Added citation and quality metrics to the content editor.
This commit is contained in:
ajaysi
@@ -2,6 +2,7 @@
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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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@@ -37,6 +38,14 @@ class SearchEngine(str, Enum):
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TAVILY = "tavily"
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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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@@ -48,8 +57,10 @@ 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.METAPHOR, description="Search engine for research")
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search_engine: SearchEngine = Field(default=SearchEngine.GOOGLE, 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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class Config:
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schema_extra = {
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@@ -63,8 +74,10 @@ class LinkedInPostRequest(BaseModel):
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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": "metaphor",
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"max_length": 2000
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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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@@ -79,8 +92,10 @@ 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.METAPHOR, description="Search engine for research")
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search_engine: SearchEngine = Field(default=SearchEngine.GOOGLE, 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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class Config:
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schema_extra = {
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@@ -93,124 +108,181 @@ class LinkedInArticleRequest(BaseModel):
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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": "metaphor",
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"word_count": 2000
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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 post generation."""
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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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slide_count: int = Field(default=8, description="Number of slides", ge=3, le=15)
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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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key_takeaways: Optional[List[str]] = Field(None, description="Key takeaways to include", max_items=10)
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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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visual_style: Optional[str] = Field("modern", description="Visual style preference")
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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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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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schema_extra = {
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"example": {
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"topic": "5 Ways to Improve Team Productivity",
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"industry": "Business Management",
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"slide_count": 8,
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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": "Team leaders and managers",
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"key_takeaways": ["Clear communication", "Goal setting", "Tool optimization"],
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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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"visual_style": "modern"
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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 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", min_length=3, max_length=200)
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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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video_length: int = Field(default=60, description="Target video length in seconds", ge=15, le=300)
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tone: LinkedInTone = Field(default=LinkedInTone.PROFESSIONAL, description="Tone of the video")
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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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key_messages: Optional[List[str]] = Field(None, description="Key messages to include", max_items=5)
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include_hook: bool = Field(default=True, description="Whether to include an attention-grabbing hook")
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include_captions: bool = Field(default=True, description="Whether to include caption suggestions")
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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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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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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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schema_extra = {
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"example": {
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"topic": "Quick tips for remote team management",
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"industry": "Human Resources",
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"video_length": 90,
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"tone": "conversational",
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"target_audience": "Remote team managers",
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"key_messages": ["Communication tools", "Regular check-ins", "Team building"],
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"include_hook": True,
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"include_captions": True
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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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"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_post: str = Field(..., description="Content of the original post", min_length=10, max_length=3000)
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comment: str = Field(..., description="Comment to respond to", min_length=1, max_length=1000)
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response_type: Literal["professional", "appreciative", "clarifying", "disagreement", "value_add"] = Field(
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default="professional", description="Type of response"
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)
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tone: LinkedInTone = Field(default=LinkedInTone.PROFESSIONAL, description="Tone of the response")
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include_question: bool = Field(default=False, description="Whether to include a follow-up question")
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brand_voice: Optional[str] = Field(None, description="Specific brand voice guidelines", max_length=500)
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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.GOOGLE, 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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schema_extra = {
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"example": {
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"original_post": "Just published an article about AI transformation in healthcare...",
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"comment": "Great insights! How do you see this affecting smaller healthcare providers?",
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"response_type": "value_add",
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"tone": "professional",
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"include_question": True,
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"brand_voice": "Expert but approachable, data-driven"
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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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"""Model for research source information."""
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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] = None
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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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"""Model for hashtag suggestions."""
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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] = None
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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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"""Model for image suggestions."""
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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] = None
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placement: Optional[str] = None
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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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# 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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"""Model for generated post content."""
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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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"""Model for generated article content."""
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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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@@ -218,43 +290,62 @@ class ArticleContent(BaseModel):
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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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"""Model for carousel slide content."""
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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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"""Model for generated carousel content."""
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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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"""Model for video script content."""
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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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"""Response model for LinkedIn post generation."""
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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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schema_extra = {
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@@ -268,55 +359,91 @@ class LinkedInPostResponse(BaseModel):
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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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"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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"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
|
||||
"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):
|
||||
"""Response model for LinkedIn article generation."""
|
||||
"""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):
|
||||
"""Response model for LinkedIn carousel generation."""
|
||||
"""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):
|
||||
"""Response model for LinkedIn video script generation."""
|
||||
"""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):
|
||||
"""Response model for LinkedIn comment response generation."""
|
||||
"""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
|
||||
error: Optional[str] = None
|
||||
grounding_status: Optional[Dict[str, Any]] = Field(None, description="Grounding operation status")
|
||||
Reference in New Issue
Block a user