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
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ajaysi
@@ -1,8 +1,9 @@
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"""
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LinkedIn Image Generator Service
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This service generates LinkedIn-optimized images using Google's Gemini API.
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It provides professional, business-appropriate imagery for LinkedIn content.
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This service generates LinkedIn-optimized images using the common
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llm_providers infrastructure. It provides professional, business-appropriate
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imagery for LinkedIn content.
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"""
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import os
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@@ -17,6 +18,7 @@ from io import BytesIO
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# Import existing infrastructure
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from ...onboarding.api_key_manager import APIKeyManager
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from ...llm_providers.main_image_generation import generate_image
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from ...llm_providers.main_image_editing import edit_image as common_edit_image
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# Set up logging
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logger = logging.getLogger(__name__)
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@@ -24,9 +26,9 @@ logger = logging.getLogger(__name__)
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class LinkedInImageGenerator:
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"""
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Handles LinkedIn-optimized image generation using Gemini API.
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Handles LinkedIn-optimized image generation using common infrastructure.
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This service integrates with the existing Gemini provider infrastructure
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This service integrates with the llm_providers image generation system
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and provides LinkedIn-specific image optimization, quality assurance,
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and professional business aesthetics.
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"""
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@@ -36,10 +38,9 @@ class LinkedInImageGenerator:
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Initialize the LinkedIn Image Generator.
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Args:
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api_key_manager: API key manager for Gemini authentication
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api_key_manager: API key manager for authentication
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"""
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self.api_key_manager = api_key_manager or APIKeyManager()
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self.model = "gemini-2.5-flash-image-preview"
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self.default_aspect_ratio = "1:1" # LinkedIn post optimal ratio
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self.max_retries = 3
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@@ -55,16 +56,18 @@ class LinkedInImageGenerator:
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prompt: str,
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content_context: Dict[str, Any],
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aspect_ratio: str = "1:1",
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style_preference: str = "professional"
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style_preference: str = "professional",
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user_id: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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Generate LinkedIn-optimized image using Gemini API.
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Generate LinkedIn-optimized image using AI provider.
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Args:
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prompt: User's image generation prompt
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content_context: LinkedIn content context (topic, industry, content_type)
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aspect_ratio: Image aspect ratio (1:1, 16:9, 4:3)
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aspect_ratio: Image aspect ratio (1:1, 16:9, 4:3, 1.91:1, 1:1.25)
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style_preference: Style preference (professional, creative, industry-specific)
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user_id: User ID for tenant provider resolution
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Returns:
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Dict containing generation result, image data, and metadata
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@@ -78,8 +81,8 @@ class LinkedInImageGenerator:
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prompt, content_context, style_preference, aspect_ratio
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)
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# Generate image using existing Gemini infrastructure
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generation_result = await self._generate_with_gemini(enhanced_prompt, aspect_ratio)
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# Generate image using tenant-aware provider selection
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generation_result = await self._generate_with_provider(enhanced_prompt, aspect_ratio, user_id)
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if not generation_result.get('success'):
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return {
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@@ -108,7 +111,7 @@ class LinkedInImageGenerator:
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'aspect_ratio': aspect_ratio,
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'content_context': content_context,
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'generation_time': generation_time,
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'model_used': self.model,
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'model_used': generation_result.get('model'),
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'image_format': processed_image['format'],
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'image_size': processed_image['size'],
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'resolution': processed_image['resolution']
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@@ -131,17 +134,19 @@ class LinkedInImageGenerator:
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async def edit_image(
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self,
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base_image: bytes,
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input_image_bytes: bytes,
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edit_prompt: str,
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content_context: Dict[str, Any]
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content_context: Dict[str, Any],
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user_id: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Edit existing image using Gemini's conversational editing capabilities.
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Edit existing image using unified image editing infrastructure.
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Args:
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base_image: Base image data in bytes
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input_image_bytes: Input image bytes to edit
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edit_prompt: Description of desired edits
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content_context: LinkedIn content context for optimization
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user_id: User ID for tenant provider resolution and subscription checks
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Returns:
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Dict containing edited image result and metadata
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@@ -155,18 +160,46 @@ class LinkedInImageGenerator:
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edit_prompt, content_context
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)
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# Use Gemini's image editing capabilities
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# Note: This will be implemented when Gemini's image editing is fully available
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# For now, we'll return a placeholder implementation
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# Use unified image editing system.
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# common_edit_image() handles: provider resolution, pre-flight validation,
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# generation, and usage tracking — all via user_id.
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result = common_edit_image(
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input_image_bytes=input_image_bytes,
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prompt=enhanced_edit_prompt,
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user_id=user_id,
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)
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return {
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'success': False,
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'error': 'Image editing not yet implemented - coming in next Gemini API update',
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'generation_time': (datetime.now() - start_time).total_seconds()
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}
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if result and result.image_bytes:
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generation_time = (datetime.now() - start_time).total_seconds()
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logger.info(
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"LinkedIn image edited successfully via provider=%s model=%s in %.2fs",
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result.provider, result.model, generation_time,
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)
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return {
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'success': True,
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'image_data': result.image_bytes,
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'image_url': None, # not using URL-based retrieval
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'width': result.width,
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'height': result.height,
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'provider': result.provider,
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'model': result.model,
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'metadata': {
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'original_prompt': edit_prompt,
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'enhanced_prompt': enhanced_edit_prompt,
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'generation_time': generation_time,
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'content_context': content_context,
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},
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}
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else:
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logger.warning("LinkedIn image editing returned no result")
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return {
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'success': False,
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'error': 'Image editing returned no result',
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'generation_time': (datetime.now() - start_time).total_seconds(),
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}
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except Exception as e:
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logger.error(f"Error in LinkedIn image editing: {str(e)}")
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logger.error(f"Error in LinkedIn image editing: {str(e)}", exc_info=True)
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return {
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'success': False,
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'error': f"Image editing failed: {str(e)}",
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@@ -268,13 +301,16 @@ class LinkedInImageGenerator:
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return enhanced_edit_prompt
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async def _generate_with_gemini(self, prompt: str, aspect_ratio: str) -> Dict[str, Any]:
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async def _generate_with_provider(self, prompt: str, aspect_ratio: str, user_id: Optional[str] = None) -> Dict[str, Any]:
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"""
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Generate image using unified image generation infrastructure.
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Provider resolution, pre-flight validation, and usage tracking
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are all handled by generate_image() from main_image_generation.
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Args:
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prompt: Enhanced prompt for image generation
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aspect_ratio: Desired aspect ratio
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user_id: User ID for tenant provider resolution and subscription checks
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Returns:
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Generation result from image generation provider
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@@ -285,26 +321,31 @@ class LinkedInImageGenerator:
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"1:1": (1024, 1024),
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"16:9": (1920, 1080),
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"4:3": (1366, 1024),
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"9:16": (1080, 1920), # Portrait for stories
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"9:16": (1080, 1920),
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"1.91:1": (1200, 627), # LinkedIn recommended landscape
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"1:1.25": (1080, 1350), # LinkedIn recommended portrait
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}
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width, height = aspect_map.get(aspect_ratio, (1024, 1024))
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# Use unified image generation system (defaults to provider based on GPT_PROVIDER)
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# Delegate to unified image generation system.
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# Generate_image() handles: provider resolution, pre-flight validation,
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# model auto-detection, generation, and usage tracking.
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# We do NOT pass explicit provider or model — let generate_image() resolve
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# them from tenant config and user defaults.
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result = generate_image(
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prompt=prompt,
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options={
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"provider": "gemini", # LinkedIn uses Gemini by default
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"model": self.model if hasattr(self, 'model') else None,
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"width": width,
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"height": height,
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}
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},
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user_id=user_id
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)
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if result and result.image_bytes:
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return {
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'success': True,
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'image_data': result.image_bytes,
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'image_path': None, # No file path, using bytes directly
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'image_path': None,
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'width': result.width,
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'height': result.height,
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'provider': result.provider,
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@@ -315,7 +356,7 @@ class LinkedInImageGenerator:
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'success': False,
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'error': 'Image generation returned no result'
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}
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except Exception as e:
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logger.error(f"Error in image generation: {str(e)}")
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return {
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@@ -487,6 +528,9 @@ class LinkedInImageGenerator:
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(1.6, 1.8), # 16:9 (landscape)
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(0.7, 0.8), # 4:3 (portrait)
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(1.2, 1.4), # 5:4 (landscape)
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(1.85, 2.0), # 1.91:1 (LinkedIn recommended landscape)
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(0.6, 0.72), # 1:1.25 (LinkedIn recommended portrait, ~0.8)
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(0.65, 0.85), # 1:1.25 broader match
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]
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for min_ratio, max_ratio in suitable_ratios:
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