Base code
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Kunthawat Greethong
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backend/models/research_persona_models.py
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155
backend/models/research_persona_models.py
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"""
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Research Persona Models
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Pydantic models for AI-generated research personas.
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"""
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from typing import Dict, Any, List, Optional
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from pydantic import BaseModel, Field
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from datetime import datetime
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class ResearchPreset(BaseModel):
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"""Research preset configuration."""
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name: str
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keywords: str
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industry: str
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target_audience: str
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research_mode: str = Field(..., description="basic, comprehensive, or targeted")
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config: Dict[str, Any] = Field(default_factory=dict, description="Complete ResearchConfig object")
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description: Optional[str] = None
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icon: Optional[str] = None
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gradient: Optional[str] = None
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class ResearchPersona(BaseModel):
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"""AI-generated research persona providing personalized defaults and suggestions."""
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# Smart Defaults
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default_industry: str = Field(..., description="Default industry from onboarding data")
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default_target_audience: str = Field(..., description="Default target audience from onboarding data")
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default_research_mode: str = Field(..., description="basic, comprehensive, or targeted")
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default_provider: str = Field(..., description="google or exa")
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# Keyword Intelligence
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suggested_keywords: List[str] = Field(default_factory=list, description="8-12 relevant keywords")
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keyword_expansion_patterns: Dict[str, List[str]] = Field(
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default_factory=dict,
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description="Mapping of keywords to expanded, industry-specific terms"
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)
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# Domain & Source Intelligence
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suggested_exa_domains: List[str] = Field(
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default_factory=list,
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description="4-6 authoritative domains for the industry"
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)
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suggested_exa_category: Optional[str] = Field(
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None,
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description="Suggested Exa category based on industry"
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)
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suggested_exa_search_type: Optional[str] = Field(
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None,
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description="Suggested Exa search algorithm: auto, neural, keyword, fast, deep"
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)
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# Tavily Provider Intelligence
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suggested_tavily_topic: Optional[str] = Field(
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None,
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description="Suggested Tavily topic: general, news, finance"
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)
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suggested_tavily_search_depth: Optional[str] = Field(
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None,
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description="Suggested Tavily search depth: basic, advanced, fast, ultra-fast"
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)
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suggested_tavily_include_answer: Optional[str] = Field(
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None,
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description="Suggested Tavily answer type: false, basic, advanced"
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)
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suggested_tavily_time_range: Optional[str] = Field(
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None,
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description="Suggested Tavily time range: day, week, month, year"
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)
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suggested_tavily_raw_content_format: Optional[str] = Field(
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None,
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description="Suggested Tavily raw content format: false, markdown, text"
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)
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# Provider Selection Logic
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provider_recommendations: Dict[str, str] = Field(
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default_factory=dict,
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description="Provider recommendations by use case: {'trends': 'tavily', 'deep_research': 'exa', 'factual': 'google'}"
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)
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# Query Enhancement Intelligence
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research_angles: List[str] = Field(
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default_factory=list,
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description="5-8 alternative research angles/focuses"
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)
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query_enhancement_rules: Dict[str, str] = Field(
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default_factory=dict,
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description="Templates for improving vague user queries"
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)
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# Research History Insights
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recommended_presets: List[ResearchPreset] = Field(
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default_factory=list,
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description="3-5 personalized research preset templates"
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)
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# Research Preferences
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research_preferences: Dict[str, Any] = Field(
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default_factory=dict,
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description="Structured research preferences from onboarding"
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)
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# Metadata
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generated_at: Optional[str] = Field(None, description="ISO timestamp of generation")
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confidence_score: Optional[float] = Field(None, ge=0.0, le=1.0, description="Confidence score 0-1")
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version: Optional[str] = Field(None, description="Schema version")
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class Config:
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json_schema_extra = {
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"example": {
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"default_industry": "Healthcare",
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"default_target_audience": "Medical professionals and healthcare administrators",
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"default_research_mode": "comprehensive",
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"default_provider": "exa",
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"suggested_keywords": ["telemedicine", "patient care", "healthcare technology"],
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"keyword_expansion_patterns": {
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"AI": ["healthcare AI", "medical AI", "clinical AI"],
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"tools": ["medical devices", "clinical tools"]
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},
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"suggested_exa_domains": ["pubmed.gov", "nejm.org", "thelancet.com"],
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"suggested_exa_category": "research paper",
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"suggested_exa_search_type": "neural",
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"suggested_tavily_topic": "news",
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"suggested_tavily_search_depth": "advanced",
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"suggested_tavily_include_answer": "advanced",
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"suggested_tavily_time_range": "month",
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"suggested_tavily_raw_content_format": "markdown",
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"provider_recommendations": {
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"trends": "tavily",
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"deep_research": "exa",
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"factual": "google",
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"news": "tavily",
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"academic": "exa"
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},
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"research_angles": [
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"Compare telemedicine platforms",
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"Telemedicine ROI analysis",
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"Latest telemedicine trends"
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],
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"query_enhancement_rules": {
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"vague_ai": "Research: AI applications in Healthcare for Medical professionals",
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"vague_tools": "Compare top Healthcare tools"
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},
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"recommended_presets": [],
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"research_preferences": {
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"research_depth": "comprehensive",
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"content_types": ["blog", "article"]
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},
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"generated_at": "2024-01-01T00:00:00Z",
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"confidence_score": 0.85,
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"version": "1.0"
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}
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}
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