ALwrity Prompts - AI Integration Plan
This commit is contained in:
ajaysi
@@ -12,6 +12,15 @@ from models.linkedin_models import (
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PostContent, ArticleContent, GroundingLevel, ResearchSource
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)
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from services.linkedin.quality_handler import QualityHandler
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from services.linkedin.content_generator_prompts import (
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PostPromptBuilder,
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ArticlePromptBuilder,
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CarouselPromptBuilder,
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VideoScriptPromptBuilder,
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CommentResponsePromptBuilder,
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CarouselGenerator,
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VideoScriptGenerator
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)
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class ContentGenerator:
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@@ -22,6 +31,10 @@ class ContentGenerator:
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self.quality_analyzer = quality_analyzer
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self.gemini_grounded = gemini_grounded
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self.fallback_provider = fallback_provider
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# Initialize specialized generators
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self.carousel_generator = CarouselGenerator(citation_manager, quality_analyzer)
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self.video_script_generator = VideoScriptGenerator(citation_manager, quality_analyzer)
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def _transform_gemini_sources(self, gemini_sources):
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"""Transform Gemini sources to ResearchSource format."""
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@@ -258,91 +271,10 @@ class ContentGenerator:
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content_result: Dict[str, Any],
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grounding_enabled: bool
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):
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"""Generate LinkedIn carousel with all processing steps."""
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try:
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start_time = datetime.now()
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# Step 3: Add citations if requested
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citations = []
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source_list = None
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if request.include_citations and research_sources:
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# Extract citations from all slides
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all_content = " ".join([slide['content'] for slide in content_result['slides']])
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citations = self.citation_manager.extract_citations(all_content) if self.citation_manager else []
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source_list = self.citation_manager.generate_source_list(research_sources) if self.citation_manager else None
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# Step 4: Analyze content quality
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quality_metrics = None
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if grounding_enabled and self.quality_analyzer:
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try:
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all_content = " ".join([slide['content'] for slide in content_result['slides']])
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quality_handler = QualityHandler(self.quality_analyzer)
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quality_metrics = quality_handler.create_quality_metrics(
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content=all_content,
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sources=research_sources,
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industry=request.industry,
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grounding_enabled=grounding_enabled
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)
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except Exception as e:
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logger.warning(f"Quality analysis failed: {e}")
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# Step 5: Build response
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slides = []
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for i, slide_data in enumerate(content_result['slides']):
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slide_citations = []
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if request.include_citations and research_sources and self.citation_manager:
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slide_citations = self.citation_manager.extract_citations(slide_data['content'])
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slides.append({
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'slide_number': i + 1,
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'title': slide_data['title'],
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'content': slide_data['content'],
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'visual_elements': slide_data.get('visual_elements', []),
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'design_notes': slide_data.get('design_notes'),
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'citations': slide_citations
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})
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carousel_content = {
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'title': content_result['title'],
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'slides': slides,
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'cover_slide': content_result.get('cover_slide'),
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'cta_slide': content_result.get('cta_slide'),
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'design_guidelines': content_result.get('design_guidelines', {}),
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'citations': citations,
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'source_list': source_list,
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'quality_metrics': quality_metrics,
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'grounding_enabled': grounding_enabled
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}
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generation_time = (datetime.now() - start_time).total_seconds()
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# Build grounding status
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grounding_status = {
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'status': 'success' if grounding_enabled else 'disabled',
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'sources_used': len(research_sources),
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'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
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'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
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} if grounding_enabled else None
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return {
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'success': True,
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'data': carousel_content,
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'research_sources': research_sources,
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'generation_metadata': {
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'model_used': 'gemini-2.0-flash-001',
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'generation_time': generation_time,
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'research_time': research_time,
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'grounding_enabled': grounding_enabled
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},
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'grounding_status': grounding_status
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}
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except Exception as e:
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logger.error(f"Error generating LinkedIn carousel: {str(e)}")
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return {
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'success': False,
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'error': f"Failed to generate LinkedIn carousel: {str(e)}"
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}
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"""Generate LinkedIn carousel using the specialized CarouselGenerator."""
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return await self.carousel_generator.generate_carousel(
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request, research_sources, research_time, content_result, grounding_enabled
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)
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async def generate_video_script(
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self,
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@@ -352,76 +284,10 @@ class ContentGenerator:
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content_result: Dict[str, Any],
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grounding_enabled: bool
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):
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"""Generate LinkedIn video script with all processing steps."""
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try:
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start_time = datetime.now()
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# Step 3: Add citations if requested
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citations = []
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source_list = None
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if request.include_citations and research_sources and self.citation_manager:
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all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
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citations = self.citation_manager.extract_citations(all_content)
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source_list = self.citation_manager.generate_source_list(research_sources)
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# Step 4: Analyze content quality
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quality_metrics = None
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if grounding_enabled and self.quality_analyzer:
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try:
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all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
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quality_handler = QualityHandler(self.quality_analyzer)
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quality_metrics = quality_handler.create_quality_metrics(
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content=all_content,
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sources=research_sources,
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industry=request.industry,
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grounding_enabled=grounding_enabled
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)
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except Exception as e:
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logger.warning(f"Quality analysis failed: {e}")
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# Step 5: Build response
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video_script = {
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'hook': content_result['hook'],
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'main_content': content_result['main_content'],
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'conclusion': content_result['conclusion'],
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'captions': content_result.get('captions'),
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'thumbnail_suggestions': content_result.get('thumbnail_suggestions', []),
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'video_description': content_result.get('video_description', ''),
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'citations': citations,
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'source_list': source_list,
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'quality_metrics': quality_metrics,
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'grounding_enabled': grounding_enabled
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}
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generation_time = (datetime.now() - start_time).total_seconds()
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# Build grounding status
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grounding_status = {
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'status': 'success' if grounding_enabled else 'disabled',
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'sources_used': len(research_sources),
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'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
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'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
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} if grounding_enabled else None
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return {
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'success': True,
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'data': video_script,
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'research_sources': research_sources,
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'generation_metadata': {
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'model_used': 'gemini-2.0-flash-001',
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'generation_time': generation_time,
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'research_time': research_time,
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'grounding_enabled': grounding_enabled
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},
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'grounding_status': grounding_status
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}
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except Exception as e:
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logger.error(f"Error generating LinkedIn video script: {str(e)}")
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return {
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'success': False,
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'error': f"Failed to generate LinkedIn video script: {str(e)}"
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}
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"""Generate LinkedIn video script using the specialized VideoScriptGenerator."""
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return await self.video_script_generator.generate_video_script(
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request, research_sources, research_time, content_result, grounding_enabled
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)
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async def generate_comment_response(
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self,
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@@ -471,11 +337,11 @@ class ContentGenerator:
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"""Generate grounded post content using the enhanced Gemini provider with native grounding."""
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try:
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if not self.gemini_grounded:
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logger.warning("Gemini Grounded Provider not available, using fallback")
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return await self.generate_fallback_post_content(request)
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logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
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raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
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# Build the prompt for grounded generation
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prompt = self._build_post_prompt(request)
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# Build the prompt for grounded generation using the new prompt builder
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prompt = PostPromptBuilder.build_post_prompt(request)
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# Generate grounded content using native Google Search grounding
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result = await self.gemini_grounded.generate_grounded_content(
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@@ -489,18 +355,17 @@ class ContentGenerator:
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except Exception as e:
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logger.error(f"Error generating grounded post content: {str(e)}")
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# Fallback to basic generation
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return await self.generate_fallback_post_content(request)
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raise Exception(f"Failed to generate grounded post content: {str(e)}")
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async def generate_grounded_article_content(self, request, research_sources: List) -> Dict[str, Any]:
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"""Generate grounded article content using the enhanced Gemini provider with native grounding."""
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try:
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if not self.gemini_grounded:
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logger.warning("Gemini Grounded Provider not available, using fallback")
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return await self.generate_fallback_article_content(request)
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logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
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raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
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# Build the prompt for grounded generation
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prompt = self._build_article_prompt(request)
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# Build the prompt for grounded generation using the new prompt builder
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prompt = ArticlePromptBuilder.build_article_prompt(request)
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# Generate grounded content using native Google Search grounding
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result = await self.gemini_grounded.generate_grounded_content(
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@@ -514,18 +379,17 @@ class ContentGenerator:
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except Exception as e:
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logger.error(f"Error generating grounded article content: {str(e)}")
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# Fallback to basic generation
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return await self.generate_fallback_article_content(request)
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raise Exception(f"Failed to generate grounded article content: {str(e)}")
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async def generate_grounded_carousel_content(self, request, research_sources: List) -> Dict[str, Any]:
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"""Generate grounded carousel content using the enhanced Gemini provider with native grounding."""
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try:
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if not self.gemini_grounded:
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logger.warning("Gemini Grounded Provider not available, using fallback")
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return await self.generate_fallback_carousel_content(request)
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logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
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raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
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# Build the prompt for grounded generation
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prompt = self._build_carousel_prompt(request)
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# Build the prompt for grounded generation using the new prompt builder
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prompt = CarouselPromptBuilder.build_carousel_prompt(request)
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# Generate grounded content using native Google Search grounding
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result = await self.gemini_grounded.generate_grounded_content(
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@@ -539,18 +403,17 @@ class ContentGenerator:
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except Exception as e:
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logger.error(f"Error generating grounded carousel content: {str(e)}")
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# Fallback to basic generation
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return await self.generate_fallback_carousel_content(request)
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raise Exception(f"Failed to generate grounded carousel content: {str(e)}")
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async def generate_grounded_video_script_content(self, request, research_sources: List) -> Dict[str, Any]:
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"""Generate grounded video script content using the enhanced Gemini provider with native grounding."""
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try:
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if not self.gemini_grounded:
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logger.warning("Gemini Grounded Provider not available, using fallback")
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return await self.generate_fallback_video_script_content(request)
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logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
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raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
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# Build the prompt for grounded generation
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prompt = self._build_video_script_prompt(request)
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# Build the prompt for grounded generation using the new prompt builder
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prompt = VideoScriptPromptBuilder.build_video_script_prompt(request)
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# Generate grounded content using native Google Search grounding
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result = await self.gemini_grounded.generate_grounded_content(
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@@ -564,185 +427,28 @@ class ContentGenerator:
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except Exception as e:
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logger.error(f"Error generating grounded video script content: {str(e)}")
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# Fallback to basic generation
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return await self.generate_fallback_video_script_content(request)
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raise Exception(f"Failed to generate grounded video script content: {str(e)}")
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async def generate_grounded_comment_response(self, request, research_sources: List) -> Dict[str, Any]:
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"""Generate grounded comment response using the enhanced Gemini provider with native grounding."""
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try:
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if not self.gemini_grounded:
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logger.warning("Gemini Grounded Provider not available, using fallback")
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return await self.generate_fallback_comment_response(request)
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logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
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raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
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# Build the prompt for grounded generation
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prompt = self._build_comment_response_prompt(request)
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# Build the prompt for grounded generation using the new prompt builder
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prompt = CommentResponsePromptBuilder.build_comment_response_prompt(request)
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# Generate grounded content using native Google Search grounding
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result = await self.gemini_grounded.generate_grounded_content(
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prompt=prompt,
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content_type="linkedin_comment_response",
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temperature=0.7,
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max_tokens=500
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max_tokens=2000
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)
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return result
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except Exception as e:
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logger.error(f"Error generating grounded comment response: {str(e)}")
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# Fallback to basic generation
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return await self.generate_fallback_comment_response(request)
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# Fallback content generation methods
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async def generate_fallback_post_content(self, request) -> Dict[str, Any]:
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"""Generate post content using fallback provider."""
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if not self.fallback_provider:
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raise Exception("No fallback provider available")
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return {
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'content': f"Professional LinkedIn post about {request.topic} in the {request.industry} industry.",
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'hashtags': [{'hashtag': f'#{request.industry.lower().replace(" ", "")}', 'category': 'industry', 'popularity_score': 0.8}],
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'call_to_action': "What are your thoughts on this? Share in the comments!",
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'engagement_prediction': {'estimated_likes': 50, 'estimated_comments': 5}
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}
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async def generate_fallback_article_content(self, request) -> Dict[str, Any]:
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"""Generate article content using fallback provider."""
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if not self.fallback_provider:
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raise Exception("No fallback provider available")
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return {
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'title': f"Comprehensive Guide to {request.topic} in {request.industry}",
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'content': f"Detailed article about {request.topic} in the {request.industry} industry.",
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'sections': [{'title': 'Introduction', 'content': 'Industry overview and context'}],
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'seo_metadata': {'keywords': [request.topic, request.industry]},
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'image_suggestions': ['Industry-related visual content'],
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'reading_time': '5 minutes'
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}
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async def generate_fallback_carousel_content(self, request) -> Dict[str, Any]:
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"""Generate carousel content using fallback provider."""
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if not self.fallback_provider:
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raise Exception("No fallback provider available")
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return {
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'title': f"Key Insights: {request.topic} in {request.industry}",
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'slides': [
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{'title': 'Overview', 'content': f'Introduction to {request.topic}', 'visual_elements': [], 'design_notes': 'Clean, professional design'},
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{'title': 'Key Points', 'content': f'Main insights about {request.topic}', 'visual_elements': [], 'design_notes': 'Bullet points with icons'}
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],
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'cover_slide': {'title': 'Cover', 'content': 'Professional cover slide', 'visual_elements': [], 'design_notes': 'Eye-catching design'},
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'cta_slide': {'title': 'Call to Action', 'content': 'Engage with this content', 'visual_elements': [], 'design_notes': 'Clear CTA design'},
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'design_guidelines': {'style': 'professional', 'colors': 'brand colors'}
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}
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async def generate_fallback_video_script_content(self, request) -> Dict[str, Any]:
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"""Generate video script content using fallback provider."""
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if not self.fallback_provider:
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raise Exception("No fallback provider available")
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return {
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'hook': f"Discover how {request.topic} is transforming the {request.industry} industry!",
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'main_content': [
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{'content': f'Introduction to {request.topic}', 'duration': '30s'},
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{'content': f'Key insights about {request.topic}', 'duration': '45s'}
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],
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'conclusion': f"Ready to explore {request.topic}? Let's dive in!",
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'captions': [f'Key point about {request.topic}'],
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'thumbnail_suggestions': ['Professional thumbnail with industry imagery'],
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'video_description': f"Video description about {request.topic}"
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}
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async def generate_fallback_comment_response(self, request) -> Dict[str, Any]:
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"""Generate comment response using fallback provider."""
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if not self.fallback_provider:
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raise Exception("No fallback provider available")
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return {
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'response': f"Thank you for your comment about {request.original_comment}",
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'alternative_responses': [],
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'tone_analysis': None
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}
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# Prompt building methods
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def _build_post_prompt(self, request) -> str:
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"""Build prompt for post generation."""
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prompt = f"""
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Generate a professional LinkedIn post about {request.topic} in the {request.industry} industry.
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Requirements:
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- Tone: {request.tone}
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- Target audience: {request.target_audience or 'Industry professionals'}
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- Maximum length: {request.max_length} characters
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- Include engaging hashtags
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- Include a call to action
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- Make it informative and shareable
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Key points to include: {', '.join(request.key_points) if request.key_points else 'Industry insights and trends'}
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"""
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return prompt.strip()
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def _build_article_prompt(self, request) -> str:
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"""Build prompt for article generation."""
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prompt = f"""
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Generate a comprehensive LinkedIn article about {request.topic} in the {request.industry} industry.
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Requirements:
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- Tone: {request.tone}
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- Target audience: {request.target_audience or 'Industry professionals'}
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- Word count: {request.word_count} words
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- Include SEO optimization
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- Include image suggestions
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- Make it informative and engaging
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Key sections to include: {', '.join(request.key_sections) if request.key_sections else 'Introduction, main content, conclusion'}
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"""
|
||||
return prompt.strip()
|
||||
|
||||
def _build_carousel_prompt(self, request) -> str:
|
||||
"""Build prompt for carousel generation."""
|
||||
prompt = f"""
|
||||
Generate a LinkedIn carousel about {request.topic} in the {request.industry} industry.
|
||||
|
||||
Requirements:
|
||||
- Tone: {request.tone}
|
||||
- Target audience: {request.target_audience or 'Industry professionals'}
|
||||
- Number of slides: {request.number_of_slides}
|
||||
- Include cover slide: {request.include_cover_slide}
|
||||
- Include CTA slide: {request.include_cta_slide}
|
||||
- Make each slide informative and visually appealing
|
||||
|
||||
Each slide should contain valuable insights and be designed for social media engagement.
|
||||
"""
|
||||
return prompt.strip()
|
||||
|
||||
def _build_video_script_prompt(self, request) -> str:
|
||||
"""Build prompt for video script generation."""
|
||||
prompt = f"""
|
||||
Generate a LinkedIn video script about {request.topic} in the {request.industry} industry.
|
||||
|
||||
Requirements:
|
||||
- Tone: {request.tone}
|
||||
- Target audience: {request.target_audience or 'Industry professionals'}
|
||||
- Duration: {request.video_duration} seconds
|
||||
- Include captions: {request.include_captions}
|
||||
- Include thumbnail suggestions: {request.include_thumbnail_suggestions}
|
||||
- Make it engaging and informative
|
||||
|
||||
Structure: Hook, main content (divided into scenes), conclusion
|
||||
"""
|
||||
return prompt.strip()
|
||||
|
||||
def _build_comment_response_prompt(self, request) -> str:
|
||||
"""Build prompt for comment response generation."""
|
||||
prompt = f"""
|
||||
Generate a LinkedIn comment response to: "{request.original_comment}"
|
||||
|
||||
Context: {request.post_context}
|
||||
Industry: {request.industry}
|
||||
Tone: {request.tone}
|
||||
Response length: {request.response_length}
|
||||
Include questions: {request.include_questions}
|
||||
|
||||
Make the response engaging, professional, and add value to the conversation.
|
||||
"""
|
||||
return prompt.strip()
|
||||
raise Exception(f"Failed to generate grounded comment response: {str(e)}")
|
||||
|
||||
Reference in New Issue
Block a user