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
@@ -36,6 +36,7 @@ class SearchEngine(str, Enum):
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METAPHOR = "metaphor"
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GOOGLE = "google"
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TAVILY = "tavily"
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EXA = "exa"
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class GroundingLevel(str, Enum):
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@@ -57,7 +58,7 @@ class LinkedInPostRequest(BaseModel):
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include_hashtags: bool = Field(default=True, description="Whether to include hashtags")
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include_call_to_action: bool = Field(default=True, description="Whether to include call to action")
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research_enabled: bool = Field(default=True, description="Whether to include research-backed content")
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search_engine: SearchEngine = Field(default=SearchEngine.GOOGLE, description="Search engine for research")
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search_engine: SearchEngine = Field(default=SearchEngine.EXA, description="Search engine for research")
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max_length: int = Field(default=3000, description="Maximum character count", ge=100, le=3000)
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grounding_level: GroundingLevel = Field(default=GroundingLevel.ENHANCED, description="Level of content grounding")
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include_citations: bool = Field(default=True, description="Whether to include inline citations")
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@@ -94,7 +95,7 @@ class LinkedInArticleRequest(BaseModel):
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include_images: bool = Field(default=True, description="Whether to generate image suggestions")
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seo_optimization: bool = Field(default=True, description="Whether to include SEO optimization")
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research_enabled: bool = Field(default=True, description="Whether to include research-backed content")
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search_engine: SearchEngine = Field(default=SearchEngine.GOOGLE, description="Search engine for research")
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search_engine: SearchEngine = Field(default=SearchEngine.EXA, description="Search engine for research")
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word_count: int = Field(default=1500, description="Target word count", ge=500, le=5000)
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grounding_level: GroundingLevel = Field(default=GroundingLevel.ENHANCED, description="Level of content grounding")
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include_citations: bool = Field(default=True, description="Whether to include inline citations")
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@@ -129,9 +130,11 @@ class LinkedInCarouselRequest(BaseModel):
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number_of_slides: int = Field(default=5, description="Number of slides", ge=3, le=10)
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include_cover_slide: bool = Field(default=True, description="Whether to include a cover slide")
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include_cta_slide: bool = Field(default=True, description="Whether to include a call-to-action slide")
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key_points: Optional[List[str]] = Field(None, description="Specific key points to cover", max_items=10)
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research_enabled: bool = Field(default=True, description="Whether to include research-backed content")
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search_engine: SearchEngine = Field(default=SearchEngine.GOOGLE, description="Search engine for research")
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search_engine: SearchEngine = Field(default=SearchEngine.EXA, description="Search engine for research")
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grounding_level: GroundingLevel = Field(default=GroundingLevel.ENHANCED, description="Level of content grounding")
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color_scheme: str = Field(default="professional", description="Color scheme for PDF rendering: professional, creative, industry, dark, minimal")
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include_citations: bool = Field(default=True, description="Whether to include inline citations")
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class Config:
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@@ -144,9 +147,11 @@ class LinkedInCarouselRequest(BaseModel):
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"number_of_slides": 6,
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"include_cover_slide": True,
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"include_cta_slide": True,
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"key_points": ["Remote collaboration tools", "Work-life balance", "Productivity metrics"],
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"research_enabled": True,
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"search_engine": "google",
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"grounding_level": "enhanced",
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"color_scheme": "professional",
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"include_citations": True
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}
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}
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@@ -161,8 +166,9 @@ class LinkedInVideoScriptRequest(BaseModel):
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video_duration: int = Field(default=60, description="Target video duration in seconds", ge=30, le=300)
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include_captions: bool = Field(default=True, description="Whether to include captions")
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include_thumbnail_suggestions: bool = Field(default=True, description="Whether to include thumbnail suggestions")
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key_points: Optional[List[str]] = Field(None, description="Specific key points to cover in the video", max_items=10)
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research_enabled: bool = Field(default=True, description="Whether to include research-backed content")
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search_engine: SearchEngine = Field(default=SearchEngine.GOOGLE, description="Search engine for research")
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search_engine: SearchEngine = Field(default=SearchEngine.EXA, description="Search engine for research")
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grounding_level: GroundingLevel = Field(default=GroundingLevel.ENHANCED, description="Level of content grounding")
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include_citations: bool = Field(default=True, description="Whether to include inline citations")
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@@ -176,6 +182,7 @@ class LinkedInVideoScriptRequest(BaseModel):
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"video_duration": 90,
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"include_captions": True,
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"include_thumbnail_suggestions": True,
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"key_points": ["Zero trust architecture", "Phishing prevention", "Incident response"],
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"research_enabled": True,
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"search_engine": "google",
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"grounding_level": "enhanced",
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@@ -193,7 +200,7 @@ class LinkedInCommentResponseRequest(BaseModel):
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response_length: str = Field(default="medium", description="Length of response: short, medium, long")
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include_questions: bool = Field(default=True, description="Whether to include engaging questions")
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research_enabled: bool = Field(default=False, description="Whether to include research-backed content")
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search_engine: SearchEngine = Field(default=SearchEngine.GOOGLE, description="Search engine for research")
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search_engine: SearchEngine = Field(default=SearchEngine.EXA, description="Search engine for research")
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grounding_level: GroundingLevel = Field(default=GroundingLevel.BASIC, description="Level of content grounding")
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class Config:
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@@ -451,4 +458,24 @@ class LinkedInCommentResponseResult(BaseModel):
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tone_analysis: Optional[Dict[str, Any]] = None
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generation_metadata: Dict[str, Any] = {}
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error: Optional[str] = None
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grounding_status: Optional[Dict[str, Any]] = Field(None, description="Grounding operation status")
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grounding_status: Optional[Dict[str, Any]] = Field(None, description="Grounding operation status")
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class LinkedInEditContentRequest(BaseModel):
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"""Request model for AI-powered LinkedIn content editing."""
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content: str = Field(..., description="Content to edit", min_length=1)
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edit_type: str = Field(..., description="Type of edit: professionalize, optimize_engagement, add_hashtags, adjust_tone, expand, condense, add_cta")
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industry: Optional[str] = Field(None, description="Industry context for the edit")
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tone: Optional[str] = Field(None, description="Target tone: professional, conversational, authoritative, educational, friendly")
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target_audience: Optional[str] = Field(None, description="Target audience for the content")
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parameters: Optional[Dict[str, Any]] = Field(None, description="Additional parameters specific to edit type")
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class LinkedInEditContentResponse(BaseModel):
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"""Response model for AI-powered LinkedIn content editing."""
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success: bool = True
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content: Optional[str] = None
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edit_type: str
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provider: Optional[str] = None
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model: Optional[str] = None
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error: Optional[str] = None
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