628 lines
22 KiB
Python
628 lines
22 KiB
Python
"""
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Intent Prompt Builder
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Builds comprehensive AI prompts for:
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1. Intent inference from user input
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2. Targeted query generation
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3. Intent-aware result analysis
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Author: ALwrity Team
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Version: 1.0
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"""
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import json
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from typing import Dict, Any, List, Optional
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from loguru import logger
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from models.research_intent_models import (
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ResearchIntent,
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ResearchPurpose,
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ContentOutput,
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ExpectedDeliverable,
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ResearchDepthLevel,
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)
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from models.research_persona_models import ResearchPersona
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class IntentPromptBuilder:
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"""Builds prompts for intent-driven research."""
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# Purpose explanations for the AI
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PURPOSE_EXPLANATIONS = {
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ResearchPurpose.LEARN: "User wants to understand a topic for personal knowledge",
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ResearchPurpose.CREATE_CONTENT: "User will create content (blog, video, podcast) from this research",
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ResearchPurpose.MAKE_DECISION: "User needs to make a choice/decision based on research",
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ResearchPurpose.COMPARE: "User wants to compare alternatives or competitors",
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ResearchPurpose.SOLVE_PROBLEM: "User is looking for a solution to a specific problem",
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ResearchPurpose.FIND_DATA: "User needs specific statistics, facts, or citations",
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ResearchPurpose.EXPLORE_TRENDS: "User wants to understand current/future trends",
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ResearchPurpose.VALIDATE: "User wants to verify or fact-check information",
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ResearchPurpose.GENERATE_IDEAS: "User wants to brainstorm content ideas",
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}
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# Deliverable descriptions
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DELIVERABLE_DESCRIPTIONS = {
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ExpectedDeliverable.KEY_STATISTICS: "Numbers, percentages, data points with citations",
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ExpectedDeliverable.EXPERT_QUOTES: "Authoritative quotes from industry experts",
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ExpectedDeliverable.CASE_STUDIES: "Real examples and success stories",
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ExpectedDeliverable.COMPARISONS: "Side-by-side analysis tables",
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ExpectedDeliverable.TRENDS: "Current and emerging industry trends",
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ExpectedDeliverable.BEST_PRACTICES: "Recommended approaches and guidelines",
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ExpectedDeliverable.STEP_BY_STEP: "Process guides and how-to instructions",
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ExpectedDeliverable.PROS_CONS: "Advantages and disadvantages analysis",
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ExpectedDeliverable.DEFINITIONS: "Clear explanations of concepts and terms",
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ExpectedDeliverable.CITATIONS: "Authoritative sources for reference",
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ExpectedDeliverable.EXAMPLES: "Concrete examples to illustrate points",
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ExpectedDeliverable.PREDICTIONS: "Future outlook and predictions",
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}
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def build_intent_inference_prompt(
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self,
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user_input: str,
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keywords: List[str],
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research_persona: Optional[ResearchPersona] = None,
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competitor_data: Optional[List[Dict]] = None,
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industry: Optional[str] = None,
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target_audience: Optional[str] = None,
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) -> str:
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"""
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Build prompt for inferring user's research intent.
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This prompt analyzes the user's input and determines:
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- What they want to accomplish
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- What questions they need answered
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- What specific deliverables they need
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"""
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# Build persona context
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persona_context = self._build_persona_context(research_persona, industry, target_audience)
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# Build competitor context
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competitor_context = self._build_competitor_context(competitor_data)
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prompt = f"""You are an expert research intent analyzer. Your job is to understand what a content creator REALLY needs from their research.
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## USER INPUT
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"{user_input}"
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{f"KEYWORDS: {', '.join(keywords)}" if keywords else ""}
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## USER CONTEXT
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{persona_context}
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{competitor_context}
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## YOUR TASK
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Analyze the user's input and infer their research intent. Determine:
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1. **INPUT TYPE**: Is this:
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- "keywords": Simple topic keywords (e.g., "AI healthcare 2025")
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- "question": A specific question (e.g., "What are the best AI tools for healthcare?")
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- "goal": A goal statement (e.g., "I need to write a blog about AI in healthcare")
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- "mixed": Combination of above
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2. **PRIMARY QUESTION**: What is the main question to answer? Convert their input into a clear question.
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3. **SECONDARY QUESTIONS**: What related questions should also be answered? (3-5 questions)
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4. **PURPOSE**: Why are they researching? Choose ONE:
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- "learn": Understand a topic for personal knowledge
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- "create_content": Create content (blog, video, podcast)
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- "make_decision": Make a choice between options
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- "compare": Compare alternatives/competitors
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- "solve_problem": Find a solution
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- "find_data": Get specific statistics/facts
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- "explore_trends": Understand industry trends
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- "validate": Verify claims/information
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- "generate_ideas": Brainstorm ideas
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5. **CONTENT OUTPUT**: What will they create? Choose ONE:
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- "blog", "podcast", "video", "social_post", "newsletter", "presentation", "report", "whitepaper", "email", "general"
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6. **EXPECTED DELIVERABLES**: What specific outputs do they need? Choose ALL that apply:
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- "key_statistics": Numbers, data points
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- "expert_quotes": Authoritative quotes
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- "case_studies": Real examples
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- "comparisons": Side-by-side analysis
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- "trends": Industry trends
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- "best_practices": Recommendations
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- "step_by_step": How-to guides
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- "pros_cons": Advantages/disadvantages
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- "definitions": Concept explanations
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- "citations": Source references
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- "examples": Concrete examples
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- "predictions": Future outlook
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7. **DEPTH**: How deep should the research go?
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- "overview": Quick summary
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- "detailed": In-depth analysis
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- "expert": Comprehensive expert-level
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8. **FOCUS AREAS**: What specific aspects should be researched? (2-4 areas)
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9. **PERSPECTIVE**: From whose viewpoint? (e.g., "marketing manager", "small business owner")
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10. **TIME SENSITIVITY**: Is recency important?
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- "real_time": Latest only (past 24-48 hours)
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- "recent": Past week/month
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- "historical": Include older content
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- "evergreen": Timeless content
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11. **CONFIDENCE**: How confident are you in this inference? (0.0-1.0)
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- If < 0.7, set needs_clarification to true and provide clarifying_questions
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## OUTPUT FORMAT
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Return a JSON object:
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```json
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{{
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"input_type": "keywords|question|goal|mixed",
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"primary_question": "The main question to answer",
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"secondary_questions": ["question 1", "question 2", "question 3"],
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"purpose": "one of the purpose options",
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"content_output": "one of the content options",
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"expected_deliverables": ["deliverable1", "deliverable2"],
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"depth": "overview|detailed|expert",
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"focus_areas": ["area1", "area2"],
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"perspective": "target perspective or null",
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"time_sensitivity": "real_time|recent|historical|evergreen",
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"confidence": 0.85,
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"needs_clarification": false,
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"clarifying_questions": [],
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"analysis_summary": "Brief summary of what the user wants"
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}}
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```
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## IMPORTANT RULES
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1. Always convert vague input into a specific primary question
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2. Infer deliverables based on purpose (e.g., create_content → statistics + examples)
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3. Use persona context to refine perspective and focus areas
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4. If input is ambiguous, provide clarifying questions
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5. Default to "detailed" depth unless input suggests otherwise
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6. For content creation, include relevant deliverables automatically
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"""
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return prompt
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def build_query_generation_prompt(
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self,
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intent: ResearchIntent,
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research_persona: Optional[ResearchPersona] = None,
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) -> str:
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"""
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Build prompt for generating targeted research queries.
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Generates multiple queries, each targeting a specific deliverable.
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"""
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deliverables_list = "\n".join([
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f"- {d}: {self.DELIVERABLE_DESCRIPTIONS.get(ExpectedDeliverable(d), d)}"
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for d in intent.expected_deliverables
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])
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persona_keywords = ""
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if research_persona and research_persona.suggested_keywords:
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persona_keywords = f"\nSUGGESTED KEYWORDS FROM PERSONA: {', '.join(research_persona.suggested_keywords[:10])}"
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prompt = f"""You are a research query optimizer. Generate multiple targeted search queries based on the user's research intent.
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## RESEARCH INTENT
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PRIMARY QUESTION: {intent.primary_question}
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SECONDARY QUESTIONS:
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{chr(10).join(f'- {q}' for q in intent.secondary_questions) if intent.secondary_questions else 'None'}
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PURPOSE: {intent.purpose} - {self.PURPOSE_EXPLANATIONS.get(ResearchPurpose(intent.purpose), intent.purpose)}
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CONTENT OUTPUT: {intent.content_output}
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EXPECTED DELIVERABLES:
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{deliverables_list}
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DEPTH: {intent.depth}
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FOCUS AREAS: {', '.join(intent.focus_areas) if intent.focus_areas else 'General'}
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PERSPECTIVE: {intent.perspective or 'General audience'}
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TIME SENSITIVITY: {intent.time_sensitivity or 'No specific requirement'}
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{persona_keywords}
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## YOUR TASK
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Generate 4-8 targeted research queries. Each query should:
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1. Target a specific deliverable or question
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2. Be optimized for semantic search (Exa/Tavily)
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3. Include relevant context for better results
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For each query, specify:
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- The query string
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- What deliverable it targets
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- Best provider (exa for semantic/deep, tavily for news/real-time, google for factual)
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- Priority (1-5, higher = more important)
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- What we expect to find
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## OUTPUT FORMAT
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Return a JSON object:
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```json
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{{
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"queries": [
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{{
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"query": "Healthcare AI adoption statistics 2025 hospitals implementation data",
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"purpose": "key_statistics",
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"provider": "exa",
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"priority": 5,
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"expected_results": "Statistics on hospital AI adoption rates"
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}},
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{{
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"query": "AI healthcare trends predictions future outlook 2025 2026",
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"purpose": "trends",
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"provider": "tavily",
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"priority": 4,
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"expected_results": "Current trends and future predictions in healthcare AI"
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}}
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],
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"enhanced_keywords": ["keyword1", "keyword2", "keyword3"],
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"research_angles": [
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"Angle 1: Focus on adoption challenges",
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"Angle 2: Focus on ROI and outcomes"
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]
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}}
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```
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## QUERY OPTIMIZATION RULES
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1. For STATISTICS: Include words like "statistics", "data", "percentage", "report", "study"
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2. For CASE STUDIES: Include "case study", "success story", "implementation", "example"
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3. For TRENDS: Include "trends", "future", "predictions", "emerging", year numbers
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4. For EXPERT QUOTES: Include expert names if known, or "expert opinion", "interview"
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5. For COMPARISONS: Include "vs", "compare", "comparison", "alternative"
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6. For NEWS/REAL-TIME: Use Tavily, include recent year/month
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7. For ACADEMIC/DEEP: Use Exa with neural search
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"""
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return prompt
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def build_intent_aware_analysis_prompt(
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self,
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raw_results: str,
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intent: ResearchIntent,
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research_persona: Optional[ResearchPersona] = None,
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) -> str:
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"""
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Build prompt for analyzing research results based on user intent.
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This is the key prompt that extracts exactly what the user needs.
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"""
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purpose_explanation = self.PURPOSE_EXPLANATIONS.get(
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ResearchPurpose(intent.purpose),
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intent.purpose
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)
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deliverables_instructions = self._build_deliverables_instructions(intent.expected_deliverables)
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perspective_instruction = ""
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if intent.perspective:
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perspective_instruction = f"\n**PERSPECTIVE**: Analyze results from the viewpoint of: {intent.perspective}"
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prompt = f"""You are a research analyst helping a content creator find exactly what they need. Your job is to analyze raw research results and extract precisely what the user is looking for.
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## USER'S RESEARCH INTENT
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PRIMARY QUESTION: {intent.primary_question}
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SECONDARY QUESTIONS:
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{chr(10).join(f'- {q}' for q in intent.secondary_questions) if intent.secondary_questions else 'None specified'}
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PURPOSE: {intent.purpose}
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→ {purpose_explanation}
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CONTENT OUTPUT: {intent.content_output}
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EXPECTED DELIVERABLES: {', '.join(intent.expected_deliverables)}
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FOCUS AREAS: {', '.join(intent.focus_areas) if intent.focus_areas else 'General'}
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{perspective_instruction}
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## RAW RESEARCH RESULTS
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{raw_results[:15000]} # Truncated for token limits
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## YOUR TASK
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Analyze the raw research results and extract EXACTLY what the user needs.
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{deliverables_instructions}
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## OUTPUT REQUIREMENTS
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Provide results in this JSON structure:
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```json
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{{
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"primary_answer": "Direct 2-3 sentence answer to the primary question",
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"secondary_answers": {{
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"Question 1?": "Answer to question 1",
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"Question 2?": "Answer to question 2"
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}},
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"executive_summary": "2-3 sentence executive summary of all findings",
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"key_takeaways": [
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"Key takeaway 1 - most important finding",
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"Key takeaway 2",
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"Key takeaway 3",
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"Key takeaway 4",
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"Key takeaway 5"
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],
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"statistics": [
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{{
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"statistic": "72% of hospitals plan to adopt AI by 2025",
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"value": "72%",
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"context": "Survey of 500 US hospitals in 2024",
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"source": "Healthcare AI Report 2024",
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"url": "https://example.com/report",
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"credibility": 0.9,
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"recency": "2024"
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}}
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],
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"expert_quotes": [
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{{
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"quote": "AI will revolutionize patient care within 5 years",
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"speaker": "Dr. Jane Smith",
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"title": "Chief Medical Officer",
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"organization": "HealthTech Inc",
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"source": "TechCrunch",
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"url": "https://example.com/article"
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}}
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],
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"case_studies": [
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{{
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"title": "Mayo Clinic AI Implementation",
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"organization": "Mayo Clinic",
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"challenge": "High patient wait times",
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"solution": "AI-powered triage system",
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"outcome": "40% reduction in wait times",
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"key_metrics": ["40% faster triage", "95% patient satisfaction"],
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"source": "Healthcare IT News",
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"url": "https://example.com"
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}}
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],
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"trends": [
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{{
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"trend": "AI-assisted diagnostics adoption",
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"direction": "growing",
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"evidence": ["25% YoY growth", "Major hospital chains investing"],
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"impact": "Could reduce misdiagnosis by 30%",
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"timeline": "Expected mainstream by 2027",
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"sources": ["url1", "url2"]
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}}
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],
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"comparisons": [
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{{
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"title": "Top AI Healthcare Platforms",
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"criteria": ["Cost", "Features", "Support"],
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"items": [
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{{
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"name": "Platform A",
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"pros": ["Easy integration", "Good support"],
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"cons": ["Higher cost"],
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"features": {{"Cost": "$500/month", "Support": "24/7"}}
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}}
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],
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"verdict": "Platform A best for large hospitals"
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}}
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],
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"best_practices": [
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"Start with a pilot program before full deployment",
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"Ensure staff training is comprehensive"
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],
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"step_by_step": [
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"Step 1: Assess current infrastructure",
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"Step 2: Define use cases",
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"Step 3: Select vendor"
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],
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"pros_cons": {{
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"subject": "AI in Healthcare",
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"pros": ["Improved accuracy", "Cost savings"],
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"cons": ["Initial investment", "Training required"],
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"balanced_verdict": "Benefits outweigh costs for most hospitals"
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}},
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"definitions": {{
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"Clinical AI": "AI systems designed for medical diagnosis and treatment recommendations"
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}},
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"examples": [
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"Example: Hospital X reduced readmissions by 25% using predictive AI"
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],
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"predictions": [
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"By 2030, AI will assist in 80% of initial diagnoses"
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],
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"suggested_outline": [
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"1. Introduction: The AI Healthcare Revolution",
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"2. Current State: Where We Are Today",
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"3. Key Statistics and Trends",
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"4. Case Studies: Success Stories",
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"5. Implementation Guide",
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"6. Future Outlook"
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],
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"sources": [
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{{
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"title": "Healthcare AI Report 2024",
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"url": "https://example.com",
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"relevance_score": 0.95,
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"relevance_reason": "Directly addresses adoption statistics",
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"content_type": "research report",
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"credibility_score": 0.9
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}}
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],
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"confidence": 0.85,
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"gaps_identified": [
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"Specific cost data for small clinics not found",
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"Limited information on regulatory challenges"
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],
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"follow_up_queries": [
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"AI healthcare regulations FDA 2025",
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"Small clinic AI implementation costs"
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]
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}}
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```
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## CRITICAL RULES
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1. **ONLY include information directly from the raw results** - do not make up data
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2. **ALWAYS include source URLs** for every statistic, quote, and case study
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3. **If a deliverable type has no relevant data**, return an empty array for it
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4. **Prioritize recency and credibility** when multiple sources conflict
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5. **Answer the PRIMARY QUESTION directly** in 2-3 clear sentences
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6. **Keep KEY TAKEAWAYS to 5-7 points** - the most important findings
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7. **Add to gaps_identified** if expected information is missing
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8. **Suggest follow_up_queries** for gaps or incomplete areas
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9. **Rate confidence** based on how well results match the user's intent
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10. **Include deliverables ONLY if they are in expected_deliverables** or critical to the question
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"""
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return prompt
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def _build_persona_context(
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self,
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research_persona: Optional[ResearchPersona],
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industry: Optional[str],
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target_audience: Optional[str],
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) -> str:
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"""Build persona context section for prompts."""
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if not research_persona and not industry:
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return "No specific persona context available."
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context_parts = []
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if research_persona:
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context_parts.append(f"INDUSTRY: {research_persona.default_industry}")
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context_parts.append(f"TARGET AUDIENCE: {research_persona.default_target_audience}")
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if research_persona.suggested_keywords:
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context_parts.append(f"TYPICAL TOPICS: {', '.join(research_persona.suggested_keywords[:5])}")
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if research_persona.research_angles:
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context_parts.append(f"RESEARCH ANGLES: {', '.join(research_persona.research_angles[:3])}")
|
|
else:
|
|
if industry:
|
|
context_parts.append(f"INDUSTRY: {industry}")
|
|
if target_audience:
|
|
context_parts.append(f"TARGET AUDIENCE: {target_audience}")
|
|
|
|
return "\n".join(context_parts)
|
|
|
|
def _build_competitor_context(self, competitor_data: Optional[List[Dict]]) -> str:
|
|
"""Build competitor context section for prompts."""
|
|
|
|
if not competitor_data:
|
|
return ""
|
|
|
|
competitor_names = []
|
|
for comp in competitor_data[:5]: # Limit to 5
|
|
name = comp.get("name") or comp.get("domain") or comp.get("url", "Unknown")
|
|
competitor_names.append(name)
|
|
|
|
if competitor_names:
|
|
return f"\nKNOWN COMPETITORS: {', '.join(competitor_names)}"
|
|
|
|
return ""
|
|
|
|
def _build_deliverables_instructions(self, expected_deliverables: List[str]) -> str:
|
|
"""Build specific extraction instructions for each expected deliverable."""
|
|
|
|
instructions = ["### EXTRACTION INSTRUCTIONS\n"]
|
|
instructions.append("For each requested deliverable, extract the following:\n")
|
|
|
|
deliverable_instructions = {
|
|
ExpectedDeliverable.KEY_STATISTICS: """
|
|
**STATISTICS**:
|
|
- Extract ALL relevant statistics with exact numbers
|
|
- Include source attribution (publication name, URL)
|
|
- Note the recency of the data
|
|
- Rate credibility based on source authority
|
|
- Format: statistic statement, value, context, source, URL, credibility score
|
|
""",
|
|
ExpectedDeliverable.EXPERT_QUOTES: """
|
|
**EXPERT QUOTES**:
|
|
- Extract authoritative quotes from named experts
|
|
- Include speaker name, title, and organization
|
|
- Provide context for the quote
|
|
- Include source URL
|
|
""",
|
|
ExpectedDeliverable.CASE_STUDIES: """
|
|
**CASE STUDIES**:
|
|
- Summarize each case study: challenge → solution → outcome
|
|
- Include key metrics and results
|
|
- Name the organization involved
|
|
- Provide source URL
|
|
""",
|
|
ExpectedDeliverable.TRENDS: """
|
|
**TRENDS**:
|
|
- Identify current and emerging trends
|
|
- Note direction: growing, declining, emerging, or stable
|
|
- List supporting evidence
|
|
- Include timeline predictions if available
|
|
- Cite sources
|
|
""",
|
|
ExpectedDeliverable.COMPARISONS: """
|
|
**COMPARISONS**:
|
|
- Build comparison tables where applicable
|
|
- Define clear comparison criteria
|
|
- List pros and cons for each option
|
|
- Provide a verdict/recommendation if data supports it
|
|
""",
|
|
ExpectedDeliverable.BEST_PRACTICES: """
|
|
**BEST PRACTICES**:
|
|
- Extract recommended approaches
|
|
- Provide actionable guidelines
|
|
- Order by importance or sequence
|
|
""",
|
|
ExpectedDeliverable.STEP_BY_STEP: """
|
|
**STEP BY STEP**:
|
|
- Extract process/how-to instructions
|
|
- Number steps clearly
|
|
- Include any prerequisites or requirements
|
|
""",
|
|
ExpectedDeliverable.PROS_CONS: """
|
|
**PROS AND CONS**:
|
|
- List advantages (pros)
|
|
- List disadvantages (cons)
|
|
- Provide a balanced verdict
|
|
""",
|
|
ExpectedDeliverable.DEFINITIONS: """
|
|
**DEFINITIONS**:
|
|
- Extract clear explanations of key terms and concepts
|
|
- Keep definitions concise but comprehensive
|
|
""",
|
|
ExpectedDeliverable.EXAMPLES: """
|
|
**EXAMPLES**:
|
|
- Extract concrete examples that illustrate key points
|
|
- Include real-world applications
|
|
""",
|
|
ExpectedDeliverable.PREDICTIONS: """
|
|
**PREDICTIONS**:
|
|
- Extract future outlook and predictions
|
|
- Note the source and their track record if known
|
|
- Include timeframes where mentioned
|
|
""",
|
|
ExpectedDeliverable.CITATIONS: """
|
|
**CITATIONS**:
|
|
- List all authoritative sources with URLs
|
|
- Rate credibility and relevance
|
|
- Note content type (research, news, opinion, etc.)
|
|
""",
|
|
}
|
|
|
|
for deliverable in expected_deliverables:
|
|
try:
|
|
d_enum = ExpectedDeliverable(deliverable)
|
|
if d_enum in deliverable_instructions:
|
|
instructions.append(deliverable_instructions[d_enum])
|
|
except ValueError:
|
|
pass
|
|
|
|
return "\n".join(instructions)
|