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

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ajaysi
2026-01-10 19:32:50 +05:30
parent 0b63ae7fc1
commit 8193cdba67
298 changed files with 45678 additions and 10952 deletions
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139
backend/services/research/intent/intent_prompt_builder.py
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@@ -11,6 +11,7 @@ Version: 1.0
"""
import json
from datetime import datetime
from typing import Dict, Any, List, Optional
from loguru import logger
@@ -27,6 +28,14 @@ from models.research_persona_models import ResearchPersona
class IntentPromptBuilder:
"""Builds prompts for intent-driven research."""
def _get_current_date_context(self) -> 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}"
# Purpose explanations for the AI
PURPOSE_EXPLANATIONS = {
ResearchPurpose.LEARN: "User wants to understand a topic for personal knowledge",
@@ -74,6 +83,11 @@ class IntentPromptBuilder:
- What specific deliverables they need
"""
# Get current date context
date_context = self._get_current_date_context()
now = datetime.now()
current_year = now.year
# Build persona context
persona_context = self._build_persona_context(research_persona, industry, target_audience)
@@ -82,6 +96,11 @@ class IntentPromptBuilder:
prompt = f"""You are an expert research intent analyzer. Your job is to understand what a content creator REALLY needs from their research.
## CURRENT DATE/TIME CONTEXT
{date_context}
**NOTE**: When user mentions time-sensitive terms (latest, current, recent, trends, predictions), prioritize {current_year} data.
## USER INPUT
"{user_input}"
@@ -97,7 +116,7 @@ class IntentPromptBuilder:
Analyze the user's input and infer their research intent. Determine:
1. **INPUT TYPE**: Is this:
- "keywords": Simple topic keywords (e.g., "AI healthcare 2025")
- "keywords": Simple topic keywords (e.g., "AI healthcare {current_year}")
- "question": A specific question (e.g., "What are the best AI tools for healthcare?")
- "goal": A goal statement (e.g., "I need to write a blog about AI in healthcare")
- "mixed": Combination of above
@@ -210,8 +229,25 @@ Return a JSON object:
if research_persona and research_persona.suggested_keywords:
persona_keywords = f"\nSUGGESTED KEYWORDS FROM PERSONA: {', '.join(research_persona.suggested_keywords[:10])}"
# Get current date context
date_context = self._get_current_date_context()
now = datetime.now()
current_year = now.year
next_year = current_year + 1
current_month_year = now.strftime("%B %Y")
prompt = f"""You are a research query optimizer. Generate multiple targeted search queries based on the user's research intent.
## CURRENT DATE/TIME CONTEXT
{date_context}
**CRITICAL**: When generating queries:
- ALWAYS use the CURRENT YEAR ({current_year}) for time-sensitive queries
- For trends, predictions, or future-looking queries, use {current_year} or {next_year}
- For recent/real-time queries, use current month/year: {current_month_year}
- NEVER use outdated years from training data (e.g., 2024, 2025 if we're past those dates)
- When user mentions "latest", "current", "recent", or time-sensitive terms, prioritize {current_year} data
## RESEARCH INTENT
PRIMARY QUESTION: {intent.primary_question}
@@ -256,14 +292,14 @@ Return a JSON object:
{{
"queries": [
{{
"query": "Healthcare AI adoption statistics 2025 hospitals implementation data",
"query": "Healthcare AI adoption statistics {current_year} hospitals implementation data",
"purpose": "key_statistics",
"provider": "exa",
"priority": 5,
"expected_results": "Statistics on hospital AI adoption rates"
}},
{{
"query": "AI healthcare trends predictions future outlook 2025 2026",
"query": "AI healthcare trends predictions future outlook {current_year} {next_year}",
"purpose": "trends",
"provider": "tavily",
"priority": 4,
@@ -280,13 +316,14 @@ Return a JSON object:
## QUERY OPTIMIZATION RULES
1. For STATISTICS: Include words like "statistics", "data", "percentage", "report", "study"
1. For STATISTICS: Include words like "statistics", "data", "percentage", "report", "study", and CURRENT YEAR ({current_year})
2. For CASE STUDIES: Include "case study", "success story", "implementation", "example"
3. For TRENDS: Include "trends", "future", "predictions", "emerging", year numbers
3. For TRENDS: Include "trends", "future", "predictions", "emerging", and CURRENT YEAR ({current_year}) or {next_year}
4. For EXPERT QUOTES: Include expert names if known, or "expert opinion", "interview"
5. For COMPARISONS: Include "vs", "compare", "comparison", "alternative"
6. For NEWS/REAL-TIME: Use Tavily, include recent year/month
6. For NEWS/REAL-TIME: Use Tavily, include CURRENT YEAR ({current_year}) and current month/year ({current_month_year})
7. For ACADEMIC/DEEP: Use Exa with neural search
8. **CRITICAL**: Always use {current_year} (not outdated years) for time-sensitive queries
"""
return prompt
@@ -314,23 +351,43 @@ Return a JSON object:
if intent.perspective:
perspective_instruction = f"\n**PERSPECTIVE**: Analyze results from the viewpoint of: {intent.perspective}"
# Get current date context
date_context = self._get_current_date_context()
now = datetime.now()
current_year = now.year
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.
## CURRENT DATE/TIME CONTEXT
{date_context}
**CRITICAL**: When analyzing results:
- Prioritize data from CURRENT YEAR ({current_year}) or recent dates
- If statistics/quotes mention outdated years, note the recency in context
- For trends/predictions, ensure timelines reference {current_year} or future years
- NEVER present outdated data as "current" or "latest" - always check dates
## USER'S RESEARCH INTENT
PRIMARY QUESTION: {intent.primary_question}
**PRIMARY QUESTION**: {intent.primary_question}
SECONDARY QUESTIONS:
**SECONDARY QUESTIONS TO ANSWER**:
{chr(10).join(f'- {q}' for q in intent.secondary_questions) if intent.secondary_questions else 'None specified'}
PURPOSE: {intent.purpose}
**FOCUS AREAS** (prioritize information related to these):
{', '.join(intent.focus_areas) if intent.focus_areas else 'General - no specific focus areas'}
**ALSO ANSWERING** (address these topics if found in results):
{', '.join(intent.also_answering) if intent.also_answering else 'None specified'}
**PURPOSE**: {intent.purpose}
→ {purpose_explanation}
CONTENT OUTPUT: {intent.content_output}
**CONTENT OUTPUT**: {intent.content_output}
EXPECTED DELIVERABLES: {', '.join(intent.expected_deliverables)}
**EXPECTED DELIVERABLES**: {', '.join(intent.expected_deliverables)}
FOCUS AREAS: {', '.join(intent.focus_areas) if intent.focus_areas else 'General'}
**PERSPECTIVE**: {intent.perspective or 'General audience'}
{perspective_instruction}
## RAW RESEARCH RESULTS
@@ -339,7 +396,33 @@ FOCUS AREAS: {', '.join(intent.focus_areas) if intent.focus_areas else 'General'
## YOUR TASK
Analyze the raw research results and extract EXACTLY what the user needs.
Analyze the raw research results and extract EXACTLY what the user needs. Use a **generalized approach** - don't over-optimize for specific fields, but ensure all intent aspects are considered naturally.
### ANALYSIS GUIDELINES:
1. **PRIMARY QUESTION**: Always provide a direct, clear answer to the primary question in 2-3 sentences.
2. **SECONDARY QUESTIONS**: For each secondary question, provide an answer if information is available in the results. If not available, note it in gaps_identified. Don't force answers - only include what's actually in the results.
3. **FOCUS AREAS**: When extracting deliverables, prioritize information that relates to the focus areas. If focus areas are specified:
- Weight relevance scores higher for sources/content matching focus areas
- Include focus area context in extracted statistics, quotes, case studies
- If results don't address focus areas, note this in gaps_identified
- Provide a brief summary of what was found for each focus area in focus_areas_coverage
4. **ALSO ANSWERING**: If results contain information about "also answering" topics, include it naturally in the analysis. Don't create separate sections unless the information is substantial. Provide a brief summary of what was found for each topic in also_answering_coverage.
5. **GENERALIZED EXTRACTION**:
- Extract deliverables based on expected_deliverables
- Use perspective to frame information appropriately
- Consider content_output when structuring results
- Don't over-optimize - let the results guide what's extracted
6. **CONTEXTUAL LINKING**: When extracting information, consider:
- How it relates to the primary question
- Which secondary questions it answers
- Which focus areas it addresses
- This helps create a cohesive research result
{deliverables_instructions}
@@ -351,8 +434,16 @@ Provide results in this JSON structure:
{{
"primary_answer": "Direct 2-3 sentence answer to the primary question",
"secondary_answers": {{
"Question 1?": "Answer to question 1",
"Question 2?": "Answer to question 2"
"Secondary Question 1?": "Answer if found in results, or null if not available",
"Secondary Question 2?": "Answer if found in results, or null if not available"
}},
"focus_areas_coverage": {{
"Focus Area 1": "Brief summary of what was found related to this focus area, or null if not covered",
"Focus Area 2": "Brief summary of what was found related to this focus area, or null if not covered"
}},
"also_answering_coverage": {{
"Topic 1": "Information found about this topic, or null if not found",
"Topic 2": "Information found about this topic, or null if not found"
}},
"executive_summary": "2-3 sentence executive summary of all findings",
"key_takeaways": [
@@ -364,13 +455,13 @@ Provide results in this JSON structure:
],
"statistics": [
{{
"statistic": "72% of hospitals plan to adopt AI by 2025",
"statistic": "72% of hospitals plan to adopt AI by {current_year}",
"value": "72%",
"context": "Survey of 500 US hospitals in 2024",
"source": "Healthcare AI Report 2024",
"context": "Survey of 500 US hospitals in {current_year}",
"source": "Healthcare AI Report {current_year}",
"url": "https://example.com/report",
"credibility": 0.9,
"recency": "2024"
"recency": "{current_year}"
}}
],
"expert_quotes": [
@@ -401,7 +492,7 @@ Provide results in this JSON structure:
"direction": "growing",
"evidence": ["25% YoY growth", "Major hospital chains investing"],
"impact": "Could reduce misdiagnosis by 30%",
"timeline": "Expected mainstream by 2027",
"timeline": "Expected mainstream by {current_year + 2}",
"sources": ["url1", "url2"]
}}
],
@@ -442,7 +533,7 @@ Provide results in this JSON structure:
"Example: Hospital X reduced readmissions by 25% using predictive AI"
],
"predictions": [
"By 2030, AI will assist in 80% of initial diagnoses"
"By {current_year + 5}, AI will assist in 80% of initial diagnoses"
],
"suggested_outline": [
"1. Introduction: The AI Healthcare Revolution",
@@ -454,7 +545,7 @@ Provide results in this JSON structure:
],
"sources": [
{{
"title": "Healthcare AI Report 2024",
"title": "Healthcare AI Report {current_year}",
"url": "https://example.com",
"relevance_score": 0.95,
"relevance_reason": "Directly addresses adoption statistics",
@@ -468,7 +559,7 @@ Provide results in this JSON structure:
"Limited information on regulatory challenges"
],
"follow_up_queries": [
"AI healthcare regulations FDA 2025",
"AI healthcare regulations FDA {current_year}",
"Small clinic AI implementation costs"
]
}}
@@ -486,6 +577,8 @@ Provide results in this JSON structure:
8. **Suggest follow_up_queries** for gaps or incomplete areas
9. **Rate confidence** based on how well results match the user's intent
10. **Include deliverables ONLY if they are in expected_deliverables** or critical to the question
11. **Don't over-optimize** - use a natural, generalized approach that considers all intent fields without forcing connections
12. **For focus_areas_coverage and also_answering_coverage**: Only include entries for focus areas/topics that actually have information in the results. Use null for areas/topics not covered.
"""
return prompt