections = await self.outline_optimizer.optimize(grounding_enhanced_sections, "comprehensive optimization")
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# Rebalance word counts for optimal distribution
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target_words = request.word_count or 1500
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balanced_sections = self.outline_optimizer.rebalance_word_counts(optimized_sections, target_words)
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# Extract title options - combine AI-generated with content angles
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ai_title_options = outline_data.get('title_options', [])
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content_angle_titles = self.title_generator.extract_content_angle_titles(research)
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# Combine AI-generated titles with content angles
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title_options = self.title_generator.combine_title_options(ai_title_options, content_angle_titles, primary_keywords)
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logger.info(f"Generated optimized outline with {len(balanced_sections)} sections and {len(title_options)} title options")
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# Collect metadata for enhanced UI
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source_mapping_stats = self.metadata_collector.collect_source_mapping_stats(mapped_sections, research)
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grounding_insights_data = self.metadata_collector.collect_grounding_insights(grounding_insights)
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optimization_results = self.metadata_collector.collect_optimization_results(optimized_sections, "comprehensive optimization")
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research_coverage = self.metadata_collector.collect_research_coverage(research)
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return BlogOutlineResponse(
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success=True,
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title_options=title_options,
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outline=outline_sections
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outline=balanced_sections,
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source_mapping_stats=source_mapping_stats,
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grounding_insights=grounding_insights_data,
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optimization_results=optimization_results,
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research_coverage=research_coverage
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)
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async def generate_with_progress(self, request: BlogOutlineRequest, task_id: str) -> BlogOutlineResponse:
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"""
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Outline generation method with progress updates for real-time feedback.
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"""
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from api.blog_writer.router import _update_progress
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from api.blog_writer.task_manager import task_manager
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# Extract research insights
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research = request.research
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@@ -80,272 +135,168 @@ class OutlineGenerator:
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# Check for custom instructions
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custom_instructions = getattr(request, 'custom_instructions', None)
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await _update_progress(task_id, "📊 Analyzing research data and building content strategy...")
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await task_manager.update_progress(task_id, "📊 Analyzing research data and building content strategy...")
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# Build comprehensive outline generation prompt with rich research data
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outline_prompt = self._build_outline_prompt(
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outline_prompt = self.prompt_builder.build_outline_prompt(
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primary_keywords, secondary_keywords, content_angles, sources,
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search_intent, request, custom_instructions
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)
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await _update_progress(task_id, "🤖 Generating AI-powered outline with research insights...")
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await task_manager.update_progress(task_id, "🤖 Generating AI-powered outline with research insights...")
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# Define schema with proper property ordering (critical for Gemini API)
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outline_schema = self._get_outline_schema()
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outline_schema = self.prompt_builder.get_outline_schema()
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await _update_progress(task_id, "🔄 Making AI request to generate structured outline...")
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await task_manager.update_progress(task_id, "🔄 Making AI request to generate structured outline...")
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# Generate outline using structured JSON response with retry logic
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outline_data = await self._generate_with_retry(outline_prompt, outline_schema, task_id)
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outline_data = await self.response_processor.generate_with_retry(outline_prompt, outline_schema, task_id)
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await _update_progress(task_id, "📝 Processing outline structure and validating sections...")
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await task_manager.update_progress(task_id, "📝 Processing outline structure and validating sections...")
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# Convert to BlogOutlineSection objects
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outline_sections = self._convert_to_sections(outline_data, sources)
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outline_sections = self.response_processor.convert_to_sections(outline_data, sources)
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# Extract title options
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title_options = outline_data.get('title_options', [])
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if not title_options:
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title_options = self._generate_fallback_titles(primary_keywords)
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# Run parallel processing for speed optimization
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mapped_sections, grounding_insights = await self.parallel_processor.run_parallel_processing(
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outline_sections, research, task_id
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)
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await _update_progress(task_id, "✅ Outline generation completed successfully!")
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# Enhance sections with grounding insights (depends on both previous tasks)
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await task_manager.update_progress(task_id, "✨ Enhancing sections with grounding insights...")
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grounding_enhanced_sections = self.grounding_engine.enhance_sections_with_grounding(
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mapped_sections, research.grounding_metadata, grounding_insights
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)
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# Optimize outline for better flow, SEO, and engagement
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await task_manager.update_progress(task_id, "🎯 Optimizing outline for better flow and engagement...")
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optimized_sections = await self.outline_optimizer.optimize(grounding_enhanced_sections, "comprehensive optimization")
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# Rebalance word counts for optimal distribution
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await task_manager.update_progress(task_id, "⚖️ Rebalancing word count distribution...")
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target_words = request.word_count or 1500
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balanced_sections = self.outline_optimizer.rebalance_word_counts(optimized_sections, target_words)
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# Extract title options - combine AI-generated with content angles
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ai_title_options = outline_data.get('title_options', [])
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content_angle_titles = self.title_generator.extract_content_angle_titles(research)
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# Combine AI-generated titles with content angles
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title_options = self.title_generator.combine_title_options(ai_title_options, content_angle_titles, primary_keywords)
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await task_manager.update_progress(task_id, "✅ Outline generation and optimization completed successfully!")
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# Collect metadata for enhanced UI
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source_mapping_stats = self.metadata_collector.collect_source_mapping_stats(mapped_sections, research)
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grounding_insights_data = self.metadata_collector.collect_grounding_insights(grounding_insights)
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optimization_results = self.metadata_collector.collect_optimization_results(optimized_sections, "comprehensive optimization")
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research_coverage = self.metadata_collector.collect_research_coverage(research)
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return BlogOutlineResponse(
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success=True,
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title_options=title_options,
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outline=outline_sections
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outline=balanced_sections,
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source_mapping_stats=source_mapping_stats,
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grounding_insights=grounding_insights_data,
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optimization_results=optimization_results,
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research_coverage=research_coverage
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)
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def _build_outline_prompt(self, primary_keywords: List[str], secondary_keywords: List[str],
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content_angles: List[str], sources: List, search_intent: str,
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request: BlogOutlineRequest, custom_instructions: str = None) -> str:
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"""Build the comprehensive outline generation prompt."""
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return f"""
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You are a world-class content strategist and SEO expert with 15+ years of experience creating viral, high-converting blog content. Your outlines have generated millions of views and driven significant business results.
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CONTENT STRATEGY BRIEF:
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Topic: {', '.join(primary_keywords)}
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Search Intent: {search_intent}
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Target Word Count: {request.word_count or 1500} words
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Industry Context: {getattr(request.persona, 'industry', 'General') if request.persona else 'General'}
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Audience: {getattr(request.persona, 'target_audience', 'General') if request.persona else 'General'}
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{f"CUSTOM USER INSTRUCTIONS: {custom_instructions}" if custom_instructions else ""}
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RESEARCH INTELLIGENCE:
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Primary Keywords: {', '.join(primary_keywords)}
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Secondary Keywords: {', '.join(secondary_keywords)}
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Long-tail Opportunities: {', '.join(request.research.keyword_analysis.get('long_tail', [])[:5])}
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Semantic Keywords: {', '.join(request.research.keyword_analysis.get('semantic_keywords', [])[:5])}
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Trending Terms: {', '.join(request.research.keyword_analysis.get('trending_terms', [])[:3])}
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Keyword Difficulty: {request.research.keyword_analysis.get('difficulty', 6)}/10
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Content Gaps: {', '.join(request.research.keyword_analysis.get('content_gaps', [])[:3])}
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Content Angles Discovered:
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{chr(10).join([f"• {angle}" for angle in content_angles[:6]])}
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Competitive Intelligence:
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Top Competitors: {', '.join(request.research.competitor_analysis.get('top_competitors', [])[:3])}
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Market Opportunities: {', '.join(request.research.competitor_analysis.get('opportunities', [])[:3])}
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Competitive Advantages: {', '.join(request.research.competitor_analysis.get('competitive_advantages', [])[:3])}
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Market Positioning: {request.research.competitor_analysis.get('market_positioning', 'Standard positioning')}
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Research Sources Available: {len(sources)} authoritative sources with current data
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Key Statistics Available: Multiple data points, percentages, and expert quotes from credible sources
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STRATEGIC OUTLINE REQUIREMENTS:
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{f"CUSTOM REQUIREMENTS: {custom_instructions}" if custom_instructions else ""}
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1. CONTENT ARCHITECTURE:
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- Create a logical, engaging narrative arc that guides readers from problem to solution
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- Structure content to build authority and trust progressively
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- Include data-driven insights and expert opinions from research
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- Ensure each section adds unique value and builds upon previous sections
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2. SEO OPTIMIZATION:
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- Naturally integrate primary keywords in headings and content
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- Use secondary keywords strategically throughout sections
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- Include long-tail keywords in subheadings and key points
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- Optimize for featured snippets and voice search
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3. READER ENGAGEMENT:
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- Start with compelling hooks and pain points
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- Use storytelling elements and real-world examples
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- Include actionable insights and practical takeaways
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- End with clear next steps and calls-to-action
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4. CONTENT DEPTH:
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- Provide comprehensive coverage of the topic
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- Include multiple perspectives and expert insights
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- Address common questions and objections
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- Offer unique angles not covered by competitors
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5. WORD COUNT DISTRIBUTION:
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- Introduction: 12% of total word count
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- Main content sections: 76% of total word count
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- Conclusion: 12% of total word count
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- Ensure balanced section lengths for optimal readability
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6. COMPETITIVE ADVANTAGE:
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- Leverage content gaps identified in research
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- Include unique data points and statistics
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- Provide fresh perspectives on trending topics
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- Address underserved audience segments
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TITLE STRATEGY:
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Create 5 compelling title options that:
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- Include primary keywords naturally
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- Promise clear value and outcomes
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- Appeal to the target audience's pain points
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- Stand out from competitor content
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- Optimize for click-through rates
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Generate a comprehensive outline with the following structure:
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{{
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"title_options": [
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"Title 1 with primary keyword",
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"Title 2 with emotional hook",
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"Title 3 with benefit-focused approach",
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"Title 4 with question format",
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"Title 5 with urgency/trending angle"
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],
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"outline": [
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{{
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"heading": "Section heading with primary keyword",
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"subheadings": ["Subheading 1", "Subheading 2", "Subheading 3"],
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"key_points": ["Key point 1", "Key point 2", "Key point 3"],
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"word_count": 300,
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"keywords": ["primary keyword", "secondary keyword"]
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}}
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]
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}}
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async def enhance_section(self, section: BlogOutlineSection, focus: str = "general improvement") -> BlogOutlineSection:
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"""
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def _get_outline_schema(self) -> Dict[str, Any]:
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"""Get the structured JSON schema for outline generation."""
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return {
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"type": "object",
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"properties": {
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"title_options": {
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"type": "array",
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"items": {"type": "string"}
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},
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"outline": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"heading": {"type": "string"},
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"subheadings": {
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"type": "array",
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"items": {"type": "string"}
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},
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"key_points": {
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"type": "array",
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"items": {"type": "string"}
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},
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"word_count": {"type": "integer"},
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"keywords": {
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"type": "array",
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"items": {"type": "string"}
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}
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},
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"required": ["heading", "subheadings", "key_points", "word_count", "keywords"]
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}
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}
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},
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"required": ["title_options", "outline"],
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"propertyOrdering": ["title_options", "outline"]
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}
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async def _generate_with_retry(self, prompt: str, schema: Dict[str, Any], task_id: str = None) -> Dict[str, Any]:
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"""Generate outline with retry logic for API failures."""
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from services.llm_providers.gemini_provider import gemini_structured_json_response
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from api.blog_writer.router import _update_progress
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Enhance a single section using AI with research context.
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max_retries = 2 # Conservative retry for expensive API calls
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retry_delay = 5 # 5 second delay between retries
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for attempt in range(max_retries + 1):
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try:
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if task_id:
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await _update_progress(task_id, f"🤖 Calling Gemini API for outline generation (attempt {attempt + 1}/{max_retries + 1})...")
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outline_data = gemini_structured_json_response(
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prompt=prompt,
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schema=schema,
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temperature=0.3,
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max_tokens=4000 # Increased to avoid MAX_TOKENS truncation
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)
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# Log response for debugging
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logger.info(f"Gemini response received: {type(outline_data)}")
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# Check for errors in the response
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if isinstance(outline_data, dict) and 'error' in outline_data:
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error_msg = str(outline_data['error'])
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if "503" in error_msg and "overloaded" in error_msg and attempt < max_retries:
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if task_id:
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await _update_progress(task_id, f"⚠️ AI service overloaded, retrying in {retry_delay} seconds...")
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logger.warning(f"Gemini API overloaded, retrying in {retry_delay} seconds (attempt {attempt + 1}/{max_retries + 1})")
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await asyncio.sleep(retry_delay)
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continue
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else:
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logger.error(f"Gemini structured response error: {outline_data['error']}")
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raise ValueError(f"AI outline generation failed: {outline_data['error']}")
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# Validate required fields
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if not isinstance(outline_data, dict) or 'outline' not in outline_data or not isinstance(outline_data['outline'], list):
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if attempt < max_retries:
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if task_id:
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await _update_progress(task_id, f"⚠️ Invalid response structure, retrying in {retry_delay} seconds...")
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logger.warning(f"Invalid response structure, retrying in {retry_delay} seconds (attempt {attempt + 1}/{max_retries + 1})")
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await asyncio.sleep(retry_delay)
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continue
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else:
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raise ValueError("Invalid outline structure in Gemini response")
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# If we get here, the response is valid
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return outline_data
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except Exception as e:
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error_str = str(e)
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if ("503" in error_str or "overloaded" in error_str) and attempt < max_retries:
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if task_id:
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await _update_progress(task_id, f"⚠️ AI service error, retrying in {retry_delay} seconds...")
|
||||
logger.warning(f"Gemini API error, retrying in {retry_delay} seconds (attempt {attempt + 1}/{max_retries + 1}): {error_str}")
|
||||
await asyncio.sleep(retry_delay)
|
||||
continue
|
||||
else:
|
||||
logger.error(f"Outline generation failed after {attempt + 1} attempts: {error_str}")
|
||||
raise ValueError(f"AI outline generation failed: {error_str}")
|
||||
Args:
|
||||
section: The section to enhance
|
||||
focus: Enhancement focus area (e.g., "SEO optimization", "engagement", "comprehensiveness")
|
||||
|
||||
Returns:
|
||||
Enhanced section with improved content
|
||||
"""
|
||||
logger.info(f"Enhancing section '{section.heading}' with focus: {focus}")
|
||||
enhanced_section = await self.section_enhancer.enhance(section, focus)
|
||||
logger.info(f"✅ Section enhancement completed for '{section.heading}'")
|
||||
return enhanced_section
|
||||
|
||||
def _convert_to_sections(self, outline_data: Dict[str, Any], sources: List) -> List[BlogOutlineSection]:
|
||||
"""Convert outline data to BlogOutlineSection objects."""
|
||||
outline_sections = []
|
||||
for i, section_data in enumerate(outline_data.get('outline', [])):
|
||||
if not isinstance(section_data, dict) or 'heading' not in section_data:
|
||||
continue
|
||||
|
||||
section = BlogOutlineSection(
|
||||
id=f"s{i+1}",
|
||||
heading=section_data.get('heading', f'Section {i+1}'),
|
||||
subheadings=section_data.get('subheadings', []),
|
||||
key_points=section_data.get('key_points', []),
|
||||
references=sources[:3], # Use first 3 sources as references
|
||||
target_words=section_data.get('word_count', 200),
|
||||
keywords=section_data.get('keywords', [])
|
||||
)
|
||||
outline_sections.append(section)
|
||||
async def optimize_outline(self, outline: List[BlogOutlineSection], focus: str = "comprehensive optimization") -> List[BlogOutlineSection]:
|
||||
"""
|
||||
Optimize an entire outline for better flow, SEO, and engagement.
|
||||
|
||||
return outline_sections
|
||||
Args:
|
||||
outline: List of sections to optimize
|
||||
focus: Optimization focus area
|
||||
|
||||
Returns:
|
||||
Optimized outline with improved flow and engagement
|
||||
"""
|
||||
logger.info(f"Optimizing outline with {len(outline)} sections, focus: {focus}")
|
||||
optimized_outline = await self.outline_optimizer.optimize(outline, focus)
|
||||
logger.info(f"✅ Outline optimization completed for {len(optimized_outline)} sections")
|
||||
return optimized_outline
|
||||
|
||||
def rebalance_outline_word_counts(self, outline: List[BlogOutlineSection], target_words: int) -> List[BlogOutlineSection]:
|
||||
"""
|
||||
Rebalance word count distribution across outline sections.
|
||||
|
||||
Args:
|
||||
outline: List of sections to rebalance
|
||||
target_words: Total target word count
|
||||
|
||||
Returns:
|
||||
Outline with rebalanced word counts
|
||||
"""
|
||||
logger.info(f"Rebalancing word counts for {len(outline)} sections, target: {target_words} words")
|
||||
rebalanced_outline = self.outline_optimizer.rebalance_word_counts(outline, target_words)
|
||||
logger.info(f"✅ Word count rebalancing completed")
|
||||
return rebalanced_outline
|
||||
|
||||
def get_grounding_insights(self, research_data) -> Dict[str, Any]:
|
||||
"""
|
||||
Get grounding metadata insights for research data.
|
||||
|
||||
Args:
|
||||
research_data: Research data with grounding metadata
|
||||
|
||||
Returns:
|
||||
Dictionary containing grounding insights and analysis
|
||||
"""
|
||||
logger.info("Extracting grounding insights from research data...")
|
||||
insights = self.grounding_engine.extract_contextual_insights(research_data.grounding_metadata)
|
||||
logger.info(f"✅ Extracted {len(insights)} grounding insight categories")
|
||||
return insights
|
||||
|
||||
def get_authority_sources(self, research_data) -> List[Tuple]:
|
||||
"""
|
||||
Get high-authority sources from grounding metadata.
|
||||
|
||||
Args:
|
||||
research_data: Research data with grounding metadata
|
||||
|
||||
Returns:
|
||||
List of (chunk, authority_score) tuples sorted by authority
|
||||
"""
|
||||
logger.info("Identifying high-authority sources from grounding metadata...")
|
||||
authority_sources = self.grounding_engine.get_authority_sources(research_data.grounding_metadata)
|
||||
logger.info(f"✅ Identified {len(authority_sources)} high-authority sources")
|
||||
return authority_sources
|
||||
|
||||
def get_high_confidence_insights(self, research_data) -> List[str]:
|
||||
"""
|
||||
Get high-confidence insights from grounding metadata.
|
||||
|
||||
Args:
|
||||
research_data: Research data with grounding metadata
|
||||
|
||||
Returns:
|
||||
List of high-confidence insights
|
||||
"""
|
||||
logger.info("Extracting high-confidence insights from grounding metadata...")
|
||||
insights = self.grounding_engine.get_high_confidence_insights(research_data.grounding_metadata)
|
||||
logger.info(f"✅ Extracted {len(insights)} high-confidence insights")
|
||||
return insights
|
||||
|
||||
|
||||
|
||||
def _generate_fallback_titles(self, primary_keywords: List[str]) -> List[str]:
|
||||
"""Generate fallback titles when AI generation fails."""
|
||||
primary_keyword = primary_keywords[0] if primary_keywords else "Topic"
|
||||
return [
|
||||
f"The Complete Guide to {primary_keyword}",
|
||||
f"{primary_keyword}: Everything You Need to Know",
|
||||
f"How to Master {primary_keyword} in 2024"
|
||||
]
|
||||
|
||||
Reference in New Issue
Block a user