feat: LinkedIn LLM alignment - Phase 1-3 complete
Phase 1: Dead Code Cleanup - Remove GeminiGroundedProvider import and property from linkedin_service.py - Remove fallback_provider property (gemini_provider imports) - Fix routers/linkedin.py edit endpoint to use llm_text_gen - Delete dead LinkedInImageEditor class - Remove dead _transform_gemini_sources from content_generator.py Phase 2: Research Infrastructure Alignment - Add user_id to _conduct_research() for pre-flight validation - Add validate_exa_research_operations() before Exa/Tavily calls - Pass user_id to provider.simple_search() for usage tracking - Inject research content into LLM prompts via _build_research_context() - Fix Google engine path to fallback to Exa - Add Exa → Tavily fallback on research failure Phase 3: Cosmetic Cleanup - Rename _generate_prompts_with_gemini → _generate_prompts_with_llm - Rename _build_gemini_prompt → _build_image_prompt - Rename _parse_gemini_response → _parse_llm_response - Remove all Gemini references from LinkedIn code (0 remaining) - Update docstrings and log messages Additional: - Research caching using existing ResearchCache - Shared ExaContentResearchProvider in services/research/ - Persona service uses llm_text_gen instead of gemini_structured_json_response - LinkedInWriter.tsx ChatMessage → ChatMsg type mapping fix - RegisterLinkedInActionsEnhanced.tsx content_format_rules typing fix
This commit is contained in:
ajaysi
@@ -1,8 +1,9 @@
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"""
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LinkedIn Content Generation Service for ALwrity
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This service generates various types of LinkedIn content with enhanced grounding capabilities.
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Integrated with Google Search, Gemini Grounded Provider, and quality analysis.
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This service generates various types of LinkedIn content with provider-agnostic
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LLM access via llm_text_gen. Research is handled by Exa/Tavily through the
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common research infrastructure.
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"""
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import asyncio
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@@ -21,57 +22,44 @@ from models.linkedin_models import (
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HashtagSuggestion, ImageSuggestion, Citation, ContentQualityMetrics,
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GroundingLevel
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)
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from services.research import GoogleSearchService
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from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
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from services.citation import CitationManager
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from services.quality import ContentQualityAnalyzer
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class LinkedInService:
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"""
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Enhanced LinkedIn content generation service with grounding capabilities.
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LinkedIn content generation service with provider-agnostic LLM access.
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This service integrates real research, grounded content generation,
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citation management, and quality analysis for enterprise-grade content.
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Uses llm_text_gen for text generation (respects GPT_PROVIDER).
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Uses Exa/Tavily for research via common infrastructure.
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"""
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def __init__(self):
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"""Initialize the LinkedIn service with all required components."""
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# Google Search Service not used - removed to avoid false warnings
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self.google_search = None
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try:
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self.gemini_grounded = GeminiGroundedProvider()
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logger.info("✅ Gemini Grounded Provider initialized")
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except Exception as e:
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logger.warning(f"⚠️ Gemini Grounded Provider not available: {e}")
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self.gemini_grounded = None
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try:
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self.citation_manager = CitationManager()
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logger.info("✅ Citation Manager initialized")
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except Exception as e:
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logger.warning(f"⚠️ Citation Manager not available: {e}")
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self.citation_manager = None
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try:
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self.quality_analyzer = ContentQualityAnalyzer()
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logger.info("✅ Content Quality Analyzer initialized")
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except Exception as e:
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logger.warning(f"⚠️ Content Quality Analyzer not available: {e}")
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self.quality_analyzer = None
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# Initialize fallback provider for non-grounded content
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try:
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from services.llm_providers.gemini_provider import gemini_structured_json_response, gemini_text_response
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self.fallback_provider = {
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'generate_structured_json': gemini_structured_json_response,
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'generate_text': gemini_text_response
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}
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logger.info("✅ Fallback Gemini provider initialized")
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except ImportError as e:
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logger.warning(f"⚠️ Fallback Gemini provider not available: {e}")
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self.fallback_provider = None
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"""Initialize the LinkedIn service with lazy provider initialization."""
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self._citation_manager = None
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self._quality_analyzer = None
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@property
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def citation_manager(self):
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if self._citation_manager is None:
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try:
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self._citation_manager = CitationManager()
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logger.info("✅ Citation Manager initialized")
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except Exception as e:
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logger.warning(f"⚠️ Citation Manager not available: {e}")
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self._citation_manager = None
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return self._citation_manager
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@property
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def quality_analyzer(self):
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if self._quality_analyzer is None:
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try:
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self._quality_analyzer = ContentQualityAnalyzer()
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logger.info("✅ Content Quality Analyzer initialized")
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except Exception as e:
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logger.warning(f"⚠️ Content Quality Analyzer not available: {e}")
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self._quality_analyzer = None
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return self._quality_analyzer
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async def generate_linkedin_post(self, request: LinkedInPostRequest) -> LinkedInPostResponse:
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"""
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@@ -94,8 +82,9 @@ class LinkedInService:
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# Step 1: Conduct research if enabled
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from services.linkedin.research_handler import ResearchHandler
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research_handler = ResearchHandler(self)
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user_id = str(getattr(request, 'user_id', '') or '')
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research_sources, research_time = await research_handler.conduct_research(
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request, request.research_enabled, request.search_engine, 10
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request, request.research_enabled, request.search_engine, 10, user_id=user_id
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)
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# Step 2: Generate content based on grounding level
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@@ -105,15 +94,14 @@ class LinkedInService:
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from services.linkedin.content_generator import ContentGenerator
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content_generator = ContentGenerator(
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self.citation_manager,
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self.quality_analyzer,
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self.gemini_grounded,
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self.fallback_provider
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self.quality_analyzer
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)
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if grounding_enabled:
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content_result = await content_generator.generate_grounded_post_content(
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request=request,
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research_sources=research_sources
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research_sources=research_sources,
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user_id=str(getattr(request, 'user_id', ''))
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)
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else:
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logger.error("Grounding not enabled, Error generating LinkedIn post")
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@@ -152,8 +140,9 @@ class LinkedInService:
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# Step 1: Conduct research if enabled
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from services.linkedin.research_handler import ResearchHandler
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research_handler = ResearchHandler(self)
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user_id = str(getattr(request, 'user_id', '') or '')
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research_sources, research_time = await research_handler.conduct_research(
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request, request.research_enabled, request.search_engine, 15
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request, request.research_enabled, request.search_engine, 15, user_id=user_id
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)
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# Step 2: Generate content based on grounding level
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@@ -163,15 +152,14 @@ class LinkedInService:
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from services.linkedin.content_generator import ContentGenerator
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content_generator = ContentGenerator(
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self.citation_manager,
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self.quality_analyzer,
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self.gemini_grounded,
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self.fallback_provider
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self.quality_analyzer
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)
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if grounding_enabled:
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content_result = await content_generator.generate_grounded_article_content(
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request=request,
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research_sources=research_sources
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research_sources=research_sources,
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user_id=str(getattr(request, 'user_id', ''))
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)
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else:
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logger.error("Grounding not enabled - cannot generate LinkedIn article without AI provider")
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@@ -210,8 +198,9 @@ class LinkedInService:
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# Step 1: Conduct research if enabled
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from services.linkedin.research_handler import ResearchHandler
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research_handler = ResearchHandler(self)
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user_id = str(getattr(request, 'user_id', '') or '')
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research_sources, research_time = await research_handler.conduct_research(
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request, request.research_enabled, request.search_engine, 12
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request, request.research_enabled, request.search_engine, 12, user_id=user_id
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)
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# Step 2: Generate content based on grounding level
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@@ -221,15 +210,14 @@ class LinkedInService:
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from services.linkedin.content_generator import ContentGenerator
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content_generator = ContentGenerator(
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self.citation_manager,
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self.quality_analyzer,
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self.gemini_grounded,
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self.fallback_provider
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self.quality_analyzer
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)
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if grounding_enabled:
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content_result = await content_generator.generate_grounded_carousel_content(
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request=request,
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research_sources=research_sources
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research_sources=research_sources,
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user_id=str(getattr(request, 'user_id', ''))
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)
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else:
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logger.error("Grounding not enabled - cannot generate LinkedIn carousel without AI provider")
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@@ -303,8 +291,9 @@ class LinkedInService:
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# Step 1: Conduct research if enabled
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from services.linkedin.research_handler import ResearchHandler
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research_handler = ResearchHandler(self)
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user_id = str(getattr(request, 'user_id', '') or '')
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research_sources, research_time = await research_handler.conduct_research(
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request, request.research_enabled, request.search_engine, 8
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request, request.research_enabled, request.search_engine, 8, user_id=user_id
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)
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# Step 2: Generate content based on grounding level
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@@ -314,15 +303,14 @@ class LinkedInService:
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from services.linkedin.content_generator import ContentGenerator
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content_generator = ContentGenerator(
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self.citation_manager,
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self.quality_analyzer,
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self.gemini_grounded,
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self.fallback_provider
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self.quality_analyzer
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)
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if grounding_enabled:
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content_result = await content_generator.generate_grounded_video_script_content(
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request=request,
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research_sources=research_sources
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research_sources=research_sources,
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user_id=str(getattr(request, 'user_id', ''))
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)
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else:
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logger.error("Grounding not enabled - cannot generate LinkedIn video script without AI provider")
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@@ -387,8 +375,9 @@ class LinkedInService:
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# Step 1: Conduct research if enabled
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from services.linkedin.research_handler import ResearchHandler
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research_handler = ResearchHandler(self)
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user_id = str(getattr(request, 'user_id', '') or '')
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research_sources, research_time = await research_handler.conduct_research(
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request, request.research_enabled, request.search_engine, 5
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request, request.research_enabled, request.search_engine, 5, user_id=user_id
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)
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# Step 2: Generate response based on grounding level
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@@ -398,15 +387,14 @@ class LinkedInService:
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from services.linkedin.content_generator import ContentGenerator
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content_generator = ContentGenerator(
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self.citation_manager,
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self.quality_analyzer,
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self.gemini_grounded,
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self.fallback_provider
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self.quality_analyzer
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)
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if grounding_enabled:
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response_result = await content_generator.generate_grounded_comment_response(
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request=request,
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research_sources=research_sources
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research_sources=research_sources,
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user_id=str(getattr(request, 'user_id', ''))
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)
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else:
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logger.error("Grounding not enabled - cannot generate LinkedIn comment response without AI provider")
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@@ -423,20 +411,13 @@ class LinkedInService:
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)
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if result['success']:
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# Convert to LinkedInCommentResponseResult
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from models.linkedin_models import CommentResponse
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comment_response = CommentResponse(
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response=result['response'],
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alternative_responses=result.get('alternative_responses', []),
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tone_analysis=result.get('tone_analysis')
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)
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return LinkedInCommentResponseResult(
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success=True,
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data=comment_response,
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research_sources=result['research_sources'],
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generation_metadata=result['generation_metadata'],
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grounding_status=result['grounding_status']
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response=result['response'],
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alternative_responses=result.get('alternative_responses', []),
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tone_analysis=result.get('tone_analysis'),
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generation_metadata=result.get('generation_metadata', {}),
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grounding_status=result.get('grounding_status')
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)
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else:
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return LinkedInCommentResponseResult(
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@@ -451,35 +432,187 @@ class LinkedInService:
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error=f"Failed to generate LinkedIn comment response: {str(e)}"
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)
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async def _conduct_research(self, topic: str, industry: str, search_engine: str, max_results: int = 10) -> List[ResearchSource]:
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async def _conduct_research(self, topic: str, industry: str, search_engine: str, max_results: int = 10, user_id: str = None) -> List[ResearchSource]:
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"""
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Use native Google Search grounding instead of custom search.
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The Gemini API handles search automatically when the google_search tool is enabled.
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Conduct research using the configured search engine with caching.
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For Exa: delegates to ExaResearchProvider.simple_search() with pre-flight validation
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For Tavily: delegates to TavilyService.search() with pre-flight validation
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For Google/unknown: falls back to Exa if available
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Args:
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topic: Research topic
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industry: Target industry
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search_engine: Search engine to use (google uses native grounding)
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search_engine: Search engine to use (exa, tavily)
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max_results: Maximum number of results to return
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user_id: User ID for subscription pre-flight validation and usage tracking
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Returns:
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List of research sources (empty for google - sources come from grounding metadata)
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List of research sources
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"""
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from services.cache.research_cache import research_cache
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search_engine_lower = search_engine.lower().strip()
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# Default to Exa if Google or unknown engine specified
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if search_engine_lower in ("google", ""):
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logger.info(f"Search engine '{search_engine}' not supported for direct research, defaulting to Exa")
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search_engine_lower = "exa"
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# Check cache first
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cached_result = research_cache.get_cached_result(
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keywords=[topic],
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industry=industry,
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target_audience="linkedin"
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)
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if cached_result:
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logger.info(f"Returning cached research result for topic: {topic[:50]}")
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# Convert cached dict back to ResearchSource objects
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sources = []
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for r in cached_result:
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sources.append(ResearchSource(
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title=r.get('title', 'Untitled'),
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url=r.get('url', ''),
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content=r.get('content', '')[:500],
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relevance_score=r.get('relevance_score', 0.5),
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credibility_score=r.get('credibility_score', 0.5),
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source_type=r.get('source_type', 'web'),
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publication_date=r.get('publication_date')
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))
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return sources
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try:
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# Debug: Log the search engine value received
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logger.info(f"Received search engine: '{search_engine}' (type: {type(search_engine)})")
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# Pre-flight validation if user_id provided
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if user_id:
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try:
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from services.subscription.preflight_validator import validate_exa_research_operations
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from services.database import get_session_for_user
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from services.subscription import PricingService
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import os
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db_val = get_session_for_user(user_id)
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if db_val:
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try:
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pricing_service = PricingService(db_val)
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gpt_provider = os.getenv("GPT_PROVIDER", "google")
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validate_exa_research_operations(pricing_service, user_id, gpt_provider)
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finally:
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db_val.close()
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except Exception as preflight_err:
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logger.warning(f"Research pre-flight validation failed: {preflight_err}")
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# Continue anyway - don't block research for pre-flight issues
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if search_engine_lower == "exa":
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from services.research import get_exa_content_provider
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try:
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provider = get_exa_content_provider()
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except RuntimeError:
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logger.warning("Exa API key not configured, falling back to Tavily")
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provider = None
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if provider:
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try:
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results = await provider.simple_search(
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query=f"{topic} {industry}",
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num_results=max_results,
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user_id=user_id
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)
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sources = []
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for r in results:
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sources.append(ResearchSource(
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title=r.get('title', 'Untitled'),
|
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url=r.get('url', ''),
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content=r.get('text', '')[:500],
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||||
relevance_score=r.get('score', 0.5),
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credibility_score=r.get('score', 0.5),
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source_type='web',
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publication_date=r.get('publishedDate')
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||||
))
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||||
|
||||
# Cache the results
|
||||
cache_data = [
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||||
{
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||||
'title': s.title,
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'url': s.url,
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||||
'content': s.content,
|
||||
'relevance_score': s.relevance_score,
|
||||
'credibility_score': s.credibility_score,
|
||||
'source_type': s.source_type,
|
||||
'publication_date': s.publication_date
|
||||
}
|
||||
for s in sources
|
||||
]
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||||
research_cache.cache_result(
|
||||
keywords=[topic],
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||||
industry=industry,
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||||
target_audience="linkedin",
|
||||
result=cache_data
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||||
)
|
||||
|
||||
logger.info(f"Exa research returned {len(sources)} sources for topic: {topic[:50]}")
|
||||
return sources
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||||
except Exception as exa_err:
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||||
logger.warning(f"Exa research failed ({exa_err}), falling back to Tavily")
|
||||
|
||||
# Fallback to Tavily
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||||
search_engine_lower = "tavily"
|
||||
|
||||
elif search_engine_lower == "tavily":
|
||||
from services.research.tavily_service import TavilyService
|
||||
|
||||
tavily_service = TavilyService()
|
||||
if not tavily_service.enabled:
|
||||
logger.warning("Tavily API key not configured, skipping Tavily research")
|
||||
return []
|
||||
|
||||
result = await tavily_service.search(
|
||||
query=f"{topic} {industry}",
|
||||
max_results=max_results
|
||||
)
|
||||
|
||||
raw_results = result.get('results', []) if isinstance(result, dict) else []
|
||||
sources = []
|
||||
for r in raw_results:
|
||||
sources.append(ResearchSource(
|
||||
title=r.get('title', 'Untitled'),
|
||||
url=r.get('url', ''),
|
||||
content=r.get('content', '')[:500],
|
||||
relevance_score=r.get('score', r.get('relevance_score', 0.5)),
|
||||
credibility_score=r.get('relevance_score', 0.5),
|
||||
source_type='web',
|
||||
publication_date=r.get('published_date')
|
||||
))
|
||||
|
||||
# Cache the results
|
||||
cache_data = [
|
||||
{
|
||||
'title': s.title,
|
||||
'url': s.url,
|
||||
'content': s.content,
|
||||
'relevance_score': s.relevance_score,
|
||||
'credibility_score': s.credibility_score,
|
||||
'source_type': s.source_type,
|
||||
'publication_date': s.publication_date
|
||||
}
|
||||
for s in sources
|
||||
]
|
||||
research_cache.cache_result(
|
||||
keywords=[topic],
|
||||
industry=industry,
|
||||
target_audience="linkedin",
|
||||
result=cache_data
|
||||
)
|
||||
|
||||
logger.info(f"Tavily research returned {len(sources)} sources for topic: {topic[:50]}")
|
||||
return sources
|
||||
|
||||
# Handle both enum value 'google' and enum name 'GOOGLE'
|
||||
if search_engine.lower() == "google":
|
||||
# No need for manual search - Gemini handles it automatically with native grounding
|
||||
logger.info("Using native Google Search grounding via Gemini API - no manual search needed")
|
||||
return [] # Return empty list - sources will come from grounding metadata
|
||||
else:
|
||||
# Fallback to basic research for other search engines
|
||||
logger.error(f"Search engine {search_engine} not fully implemented, using fallback")
|
||||
raise Exception(f"Search engine {search_engine} not fully implemented, using fallback")
|
||||
logger.warning(f"Unknown search engine '{search_engine}', no research performed")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error conducting research: {str(e)}")
|
||||
# Fallback to basic research
|
||||
raise Exception(f"Error conducting research: {str(e)}")
|
||||
logger.error(f"Research failed for engine {search_engine}: {e}")
|
||||
return []
|
||||
|
||||
Reference in New Issue
Block a user