63a0df2536
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
591 lines
26 KiB
Python
591 lines
26 KiB
Python
"""
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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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from datetime import datetime
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from loguru import logger
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from models.linkedin_models import (
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LinkedInPostRequest, LinkedInArticleRequest, LinkedInPostResponse, LinkedInArticleResponse,
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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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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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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):
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self.citation_manager = citation_manager
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self.quality_analyzer = quality_analyzer
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# Persona caching
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self._persona_cache: Dict[str, Dict[str, Any]] = {}
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self._cache_timestamps: Dict[str, float] = {}
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self._cache_duration = 300 # 5 minutes cache duration
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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 _get_cached_persona_data(self, user_id: int, platform: str) -> Optional[Dict[str, Any]]:
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"""
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Get persona data with caching for LinkedIn platform.
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Args:
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user_id: User ID to get persona for
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platform: Platform type (linkedin)
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Returns:
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Persona data or None if not available
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"""
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cache_key = f"{platform}_persona_{user_id}"
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current_time = time.time()
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# Check cache first
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if cache_key in self._persona_cache and cache_key in self._cache_timestamps:
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cache_age = current_time - self._cache_timestamps[cache_key]
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if cache_age < self._cache_duration:
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logger.debug(f"Using cached persona data for user {user_id} (age: {cache_age:.1f}s)")
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return self._persona_cache[cache_key]
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else:
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# Cache expired, remove it
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logger.debug(f"Cache expired for user {user_id}, refreshing...")
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del self._persona_cache[cache_key]
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del self._cache_timestamps[cache_key]
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# Fetch fresh data
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try:
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persona_service = PersonaAnalysisService()
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persona_data = persona_service.get_persona_for_platform(user_id, platform)
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# Cache the result
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if persona_data:
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self._persona_cache[cache_key] = persona_data
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self._cache_timestamps[cache_key] = current_time
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logger.debug(f"Cached persona data for user {user_id}")
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return persona_data
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except Exception as e:
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logger.warning(f"Could not load persona data for {platform} content generation: {e}")
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return None
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def _clear_persona_cache(self, user_id: int = None):
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"""
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Clear persona cache for a specific user or all users.
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Args:
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user_id: User ID to clear cache for, or None to clear all
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"""
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if user_id is None:
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self._persona_cache.clear()
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self._cache_timestamps.clear()
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logger.info("Cleared all persona cache")
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else:
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# Clear cache for all platforms for this user
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keys_to_remove = [key for key in self._persona_cache.keys() if key.endswith(f"_{user_id}")]
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for key in keys_to_remove:
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del self._persona_cache[key]
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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 _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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request: LinkedInPostRequest,
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research_sources: List,
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research_time: float,
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content_result: Dict[str, Any],
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grounding_enabled: bool
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) -> LinkedInPostResponse:
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"""Generate LinkedIn post with all processing steps."""
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try:
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start_time = datetime.now()
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# Debug: Log what we received
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logger.info(f"ContentGenerator.generate_post called with:")
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logger.info(f" - research_sources count: {len(research_sources) if research_sources else 0}")
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logger.info(f" - research_sources type: {type(research_sources)}")
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logger.info(f" - content_result keys: {list(content_result.keys()) if content_result else 'None'}")
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logger.info(f" - grounding_enabled: {grounding_enabled}")
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logger.info(f" - include_citations: {request.include_citations}")
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# Debug: Log content_result details
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if content_result:
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logger.info(f" - content_result has citations: {'citations' in content_result}")
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logger.info(f" - content_result has sources: {'sources' in content_result}")
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if 'citations' in content_result:
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logger.info(f" - citations count: {len(content_result['citations']) if content_result['citations'] else 0}")
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if 'sources' in content_result:
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logger.info(f" - sources count: {len(content_result['sources']) if content_result['sources'] else 0}")
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if research_sources:
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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
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citations = []
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source_list = None
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final_research_sources = research_sources
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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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logger.info(f"Extracted {len(citations)} citations from content")
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source_list = self.citation_manager.generate_source_list(research_sources)
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logger.info(f"Generated source list: {source_list[:200] if source_list else 'None'}")
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except Exception as e:
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logger.warning(f"Citation processing failed: {e}")
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else:
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logger.info(f"Citation processing skipped: include_citations={request.include_citations}, research_sources={len(research_sources) if research_sources else 0}, citation_manager={self.citation_manager is not 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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quality_handler = QualityHandler(self.quality_analyzer)
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quality_metrics = quality_handler.create_quality_metrics(
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content=content_result['content'],
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sources=final_research_sources, # Use final_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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post_content = PostContent(
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content=content_result['content'],
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character_count=len(content_result['content']),
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hashtags=content_result.get('hashtags', []),
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call_to_action=content_result.get('call_to_action'),
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engagement_prediction=content_result.get('engagement_prediction'),
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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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search_queries=content_result.get('search_queries', [])
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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(final_research_sources), # Use final_research_sources
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'citation_coverage': len(citations) / max(len(final_research_sources), 1) if final_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 LinkedInPostResponse(
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success=True,
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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': '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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},
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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 post: {str(e)}")
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return LinkedInPostResponse(
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success=False,
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error=f"Failed to generate LinkedIn post: {str(e)}"
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)
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async def generate_article(
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self,
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request: LinkedInArticleRequest,
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research_sources: List,
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research_time: float,
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content_result: Dict[str, Any],
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grounding_enabled: bool
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) -> LinkedInArticleResponse:
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"""Generate LinkedIn article 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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final_research_sources = research_sources
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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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except Exception as e:
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logger.warning(f"Citation processing failed: {e}")
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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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quality_handler = QualityHandler(self.quality_analyzer)
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quality_metrics = quality_handler.create_quality_metrics(
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content=content_result['content'],
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sources=final_research_sources, # Use final_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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article_content = ArticleContent(
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title=content_result['title'],
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content=content_result['content'],
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word_count=len(content_result['content'].split()),
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sections=content_result.get('sections', []),
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seo_metadata=content_result.get('seo_metadata'),
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image_suggestions=content_result.get('image_suggestions', []),
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reading_time=content_result.get('reading_time'),
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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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search_queries=content_result.get('search_queries', [])
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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(final_research_sources), # Use final_research_sources
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'citation_coverage': len(citations) / max(len(final_research_sources), 1) if final_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 LinkedInArticleResponse(
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success=True,
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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': '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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},
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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 article: {str(e)}")
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return LinkedInArticleResponse(
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success=False,
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error=f"Failed to generate LinkedIn article: {str(e)}"
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)
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async def generate_carousel(
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self,
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request,
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research_sources: List,
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research_time: float,
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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 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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request,
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research_sources: List,
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research_time: float,
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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 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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request,
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research_sources: List,
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research_time: float,
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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 comment response with all processing steps."""
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try:
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start_time = datetime.now()
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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': 0, # Comments typically don't have citations
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'quality_score': 0.8 # Default quality for comments
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} if grounding_enabled else None
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return {
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'success': True,
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'response': content_result['response'],
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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': '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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},
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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 comment response: {str(e)}")
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return {
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'success': False,
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'error': f"Failed to generate LinkedIn comment response: {str(e)}"
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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, 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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# 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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if persona_data:
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core = persona_data.get('core_persona', {})
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platform_adapt = persona_data.get('platform_adaptation', {})
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if 'core_persona' in override:
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core.update(override['core_persona'])
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if 'platform_adaptation' in override:
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platform_adapt.update(override['platform_adaptation'])
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persona_data['core_persona'] = core
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persona_data['platform_adaptation'] = platform_adapt
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else:
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persona_data = override
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except Exception:
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pass
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prompt = PostPromptBuilder.build_post_prompt(request, persona=persona_data)
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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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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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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 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, 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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# 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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if persona_data:
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core = persona_data.get('core_persona', {})
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platform_adapt = persona_data.get('platform_adaptation', {})
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if 'core_persona' in override:
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core.update(override['core_persona'])
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if 'platform_adaptation' in override:
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platform_adapt.update(override['platform_adaptation'])
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persona_data['core_persona'] = core
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persona_data['platform_adaptation'] = platform_adapt
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else:
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persona_data = override
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except Exception:
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pass
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prompt = ArticlePromptBuilder.build_article_prompt(request, persona=persona_data)
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# 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,
|
|
user_id=user_id,
|
|
flow_type="linkedin_article",
|
|
max_tokens=request.word_count * 10,
|
|
temperature=0.7
|
|
)
|
|
|
|
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 article content: {str(e)}")
|
|
raise Exception(f"Failed to generate LinkedIn article: {str(e)}")
|
|
|
|
async def generate_grounded_carousel_content(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
|
|
"""Generate carousel content using provider-agnostic llm_text_gen."""
|
|
try:
|
|
prompt = CarouselPromptBuilder.build_carousel_prompt(request)
|
|
|
|
# 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,
|
|
user_id=user_id,
|
|
flow_type="linkedin_carousel",
|
|
max_tokens=2000,
|
|
temperature=0.7
|
|
)
|
|
|
|
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 carousel content: {str(e)}")
|
|
raise Exception(f"Failed to generate LinkedIn carousel: {str(e)}")
|
|
|
|
async def generate_grounded_video_script_content(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
|
|
"""Generate video script content using provider-agnostic llm_text_gen."""
|
|
try:
|
|
prompt = VideoScriptPromptBuilder.build_video_script_prompt(request)
|
|
|
|
# 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,
|
|
user_id=user_id,
|
|
flow_type="linkedin_video_script",
|
|
max_tokens=1500,
|
|
temperature=0.7
|
|
)
|
|
|
|
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 video script content: {str(e)}")
|
|
raise Exception(f"Failed to generate LinkedIn video script: {str(e)}")
|
|
|
|
async def generate_grounded_comment_response(self, request, research_sources: List, user_id: str = None) -> Dict[str, Any]:
|
|
"""Generate comment response using provider-agnostic llm_text_gen."""
|
|
try:
|
|
prompt = CommentResponsePromptBuilder.build_comment_response_prompt(request)
|
|
|
|
# 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,
|
|
user_id=user_id,
|
|
flow_type="linkedin_comment_response",
|
|
max_tokens=2000,
|
|
temperature=0.7
|
|
)
|
|
|
|
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 comment response: {str(e)}")
|
|
raise Exception(f"Failed to generate LinkedIn comment response: {str(e)}")
|