Added video studio router and endpoints. Added research router and endpoints. Added youtube router and endpoints. Added onboarding utils router and endpoints. Added onboarding utils service. Added onboarding utils models. Added onboarding utils routes. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils.
This commit is contained in:
ajaysi
355
backend/models/research_intent_models.py
Normal file
355
backend/models/research_intent_models.py
Normal file
@@ -0,0 +1,355 @@
|
||||
"""
|
||||
Research Intent Models
|
||||
|
||||
Pydantic models for understanding user research intent.
|
||||
These models capture what the user actually wants to accomplish from their research,
|
||||
enabling targeted query generation and intent-aware result analysis.
|
||||
|
||||
Author: ALwrity Team
|
||||
Version: 1.0
|
||||
"""
|
||||
|
||||
from enum import Enum
|
||||
from typing import Dict, Any, List, Optional, Union
|
||||
from pydantic import BaseModel, Field
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class ResearchPurpose(str, Enum):
|
||||
"""Why is the user researching?"""
|
||||
LEARN = "learn" # Understand a topic for personal knowledge
|
||||
CREATE_CONTENT = "create_content" # Write article/blog/podcast/video
|
||||
MAKE_DECISION = "make_decision" # Choose between options
|
||||
COMPARE = "compare" # Compare alternatives/competitors
|
||||
SOLVE_PROBLEM = "solve_problem" # Find solution to a problem
|
||||
FIND_DATA = "find_data" # Get statistics/facts/citations
|
||||
EXPLORE_TRENDS = "explore_trends" # Understand market/industry trends
|
||||
VALIDATE = "validate" # Verify claims/information
|
||||
GENERATE_IDEAS = "generate_ideas" # Brainstorm content ideas
|
||||
|
||||
|
||||
class ContentOutput(str, Enum):
|
||||
"""What content type will be created from this research?"""
|
||||
BLOG = "blog"
|
||||
PODCAST = "podcast"
|
||||
VIDEO = "video"
|
||||
SOCIAL_POST = "social_post"
|
||||
NEWSLETTER = "newsletter"
|
||||
PRESENTATION = "presentation"
|
||||
REPORT = "report"
|
||||
WHITEPAPER = "whitepaper"
|
||||
EMAIL = "email"
|
||||
GENERAL = "general" # No specific output
|
||||
|
||||
|
||||
class ExpectedDeliverable(str, Enum):
|
||||
"""What specific outputs the user expects from research."""
|
||||
KEY_STATISTICS = "key_statistics" # Numbers, data points, percentages
|
||||
EXPERT_QUOTES = "expert_quotes" # Authoritative statements
|
||||
CASE_STUDIES = "case_studies" # Real examples and success stories
|
||||
COMPARISONS = "comparisons" # Side-by-side analysis
|
||||
TRENDS = "trends" # Market/industry trends
|
||||
BEST_PRACTICES = "best_practices" # Recommendations and guidelines
|
||||
STEP_BY_STEP = "step_by_step" # Process/how-to instructions
|
||||
PROS_CONS = "pros_cons" # Advantages/disadvantages
|
||||
DEFINITIONS = "definitions" # Clear explanations of concepts
|
||||
CITATIONS = "citations" # Authoritative sources
|
||||
EXAMPLES = "examples" # Concrete examples
|
||||
PREDICTIONS = "predictions" # Future outlook
|
||||
|
||||
|
||||
class ResearchDepthLevel(str, Enum):
|
||||
"""How deep the research should go."""
|
||||
OVERVIEW = "overview" # Quick summary, surface level
|
||||
DETAILED = "detailed" # In-depth analysis
|
||||
EXPERT = "expert" # Comprehensive, expert-level research
|
||||
|
||||
|
||||
class InputType(str, Enum):
|
||||
"""Type of user input detected."""
|
||||
KEYWORDS = "keywords" # Simple keywords: "AI healthcare 2025"
|
||||
QUESTION = "question" # A question: "What are the best AI tools?"
|
||||
GOAL = "goal" # Goal statement: "I need to write a blog about..."
|
||||
MIXED = "mixed" # Combination of above
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Structured Deliverable Models
|
||||
# ============================================================================
|
||||
|
||||
class StatisticWithCitation(BaseModel):
|
||||
"""A statistic with full attribution."""
|
||||
statistic: str = Field(..., description="The full statistical statement")
|
||||
value: Optional[str] = Field(None, description="The numeric value (e.g., '72%')")
|
||||
context: str = Field(..., description="Context of when/where this was measured")
|
||||
source: str = Field(..., description="Source name/publication")
|
||||
url: str = Field(..., description="Source URL")
|
||||
credibility: float = Field(0.8, ge=0.0, le=1.0, description="Credibility score 0-1")
|
||||
recency: Optional[str] = Field(None, description="How recent the data is")
|
||||
|
||||
|
||||
class ExpertQuote(BaseModel):
|
||||
"""A quote from an authoritative source."""
|
||||
quote: str = Field(..., description="The actual quote")
|
||||
speaker: str = Field(..., description="Name of the speaker")
|
||||
title: Optional[str] = Field(None, description="Title/role of the speaker")
|
||||
organization: Optional[str] = Field(None, description="Organization/company")
|
||||
context: Optional[str] = Field(None, description="Context of the quote")
|
||||
source: str = Field(..., description="Source name")
|
||||
url: str = Field(..., description="Source URL")
|
||||
|
||||
|
||||
class CaseStudySummary(BaseModel):
|
||||
"""Summary of a case study."""
|
||||
title: str = Field(..., description="Case study title")
|
||||
organization: str = Field(..., description="Organization featured")
|
||||
challenge: str = Field(..., description="The challenge/problem faced")
|
||||
solution: str = Field(..., description="The solution implemented")
|
||||
outcome: str = Field(..., description="The results achieved")
|
||||
key_metrics: List[str] = Field(default_factory=list, description="Key metrics/numbers")
|
||||
source: str = Field(..., description="Source name")
|
||||
url: str = Field(..., description="Source URL")
|
||||
|
||||
|
||||
class TrendAnalysis(BaseModel):
|
||||
"""Analysis of a trend."""
|
||||
trend: str = Field(..., description="The trend description")
|
||||
direction: str = Field(..., description="growing, declining, emerging, stable")
|
||||
evidence: List[str] = Field(default_factory=list, description="Supporting evidence")
|
||||
impact: Optional[str] = Field(None, description="Potential impact")
|
||||
timeline: Optional[str] = Field(None, description="Timeline of the trend")
|
||||
sources: List[str] = Field(default_factory=list, description="Source URLs")
|
||||
|
||||
|
||||
class ComparisonItem(BaseModel):
|
||||
"""An item in a comparison."""
|
||||
name: str
|
||||
description: Optional[str] = None
|
||||
pros: List[str] = Field(default_factory=list)
|
||||
cons: List[str] = Field(default_factory=list)
|
||||
features: Dict[str, str] = Field(default_factory=dict)
|
||||
rating: Optional[float] = None
|
||||
source: Optional[str] = None
|
||||
|
||||
|
||||
class ComparisonTable(BaseModel):
|
||||
"""Comparison between options."""
|
||||
title: str = Field(..., description="Comparison title")
|
||||
criteria: List[str] = Field(default_factory=list, description="Comparison criteria")
|
||||
items: List[ComparisonItem] = Field(default_factory=list, description="Items being compared")
|
||||
winner: Optional[str] = Field(None, description="Recommended option if applicable")
|
||||
verdict: Optional[str] = Field(None, description="Summary verdict")
|
||||
|
||||
|
||||
class ProsCons(BaseModel):
|
||||
"""Pros and cons analysis."""
|
||||
subject: str = Field(..., description="What is being analyzed")
|
||||
pros: List[str] = Field(default_factory=list, description="Advantages")
|
||||
cons: List[str] = Field(default_factory=list, description="Disadvantages")
|
||||
balanced_verdict: str = Field(..., description="Balanced conclusion")
|
||||
|
||||
|
||||
class SourceWithRelevance(BaseModel):
|
||||
"""A source with relevance information."""
|
||||
title: str
|
||||
url: str
|
||||
excerpt: Optional[str] = None
|
||||
relevance_score: float = Field(0.8, ge=0.0, le=1.0)
|
||||
relevance_reason: Optional[str] = None
|
||||
content_type: Optional[str] = None # article, research paper, news, etc.
|
||||
published_date: Optional[str] = None
|
||||
credibility_score: float = Field(0.8, ge=0.0, le=1.0)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Intent Models
|
||||
# ============================================================================
|
||||
|
||||
class ResearchIntent(BaseModel):
|
||||
"""
|
||||
What the user actually wants from their research.
|
||||
This is inferred from user input + research persona.
|
||||
"""
|
||||
|
||||
# Core understanding
|
||||
primary_question: str = Field(..., description="The main question to answer")
|
||||
secondary_questions: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Related questions that should be answered"
|
||||
)
|
||||
|
||||
# Purpose classification
|
||||
purpose: ResearchPurpose = Field(
|
||||
ResearchPurpose.LEARN,
|
||||
description="Why the user is researching"
|
||||
)
|
||||
content_output: ContentOutput = Field(
|
||||
ContentOutput.GENERAL,
|
||||
description="What content type will be created"
|
||||
)
|
||||
|
||||
# What they need from results
|
||||
expected_deliverables: List[ExpectedDeliverable] = Field(
|
||||
default_factory=list,
|
||||
description="Specific outputs the user expects"
|
||||
)
|
||||
|
||||
# Depth and focus
|
||||
depth: ResearchDepthLevel = Field(
|
||||
ResearchDepthLevel.DETAILED,
|
||||
description="How deep the research should go"
|
||||
)
|
||||
focus_areas: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Specific aspects to focus on"
|
||||
)
|
||||
|
||||
# Constraints
|
||||
perspective: Optional[str] = Field(
|
||||
None,
|
||||
description="Perspective to research from (e.g., 'hospital administrator')"
|
||||
)
|
||||
time_sensitivity: Optional[str] = Field(
|
||||
None,
|
||||
description="Time constraint: 'real_time', 'recent', 'historical', 'evergreen'"
|
||||
)
|
||||
|
||||
# Detected input type
|
||||
input_type: InputType = Field(
|
||||
InputType.KEYWORDS,
|
||||
description="Type of user input detected"
|
||||
)
|
||||
|
||||
# Original user input (for reference)
|
||||
original_input: str = Field(..., description="The original user input")
|
||||
|
||||
# Confidence in inference
|
||||
confidence: float = Field(
|
||||
0.8,
|
||||
ge=0.0,
|
||||
le=1.0,
|
||||
description="Confidence in the intent inference"
|
||||
)
|
||||
needs_clarification: bool = Field(
|
||||
False,
|
||||
description="True if AI is uncertain and needs user clarification"
|
||||
)
|
||||
clarifying_questions: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Questions to ask user if uncertain"
|
||||
)
|
||||
|
||||
class Config:
|
||||
use_enum_values = True
|
||||
|
||||
|
||||
class ResearchQuery(BaseModel):
|
||||
"""A targeted research query with purpose."""
|
||||
query: str = Field(..., description="The search query")
|
||||
purpose: ExpectedDeliverable = Field(..., description="What this query targets")
|
||||
provider: str = Field("exa", description="Preferred provider: exa, tavily, google")
|
||||
priority: int = Field(1, ge=1, le=5, description="Priority 1-5, higher = more important")
|
||||
expected_results: str = Field(..., description="What we expect to find with this query")
|
||||
|
||||
|
||||
class IntentInferenceRequest(BaseModel):
|
||||
"""Request to infer research intent from user input."""
|
||||
user_input: str = Field(..., description="User's keywords, question, or goal")
|
||||
keywords: List[str] = Field(default_factory=list, description="Extracted keywords")
|
||||
use_persona: bool = Field(True, description="Use research persona for context")
|
||||
use_competitor_data: bool = Field(True, description="Use competitor data for context")
|
||||
|
||||
|
||||
class IntentInferenceResponse(BaseModel):
|
||||
"""Response from intent inference."""
|
||||
success: bool = True
|
||||
intent: ResearchIntent
|
||||
analysis_summary: str = Field(..., description="AI's understanding of user intent")
|
||||
suggested_queries: List[ResearchQuery] = Field(
|
||||
default_factory=list,
|
||||
description="Generated research queries based on intent"
|
||||
)
|
||||
suggested_keywords: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Enhanced/expanded keywords"
|
||||
)
|
||||
suggested_angles: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Research angles to explore"
|
||||
)
|
||||
quick_options: List[Dict[str, Any]] = Field(
|
||||
default_factory=list,
|
||||
description="Quick options for user to confirm/modify intent"
|
||||
)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Intent-Driven Research Result
|
||||
# ============================================================================
|
||||
|
||||
class IntentDrivenResearchResult(BaseModel):
|
||||
"""
|
||||
Research results organized by what user needs.
|
||||
This is the final output after intent-aware analysis.
|
||||
"""
|
||||
|
||||
success: bool = True
|
||||
|
||||
# Direct answers
|
||||
primary_answer: str = Field(..., description="Direct answer to primary question")
|
||||
secondary_answers: Dict[str, str] = Field(
|
||||
default_factory=dict,
|
||||
description="Answers to secondary questions (question → answer)"
|
||||
)
|
||||
|
||||
# Deliverables (populated based on user's expected_deliverables)
|
||||
statistics: List[StatisticWithCitation] = Field(default_factory=list)
|
||||
expert_quotes: List[ExpertQuote] = Field(default_factory=list)
|
||||
case_studies: List[CaseStudySummary] = Field(default_factory=list)
|
||||
comparisons: List[ComparisonTable] = Field(default_factory=list)
|
||||
trends: List[TrendAnalysis] = Field(default_factory=list)
|
||||
best_practices: List[str] = Field(default_factory=list)
|
||||
step_by_step: List[str] = Field(default_factory=list)
|
||||
pros_cons: Optional[ProsCons] = None
|
||||
definitions: Dict[str, str] = Field(
|
||||
default_factory=dict,
|
||||
description="Term → definition mappings"
|
||||
)
|
||||
examples: List[str] = Field(default_factory=list)
|
||||
predictions: List[str] = Field(default_factory=list)
|
||||
|
||||
# Content-ready outputs
|
||||
executive_summary: str = Field("", description="2-3 sentence summary")
|
||||
key_takeaways: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="5-7 key bullet points"
|
||||
)
|
||||
suggested_outline: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Suggested content outline if creating content"
|
||||
)
|
||||
|
||||
# Supporting data
|
||||
sources: List[SourceWithRelevance] = Field(default_factory=list)
|
||||
raw_content: Optional[str] = Field(None, description="Raw content for further processing")
|
||||
|
||||
# Research quality metadata
|
||||
confidence: float = Field(0.8, ge=0.0, le=1.0)
|
||||
gaps_identified: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="What we couldn't find"
|
||||
)
|
||||
follow_up_queries: List[str] = Field(
|
||||
default_factory=list,
|
||||
description="Suggested additional research"
|
||||
)
|
||||
|
||||
# Original intent for reference
|
||||
original_intent: Optional[ResearchIntent] = None
|
||||
|
||||
# Error handling
|
||||
error_message: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
use_enum_values = True
|
||||
|
||||
@@ -39,13 +39,45 @@ class ResearchPersona(BaseModel):
|
||||
|
||||
# Domain & Source Intelligence
|
||||
suggested_exa_domains: List[str] = Field(
|
||||
default_factory=list,
|
||||
default_factory=list,
|
||||
description="4-6 authoritative domains for the industry"
|
||||
)
|
||||
suggested_exa_category: Optional[str] = Field(
|
||||
None,
|
||||
None,
|
||||
description="Suggested Exa category based on industry"
|
||||
)
|
||||
suggested_exa_search_type: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Exa search algorithm: auto, neural, keyword, fast, deep"
|
||||
)
|
||||
|
||||
# Tavily Provider Intelligence
|
||||
suggested_tavily_topic: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily topic: general, news, finance"
|
||||
)
|
||||
suggested_tavily_search_depth: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily search depth: basic, advanced, fast, ultra-fast"
|
||||
)
|
||||
suggested_tavily_include_answer: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily answer type: false, basic, advanced"
|
||||
)
|
||||
suggested_tavily_time_range: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily time range: day, week, month, year"
|
||||
)
|
||||
suggested_tavily_raw_content_format: Optional[str] = Field(
|
||||
None,
|
||||
description="Suggested Tavily raw content format: false, markdown, text"
|
||||
)
|
||||
|
||||
# Provider Selection Logic
|
||||
provider_recommendations: Dict[str, str] = Field(
|
||||
default_factory=dict,
|
||||
description="Provider recommendations by use case: {'trends': 'tavily', 'deep_research': 'exa', 'factual': 'google'}"
|
||||
)
|
||||
|
||||
# Query Enhancement Intelligence
|
||||
research_angles: List[str] = Field(
|
||||
@@ -88,6 +120,19 @@ class ResearchPersona(BaseModel):
|
||||
},
|
||||
"suggested_exa_domains": ["pubmed.gov", "nejm.org", "thelancet.com"],
|
||||
"suggested_exa_category": "research paper",
|
||||
"suggested_exa_search_type": "neural",
|
||||
"suggested_tavily_topic": "news",
|
||||
"suggested_tavily_search_depth": "advanced",
|
||||
"suggested_tavily_include_answer": "advanced",
|
||||
"suggested_tavily_time_range": "month",
|
||||
"suggested_tavily_raw_content_format": "markdown",
|
||||
"provider_recommendations": {
|
||||
"trends": "tavily",
|
||||
"deep_research": "exa",
|
||||
"factual": "google",
|
||||
"news": "tavily",
|
||||
"academic": "exa"
|
||||
},
|
||||
"research_angles": [
|
||||
"Compare telemedicine platforms",
|
||||
"Telemedicine ROI analysis",
|
||||
|
||||
Reference in New Issue
Block a user