Added video studio router and endpoints. Added research router and endpoints. Added youtube router and endpoints. Added onboarding utils router and endpoints. Added onboarding utils service. Added onboarding utils models. Added onboarding utils routes. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. 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Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils. Added onboarding utils utils.

2026-01-01 17:56:25 +05:30
parent 7512933c65
commit b134e9dc7e
252 changed files with 40333 additions and 2712 deletions
--- a/backend/services/research/intent/intent_aware_analyzer.py
+++ b/backend/services/research/intent/intent_aware_analyzer.py
@@ -0,0 +1,547 @@
+"""
+Intent-Aware Result Analyzer
+
+Analyzes research results based on user intent.
+Extracts exactly what the user needs from raw research data.
+
+This is the key innovation - instead of generic analysis,
+we analyze results through the lens of what the user wants to accomplish.
+
+Author: ALwrity Team
+Version: 1.0
+"""
+
+import json
+from typing import Dict, Any, List, Optional
+from loguru import logger
+
+from models.research_intent_models import (
+    ResearchIntent,
+    IntentDrivenResearchResult,
+    ExpectedDeliverable,
+    StatisticWithCitation,
+    ExpertQuote,
+    CaseStudySummary,
+    TrendAnalysis,
+    ComparisonTable,
+    ComparisonItem,
+    ProsCons,
+    SourceWithRelevance,
+)
+from models.research_persona_models import ResearchPersona
+from .intent_prompt_builder import IntentPromptBuilder
+
+
+class IntentAwareAnalyzer:
+    """
+    Analyzes research results based on user intent.
+    
+    Instead of generic summaries, this extracts exactly what the user
+    needs: statistics, quotes, case studies, trends, etc.
+    """
+    
+    def __init__(self):
+        """Initialize the analyzer."""
+        self.prompt_builder = IntentPromptBuilder()
+        logger.info("IntentAwareAnalyzer initialized")
+    
+    async def analyze(
+        self,
+        raw_results: Dict[str, Any],
+        intent: ResearchIntent,
+        research_persona: Optional[ResearchPersona] = None,
+    ) -> IntentDrivenResearchResult:
+        """
+        Analyze raw research results based on user intent.
+        
+        Args:
+            raw_results: Raw results from Exa/Tavily/Google
+            intent: The user's research intent
+            research_persona: Optional persona for context
+            
+        Returns:
+            IntentDrivenResearchResult with extracted deliverables
+        """
+        try:
+            logger.info(f"Analyzing results for intent: {intent.primary_question[:50]}...")
+            
+            # Format raw results for analysis
+            formatted_results = self._format_raw_results(raw_results)
+            
+            # Build the analysis prompt
+            prompt = self.prompt_builder.build_intent_aware_analysis_prompt(
+                raw_results=formatted_results,
+                intent=intent,
+                research_persona=research_persona,
+            )
+            
+            # Define the expected JSON schema
+            analysis_schema = self._build_analysis_schema(intent.expected_deliverables)
+            
+            # Call LLM for analysis
+            from services.llm_providers.main_text_generation import llm_text_gen
+            
+            result = llm_text_gen(
+                prompt=prompt,
+                json_struct=analysis_schema,
+                user_id=None
+            )
+            
+            if isinstance(result, dict) and "error" in result:
+                logger.error(f"Intent-aware analysis failed: {result.get('error')}")
+                return self._create_fallback_result(raw_results, intent)
+            
+            # Parse and validate the result
+            analyzed_result = self._parse_analysis_result(result, intent, raw_results)
+            
+            logger.info(
+                f"Analysis complete: {len(analyzed_result.key_takeaways)} takeaways, "
+                f"{len(analyzed_result.statistics)} stats, "
+                f"{len(analyzed_result.sources)} sources"
+            )
+            
+            return analyzed_result
+            
+        except Exception as e:
+            logger.error(f"Error in intent-aware analysis: {e}")
+            return self._create_fallback_result(raw_results, intent)
+    
+    def _format_raw_results(self, raw_results: Dict[str, Any]) -> str:
+        """Format raw research results for LLM analysis."""
+        
+        formatted_parts = []
+        
+        # Extract content
+        content = raw_results.get("content", "")
+        if content:
+            formatted_parts.append(f"=== MAIN CONTENT ===\n{content[:8000]}")
+        
+        # Extract sources with their content
+        sources = raw_results.get("sources", [])
+        if sources:
+            formatted_parts.append("\n=== SOURCES ===")
+            for i, source in enumerate(sources[:15], 1):  # Limit to 15 sources
+                title = source.get("title", "Untitled")
+                url = source.get("url", "")
+                excerpt = source.get("excerpt", source.get("text", source.get("content", "")))
+                
+                formatted_parts.append(f"\nSource {i}: {title}")
+                formatted_parts.append(f"URL: {url}")
+                if excerpt:
+                    formatted_parts.append(f"Content: {excerpt[:500]}")
+        
+        # Extract grounding metadata if available (from Google)
+        grounding = raw_results.get("grounding_metadata", {})
+        if grounding:
+            formatted_parts.append("\n=== GROUNDING DATA ===")
+            formatted_parts.append(json.dumps(grounding, indent=2)[:2000])
+        
+        # Extract any AI answers (from Tavily)
+        answer = raw_results.get("answer", "")
+        if answer:
+            formatted_parts.append(f"\n=== AI-GENERATED ANSWER ===\n{answer}")
+        
+        return "\n".join(formatted_parts)
+    
+    def _build_analysis_schema(self, expected_deliverables: List[str]) -> Dict[str, Any]:
+        """Build JSON schema based on expected deliverables."""
+        
+        # Base schema
+        schema = {
+            "type": "object",
+            "properties": {
+                "primary_answer": {"type": "string"},
+                "secondary_answers": {
+                    "type": "object",
+                    "additionalProperties": {"type": "string"}
+                },
+                "executive_summary": {"type": "string"},
+                "key_takeaways": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "maxItems": 7
+                },
+                "confidence": {"type": "number"},
+                "gaps_identified": {
+                    "type": "array",
+                    "items": {"type": "string"}
+                },
+                "follow_up_queries": {
+                    "type": "array",
+                    "items": {"type": "string"}
+                },
+            },
+            "required": ["primary_answer", "executive_summary", "key_takeaways", "confidence"]
+        }
+        
+        # Add deliverable-specific properties
+        if ExpectedDeliverable.KEY_STATISTICS.value in expected_deliverables:
+            schema["properties"]["statistics"] = {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "statistic": {"type": "string"},
+                        "value": {"type": "string"},
+                        "context": {"type": "string"},
+                        "source": {"type": "string"},
+                        "url": {"type": "string"},
+                        "credibility": {"type": "number"},
+                        "recency": {"type": "string"}
+                    },
+                    "required": ["statistic", "context", "source", "url"]
+                }
+            }
+        
+        if ExpectedDeliverable.EXPERT_QUOTES.value in expected_deliverables:
+            schema["properties"]["expert_quotes"] = {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "quote": {"type": "string"},
+                        "speaker": {"type": "string"},
+                        "title": {"type": "string"},
+                        "organization": {"type": "string"},
+                        "source": {"type": "string"},
+                        "url": {"type": "string"}
+                    },
+                    "required": ["quote", "speaker", "source", "url"]
+                }
+            }
+        
+        if ExpectedDeliverable.CASE_STUDIES.value in expected_deliverables:
+            schema["properties"]["case_studies"] = {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "title": {"type": "string"},
+                        "organization": {"type": "string"},
+                        "challenge": {"type": "string"},
+                        "solution": {"type": "string"},
+                        "outcome": {"type": "string"},
+                        "key_metrics": {"type": "array", "items": {"type": "string"}},
+                        "source": {"type": "string"},
+                        "url": {"type": "string"}
+                    },
+                    "required": ["title", "organization", "challenge", "solution", "outcome"]
+                }
+            }
+        
+        if ExpectedDeliverable.TRENDS.value in expected_deliverables:
+            schema["properties"]["trends"] = {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "trend": {"type": "string"},
+                        "direction": {"type": "string"},
+                        "evidence": {"type": "array", "items": {"type": "string"}},
+                        "impact": {"type": "string"},
+                        "timeline": {"type": "string"},
+                        "sources": {"type": "array", "items": {"type": "string"}}
+                    },
+                    "required": ["trend", "direction", "evidence"]
+                }
+            }
+        
+        if ExpectedDeliverable.COMPARISONS.value in expected_deliverables:
+            schema["properties"]["comparisons"] = {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "title": {"type": "string"},
+                        "criteria": {"type": "array", "items": {"type": "string"}},
+                        "items": {
+                            "type": "array",
+                            "items": {
+                                "type": "object",
+                                "properties": {
+                                    "name": {"type": "string"},
+                                    "pros": {"type": "array", "items": {"type": "string"}},
+                                    "cons": {"type": "array", "items": {"type": "string"}},
+                                    "features": {"type": "object"}
+                                }
+                            }
+                        },
+                        "verdict": {"type": "string"}
+                    }
+                }
+            }
+        
+        if ExpectedDeliverable.PROS_CONS.value in expected_deliverables:
+            schema["properties"]["pros_cons"] = {
+                "type": "object",
+                "properties": {
+                    "subject": {"type": "string"},
+                    "pros": {"type": "array", "items": {"type": "string"}},
+                    "cons": {"type": "array", "items": {"type": "string"}},
+                    "balanced_verdict": {"type": "string"}
+                }
+            }
+        
+        if ExpectedDeliverable.BEST_PRACTICES.value in expected_deliverables:
+            schema["properties"]["best_practices"] = {
+                "type": "array",
+                "items": {"type": "string"}
+            }
+        
+        if ExpectedDeliverable.STEP_BY_STEP.value in expected_deliverables:
+            schema["properties"]["step_by_step"] = {
+                "type": "array",
+                "items": {"type": "string"}
+            }
+        
+        if ExpectedDeliverable.DEFINITIONS.value in expected_deliverables:
+            schema["properties"]["definitions"] = {
+                "type": "object",
+                "additionalProperties": {"type": "string"}
+            }
+        
+        if ExpectedDeliverable.EXAMPLES.value in expected_deliverables:
+            schema["properties"]["examples"] = {
+                "type": "array",
+                "items": {"type": "string"}
+            }
+        
+        if ExpectedDeliverable.PREDICTIONS.value in expected_deliverables:
+            schema["properties"]["predictions"] = {
+                "type": "array",
+                "items": {"type": "string"}
+            }
+        
+        # Always include sources and suggested outline
+        schema["properties"]["sources"] = {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "title": {"type": "string"},
+                    "url": {"type": "string"},
+                    "relevance_score": {"type": "number"},
+                    "relevance_reason": {"type": "string"},
+                    "content_type": {"type": "string"},
+                    "credibility_score": {"type": "number"}
+                },
+                "required": ["title", "url"]
+            }
+        }
+        
+        schema["properties"]["suggested_outline"] = {
+            "type": "array",
+            "items": {"type": "string"}
+        }
+        
+        return schema
+    
+    def _parse_analysis_result(
+        self,
+        result: Dict[str, Any],
+        intent: ResearchIntent,
+        raw_results: Dict[str, Any],
+    ) -> IntentDrivenResearchResult:
+        """Parse LLM analysis result into structured format."""
+        
+        # Parse statistics
+        statistics = []
+        for stat in result.get("statistics", []):
+            try:
+                statistics.append(StatisticWithCitation(
+                    statistic=stat.get("statistic", ""),
+                    value=stat.get("value"),
+                    context=stat.get("context", ""),
+                    source=stat.get("source", ""),
+                    url=stat.get("url", ""),
+                    credibility=float(stat.get("credibility", 0.8)),
+                    recency=stat.get("recency"),
+                ))
+            except Exception as e:
+                logger.warning(f"Failed to parse statistic: {e}")
+        
+        # Parse expert quotes
+        expert_quotes = []
+        for quote in result.get("expert_quotes", []):
+            try:
+                expert_quotes.append(ExpertQuote(
+                    quote=quote.get("quote", ""),
+                    speaker=quote.get("speaker", ""),
+                    title=quote.get("title"),
+                    organization=quote.get("organization"),
+                    context=quote.get("context"),
+                    source=quote.get("source", ""),
+                    url=quote.get("url", ""),
+                ))
+            except Exception as e:
+                logger.warning(f"Failed to parse expert quote: {e}")
+        
+        # Parse case studies
+        case_studies = []
+        for cs in result.get("case_studies", []):
+            try:
+                case_studies.append(CaseStudySummary(
+                    title=cs.get("title", ""),
+                    organization=cs.get("organization", ""),
+                    challenge=cs.get("challenge", ""),
+                    solution=cs.get("solution", ""),
+                    outcome=cs.get("outcome", ""),
+                    key_metrics=cs.get("key_metrics", []),
+                    source=cs.get("source", ""),
+                    url=cs.get("url", ""),
+                ))
+            except Exception as e:
+                logger.warning(f"Failed to parse case study: {e}")
+        
+        # Parse trends
+        trends = []
+        for trend in result.get("trends", []):
+            try:
+                trends.append(TrendAnalysis(
+                    trend=trend.get("trend", ""),
+                    direction=trend.get("direction", "growing"),
+                    evidence=trend.get("evidence", []),
+                    impact=trend.get("impact"),
+                    timeline=trend.get("timeline"),
+                    sources=trend.get("sources", []),
+                ))
+            except Exception as e:
+                logger.warning(f"Failed to parse trend: {e}")
+        
+        # Parse comparisons
+        comparisons = []
+        for comp in result.get("comparisons", []):
+            try:
+                items = []
+                for item in comp.get("items", []):
+                    items.append(ComparisonItem(
+                        name=item.get("name", ""),
+                        description=item.get("description"),
+                        pros=item.get("pros", []),
+                        cons=item.get("cons", []),
+                        features=item.get("features", {}),
+                        rating=item.get("rating"),
+                        source=item.get("source"),
+                    ))
+                comparisons.append(ComparisonTable(
+                    title=comp.get("title", ""),
+                    criteria=comp.get("criteria", []),
+                    items=items,
+                    winner=comp.get("winner"),
+                    verdict=comp.get("verdict"),
+                ))
+            except Exception as e:
+                logger.warning(f"Failed to parse comparison: {e}")
+        
+        # Parse pros/cons
+        pros_cons = None
+        pc_data = result.get("pros_cons")
+        if pc_data:
+            try:
+                pros_cons = ProsCons(
+                    subject=pc_data.get("subject", intent.original_input),
+                    pros=pc_data.get("pros", []),
+                    cons=pc_data.get("cons", []),
+                    balanced_verdict=pc_data.get("balanced_verdict", ""),
+                )
+            except Exception as e:
+                logger.warning(f"Failed to parse pros/cons: {e}")
+        
+        # Parse sources
+        sources = []
+        for src in result.get("sources", []):
+            try:
+                sources.append(SourceWithRelevance(
+                    title=src.get("title", ""),
+                    url=src.get("url", ""),
+                    excerpt=src.get("excerpt"),
+                    relevance_score=float(src.get("relevance_score", 0.8)),
+                    relevance_reason=src.get("relevance_reason"),
+                    content_type=src.get("content_type"),
+                    published_date=src.get("published_date"),
+                    credibility_score=float(src.get("credibility_score", 0.8)),
+                ))
+            except Exception as e:
+                logger.warning(f"Failed to parse source: {e}")
+        
+        # If no sources from analysis, extract from raw results
+        if not sources:
+            sources = self._extract_sources_from_raw(raw_results)
+        
+        return IntentDrivenResearchResult(
+            success=True,
+            primary_answer=result.get("primary_answer", ""),
+            secondary_answers=result.get("secondary_answers", {}),
+            statistics=statistics,
+            expert_quotes=expert_quotes,
+            case_studies=case_studies,
+            comparisons=comparisons,
+            trends=trends,
+            best_practices=result.get("best_practices", []),
+            step_by_step=result.get("step_by_step", []),
+            pros_cons=pros_cons,
+            definitions=result.get("definitions", {}),
+            examples=result.get("examples", []),
+            predictions=result.get("predictions", []),
+            executive_summary=result.get("executive_summary", ""),
+            key_takeaways=result.get("key_takeaways", []),
+            suggested_outline=result.get("suggested_outline", []),
+            sources=sources,
+            raw_content=self._format_raw_results(raw_results)[:5000],
+            confidence=float(result.get("confidence", 0.7)),
+            gaps_identified=result.get("gaps_identified", []),
+            follow_up_queries=result.get("follow_up_queries", []),
+            original_intent=intent,
+        )
+    
+    def _extract_sources_from_raw(self, raw_results: Dict[str, Any]) -> List[SourceWithRelevance]:
+        """Extract sources from raw results when analysis doesn't provide them."""
+        
+        sources = []
+        for src in raw_results.get("sources", [])[:10]:
+            try:
+                sources.append(SourceWithRelevance(
+                    title=src.get("title", "Untitled"),
+                    url=src.get("url", ""),
+                    excerpt=src.get("excerpt", src.get("text", ""))[:200],
+                    relevance_score=0.8,
+                    credibility_score=float(src.get("credibility_score", 0.8)),
+                ))
+            except Exception as e:
+                logger.warning(f"Failed to extract source: {e}")
+        
+        return sources
+    
+    def _create_fallback_result(
+        self,
+        raw_results: Dict[str, Any],
+        intent: ResearchIntent,
+    ) -> IntentDrivenResearchResult:
+        """Create a fallback result when AI analysis fails."""
+        
+        # Extract basic information from raw results
+        content = raw_results.get("content", "")
+        sources = self._extract_sources_from_raw(raw_results)
+        
+        # Create basic takeaways from content
+        key_takeaways = []
+        if content:
+            sentences = content.split(". ")[:5]
+            key_takeaways = [s.strip() + "." for s in sentences if len(s) > 20]
+        
+        return IntentDrivenResearchResult(
+            success=True,
+            primary_answer=f"Research findings for: {intent.primary_question}",
+            secondary_answers={},
+            executive_summary=content[:300] if content else "Research completed",
+            key_takeaways=key_takeaways,
+            sources=sources,
+            raw_content=self._format_raw_results(raw_results)[:5000],
+            confidence=0.5,
+            gaps_identified=[
+                "AI analysis failed - showing raw results",
+                "Manual review recommended"
+            ],
+            follow_up_queries=[],
+            original_intent=intent,
+        )