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
@@ -2,6 +2,7 @@
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Content Generator for LinkedIn Content Generation
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Handles the main content generation logic for posts and articles.
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Uses llm_text_gen for provider-agnostic LLM access (respects GPT_PROVIDER).
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"""
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from typing import Dict, Any, List, Optional
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@@ -21,6 +22,7 @@ from services.linkedin.content_generator_prompts import (
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CarouselGenerator,
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VideoScriptGenerator
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)
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from services.llm_providers.main_text_generation import llm_text_gen
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from services.persona_analysis_service import PersonaAnalysisService
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import time
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@@ -28,11 +30,9 @@ import time
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class ContentGenerator:
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"""Handles content generation for all LinkedIn content types."""
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def __init__(self, citation_manager=None, quality_analyzer=None, gemini_grounded=None, fallback_provider=None):
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def __init__(self, citation_manager=None, quality_analyzer=None):
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self.citation_manager = citation_manager
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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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# Persona caching
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self._persona_cache: Dict[str, Dict[str, Any]] = {}
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@@ -105,22 +105,24 @@ class ContentGenerator:
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del self._cache_timestamps[key]
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logger.info(f"Cleared persona cache for user {user_id}")
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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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transformed_sources = []
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for source in gemini_sources:
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transformed_source = ResearchSource(
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title=source.get('title', 'Unknown Source'),
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url=source.get('url', ''),
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content=f"Source from {source.get('title', 'Unknown')}",
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relevance_score=0.8, # Default relevance score
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credibility_score=0.7, # Default credibility score
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domain_authority=0.6, # Default domain authority
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source_type=source.get('type', 'web'),
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publication_date=datetime.now().strftime('%Y-%m-%d')
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)
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transformed_sources.append(transformed_source)
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return transformed_sources
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def _build_research_context(self, research_sources: List) -> str:
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"""Build research context string from research sources for prompt injection."""
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if not research_sources:
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return ""
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context_parts = ["\n\nRESEARCH CONTEXT (use this information to ground your content with facts and data):"]
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for i, source in enumerate(research_sources[:5], 1): # Limit to top 5 sources
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title = getattr(source, 'title', f'Source {i}')
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url = getattr(source, 'url', '')
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content = getattr(source, 'content', '')
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context_parts.append(f"\n{i}. {title}")
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if url:
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context_parts.append(f" URL: {url}")
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if content:
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context_parts.append(f" Key insight: {content[:300]}")
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context_parts.append("\nInstructions: Use the research above to include specific data points, statistics, and factual claims in your content. Cite sources where appropriate.")
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return "\n".join(context_parts)
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async def generate_post(
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self,
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@@ -155,21 +157,12 @@ class ContentGenerator:
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logger.info(f" - First research source: {research_sources[0] if research_sources else 'None'}")
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logger.info(f" - Research sources types: {[type(s) for s in research_sources[:3]]}")
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# Step 3: Add citations if requested - POST METHOD
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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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final_research_sources = research_sources # Default to passed research_sources
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final_research_sources = research_sources
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# Use sources and citations from content_result if available (from Gemini grounding)
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if content_result.get('citations') and content_result.get('sources'):
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logger.info(f"Using citations and sources from Gemini grounding: {len(content_result['citations'])} citations, {len(content_result['sources'])} sources")
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citations = content_result['citations']
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# Transform Gemini sources to ResearchSource format
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gemini_sources = self._transform_gemini_sources(content_result['sources'])
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source_list = self.citation_manager.generate_source_list(gemini_sources) if self.citation_manager else None
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# Use transformed sources for the response
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final_research_sources = gemini_sources
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elif request.include_citations and research_sources and self.citation_manager:
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if request.include_citations and research_sources and self.citation_manager:
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try:
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logger.info(f"Processing citations for content length: {len(content_result['content'])}")
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citations = self.citation_manager.extract_citations(content_result['content'])
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@@ -224,7 +217,7 @@ class ContentGenerator:
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data=post_content,
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research_sources=final_research_sources, # Use final_research_sources
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generation_metadata={
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'model_used': 'gemini-2.0-flash-001',
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'model_used': 'llm_text_gen',
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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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@@ -251,21 +244,12 @@ class ContentGenerator:
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try:
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start_time = datetime.now()
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# Step 3: Add citations if requested - ARTICLE METHOD
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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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final_research_sources = research_sources # Default to passed research_sources
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final_research_sources = research_sources
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# Use sources and citations from content_result if available (from Gemini grounding)
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if content_result.get('citations') and content_result.get('sources'):
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logger.info(f"Using citations and sources from Gemini grounding: {len(content_result['citations'])} citations, {len(content_result['sources'])} sources")
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citations = content_result['citations']
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# Transform Gemini sources to ResearchSource format
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gemini_sources = self._transform_gemini_sources(content_result['sources'])
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source_list = self.citation_manager.generate_source_list(gemini_sources) if self.citation_manager else None
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# Use transformed sources for the response
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final_research_sources = gemini_sources
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elif request.include_citations and research_sources and self.citation_manager:
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if request.include_citations and research_sources and self.citation_manager:
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try:
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citations = self.citation_manager.extract_citations(content_result['content'])
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source_list = self.citation_manager.generate_source_list(research_sources)
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@@ -317,7 +301,7 @@ class ContentGenerator:
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data=article_content,
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research_sources=final_research_sources, # Use final_research_sources
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generation_metadata={
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'model_used': 'gemini-2.0-flash-001',
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'model_used': 'llm_text_gen',
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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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@@ -386,7 +370,7 @@ class ContentGenerator:
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'alternative_responses': content_result.get('alternative_responses', []),
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'tone_analysis': content_result.get('tone_analysis'),
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'generation_metadata': {
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'model_used': 'gemini-2.0-flash-001',
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'model_used': 'llm_text_gen',
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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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@@ -402,19 +386,14 @@ class ContentGenerator:
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}
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# Grounded content generation methods
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async def generate_grounded_post_content(self, request, research_sources: List) -> Dict[str, Any]:
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"""Generate grounded post content using the enhanced Gemini provider with native grounding."""
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async def generate_grounded_post_content(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
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"""Generate post content using provider-agnostic llm_text_gen."""
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try:
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if not self.gemini_grounded:
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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 using persona if available (DB vs session override)
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user_id = int(getattr(request, "user_id", 0) or 0)
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persona_data = self._get_cached_persona_data(user_id, 'linkedin')
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# Build the prompt using persona if available
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uid = int(getattr(request, "user_id", 0) or 0)
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persona_data = self._get_cached_persona_data(uid, 'linkedin')
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if getattr(request, 'persona_override', None):
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try:
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# Merge shallowly: override core and platform adaptation parts
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override = request.persona_override
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if persona_data:
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core = persona_data.get('core_persona', {})
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@@ -431,61 +410,40 @@ class ContentGenerator:
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pass
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prompt = PostPromptBuilder.build_post_prompt(request, persona=persona_data)
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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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# Inject research context into prompt
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research_context = self._build_research_context(research_sources)
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if research_context:
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prompt += research_context
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# Generate content using provider-agnostic gateway
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raw_response = llm_text_gen(
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prompt=prompt,
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content_type="linkedin_post",
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temperature=0.7,
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max_tokens=request.max_length
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user_id=user_id,
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flow_type="linkedin_post",
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max_tokens=request.max_length,
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temperature=0.7
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)
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return result
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content_text = raw_response if isinstance(raw_response, str) else str(raw_response or "")
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return {
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'content': content_text,
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'sources': [],
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'citations': [],
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'grounding_enabled': bool(research_sources),
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'fallback_used': False
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}
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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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logger.info("Attempting fallback to standard content generation...")
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# Fallback to standard content generation without grounding
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try:
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if not self.fallback_provider:
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raise Exception("No fallback provider available")
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# Build a simpler prompt for fallback generation
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prompt = PostPromptBuilder.build_post_prompt(request)
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# Generate content using fallback provider (it's a dict with functions)
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if 'generate_text' in self.fallback_provider:
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result = await self.fallback_provider['generate_text'](
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prompt=prompt,
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temperature=0.7,
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max_tokens=request.max_length
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)
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else:
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raise Exception("Fallback provider doesn't have generate_text method")
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# Return result in the expected format
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return {
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'content': result.get('content', '') if isinstance(result, dict) else str(result),
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'sources': [],
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'citations': [],
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'grounding_enabled': False,
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'fallback_used': True
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}
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except Exception as fallback_error:
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logger.error(f"Fallback generation also failed: {str(fallback_error)}")
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raise Exception(f"Failed to generate content: {str(e)}. Fallback also failed: {str(fallback_error)}")
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logger.error(f"Error generating post content: {str(e)}")
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raise Exception(f"Failed to generate LinkedIn post: {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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async def generate_grounded_article_content(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
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"""Generate article content using provider-agnostic llm_text_gen."""
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try:
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if not self.gemini_grounded:
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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 using persona if available (DB vs session override)
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user_id = int(getattr(request, "user_id", 0) or 0)
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persona_data = self._get_cached_persona_data(user_id, 'linkedin')
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# Build the prompt using persona if available
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uid = int(getattr(request, "user_id", 0) or 0)
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persona_data = self._get_cached_persona_data(uid, 'linkedin')
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if getattr(request, 'persona_override', None):
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try:
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override = request.persona_override
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@@ -504,88 +462,129 @@ class ContentGenerator:
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pass
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prompt = ArticlePromptBuilder.build_article_prompt(request, persona=persona_data)
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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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# Inject research context into prompt
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research_context = self._build_research_context(research_sources)
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if research_context:
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prompt += research_context
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# Generate content using provider-agnostic gateway
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raw_response = llm_text_gen(
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prompt=prompt,
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content_type="linkedin_article",
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temperature=0.7,
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max_tokens=request.word_count * 10 # Approximate character count
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user_id=user_id,
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flow_type="linkedin_article",
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max_tokens=request.word_count * 10,
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temperature=0.7
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)
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return result
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content_text = raw_response if isinstance(raw_response, str) else str(raw_response or "")
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return {
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'content': content_text,
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'sources': [],
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'citations': [],
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'grounding_enabled': bool(research_sources),
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'fallback_used': False
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}
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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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raise Exception(f"Failed to generate grounded article content: {str(e)}")
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logger.error(f"Error generating article content: {str(e)}")
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raise Exception(f"Failed to generate LinkedIn article: {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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async def generate_grounded_carousel_content(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
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"""Generate carousel content using provider-agnostic llm_text_gen."""
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try:
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if not self.gemini_grounded:
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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 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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# Inject research context into prompt
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research_context = self._build_research_context(research_sources)
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if research_context:
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prompt += research_context
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# Generate content using provider-agnostic gateway
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raw_response = llm_text_gen(
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prompt=prompt,
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content_type="linkedin_carousel",
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temperature=0.7,
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max_tokens=2000
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user_id=user_id,
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flow_type="linkedin_carousel",
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max_tokens=2000,
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temperature=0.7
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)
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return result
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content_text = raw_response if isinstance(raw_response, str) else str(raw_response or "")
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return {
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'content': content_text,
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'sources': [],
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'citations': [],
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'grounding_enabled': bool(research_sources),
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'fallback_used': False
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}
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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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raise Exception(f"Failed to generate grounded carousel content: {str(e)}")
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logger.error(f"Error generating carousel content: {str(e)}")
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raise Exception(f"Failed to generate LinkedIn carousel: {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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async def generate_grounded_video_script_content(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
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"""Generate video script content using provider-agnostic llm_text_gen."""
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try:
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if not self.gemini_grounded:
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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 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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# Inject research context into prompt
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research_context = self._build_research_context(research_sources)
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if research_context:
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prompt += research_context
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# Generate content using provider-agnostic gateway
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raw_response = llm_text_gen(
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prompt=prompt,
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content_type="linkedin_video_script",
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temperature=0.7,
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max_tokens=1500
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user_id=user_id,
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flow_type="linkedin_video_script",
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max_tokens=1500,
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temperature=0.7
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)
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return result
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content_text = raw_response if isinstance(raw_response, str) else str(raw_response or "")
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return {
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'content': content_text,
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'sources': [],
|
||||
'citations': [],
|
||||
'grounding_enabled': bool(research_sources),
|
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'fallback_used': False
|
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}
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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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raise Exception(f"Failed to generate grounded video script content: {str(e)}")
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logger.error(f"Error generating video script content: {str(e)}")
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raise Exception(f"Failed to generate LinkedIn video script: {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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async def generate_grounded_comment_response(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
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"""Generate comment response using provider-agnostic llm_text_gen."""
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try:
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if not self.gemini_grounded:
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logger.error("Gemini Grounded Provider not available - cannot generate content without AI provider")
|
||||
raise Exception("Gemini Grounded Provider not available - cannot generate content without AI provider")
|
||||
|
||||
# Build the prompt for grounded generation using the new prompt builder
|
||||
prompt = CommentResponsePromptBuilder.build_comment_response_prompt(request)
|
||||
|
||||
# Generate grounded content using native Google Search grounding
|
||||
result = await self.gemini_grounded.generate_grounded_content(
|
||||
# Inject research context into prompt
|
||||
research_context = self._build_research_context(research_sources)
|
||||
if research_context:
|
||||
prompt += research_context
|
||||
|
||||
# Generate content using provider-agnostic gateway
|
||||
raw_response = llm_text_gen(
|
||||
prompt=prompt,
|
||||
content_type="linkedin_comment_response",
|
||||
temperature=0.7,
|
||||
max_tokens=2000
|
||||
user_id=user_id,
|
||||
flow_type="linkedin_comment_response",
|
||||
max_tokens=2000,
|
||||
temperature=0.7
|
||||
)
|
||||
|
||||
return result
|
||||
content_text = raw_response if isinstance(raw_response, str) else str(raw_response or "")
|
||||
|
||||
return {
|
||||
'content': content_text,
|
||||
'sources': [],
|
||||
'citations': [],
|
||||
'grounding_enabled': bool(research_sources),
|
||||
'fallback_used': False
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating grounded comment response: {str(e)}")
|
||||
raise Exception(f"Failed to generate grounded comment response: {str(e)}")
|
||||
logger.error(f"Error generating comment response: {str(e)}")
|
||||
raise Exception(f"Failed to generate LinkedIn comment response: {str(e)}")
|
||||
|
||||
Reference in New Issue
Block a user