AI Analysis and Content Strategy fixes. Enhanced Strategy Routes refactoring.

This commit is contained in:
ajaysi
2026-01-10 19:32:50 +05:30
parent 0b63ae7fc1
commit 8193cdba67
298 changed files with 45678 additions and 10952 deletions

View File

@@ -0,0 +1,112 @@
"""
Utility functions for unified research analyzer.
Provides helper functions for date context, persona context,
competitor context, and fallback response creation.
"""
from datetime import datetime
from typing import Dict, Any, List, Optional
from models.research_intent_models import ResearchIntent, ResearchQuery
from models.research_persona_models import ResearchPersona
def get_current_date_context() -> str:
"""Get current date/time context for prompts."""
now = datetime.now()
current_year = now.year
current_month = now.strftime("%B") # Full month name
current_date = now.strftime("%Y-%m-%d")
return f"CURRENT DATE: {current_date} ({current_month} {current_year})\nCURRENT YEAR: {current_year}"
def build_persona_context(
research_persona: Optional[ResearchPersona],
industry: Optional[str],
target_audience: Optional[str],
) -> str:
"""Build persona context section."""
parts = []
if research_persona:
if research_persona.default_industry:
parts.append(f"Industry: {research_persona.default_industry}")
if research_persona.default_target_audience:
parts.append(f"Target Audience: {research_persona.default_target_audience}")
if research_persona.research_angles:
parts.append(f"Preferred Research Angles: {', '.join(research_persona.research_angles[:3])}")
if research_persona.suggested_keywords:
parts.append(f"Relevant Keywords: {', '.join(research_persona.suggested_keywords[:5])}")
else:
if industry:
parts.append(f"Industry: {industry}")
if target_audience:
parts.append(f"Target Audience: {target_audience}")
if not parts:
return "No specific user context available. Use general best practices."
return "\n".join(parts)
def build_competitor_context(competitor_data: Optional[List[Dict]]) -> str:
"""Build competitor context section."""
if not competitor_data:
return ""
competitor_names = [c.get("name", c.get("url", "")) for c in competitor_data[:5]]
if competitor_names:
return f"\nKnown Competitors: {', '.join(competitor_names)}"
return ""
def create_fallback_response(user_input: str, keywords: List[str]) -> Dict[str, Any]:
"""Create fallback response when analysis fails."""
return {
"success": False,
"intent": ResearchIntent(
primary_question=f"What are the key insights about: {user_input}?",
purpose="learn",
content_output="general",
expected_deliverables=["key_statistics", "best_practices"],
depth="detailed",
focus_areas=[],
also_answering=[],
original_input=user_input,
confidence=0.5,
),
"queries": [
ResearchQuery(
query=user_input,
purpose="key_statistics",
provider="exa",
priority=5,
expected_results="General research results",
addresses_primary_question=True,
addresses_secondary_questions=[],
targets_focus_areas=[],
covers_also_answering=[],
)
],
"enhanced_keywords": keywords,
"research_angles": [],
"recommended_provider": "exa",
"provider_justification": "Default fallback to Exa for semantic search",
"exa_config": {
"enabled": True,
"type": "auto",
"type_justification": "Auto mode for balanced results",
"numResults": 10,
"highlights": True,
},
"tavily_config": {
"enabled": True,
"topic": "general",
"search_depth": "advanced",
"include_answer": True,
},
"trends_config": {
"enabled": False, # Disabled in fallback
},
}