479 lines
22 KiB
Python
479 lines
22 KiB
Python
"""
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Research Persona Service
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Handles generation, caching, and retrieval of AI-powered research personas.
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"""
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from typing import Dict, Any, Optional
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from datetime import datetime, timedelta
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from loguru import logger
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from fastapi import HTTPException
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from services.database import get_db_session
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from models.onboarding import PersonaData, OnboardingSession
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from models.research_persona_models import ResearchPersona
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from .research_persona_prompt_builder import ResearchPersonaPromptBuilder
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from services.llm_providers.main_text_generation import llm_text_gen
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from services.onboarding.database_service import OnboardingDatabaseService
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from services.persona_data_service import PersonaDataService
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class ResearchPersonaService:
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"""Service for generating and managing research personas."""
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CACHE_TTL_DAYS = 7 # 7-day cache TTL
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def __init__(self, db_session=None):
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self.db = db_session or get_db_session()
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self.prompt_builder = ResearchPersonaPromptBuilder()
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self.onboarding_service = OnboardingDatabaseService(db=self.db)
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self.persona_data_service = PersonaDataService(db_session=self.db)
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def get_cached_only(
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self,
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user_id: str
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) -> Optional[ResearchPersona]:
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"""
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Get research persona for user if it exists in database (regardless of cache validity).
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This method NEVER generates - it only returns existing personas.
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Use this for config endpoints to avoid triggering rate limit checks.
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Note: Returns persona even if cache is expired - cache validity only matters for regeneration.
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Args:
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user_id: User ID (Clerk string)
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Returns:
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ResearchPersona if exists in database, None otherwise
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"""
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try:
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# Get persona data record
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persona_data = self._get_persona_data_record(user_id)
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if not persona_data:
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logger.debug(f"[get_cached_only] No persona data record found for user {user_id}")
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return None
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# Check if research_persona field exists and is not None/empty
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# Handle cases where it might be None, empty dict {}, or empty string ""
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research_persona_raw = persona_data.research_persona
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has_persona = (
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research_persona_raw is not None
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and research_persona_raw != {}
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and research_persona_raw != ""
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and (isinstance(research_persona_raw, dict) and len(research_persona_raw) > 0)
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)
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logger.info(
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f"[get_cached_only] Checking research persona for user {user_id}: "
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f"persona_data exists=True, research_persona_raw={research_persona_raw is not None}, "
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f"research_persona type={type(research_persona_raw)}, "
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f"has_persona={has_persona}, "
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f"generated_at={persona_data.research_persona_generated_at}"
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)
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# Return persona if it exists, regardless of cache validity
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# Cache validity only matters when deciding whether to regenerate
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if has_persona:
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try:
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cache_valid = self.is_cache_valid(persona_data)
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cache_status = "valid" if cache_valid else "expired"
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logger.info(
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f"[get_cached_only] ✅ Returning research persona for user {user_id} "
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f"(cache: {cache_status}, generated_at: {persona_data.research_persona_generated_at})"
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)
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# Ensure we're passing a dict to ResearchPersona
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if not isinstance(research_persona_raw, dict):
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logger.error(f"[get_cached_only] research_persona_raw is not a dict: {type(research_persona_raw)}")
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return None
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parsed_persona = ResearchPersona(**research_persona_raw)
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logger.info(
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f"[get_cached_only] ✅ Successfully parsed persona for user {user_id}: "
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f"industry={parsed_persona.default_industry}, "
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f"target_audience={parsed_persona.default_target_audience}"
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)
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return parsed_persona
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except Exception as e:
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logger.error(f"[get_cached_only] ❌ Failed to parse research persona for user {user_id}: {e}", exc_info=True)
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logger.debug(
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f"[get_cached_only] Persona data details: "
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f"type={type(research_persona_raw)}, "
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f"is_dict={isinstance(research_persona_raw, dict)}, "
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f"value sample: {str(research_persona_raw)[:500] if research_persona_raw else 'None'}"
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)
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return None
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# Persona doesn't exist in database
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logger.info(f"[get_cached_only] ⚠️ No research persona found in database for user {user_id}")
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return None
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except Exception as e:
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logger.error(f"[get_cached_only] ❌ Error getting research persona for user {user_id}: {e}", exc_info=True)
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return None
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def get_or_generate(
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self,
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user_id: str,
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force_refresh: bool = False
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) -> Optional[ResearchPersona]:
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"""
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Get research persona for user, generating if missing or expired.
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Args:
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user_id: User ID (Clerk string)
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force_refresh: If True, regenerate even if cache is valid
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Returns:
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ResearchPersona if successful, None otherwise
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"""
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try:
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# Get persona data record
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persona_data = self._get_persona_data_record(user_id)
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if not persona_data:
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logger.warning(f"No persona data found for user {user_id}, cannot generate research persona")
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return None
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# Check if persona exists in database
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if persona_data.research_persona:
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# Persona exists - check if we should return it or regenerate
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cache_valid = self.is_cache_valid(persona_data)
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if not force_refresh and cache_valid:
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# Cache is valid - return existing persona
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logger.info(f"Using cached research persona for user {user_id}")
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try:
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return ResearchPersona(**persona_data.research_persona)
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except Exception as e:
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logger.warning(f"Failed to parse cached research persona: {e}, regenerating...")
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# Fall through to regeneration if parsing fails
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elif not force_refresh:
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# Persona exists but cache expired - return it anyway (don't regenerate unless forced)
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logger.info(f"Research persona exists for user {user_id} but cache expired - returning existing persona (use force_refresh=true to regenerate)")
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try:
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return ResearchPersona(**persona_data.research_persona)
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except Exception as e:
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logger.warning(f"Failed to parse existing research persona: {e}, regenerating...")
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# Fall through to regeneration if parsing fails
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else:
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# force_refresh=True - regenerate even though persona exists
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logger.info(f"Forcing refresh of research persona for user {user_id}")
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else:
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# Persona doesn't exist - generate new one
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logger.info(f"Research persona missing for user {user_id}, generating...")
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# Generate new research persona (only reaches here if:
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# 1. Persona doesn't exist, OR
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# 2. force_refresh=True, OR
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# 3. Parsing of existing persona failed
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try:
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logger.info(f"Generating research persona for user {user_id}")
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research_persona = self.generate_research_persona(user_id)
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except HTTPException:
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# Re-raise HTTPExceptions (e.g., 429 subscription limit) so they propagate to API
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raise
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if research_persona:
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# Save to database
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if self.save_research_persona(user_id, research_persona):
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logger.info(f"✅ Research persona generated and saved for user {user_id}")
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else:
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logger.warning(f"Failed to save research persona for user {user_id}")
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return research_persona
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else:
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# Log detailed error for debugging expensive failures
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logger.error(
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f"❌ Failed to generate research persona for user {user_id} - "
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f"This is an expensive failure (API call consumed). Check logs above for details."
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)
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# Don't return None silently - let the caller know this failed
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return None
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except HTTPException:
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# Re-raise HTTPExceptions (e.g., 429 subscription limit) so they propagate to API
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raise
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except Exception as e:
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logger.error(f"Error getting/generating research persona for user {user_id}: {e}")
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return None
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def generate_research_persona(self, user_id: str) -> Optional[ResearchPersona]:
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"""
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Generate a new research persona for the user.
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Args:
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user_id: User ID (Clerk string)
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Returns:
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ResearchPersona if successful, None otherwise
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"""
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try:
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logger.info(f"Generating research persona for user {user_id}")
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# Collect onboarding data
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onboarding_data = self._collect_onboarding_data(user_id)
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if not onboarding_data:
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logger.warning(f"Insufficient onboarding data for user {user_id}")
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return None
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# Build prompt
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prompt = self.prompt_builder.build_research_persona_prompt(onboarding_data)
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# Get JSON schema for structured response
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json_schema = self.prompt_builder.get_json_schema()
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# Call LLM with structured JSON response
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logger.info(f"Calling LLM for research persona generation (user: {user_id})")
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try:
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response_text = llm_text_gen(
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prompt=prompt,
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json_struct=json_schema,
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user_id=user_id
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)
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except HTTPException:
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# Re-raise HTTPExceptions (e.g., 429 subscription limit) so they propagate to API
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logger.warning(f"HTTPException during LLM call for user {user_id} - re-raising")
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raise
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except RuntimeError as e:
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# Re-raise RuntimeError (subscription limits) as HTTPException
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logger.warning(f"RuntimeError during LLM call for user {user_id}: {e}")
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raise HTTPException(status_code=429, detail=str(e))
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if not response_text:
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logger.error("Empty response from LLM")
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return None
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# Parse JSON response
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import json
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try:
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# When json_struct is provided, llm_text_gen may return a dict directly
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if isinstance(response_text, dict):
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# Already parsed, use directly
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persona_dict = response_text
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elif isinstance(response_text, str):
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# Handle case where LLM returns markdown-wrapped JSON or plain JSON string
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response_text = response_text.strip()
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if response_text.startswith("```json"):
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response_text = response_text[7:]
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if response_text.startswith("```"):
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response_text = response_text[3:]
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if response_text.endswith("```"):
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response_text = response_text[:-3]
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response_text = response_text.strip()
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persona_dict = json.loads(response_text)
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else:
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logger.error(f"Unexpected response type from LLM: {type(response_text)}")
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return None
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# Add generated_at timestamp
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persona_dict["generated_at"] = datetime.utcnow().isoformat()
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# Validate and create ResearchPersona
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# Log the dict structure for debugging if validation fails
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try:
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research_persona = ResearchPersona(**persona_dict)
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logger.info(f"✅ Research persona generated successfully for user {user_id}")
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return research_persona
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except Exception as validation_error:
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logger.error(f"Failed to validate ResearchPersona from dict: {validation_error}")
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logger.debug(f"Persona dict keys: {list(persona_dict.keys()) if isinstance(persona_dict, dict) else 'Not a dict'}")
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logger.debug(f"Persona dict sample: {str(persona_dict)[:500]}")
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# Re-raise to be caught by outer exception handler
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raise
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse LLM response as JSON: {e}")
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logger.debug(f"Response text: {response_text[:500] if isinstance(response_text, str) else str(response_text)[:500]}")
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return None
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except Exception as e:
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logger.error(f"Failed to create ResearchPersona from response: {e}")
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return None
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except HTTPException:
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# Re-raise HTTPExceptions (e.g., 429 subscription limit) so they propagate to API
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raise
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except Exception as e:
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logger.error(f"Error generating research persona for user {user_id}: {e}")
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return None
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def is_cache_valid(self, persona_data: PersonaData) -> bool:
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"""
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Check if cached research persona is still valid (within TTL).
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Args:
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persona_data: PersonaData database record
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Returns:
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True if cache is valid, False otherwise
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"""
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if not persona_data.research_persona_generated_at:
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return False
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# Check if within TTL
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cache_age = datetime.utcnow() - persona_data.research_persona_generated_at
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is_valid = cache_age < timedelta(days=self.CACHE_TTL_DAYS)
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if not is_valid:
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logger.debug(f"Cache expired (age: {cache_age.days} days, TTL: {self.CACHE_TTL_DAYS} days)")
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return is_valid
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def save_research_persona(
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self,
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user_id: str,
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research_persona: ResearchPersona
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) -> bool:
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"""
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Save research persona to database.
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Args:
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user_id: User ID (Clerk string)
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research_persona: ResearchPersona to save
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Returns:
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True if successful, False otherwise
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"""
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try:
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persona_data = self._get_persona_data_record(user_id)
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if not persona_data:
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logger.error(f"No persona data record found for user {user_id}")
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return False
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# Convert ResearchPersona to dict for JSON storage
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persona_dict = research_persona.dict()
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# Update database record
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persona_data.research_persona = persona_dict
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persona_data.research_persona_generated_at = datetime.utcnow()
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self.db.commit()
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logger.info(f"✅ Research persona saved for user {user_id}")
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return True
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except Exception as e:
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logger.error(f"Error saving research persona for user {user_id}: {e}")
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self.db.rollback()
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return False
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def _get_persona_data_record(self, user_id: str) -> Optional[PersonaData]:
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"""Get PersonaData database record for user."""
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try:
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# Ensure research_persona columns exist before querying
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self.onboarding_service._ensure_research_persona_columns(self.db)
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# Get onboarding session
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session = self.db.query(OnboardingSession).filter(
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OnboardingSession.user_id == user_id
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).first()
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if not session:
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return None
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# Get persona data
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persona_data = self.db.query(PersonaData).filter(
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PersonaData.session_id == session.id
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).first()
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return persona_data
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except Exception as e:
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logger.error(f"Error getting persona data record for user {user_id}: {e}")
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return None
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def _collect_onboarding_data(self, user_id: str) -> Optional[Dict[str, Any]]:
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"""
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Collect all onboarding data needed for research persona generation.
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Returns:
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Dictionary with website_analysis, persona_data, research_preferences, business_info
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"""
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try:
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# Get website analysis
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website_analysis = self.onboarding_service.get_website_analysis(user_id, self.db) or {}
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# Get persona data
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persona_data_dict = self.onboarding_service.get_persona_data(user_id, self.db) or {}
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# Get research preferences
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research_prefs = self.onboarding_service.get_research_preferences(user_id, self.db) or {}
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# Get business info - construct from persona data and website analysis
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business_info = {}
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# Try to extract from persona data
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if persona_data_dict:
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core_persona = persona_data_dict.get('corePersona') or persona_data_dict.get('core_persona')
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if core_persona:
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if core_persona.get('industry'):
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business_info['industry'] = core_persona['industry']
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if core_persona.get('target_audience'):
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business_info['target_audience'] = core_persona['target_audience']
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# Fallback to website analysis if not in persona
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if not business_info.get('industry') and website_analysis:
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target_audience_data = website_analysis.get('target_audience', {})
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if isinstance(target_audience_data, dict):
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industry_focus = target_audience_data.get('industry_focus')
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if industry_focus:
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business_info['industry'] = industry_focus
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demographics = target_audience_data.get('demographics')
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if demographics:
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business_info['target_audience'] = demographics if isinstance(demographics, str) else str(demographics)
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# Check if we have enough data - be more lenient since we can infer from minimal data
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# We need at least some basic information to generate a meaningful persona
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has_basic_data = bool(
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website_analysis or
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persona_data_dict or
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research_prefs.get('content_types') or
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business_info.get('industry')
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)
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if not has_basic_data:
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logger.warning(f"Insufficient onboarding data for user {user_id} - no basic data found")
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return None
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# If we have minimal data, add intelligent defaults to help the AI
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if not business_info.get('industry'):
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# Try to infer industry from research preferences or content types
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content_types = research_prefs.get('content_types', [])
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if 'blog' in content_types or 'article' in content_types:
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business_info['industry'] = 'Content Marketing'
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business_info['inferred'] = True
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elif 'social_media' in content_types:
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business_info['industry'] = 'Social Media Marketing'
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business_info['inferred'] = True
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elif 'video' in content_types:
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business_info['industry'] = 'Video Content Creation'
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business_info['inferred'] = True
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if not business_info.get('target_audience'):
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# Default to professionals for content creators
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business_info['target_audience'] = 'Professionals and content consumers'
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business_info['inferred'] = True
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# Get competitor analysis data (if available)
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competitor_analysis = None
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try:
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competitor_analysis = self.onboarding_service.get_competitor_analysis(user_id, self.db)
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if competitor_analysis:
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logger.info(f"Found {len(competitor_analysis)} competitors for research persona generation")
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except Exception as e:
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logger.debug(f"Could not retrieve competitor analysis for persona generation: {e}")
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return {
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"website_analysis": website_analysis,
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"persona_data": persona_data_dict,
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"research_preferences": research_prefs,
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"business_info": business_info,
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"competitor_analysis": competitor_analysis # Add competitor data for better preset generation
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}
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except Exception as e:
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logger.error(f"Error collecting onboarding data for user {user_id}: {e}")
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return None
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