AI Researcher and Video Studio implementation complete
This commit is contained in:
ajaysi
559
backend/services/research/intent/unified_research_analyzer.py
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559
backend/services/research/intent/unified_research_analyzer.py
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@@ -0,0 +1,559 @@
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"""
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Unified Research Analyzer
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Combines intent inference, query generation, and parameter optimization
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into a single AI call with justifications for each decision.
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This reduces 2 LLM calls to 1, improves coherence, and provides
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user-friendly justifications for all settings.
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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, Tuple
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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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ResearchQuery,
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IntentInferenceResponse,
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ResearchPurpose,
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ContentOutput,
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ExpectedDeliverable,
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ResearchDepthLevel,
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InputType,
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)
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from models.research_persona_models import ResearchPersona
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class UnifiedResearchAnalyzer:
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"""
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Unified AI-driven analyzer that performs:
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1. Intent inference (what user wants)
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2. Query generation (how to search)
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3. Parameter optimization (Exa/Tavily settings)
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All in a single LLM call with justifications.
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"""
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def __init__(self):
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"""Initialize the unified analyzer."""
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logger.info("UnifiedResearchAnalyzer initialized")
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async def analyze(
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self,
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user_input: str,
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keywords: Optional[List[str]] = None,
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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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user_id: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Perform unified analysis of user research request.
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Returns:
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Dict containing:
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- intent: ResearchIntent
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- queries: List[ResearchQuery]
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- exa_config: Dict with settings and justifications
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- tavily_config: Dict with settings and justifications
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- recommended_provider: str
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- provider_justification: str
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"""
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try:
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logger.info(f"Unified analysis for: {user_input[:100]}...")
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keywords = keywords or []
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# Build the unified prompt
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prompt = self._build_unified_prompt(
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user_input=user_input,
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keywords=keywords,
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research_persona=research_persona,
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competitor_data=competitor_data,
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industry=industry,
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target_audience=target_audience,
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)
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# Define the comprehensive JSON schema
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unified_schema = self._build_unified_schema()
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# Call LLM (single call for everything)
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from services.llm_providers.main_text_generation import llm_text_gen
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result = llm_text_gen(
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prompt=prompt,
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json_struct=unified_schema,
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user_id=user_id
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)
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if isinstance(result, dict) and "error" in result:
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logger.error(f"Unified analysis failed: {result.get('error')}")
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return self._create_fallback_response(user_input, keywords)
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# Parse the unified result
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return self._parse_unified_result(result, user_input)
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except Exception as e:
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logger.error(f"Error in unified analysis: {e}")
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return self._create_fallback_response(user_input, keywords or [])
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def _build_unified_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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"""Build the unified prompt for intent + queries + parameters."""
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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 AI research strategist. Analyze the user's research request and provide a complete research plan including intent understanding, search queries, and optimal API settings.
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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: Provide a Complete Research Plan
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### PART 1: INTENT ANALYSIS
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Understand what the user really wants from their research.
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### PART 2: SEARCH QUERIES
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Generate 4-8 targeted search queries optimized for semantic search.
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### PART 3: PROVIDER SETTINGS
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Configure Exa and Tavily API parameters with justifications.
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### PART 4: GOOGLE TRENDS KEYWORDS (if trends in deliverables)
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If "trends" is in expected_deliverables OR purpose is "explore_trends":
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- Suggest 1-3 optimized keywords for Google Trends analysis
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- These may differ from research queries (trends need broader, searchable terms)
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- Consider: What keywords will show meaningful trends over time?
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- Consider: What timeframe will show relevant trends? (1 year, 12 months, etc.)
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- Consider: What geographic region is most relevant for the user?
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- Explain what insights trends will uncover for content generation:
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* Search interest trends over time (optimal publication timing)
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* Regional interest distribution (audience targeting)
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* Related topics for content expansion
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* Related queries for FAQ sections
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* Rising topics for timely content opportunities
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---
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## AVAILABLE PROVIDER OPTIONS
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### EXA API OPTIONS (Semantic Search Engine)
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| Parameter | Options | Description |
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|-----------|---------|-------------|
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| type | "auto", "neural", "fast", "deep" | "neural" = semantic understanding, "deep" = comprehensive with query expansion |
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| category | "company", "research paper", "news", "github", "tweet", "personal site", "pdf", "financial report", "people" | Focus on specific content types |
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| numResults | 5-25 | Number of results (10 recommended) |
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| includeDomains | string[] | Domains to include (e.g., ["arxiv.org", "nature.com"]) |
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| excludeDomains | string[] | Domains to exclude |
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| startPublishedDate | ISO date | Filter by publish date (e.g., "2024-01-01T00:00:00.000Z") |
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| text | boolean | Include full text content |
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| highlights | boolean | Extract key highlights |
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| context | boolean | Return as single context string for RAG |
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**WHEN TO USE EXA:**
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- Semantic understanding needed (finding similar content)
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- Academic/research papers
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- Company/competitor research
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- Deep, comprehensive results
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- Historical content
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### TAVILY API OPTIONS (AI-Powered Search)
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| Parameter | Options | Description |
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|-----------|---------|-------------|
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| topic | "general", "news", "finance" | Search topic category |
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| search_depth | "basic", "advanced" | "advanced" = multiple semantic snippets per URL |
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| include_answer | false, true, "basic", "advanced" | AI-generated answer from results |
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| include_raw_content | false, true, "markdown", "text" | Raw page content format |
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| time_range | "day", "week", "month", "year" | Filter by recency |
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| max_results | 5-20 | Number of results |
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| include_domains | string[] | Domains to include |
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| exclude_domains | string[] | Domains to exclude |
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**WHEN TO USE TAVILY:**
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- Real-time/current events
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- News and trending topics
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- Quick facts with AI answers
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- Financial data
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- Recent time-sensitive content
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---
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## OUTPUT FORMAT
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Return a JSON object with this exact structure:
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```json
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{{
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"intent": {{
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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"],
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"purpose": "learn|create_content|make_decision|compare|solve_problem|find_data|explore_trends|validate|generate_ideas",
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"content_output": "blog|podcast|video|social_post|newsletter|presentation|report|whitepaper|email|general",
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"expected_deliverables": ["key_statistics", "expert_quotes", "case_studies", "trends", "best_practices"],
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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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"confidence_reason": "Why this confidence level",
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"great_example": "Example of better input if confidence < 0.8",
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"needs_clarification": false,
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"clarifying_questions": [],
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"analysis_summary": "Brief summary of research plan"
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}},
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"queries": [
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{{
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"query": "Optimized search query string",
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"purpose": "key_statistics|expert_quotes|case_studies|trends|etc",
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"provider": "exa|tavily",
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"priority": 5,
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"expected_results": "What we expect to find",
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"justification": "Why this query and provider"
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}}
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],
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"enhanced_keywords": ["expanded", "related", "keywords"],
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"research_angles": ["Angle 1: ...", "Angle 2: ..."],
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"recommended_provider": "exa|tavily",
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"provider_justification": "Why this provider is best for this research",
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"exa_config": {{
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"enabled": true,
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"type": "auto|neural|fast|deep",
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"type_justification": "Why this search type",
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"category": "news|research paper|company|etc or null",
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"category_justification": "Why this category or null",
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"numResults": 10,
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"numResults_justification": "Why this number",
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"includeDomains": [],
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"includeDomains_justification": "Why these domains or empty",
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"startPublishedDate": "2024-01-01T00:00:00.000Z or null",
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"date_justification": "Why this date filter or null",
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"highlights": true,
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"highlights_justification": "Why enable/disable highlights",
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"context": true,
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"context_justification": "Why enable/disable context string"
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}},
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"tavily_config": {{
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"enabled": true,
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"topic": "general|news|finance",
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"topic_justification": "Why this topic",
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"search_depth": "basic|advanced",
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"search_depth_justification": "Why this depth",
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"include_answer": "true|false|basic|advanced",
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"include_answer_justification": "Why this answer mode",
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"time_range": "day|week|month|year|null",
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"time_range_justification": "Why this time range or null",
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"max_results": 10,
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"max_results_justification": "Why this number",
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"include_raw_content": "false|true|markdown|text",
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"include_raw_content_justification": "Why this content mode"
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}},
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"trends_config": {{
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"enabled": true|false,
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"keywords": ["keyword1", "keyword2"],
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"keywords_justification": "Why these keywords for trends analysis",
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"timeframe": "today 1-y|today 12-m|all",
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"timeframe_justification": "Why this timeframe",
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"geo": "US|GB|IN|etc",
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"geo_justification": "Why this geographic region",
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"expected_insights": [
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"Search interest trends over the past year",
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"Regional interest distribution",
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"Related topics for content expansion",
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"Related queries for FAQ sections",
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"Optimal publication timing based on interest peaks"
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]
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}}
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}}
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```
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## DECISION RULES
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1. **Provider Selection:**
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- Use EXA for: academic research, competitor analysis, deep understanding, finding similar content
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- Use TAVILY for: news, current events, quick facts, financial data, real-time info
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2. **Query Optimization:**
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- Include relevant keywords for semantic matching
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- Add context words based on deliverables (e.g., "statistics 2024" for key_statistics)
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- Match query style to provider (natural language for Exa, keyword-rich for Tavily)
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3. **Parameter Selection:**
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- ALWAYS provide justification for each parameter choice
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- Consider time sensitivity when setting date filters
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- Match category/topic to content type
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- Use "advanced" depth when quality matters more than speed
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4. **Google Trends Keywords (if trends enabled):**
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- Suggest 1-3 keywords optimized for trends analysis
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- Keywords should be broader than research queries (e.g., "AI marketing" vs "AI marketing tools for small businesses")
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- Consider what will show meaningful search interest trends
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- Choose timeframe based on content type (12 months for blogs, 1 year for comprehensive)
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- Select geo based on user's target audience or industry
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- List specific insights trends will uncover
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5. **Justifications:**
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- Keep justifications concise (1 sentence)
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- Explain the "why" not the "what"
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- Reference user's intent when relevant
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'''
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return prompt
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def _build_unified_schema(self) -> Dict[str, Any]:
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"""Build the JSON schema for unified response."""
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return {
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"type": "object",
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"properties": {
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"intent": {
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"type": "object",
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"properties": {
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"input_type": {"type": "string", "enum": ["keywords", "question", "goal", "mixed"]},
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"primary_question": {"type": "string"},
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"secondary_questions": {"type": "array", "items": {"type": "string"}},
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"purpose": {"type": "string"},
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"content_output": {"type": "string"},
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"expected_deliverables": {"type": "array", "items": {"type": "string"}},
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"depth": {"type": "string", "enum": ["overview", "detailed", "expert"]},
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"focus_areas": {"type": "array", "items": {"type": "string"}},
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"perspective": {"type": "string"},
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"time_sensitivity": {"type": "string"},
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"confidence": {"type": "number"},
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"confidence_reason": {"type": "string"},
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"great_example": {"type": "string"},
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"needs_clarification": {"type": "boolean"},
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"clarifying_questions": {"type": "array", "items": {"type": "string"}},
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"analysis_summary": {"type": "string"}
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},
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"required": ["primary_question", "purpose", "expected_deliverables", "confidence"]
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},
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"queries": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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"purpose": {"type": "string"},
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"provider": {"type": "string"},
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"priority": {"type": "integer"},
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"expected_results": {"type": "string"},
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"justification": {"type": "string"}
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},
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"required": ["query", "purpose", "provider", "priority"]
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}
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},
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"enhanced_keywords": {"type": "array", "items": {"type": "string"}},
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"research_angles": {"type": "array", "items": {"type": "string"}},
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"recommended_provider": {"type": "string"},
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"provider_justification": {"type": "string"},
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"exa_config": {
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"type": "object",
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"properties": {
|
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"enabled": {"type": "boolean"},
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"type": {"type": "string"},
|
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"type_justification": {"type": "string"},
|
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"category": {"type": "string"},
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"category_justification": {"type": "string"},
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"numResults": {"type": "integer"},
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"numResults_justification": {"type": "string"},
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"includeDomains": {"type": "array", "items": {"type": "string"}},
|
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"includeDomains_justification": {"type": "string"},
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"startPublishedDate": {"type": "string"},
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"date_justification": {"type": "string"},
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"highlights": {"type": "boolean"},
|
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"highlights_justification": {"type": "string"},
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"context": {"type": "boolean"},
|
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"context_justification": {"type": "string"}
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}
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},
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"tavily_config": {
|
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"type": "object",
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"properties": {
|
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"enabled": {"type": "boolean"},
|
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"topic": {"type": "string"},
|
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"topic_justification": {"type": "string"},
|
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"search_depth": {"type": "string"},
|
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"search_depth_justification": {"type": "string"},
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"include_answer": {"type": "string"},
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"include_answer_justification": {"type": "string"},
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"time_range": {"type": "string"},
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"time_range_justification": {"type": "string"},
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"max_results": {"type": "integer"},
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"max_results_justification": {"type": "string"},
|
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"include_raw_content": {"type": "string"},
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"include_raw_content_justification": {"type": "string"}
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}
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},
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"trends_config": {
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"type": "object",
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"properties": {
|
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"enabled": {"type": "boolean"},
|
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"keywords": {"type": "array", "items": {"type": "string"}},
|
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"keywords_justification": {"type": "string"},
|
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"timeframe": {"type": "string"},
|
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"timeframe_justification": {"type": "string"},
|
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"geo": {"type": "string"},
|
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"geo_justification": {"type": "string"},
|
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"expected_insights": {"type": "array", "items": {"type": "string"}}
|
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}
|
||||
}
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},
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"required": ["intent", "queries", "recommended_provider", "exa_config", "tavily_config"]
|
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}
|
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|
||||
def _build_persona_context(
|
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self,
|
||||
research_persona: Optional[ResearchPersona],
|
||||
industry: Optional[str],
|
||||
target_audience: Optional[str],
|
||||
) -> str:
|
||||
"""Build persona context section."""
|
||||
parts = []
|
||||
|
||||
if research_persona:
|
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if research_persona.default_industry:
|
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parts.append(f"Industry: {research_persona.default_industry}")
|
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if research_persona.default_target_audience:
|
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parts.append(f"Target Audience: {research_persona.default_target_audience}")
|
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if research_persona.research_angles:
|
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parts.append(f"Preferred Research Angles: {', '.join(research_persona.research_angles[:3])}")
|
||||
if research_persona.suggested_keywords:
|
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parts.append(f"Relevant Keywords: {', '.join(research_persona.suggested_keywords[:5])}")
|
||||
else:
|
||||
if industry:
|
||||
parts.append(f"Industry: {industry}")
|
||||
if target_audience:
|
||||
parts.append(f"Target Audience: {target_audience}")
|
||||
|
||||
if not parts:
|
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return "No specific user context available. Use general best practices."
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
def _build_competitor_context(self, competitor_data: Optional[List[Dict]]) -> str:
|
||||
"""Build competitor context section."""
|
||||
if not competitor_data:
|
||||
return ""
|
||||
|
||||
competitor_names = [c.get("name", c.get("url", "")) for c in competitor_data[:5]]
|
||||
if competitor_names:
|
||||
return f"\nKnown Competitors: {', '.join(competitor_names)}"
|
||||
return ""
|
||||
|
||||
def _parse_unified_result(self, result: Dict[str, Any], user_input: str) -> Dict[str, Any]:
|
||||
"""Parse the unified LLM result into structured response."""
|
||||
|
||||
intent_data = result.get("intent", {})
|
||||
|
||||
# Build ResearchIntent
|
||||
intent = ResearchIntent(
|
||||
primary_question=intent_data.get("primary_question", user_input),
|
||||
secondary_questions=intent_data.get("secondary_questions", []),
|
||||
purpose=intent_data.get("purpose", "learn"),
|
||||
content_output=intent_data.get("content_output", "general"),
|
||||
expected_deliverables=intent_data.get("expected_deliverables", ["key_statistics"]),
|
||||
depth=intent_data.get("depth", "detailed"),
|
||||
focus_areas=intent_data.get("focus_areas", []),
|
||||
perspective=intent_data.get("perspective"),
|
||||
time_sensitivity=intent_data.get("time_sensitivity"),
|
||||
input_type=intent_data.get("input_type", "keywords"),
|
||||
original_input=user_input,
|
||||
confidence=float(intent_data.get("confidence", 0.7)),
|
||||
confidence_reason=intent_data.get("confidence_reason"),
|
||||
great_example=intent_data.get("great_example"),
|
||||
needs_clarification=intent_data.get("needs_clarification", False),
|
||||
clarifying_questions=intent_data.get("clarifying_questions", []),
|
||||
)
|
||||
|
||||
# Build queries
|
||||
queries = []
|
||||
for q in result.get("queries", []):
|
||||
try:
|
||||
queries.append(ResearchQuery(
|
||||
query=q.get("query", ""),
|
||||
purpose=q.get("purpose", "key_statistics"),
|
||||
provider=q.get("provider", "exa"),
|
||||
priority=int(q.get("priority", 3)),
|
||||
expected_results=q.get("expected_results", ""),
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse query: {e}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"intent": intent,
|
||||
"queries": queries,
|
||||
"enhanced_keywords": result.get("enhanced_keywords", []),
|
||||
"research_angles": result.get("research_angles", []),
|
||||
"recommended_provider": result.get("recommended_provider", "exa"),
|
||||
"provider_justification": result.get("provider_justification", ""),
|
||||
"exa_config": result.get("exa_config", {}),
|
||||
"tavily_config": result.get("tavily_config", {}),
|
||||
"trends_config": result.get("trends_config", {}), # NEW: Google Trends configuration
|
||||
"analysis_summary": intent_data.get("analysis_summary", ""),
|
||||
}
|
||||
|
||||
def _create_fallback_response(self, user_input: str, keywords: List[str]) -> Dict[str, Any]:
|
||||
"""Create fallback response when analysis fails."""
|
||||
return {
|
||||
"success": False,
|
||||
"intent": ResearchIntent(
|
||||
primary_question=f"What are the key insights about: {user_input}?",
|
||||
purpose="learn",
|
||||
content_output="general",
|
||||
expected_deliverables=["key_statistics", "best_practices"],
|
||||
depth="detailed",
|
||||
original_input=user_input,
|
||||
confidence=0.5,
|
||||
),
|
||||
"queries": [
|
||||
ResearchQuery(
|
||||
query=user_input,
|
||||
purpose="key_statistics",
|
||||
provider="exa",
|
||||
priority=5,
|
||||
expected_results="General research results",
|
||||
)
|
||||
],
|
||||
"enhanced_keywords": keywords,
|
||||
"research_angles": [],
|
||||
"recommended_provider": "exa",
|
||||
"provider_justification": "Default fallback to Exa for semantic search",
|
||||
"exa_config": {
|
||||
"enabled": True,
|
||||
"type": "auto",
|
||||
"type_justification": "Auto mode for balanced results",
|
||||
"numResults": 10,
|
||||
"highlights": True,
|
||||
},
|
||||
"tavily_config": {
|
||||
"enabled": True,
|
||||
"topic": "general",
|
||||
"search_depth": "advanced",
|
||||
"include_answer": True,
|
||||
},
|
||||
"trends_config": {
|
||||
"enabled": False, # Disabled in fallback
|
||||
},
|
||||
}
|
||||
Reference in New Issue
Block a user