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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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/research_intent_inference.py
+++ b/backend/services/research/intent/research_intent_inference.py
@@ -0,0 +1,378 @@
+"""
+Research Intent Inference Service
+
+Analyzes user input to understand their research intent.
+Uses AI to infer:
+- What the user wants to accomplish
+- What questions need answering
+- What deliverables they expect
+
+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,
+    ResearchPurpose,
+    ContentOutput,
+    ExpectedDeliverable,
+    ResearchDepthLevel,
+    InputType,
+    IntentInferenceRequest,
+    IntentInferenceResponse,
+    ResearchQuery,
+)
+from models.research_persona_models import ResearchPersona
+from .intent_prompt_builder import IntentPromptBuilder
+
+
+class ResearchIntentInference:
+    """
+    Infers user research intent from minimal input.
+    
+    Instead of asking a formal questionnaire, this service
+    uses AI to understand what the user really wants.
+    """
+    
+    def __init__(self):
+        """Initialize the intent inference service."""
+        self.prompt_builder = IntentPromptBuilder()
+        logger.info("ResearchIntentInference initialized")
+    
+    async def infer_intent(
+        self,
+        user_input: str,
+        keywords: Optional[List[str]] = None,
+        research_persona: Optional[ResearchPersona] = None,
+        competitor_data: Optional[List[Dict]] = None,
+        industry: Optional[str] = None,
+        target_audience: Optional[str] = None,
+    ) -> IntentInferenceResponse:
+        """
+        Analyze user input and infer their research intent.
+        
+        Args:
+            user_input: User's keywords, question, or goal
+            keywords: Extracted keywords (optional)
+            research_persona: User's research persona (optional)
+            competitor_data: Competitor analysis data (optional)
+            industry: Industry context (optional)
+            target_audience: Target audience context (optional)
+            
+        Returns:
+            IntentInferenceResponse with inferred intent and suggested queries
+        """
+        try:
+            logger.info(f"Inferring intent for: {user_input[:100]}...")
+            
+            keywords = keywords or []
+            
+            # Build the inference prompt
+            prompt = self.prompt_builder.build_intent_inference_prompt(
+                user_input=user_input,
+                keywords=keywords,
+                research_persona=research_persona,
+                competitor_data=competitor_data,
+                industry=industry,
+                target_audience=target_audience,
+            )
+            
+            # Define the expected JSON schema
+            intent_schema = {
+                "type": "object",
+                "properties": {
+                    "input_type": {"type": "string", "enum": ["keywords", "question", "goal", "mixed"]},
+                    "primary_question": {"type": "string"},
+                    "secondary_questions": {"type": "array", "items": {"type": "string"}},
+                    "purpose": {"type": "string"},
+                    "content_output": {"type": "string"},
+                    "expected_deliverables": {"type": "array", "items": {"type": "string"}},
+                    "depth": {"type": "string", "enum": ["overview", "detailed", "expert"]},
+                    "focus_areas": {"type": "array", "items": {"type": "string"}},
+                    "perspective": {"type": "string"},
+                    "time_sensitivity": {"type": "string"},
+                    "confidence": {"type": "number"},
+                    "needs_clarification": {"type": "boolean"},
+                    "clarifying_questions": {"type": "array", "items": {"type": "string"}},
+                    "analysis_summary": {"type": "string"}
+                },
+                "required": [
+                    "input_type", "primary_question", "purpose", "content_output",
+                    "expected_deliverables", "depth", "confidence", "analysis_summary"
+                ]
+            }
+            
+            # Call LLM for intent inference
+            from services.llm_providers.main_text_generation import llm_text_gen
+            
+            result = llm_text_gen(
+                prompt=prompt,
+                json_struct=intent_schema,
+                user_id=None
+            )
+            
+            if isinstance(result, dict) and "error" in result:
+                logger.error(f"Intent inference failed: {result.get('error')}")
+                return self._create_fallback_response(user_input, keywords)
+            
+            # Parse and validate the result
+            intent = self._parse_intent_result(result, user_input)
+            
+            # Generate quick options for UI
+            quick_options = self._generate_quick_options(intent, result)
+            
+            # Create response
+            response = IntentInferenceResponse(
+                success=True,
+                intent=intent,
+                analysis_summary=result.get("analysis_summary", "Research intent analyzed"),
+                suggested_queries=[],  # Will be populated by query generator
+                suggested_keywords=self._extract_keywords_from_input(user_input, keywords),
+                suggested_angles=result.get("focus_areas", []),
+                quick_options=quick_options,
+            )
+            
+            logger.info(f"Intent inferred: purpose={intent.purpose}, confidence={intent.confidence}")
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error inferring intent: {e}")
+            return self._create_fallback_response(user_input, keywords or [])
+    
+    def _parse_intent_result(self, result: Dict[str, Any], user_input: str) -> ResearchIntent:
+        """Parse LLM result into ResearchIntent model."""
+        
+        # Map string values to enums safely
+        input_type = self._safe_enum(InputType, result.get("input_type", "keywords"), InputType.KEYWORDS)
+        purpose = self._safe_enum(ResearchPurpose, result.get("purpose", "learn"), ResearchPurpose.LEARN)
+        content_output = self._safe_enum(ContentOutput, result.get("content_output", "general"), ContentOutput.GENERAL)
+        depth = self._safe_enum(ResearchDepthLevel, result.get("depth", "detailed"), ResearchDepthLevel.DETAILED)
+        
+        # Parse expected deliverables
+        raw_deliverables = result.get("expected_deliverables", [])
+        expected_deliverables = []
+        for d in raw_deliverables:
+            try:
+                expected_deliverables.append(ExpectedDeliverable(d).value)
+            except ValueError:
+                # Skip invalid deliverables
+                pass
+        
+        # Ensure we have at least some deliverables
+        if not expected_deliverables:
+            expected_deliverables = self._infer_deliverables_from_purpose(purpose)
+        
+        return ResearchIntent(
+            primary_question=result.get("primary_question", user_input),
+            secondary_questions=result.get("secondary_questions", []),
+            purpose=purpose.value,
+            content_output=content_output.value,
+            expected_deliverables=expected_deliverables,
+            depth=depth.value,
+            focus_areas=result.get("focus_areas", []),
+            perspective=result.get("perspective"),
+            time_sensitivity=result.get("time_sensitivity"),
+            input_type=input_type.value,
+            original_input=user_input,
+            confidence=float(result.get("confidence", 0.7)),
+            needs_clarification=result.get("needs_clarification", False),
+            clarifying_questions=result.get("clarifying_questions", []),
+        )
+    
+    def _safe_enum(self, enum_class, value: str, default):
+        """Safely convert string to enum, returning default if invalid."""
+        try:
+            return enum_class(value)
+        except ValueError:
+            return default
+    
+    def _infer_deliverables_from_purpose(self, purpose: ResearchPurpose) -> List[str]:
+        """Infer expected deliverables based on research purpose."""
+        
+        purpose_deliverables = {
+            ResearchPurpose.LEARN: [
+                ExpectedDeliverable.DEFINITIONS.value,
+                ExpectedDeliverable.EXAMPLES.value,
+                ExpectedDeliverable.KEY_STATISTICS.value,
+            ],
+            ResearchPurpose.CREATE_CONTENT: [
+                ExpectedDeliverable.KEY_STATISTICS.value,
+                ExpectedDeliverable.EXPERT_QUOTES.value,
+                ExpectedDeliverable.EXAMPLES.value,
+                ExpectedDeliverable.CASE_STUDIES.value,
+            ],
+            ResearchPurpose.MAKE_DECISION: [
+                ExpectedDeliverable.PROS_CONS.value,
+                ExpectedDeliverable.COMPARISONS.value,
+                ExpectedDeliverable.BEST_PRACTICES.value,
+            ],
+            ResearchPurpose.COMPARE: [
+                ExpectedDeliverable.COMPARISONS.value,
+                ExpectedDeliverable.PROS_CONS.value,
+                ExpectedDeliverable.KEY_STATISTICS.value,
+            ],
+            ResearchPurpose.SOLVE_PROBLEM: [
+                ExpectedDeliverable.STEP_BY_STEP.value,
+                ExpectedDeliverable.BEST_PRACTICES.value,
+                ExpectedDeliverable.CASE_STUDIES.value,
+            ],
+            ResearchPurpose.FIND_DATA: [
+                ExpectedDeliverable.KEY_STATISTICS.value,
+                ExpectedDeliverable.CITATIONS.value,
+            ],
+            ResearchPurpose.EXPLORE_TRENDS: [
+                ExpectedDeliverable.TRENDS.value,
+                ExpectedDeliverable.PREDICTIONS.value,
+                ExpectedDeliverable.KEY_STATISTICS.value,
+            ],
+            ResearchPurpose.VALIDATE: [
+                ExpectedDeliverable.CITATIONS.value,
+                ExpectedDeliverable.KEY_STATISTICS.value,
+                ExpectedDeliverable.EXPERT_QUOTES.value,
+            ],
+            ResearchPurpose.GENERATE_IDEAS: [
+                ExpectedDeliverable.EXAMPLES.value,
+                ExpectedDeliverable.TRENDS.value,
+                ExpectedDeliverable.CASE_STUDIES.value,
+            ],
+        }
+        
+        return purpose_deliverables.get(purpose, [ExpectedDeliverable.KEY_STATISTICS.value])
+    
+    def _generate_quick_options(self, intent: ResearchIntent, result: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Generate quick options for UI confirmation."""
+        
+        options = []
+        
+        # Purpose option
+        options.append({
+            "id": "purpose",
+            "label": "Research Purpose",
+            "value": intent.purpose,
+            "display": self._purpose_display(intent.purpose),
+            "alternatives": [p.value for p in ResearchPurpose],
+            "confidence": result.get("confidence", 0.7),
+        })
+        
+        # Content output option
+        if intent.content_output != ContentOutput.GENERAL.value:
+            options.append({
+                "id": "content_output",
+                "label": "Content Type",
+                "value": intent.content_output,
+                "display": intent.content_output.replace("_", " ").title(),
+                "alternatives": [c.value for c in ContentOutput],
+                "confidence": result.get("confidence", 0.7),
+            })
+        
+        # Deliverables option
+        options.append({
+            "id": "deliverables",
+            "label": "What I'll Find",
+            "value": intent.expected_deliverables,
+            "display": [d.replace("_", " ").title() for d in intent.expected_deliverables[:4]],
+            "alternatives": [d.value for d in ExpectedDeliverable],
+            "confidence": result.get("confidence", 0.7),
+            "multi_select": True,
+        })
+        
+        # Depth option
+        options.append({
+            "id": "depth",
+            "label": "Research Depth",
+            "value": intent.depth,
+            "display": intent.depth.title(),
+            "alternatives": [d.value for d in ResearchDepthLevel],
+            "confidence": result.get("confidence", 0.7),
+        })
+        
+        return options
+    
+    def _purpose_display(self, purpose: str) -> str:
+        """Get display-friendly purpose text."""
+        display_map = {
+            "learn": "Understand this topic",
+            "create_content": "Create content about this",
+            "make_decision": "Make a decision",
+            "compare": "Compare options",
+            "solve_problem": "Solve a problem",
+            "find_data": "Find specific data",
+            "explore_trends": "Explore trends",
+            "validate": "Validate information",
+            "generate_ideas": "Generate ideas",
+        }
+        return display_map.get(purpose, purpose.replace("_", " ").title())
+    
+    def _extract_keywords_from_input(self, user_input: str, keywords: List[str]) -> List[str]:
+        """Extract and enhance keywords from user input."""
+        
+        # Start with provided keywords
+        extracted = list(keywords) if keywords else []
+        
+        # Simple extraction from input (split on common delimiters)
+        words = user_input.lower().replace(",", " ").replace(";", " ").split()
+        
+        # Filter out common words
+        stop_words = {
+            "the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
+            "have", "has", "had", "do", "does", "did", "will", "would", "could",
+            "should", "may", "might", "must", "shall", "can", "need", "dare",
+            "to", "of", "in", "for", "on", "with", "at", "by", "from", "up",
+            "about", "into", "through", "during", "before", "after", "above",
+            "below", "between", "under", "again", "further", "then", "once",
+            "here", "there", "when", "where", "why", "how", "all", "each",
+            "few", "more", "most", "other", "some", "such", "no", "nor", "not",
+            "only", "own", "same", "so", "than", "too", "very", "just", "and",
+            "but", "if", "or", "because", "as", "until", "while", "i", "we",
+            "you", "they", "what", "which", "who", "whom", "this", "that",
+            "these", "those", "am", "want", "write", "blog", "post", "article",
+        }
+        
+        for word in words:
+            if word not in stop_words and len(word) > 2 and word not in extracted:
+                extracted.append(word)
+        
+        return extracted[:15]  # Limit to 15 keywords
+    
+    def _create_fallback_response(self, user_input: str, keywords: List[str]) -> IntentInferenceResponse:
+        """Create a fallback response when AI inference fails."""
+        
+        # Create a basic intent from the input
+        fallback_intent = ResearchIntent(
+            primary_question=f"What are the key insights about: {user_input}?",
+            secondary_questions=[
+                f"What are the latest trends in {user_input}?",
+                f"What are best practices for {user_input}?",
+            ],
+            purpose=ResearchPurpose.LEARN.value,
+            content_output=ContentOutput.GENERAL.value,
+            expected_deliverables=[
+                ExpectedDeliverable.KEY_STATISTICS.value,
+                ExpectedDeliverable.EXAMPLES.value,
+                ExpectedDeliverable.BEST_PRACTICES.value,
+            ],
+            depth=ResearchDepthLevel.DETAILED.value,
+            focus_areas=[],
+            input_type=InputType.KEYWORDS.value,
+            original_input=user_input,
+            confidence=0.5,
+            needs_clarification=True,
+            clarifying_questions=[
+                "What type of content are you creating?",
+                "What specific aspects are you most interested in?",
+            ],
+        )
+        
+        return IntentInferenceResponse(
+            success=True,  # Still return success, just with lower confidence
+            intent=fallback_intent,
+            analysis_summary=f"Basic research analysis for: {user_input}",
+            suggested_queries=[],
+            suggested_keywords=keywords,
+            suggested_angles=[],
+            quick_options=[],
+        )