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
481 lines
24 KiB
Python
481 lines
24 KiB
Python
"""
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LinkedIn Content Generation Models for ALwrity
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This module defines the data models for LinkedIn content generation endpoints.
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Enhanced to support grounding capabilities with source integration and quality metrics.
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"""
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from pydantic import BaseModel, Field, validator
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from typing import List, Optional, Dict, Any, Literal
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from datetime import datetime
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from enum import Enum
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class LinkedInPostType(str, Enum):
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"""Types of LinkedIn posts."""
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PROFESSIONAL = "professional"
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THOUGHT_LEADERSHIP = "thought_leadership"
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INDUSTRY_NEWS = "industry_news"
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PERSONAL_STORY = "personal_story"
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COMPANY_UPDATE = "company_update"
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POLL = "poll"
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class LinkedInTone(str, Enum):
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"""LinkedIn content tone options."""
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PROFESSIONAL = "professional"
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CONVERSATIONAL = "conversational"
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AUTHORITATIVE = "authoritative"
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INSPIRATIONAL = "inspirational"
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EDUCATIONAL = "educational"
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FRIENDLY = "friendly"
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class SearchEngine(str, Enum):
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"""Available search engines for research."""
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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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"""Levels of content grounding."""
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NONE = "none"
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BASIC = "basic"
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ENHANCED = "enhanced"
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ENTERPRISE = "enterprise"
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class LinkedInPostRequest(BaseModel):
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"""Request model for LinkedIn post generation."""
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topic: str = Field(..., description="Main topic for the post", min_length=3, max_length=200)
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industry: str = Field(..., description="Target industry context", min_length=2, max_length=100)
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post_type: LinkedInPostType = Field(default=LinkedInPostType.PROFESSIONAL, description="Type of LinkedIn post")
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tone: LinkedInTone = Field(default=LinkedInTone.PROFESSIONAL, description="Tone of the post")
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target_audience: Optional[str] = Field(None, description="Specific target audience", max_length=200)
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key_points: Optional[List[str]] = Field(None, description="Key points to include", max_items=10)
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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.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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user_id: Optional[int] = Field(default=1, description="User id for persona lookup")
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persona_override: Optional[Dict[str, Any]] = Field(default=None, description="Session-only persona overrides to apply without saving")
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class Config:
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json_schema_extra = {
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"example": {
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"topic": "AI in healthcare transformation",
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"industry": "Healthcare",
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"post_type": "thought_leadership",
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"tone": "professional",
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"target_audience": "Healthcare executives and professionals",
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"key_points": ["AI diagnostics", "Patient outcomes", "Cost reduction"],
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"include_hashtags": True,
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"include_call_to_action": True,
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"research_enabled": True,
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"search_engine": "google",
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"max_length": 2000,
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"grounding_level": "enhanced",
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"include_citations": True
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}
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}
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class LinkedInArticleRequest(BaseModel):
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"""Request model for LinkedIn article generation."""
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topic: str = Field(..., description="Main topic for the article", min_length=3, max_length=200)
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industry: str = Field(..., description="Target industry context", min_length=2, max_length=100)
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tone: LinkedInTone = Field(default=LinkedInTone.PROFESSIONAL, description="Tone of the article")
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target_audience: Optional[str] = Field(None, description="Specific target audience", max_length=200)
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key_sections: Optional[List[str]] = Field(None, description="Key sections to include", max_items=10)
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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.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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user_id: Optional[int] = Field(default=1, description="User id for persona lookup")
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persona_override: Optional[Dict[str, Any]] = Field(default=None, description="Session-only persona overrides to apply without saving")
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class Config:
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json_schema_extra = {
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"example": {
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"topic": "Digital transformation in manufacturing",
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"industry": "Manufacturing",
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"tone": "professional",
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"target_audience": "Manufacturing leaders and technology professionals",
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"key_sections": ["Current challenges", "Technology solutions", "Implementation strategies"],
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"include_images": True,
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"seo_optimization": True,
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"research_enabled": True,
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"search_engine": "google",
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"word_count": 2000,
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"grounding_level": "enhanced",
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"include_citations": True
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}
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}
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class LinkedInCarouselRequest(BaseModel):
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"""Request model for LinkedIn carousel generation."""
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topic: str = Field(..., description="Main topic for the carousel", min_length=3, max_length=200)
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industry: str = Field(..., description="Target industry context", min_length=2, max_length=100)
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tone: LinkedInTone = Field(default=LinkedInTone.PROFESSIONAL, description="Tone of the carousel")
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target_audience: Optional[str] = Field(None, description="Specific target audience", max_length=200)
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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.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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json_schema_extra = {
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"example": {
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"topic": "Future of remote work",
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"industry": "Technology",
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"tone": "professional",
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"target_audience": "HR professionals and business leaders",
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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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class LinkedInVideoScriptRequest(BaseModel):
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"""Request model for LinkedIn video script generation."""
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topic: str = Field(..., description="Main topic for the video script", min_length=3, max_length=200)
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industry: str = Field(..., description="Target industry context", min_length=2, max_length=100)
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tone: LinkedInTone = Field(default=LinkedInTone.PROFESSIONAL, description="Tone of the video script")
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target_audience: Optional[str] = Field(None, description="Specific target audience", max_length=200)
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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.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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class Config:
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json_schema_extra = {
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"example": {
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"topic": "Cybersecurity best practices",
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"industry": "Technology",
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"tone": "educational",
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"target_audience": "IT professionals and business leaders",
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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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"include_citations": True
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}
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}
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class LinkedInCommentResponseRequest(BaseModel):
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"""Request model for LinkedIn comment response generation."""
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original_comment: str = Field(..., description="Original comment to respond to", min_length=10, max_length=1000)
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post_context: str = Field(..., description="Context of the post being commented on", min_length=10, max_length=500)
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industry: str = Field(..., description="Industry context", min_length=2, max_length=100)
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tone: LinkedInTone = Field(default=LinkedInTone.FRIENDLY, description="Tone of the response")
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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.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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json_schema_extra = {
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"example": {
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"original_comment": "Great insights on AI implementation!",
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"post_context": "Post about AI transformation in healthcare",
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"industry": "Healthcare",
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"tone": "friendly",
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"response_length": "medium",
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"include_questions": True,
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"research_enabled": False,
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"search_engine": "google",
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"grounding_level": "basic"
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}
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}
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# Enhanced Research Source Model
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class ResearchSource(BaseModel):
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"""Enhanced model for research source information with grounding capabilities."""
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title: str
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url: str
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content: str
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relevance_score: Optional[float] = Field(None, description="Relevance score (0.0-1.0)")
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credibility_score: Optional[float] = Field(None, description="Credibility score (0.0-1.0)")
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domain_authority: Optional[float] = Field(None, description="Domain authority score (0.0-1.0)")
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source_type: Optional[str] = Field(None, description="Type of source (academic, business_news, etc.)")
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publication_date: Optional[str] = Field(None, description="Publication date if available")
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raw_result: Optional[Dict[str, Any]] = Field(None, description="Raw search result data")
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# Enhanced Hashtag Suggestion Model
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class HashtagSuggestion(BaseModel):
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"""Enhanced model for hashtag suggestions."""
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hashtag: str
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category: str
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popularity_score: Optional[float] = Field(None, description="Popularity score (0.0-1.0)")
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relevance_score: Optional[float] = Field(None, description="Relevance to topic (0.0-1.0)")
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industry_alignment: Optional[float] = Field(None, description="Industry alignment score (0.0-1.0)")
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# Enhanced Image Suggestion Model
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class ImageSuggestion(BaseModel):
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"""Enhanced model for image suggestions."""
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description: str
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alt_text: str
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style: Optional[str] = Field(None, description="Visual style description")
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placement: Optional[str] = Field(None, description="Suggested placement in content")
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relevance_score: Optional[float] = Field(None, description="Relevance to content (0.0-1.0)")
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# New Quality Metrics Model
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class ContentQualityMetrics(BaseModel):
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"""Model for content quality assessment metrics."""
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overall_score: float = Field(..., description="Overall quality score (0.0-1.0)")
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factual_accuracy: float = Field(..., description="Factual accuracy score (0.0-1.0)")
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source_verification: float = Field(..., description="Source verification score (0.0-1.0)")
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professional_tone: float = Field(..., description="Professional tone score (0.0-1.0)")
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industry_relevance: float = Field(..., description="Industry relevance score (0.0-1.0)")
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citation_coverage: float = Field(..., description="Citation coverage score (0.0-1.0)")
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content_length: int = Field(..., description="Content length in characters")
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word_count: int = Field(..., description="Word count")
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analysis_timestamp: str = Field(..., description="Timestamp of quality analysis")
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recommendations: Optional[List[str]] = Field(default_factory=list, description="List of improvement recommendations")
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# New Citation Model
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class Citation(BaseModel):
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"""Model for inline citations in content."""
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type: str = Field(..., description="Type of citation (inline, footnote, etc.)")
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reference: str = Field(..., description="Citation reference (e.g., 'Source 1')")
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position: Optional[int] = Field(None, description="Position in content")
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source_index: Optional[int] = Field(None, description="Index of source in research_sources")
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# Enhanced Post Content Model
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class PostContent(BaseModel):
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"""Enhanced model for generated post content with grounding capabilities."""
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content: str
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character_count: int
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hashtags: List[HashtagSuggestion]
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call_to_action: Optional[str] = None
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engagement_prediction: Optional[Dict[str, Any]] = None
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citations: List[Citation] = Field(default_factory=list, description="Inline citations")
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source_list: Optional[str] = Field(None, description="Formatted source list")
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quality_metrics: Optional[ContentQualityMetrics] = Field(None, description="Content quality metrics")
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grounding_enabled: bool = Field(default=False, description="Whether grounding was used")
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search_queries: Optional[List[str]] = Field(default_factory=list, description="Search queries used for research")
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# Enhanced Article Content Model
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class ArticleContent(BaseModel):
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"""Enhanced model for generated article content with grounding capabilities."""
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title: str
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content: str
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word_count: int
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sections: List[Dict[str, str]]
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seo_metadata: Optional[Dict[str, Any]] = None
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image_suggestions: List[ImageSuggestion]
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reading_time: Optional[int] = None
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citations: List[Citation] = Field(default_factory=list, description="Inline citations")
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source_list: Optional[str] = Field(None, description="Formatted source list")
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quality_metrics: Optional[ContentQualityMetrics] = Field(None, description="Content quality metrics")
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grounding_enabled: bool = Field(default=False, description="Whether grounding was used")
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search_queries: Optional[List[str]] = Field(default_factory=list, description="Search queries used for research")
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# Enhanced Carousel Slide Model
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class CarouselSlide(BaseModel):
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"""Enhanced model for carousel slide content."""
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slide_number: int
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title: str
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content: str
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visual_elements: List[str]
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design_notes: Optional[str] = None
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citations: List[Citation] = Field(default_factory=list, description="Inline citations for this slide")
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# Enhanced Carousel Content Model
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class CarouselContent(BaseModel):
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"""Enhanced model for generated carousel content with grounding capabilities."""
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title: str
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slides: List[CarouselSlide]
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cover_slide: Optional[CarouselSlide] = None
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cta_slide: Optional[CarouselSlide] = None
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design_guidelines: Dict[str, str]
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citations: List[Citation] = Field(default_factory=list, description="Overall citations")
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source_list: Optional[str] = Field(None, description="Formatted source list")
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quality_metrics: Optional[ContentQualityMetrics] = Field(None, description="Content quality metrics")
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grounding_enabled: bool = Field(default=False, description="Whether grounding was used")
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# Enhanced Video Script Model
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class VideoScript(BaseModel):
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"""Enhanced model for video script content with grounding capabilities."""
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hook: str
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main_content: List[Dict[str, str]] # scene_number, content, duration, visual_notes
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conclusion: str
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captions: Optional[List[str]] = None
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thumbnail_suggestions: List[str]
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video_description: str
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citations: List[Citation] = Field(default_factory=list, description="Inline citations")
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source_list: Optional[str] = Field(None, description="Formatted source list")
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quality_metrics: Optional[ContentQualityMetrics] = Field(None, description="Content quality metrics")
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grounding_enabled: bool = Field(default=False, description="Whether grounding was used")
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# Enhanced LinkedIn Post Response Model
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class LinkedInPostResponse(BaseModel):
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"""Enhanced response model for LinkedIn post generation with grounding capabilities."""
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success: bool = True
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data: Optional[PostContent] = None
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research_sources: List[ResearchSource] = []
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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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class Config:
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json_schema_extra = {
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"example": {
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"success": True,
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"data": {
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"content": "🚀 AI is revolutionizing healthcare...",
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"character_count": 1250,
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"hashtags": [
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{"hashtag": "#AIinHealthcare", "category": "industry", "popularity_score": 0.9},
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{"hashtag": "#DigitalTransformation", "category": "general", "popularity_score": 0.8}
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],
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"call_to_action": "What's your experience with AI in healthcare? Share in the comments!",
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"engagement_prediction": {"estimated_likes": 120, "estimated_comments": 15},
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"citations": [
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{"type": "inline", "reference": "Source 1", "position": 45}
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],
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"source_list": "**Sources:**\n1. **AI in Healthcare: Current Trends**\n - URL: [https://example.com/ai-healthcare](https://example.com/ai-healthcare)",
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"quality_metrics": {
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"overall_score": 0.85,
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"factual_accuracy": 0.9,
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"source_verification": 0.8,
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"professional_tone": 0.9,
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"industry_relevance": 0.85,
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"citation_coverage": 0.8,
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"content_length": 1250,
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"word_count": 180,
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"analysis_timestamp": "2025-01-15T10:30:00Z"
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},
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"grounding_enabled": True
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},
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"research_sources": [
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{
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"title": "AI in Healthcare: Current Trends",
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"url": "https://example.com/ai-healthcare",
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"content": "Summary of AI healthcare trends...",
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"relevance_score": 0.95,
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"credibility_score": 0.85,
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|
"domain_authority": 0.9,
|
|
"source_type": "business_news"
|
|
}
|
|
],
|
|
"generation_metadata": {
|
|
"model_used": "gemini-2.0-flash-001",
|
|
"generation_time": 3.2,
|
|
"research_time": 5.1,
|
|
"grounding_enabled": True
|
|
},
|
|
"grounding_status": {
|
|
"status": "success",
|
|
"sources_used": 3,
|
|
"citation_coverage": 0.8,
|
|
"quality_score": 0.85
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
# Enhanced LinkedIn Article Response Model
|
|
class LinkedInArticleResponse(BaseModel):
|
|
"""Enhanced response model for LinkedIn article generation with grounding capabilities."""
|
|
success: bool = True
|
|
data: Optional[ArticleContent] = None
|
|
research_sources: List[ResearchSource] = []
|
|
generation_metadata: Dict[str, Any] = {}
|
|
error: Optional[str] = None
|
|
grounding_status: Optional[Dict[str, Any]] = Field(None, description="Grounding operation status")
|
|
|
|
|
|
# Enhanced LinkedIn Carousel Response Model
|
|
class LinkedInCarouselResponse(BaseModel):
|
|
"""Enhanced response model for LinkedIn carousel generation with grounding capabilities."""
|
|
success: bool = True
|
|
data: Optional[CarouselContent] = None
|
|
research_sources: List[ResearchSource] = []
|
|
generation_metadata: Dict[str, Any] = {}
|
|
error: Optional[str] = None
|
|
grounding_status: Optional[Dict[str, Any]] = Field(None, description="Grounding operation status")
|
|
|
|
|
|
# Enhanced LinkedIn Video Script Response Model
|
|
class LinkedInVideoScriptResponse(BaseModel):
|
|
"""Enhanced response model for LinkedIn video script generation with grounding capabilities."""
|
|
success: bool = True
|
|
data: Optional[VideoScript] = None
|
|
research_sources: List[ResearchSource] = []
|
|
generation_metadata: Dict[str, Any] = {}
|
|
error: Optional[str] = None
|
|
grounding_status: Optional[Dict[str, Any]] = Field(None, description="Grounding operation status")
|
|
|
|
|
|
# Enhanced LinkedIn Comment Response Result Model
|
|
class LinkedInCommentResponseResult(BaseModel):
|
|
"""Enhanced response model for LinkedIn comment response generation with grounding capabilities."""
|
|
success: bool = True
|
|
response: Optional[str] = None
|
|
alternative_responses: List[str] = []
|
|
tone_analysis: Optional[Dict[str, Any]] = None
|
|
generation_metadata: Dict[str, Any] = {}
|
|
error: Optional[str] = None
|
|
grounding_status: Optional[Dict[str, Any]] = Field(None, description="Grounding operation status")
|
|
|
|
|
|
class LinkedInEditContentRequest(BaseModel):
|
|
"""Request model for AI-powered LinkedIn content editing."""
|
|
content: str = Field(..., description="Content to edit", min_length=1)
|
|
edit_type: str = Field(..., description="Type of edit: professionalize, optimize_engagement, add_hashtags, adjust_tone, expand, condense, add_cta")
|
|
industry: Optional[str] = Field(None, description="Industry context for the edit")
|
|
tone: Optional[str] = Field(None, description="Target tone: professional, conversational, authoritative, educational, friendly")
|
|
target_audience: Optional[str] = Field(None, description="Target audience for the content")
|
|
parameters: Optional[Dict[str, Any]] = Field(None, description="Additional parameters specific to edit type")
|
|
|
|
|
|
class LinkedInEditContentResponse(BaseModel):
|
|
"""Response model for AI-powered LinkedIn content editing."""
|
|
success: bool = True
|
|
content: Optional[str] = None
|
|
edit_type: str
|
|
provider: Optional[str] = None
|
|
model: Optional[str] = None
|
|
error: Optional[str] = None |