Scheduled research persona generation
This commit is contained in:
ajaysi
171
backend/services/research/research_persona_prompt_builder.py
Normal file
171
backend/services/research/research_persona_prompt_builder.py
Normal file
@@ -0,0 +1,171 @@
|
||||
"""
|
||||
Research Persona Prompt Builder
|
||||
|
||||
Handles building comprehensive prompts for research persona generation.
|
||||
Generates personalized research defaults, suggestions, and configurations.
|
||||
"""
|
||||
|
||||
from typing import Dict, Any
|
||||
import json
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class ResearchPersonaPromptBuilder:
|
||||
"""Builds comprehensive prompts for research persona generation."""
|
||||
|
||||
def build_research_persona_prompt(self, onboarding_data: Dict[str, Any]) -> str:
|
||||
"""Build the research persona generation prompt with comprehensive data."""
|
||||
|
||||
# Extract data from onboarding_data
|
||||
website_analysis = onboarding_data.get("website_analysis", {}) or {}
|
||||
persona_data = onboarding_data.get("persona_data", {}) or {}
|
||||
research_prefs = onboarding_data.get("research_preferences", {}) or {}
|
||||
business_info = onboarding_data.get("business_info", {}) or {}
|
||||
|
||||
# Extract core persona
|
||||
core_persona = persona_data.get("core_persona", {}) or {}
|
||||
|
||||
prompt = f"""
|
||||
COMPREHENSIVE RESEARCH PERSONA GENERATION TASK: Create a highly detailed, personalized research persona based on the user's business, writing style, and content strategy. This persona will provide intelligent defaults and suggestions for research inputs.
|
||||
|
||||
=== USER CONTEXT ===
|
||||
|
||||
BUSINESS INFORMATION:
|
||||
{json.dumps(business_info, indent=2)}
|
||||
|
||||
WEBSITE ANALYSIS:
|
||||
{json.dumps(website_analysis, indent=2)}
|
||||
|
||||
CORE PERSONA:
|
||||
{json.dumps(core_persona, indent=2)}
|
||||
|
||||
RESEARCH PREFERENCES:
|
||||
{json.dumps(research_prefs, indent=2)}
|
||||
|
||||
=== RESEARCH PERSONA GENERATION REQUIREMENTS ===
|
||||
|
||||
Generate a comprehensive research persona in JSON format with the following structure:
|
||||
|
||||
1. DEFAULT VALUES:
|
||||
- "default_industry": Extract from core_persona.industry, business_info.industry, or website_analysis target_audience. Use "General" only if none available.
|
||||
- "default_target_audience": Extract from core_persona.target_audience, website_analysis.target_audience, or business_info.target_audience. Be specific and descriptive.
|
||||
- "default_research_mode": Suggest "basic", "comprehensive", or "targeted" based on research_preferences.research_depth and content_type preferences.
|
||||
- "default_provider": Suggest "google" for news/trends, "exa" for academic/technical deep-dives, or "google" as default.
|
||||
|
||||
2. KEYWORD INTELLIGENCE:
|
||||
- "suggested_keywords": Generate 8-12 keywords relevant to the user's industry, interests (from core_persona), and content goals.
|
||||
- "keyword_expansion_patterns": Create a dictionary mapping common keywords to expanded, industry-specific terms. Include 10-15 patterns like:
|
||||
{{"AI": ["healthcare AI", "medical AI", "clinical AI", "diagnostic AI"], "tools": ["medical devices", "clinical tools"], ...}}
|
||||
Focus on industry-specific terminology from the user's domain.
|
||||
|
||||
3. DOMAIN EXPERTISE:
|
||||
- "suggested_exa_domains": List 4-6 authoritative domains for the user's industry (e.g., Healthcare: ["pubmed.gov", "nejm.org", "thelancet.com"]).
|
||||
- "suggested_exa_category": Suggest appropriate Exa category based on industry:
|
||||
- Healthcare/Science: "research paper"
|
||||
- Finance: "financial report"
|
||||
- Technology/Business: "company" or "news"
|
||||
- Default: null (empty string for all categories)
|
||||
|
||||
4. RESEARCH ANGLES:
|
||||
- "research_angles": Generate 5-8 alternative research angles/focuses based on:
|
||||
- User's pain points and challenges (from core_persona)
|
||||
- Industry trends and opportunities
|
||||
- Content goals (from research_preferences)
|
||||
- Audience interests (from core_persona.interests)
|
||||
Examples: "Compare {{topic}} tools", "{{topic}} ROI analysis", "Latest {{topic}} trends", etc.
|
||||
|
||||
5. QUERY ENHANCEMENT:
|
||||
- "query_enhancement_rules": Create templates for improving vague user queries:
|
||||
{{"vague_ai": "Research: AI applications in {{industry}} for {{audience}}", "vague_tools": "Compare top {{industry}} tools", ...}}
|
||||
Include 5-8 enhancement patterns.
|
||||
|
||||
6. RECOMMENDED PRESETS:
|
||||
- "recommended_presets": Generate 3-5 personalized research preset templates. Each preset should include:
|
||||
- name: Descriptive name (e.g., "{{Industry}} Trends", "{{Audience}} Insights")
|
||||
- keywords: Research query string
|
||||
- industry: User's industry
|
||||
- target_audience: User's target audience
|
||||
- research_mode: "basic", "comprehensive", or "targeted"
|
||||
- config: Complete ResearchConfig object with appropriate settings
|
||||
- description: Brief explanation of what this preset researches
|
||||
Make presets relevant to the user's specific industry, audience, and content goals.
|
||||
|
||||
7. RESEARCH PREFERENCES:
|
||||
- "research_preferences": Extract and structure research preferences from onboarding:
|
||||
- research_depth: From research_preferences.research_depth
|
||||
- content_types: From research_preferences.content_types
|
||||
- auto_research: From research_preferences.auto_research
|
||||
- factual_content: From research_preferences.factual_content
|
||||
|
||||
=== OUTPUT REQUIREMENTS ===
|
||||
|
||||
Return a valid JSON object matching this exact structure:
|
||||
{{
|
||||
"default_industry": "string",
|
||||
"default_target_audience": "string",
|
||||
"default_research_mode": "basic" | "comprehensive" | "targeted",
|
||||
"default_provider": "google" | "exa",
|
||||
"suggested_keywords": ["keyword1", "keyword2", ...],
|
||||
"keyword_expansion_patterns": {{
|
||||
"keyword": ["expansion1", "expansion2", ...]
|
||||
}},
|
||||
"suggested_exa_domains": ["domain1.com", "domain2.com", ...],
|
||||
"suggested_exa_category": "string or null",
|
||||
"research_angles": ["angle1", "angle2", ...],
|
||||
"query_enhancement_rules": {{
|
||||
"pattern": "template"
|
||||
}},
|
||||
"recommended_presets": [
|
||||
{{
|
||||
"name": "string",
|
||||
"keywords": "string",
|
||||
"industry": "string",
|
||||
"target_audience": "string",
|
||||
"research_mode": "basic" | "comprehensive" | "targeted",
|
||||
"config": {{
|
||||
"mode": "basic" | "comprehensive" | "targeted",
|
||||
"provider": "google" | "exa",
|
||||
"max_sources": 10 | 15 | 12,
|
||||
"include_statistics": true | false,
|
||||
"include_expert_quotes": true | false,
|
||||
"include_competitors": true | false,
|
||||
"include_trends": true | false,
|
||||
"exa_category": "string or null",
|
||||
"exa_include_domains": ["domain1.com", ...],
|
||||
"exa_search_type": "auto" | "keyword" | "neural"
|
||||
}},
|
||||
"description": "string"
|
||||
}}
|
||||
],
|
||||
"research_preferences": {{
|
||||
"research_depth": "string",
|
||||
"content_types": ["type1", "type2", ...],
|
||||
"auto_research": true | false,
|
||||
"factual_content": true | false
|
||||
}},
|
||||
"version": "1.0",
|
||||
"confidence_score": 85.0
|
||||
}}
|
||||
|
||||
=== IMPORTANT INSTRUCTIONS ===
|
||||
|
||||
1. Be highly specific and personalized - use actual data from the user's business, persona, and preferences.
|
||||
2. Avoid generic suggestions - every field should reflect the user's unique context.
|
||||
3. For industries not clearly identified, infer from website_analysis.content_characteristics or writing_style.
|
||||
4. Ensure all suggested keywords, domains, and angles are relevant to the user's industry and audience.
|
||||
5. Generate realistic, actionable presets that the user would actually want to use.
|
||||
6. Confidence score should reflect data richness (0-100): higher if rich onboarding data, lower if minimal data.
|
||||
7. Return ONLY valid JSON - no markdown formatting, no explanatory text.
|
||||
|
||||
Generate the research persona now:
|
||||
"""
|
||||
|
||||
return prompt
|
||||
|
||||
def get_json_schema(self) -> Dict[str, Any]:
|
||||
"""Return JSON schema for structured LLM response."""
|
||||
# This will be used with llm_text_gen(json_struct=...)
|
||||
from models.research_persona_models import ResearchPersona, ResearchPreset
|
||||
|
||||
# Convert Pydantic model to JSON schema
|
||||
return ResearchPersona.schema()
|
||||
Reference in New Issue
Block a user