""" Research API Router Standalone API endpoints for the Research Engine. These endpoints can be used by: - Frontend Research UI - Blog Writer (via adapter) - Podcast Maker - YouTube Creator - Any other content tool Author: ALwrity Team Version: 2.0 """ from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks from pydantic import BaseModel, Field from typing import Optional, List, Dict, Any from loguru import logger import uuid import asyncio from services.database import get_db from services.research.core import ( ResearchEngine, ResearchContext, ResearchPersonalizationContext, ContentType, ResearchGoal, ResearchDepth, ProviderPreference, ) from services.research.core.research_context import ResearchResult from middleware.auth_middleware import get_current_user # Intent-driven research imports from models.research_intent_models import ( ResearchIntent, IntentInferenceRequest, IntentInferenceResponse, IntentDrivenResearchResult, ResearchQuery, ExpectedDeliverable, ResearchPurpose, ContentOutput, ResearchDepthLevel, ) from services.research.intent import ( ResearchIntentInference, IntentQueryGenerator, IntentAwareAnalyzer, ) router = APIRouter(prefix="/api/research", tags=["Research Engine"]) # Request/Response models class ResearchRequest(BaseModel): """API request for research.""" query: str = Field(..., description="Main research query or topic") keywords: List[str] = Field(default_factory=list, description="Additional keywords") # Research configuration goal: Optional[str] = Field(default="factual", description="Research goal: factual, trending, competitive, etc.") depth: Optional[str] = Field(default="standard", description="Research depth: quick, standard, comprehensive, expert") provider: Optional[str] = Field(default="auto", description="Provider preference: auto, exa, tavily, google") # Personalization content_type: Optional[str] = Field(default="general", description="Content type: blog, podcast, video, etc.") industry: Optional[str] = None target_audience: Optional[str] = None tone: Optional[str] = None # Constraints max_sources: int = Field(default=10, ge=1, le=25) recency: Optional[str] = None # day, week, month, year # Domain filtering include_domains: List[str] = Field(default_factory=list) exclude_domains: List[str] = Field(default_factory=list) # Advanced mode advanced_mode: bool = False # Raw provider parameters (only if advanced_mode=True) exa_category: Optional[str] = None exa_search_type: Optional[str] = None tavily_topic: Optional[str] = None tavily_search_depth: Optional[str] = None tavily_include_answer: bool = False tavily_time_range: Optional[str] = None class ResearchResponse(BaseModel): """API response for research.""" success: bool task_id: Optional[str] = None # For async requests # Results (if synchronous) sources: List[Dict[str, Any]] = Field(default_factory=list) keyword_analysis: Dict[str, Any] = Field(default_factory=dict) competitor_analysis: Dict[str, Any] = Field(default_factory=dict) suggested_angles: List[str] = Field(default_factory=list) # Metadata provider_used: Optional[str] = None search_queries: List[str] = Field(default_factory=list) # Error handling error_message: Optional[str] = None error_code: Optional[str] = None class ProviderStatusResponse(BaseModel): """API response for provider status.""" exa: Dict[str, Any] tavily: Dict[str, Any] google: Dict[str, Any] # In-memory task storage for async research _research_tasks: Dict[str, Dict[str, Any]] = {} def _convert_to_research_context(request: ResearchRequest, user_id: str) -> ResearchContext: """Convert API request to ResearchContext.""" # Map string enums goal_map = { "factual": ResearchGoal.FACTUAL, "trending": ResearchGoal.TRENDING, "competitive": ResearchGoal.COMPETITIVE, "educational": ResearchGoal.EDUCATIONAL, "technical": ResearchGoal.TECHNICAL, "inspirational": ResearchGoal.INSPIRATIONAL, } depth_map = { "quick": ResearchDepth.QUICK, "standard": ResearchDepth.STANDARD, "comprehensive": ResearchDepth.COMPREHENSIVE, "expert": ResearchDepth.EXPERT, } provider_map = { "auto": ProviderPreference.AUTO, "exa": ProviderPreference.EXA, "tavily": ProviderPreference.TAVILY, "google": ProviderPreference.GOOGLE, "hybrid": ProviderPreference.HYBRID, } content_type_map = { "blog": ContentType.BLOG, "podcast": ContentType.PODCAST, "video": ContentType.VIDEO, "social": ContentType.SOCIAL, "email": ContentType.EMAIL, "newsletter": ContentType.NEWSLETTER, "whitepaper": ContentType.WHITEPAPER, "general": ContentType.GENERAL, } # Build personalization context personalization = ResearchPersonalizationContext( creator_id=user_id, content_type=content_type_map.get(request.content_type or "general", ContentType.GENERAL), industry=request.industry, target_audience=request.target_audience, tone=request.tone, ) return ResearchContext( query=request.query, keywords=request.keywords, goal=goal_map.get(request.goal or "factual", ResearchGoal.FACTUAL), depth=depth_map.get(request.depth or "standard", ResearchDepth.STANDARD), provider_preference=provider_map.get(request.provider or "auto", ProviderPreference.AUTO), personalization=personalization, max_sources=request.max_sources, recency=request.recency, include_domains=request.include_domains, exclude_domains=request.exclude_domains, advanced_mode=request.advanced_mode, exa_category=request.exa_category, exa_search_type=request.exa_search_type, tavily_topic=request.tavily_topic, tavily_search_depth=request.tavily_search_depth, tavily_include_answer=request.tavily_include_answer, tavily_time_range=request.tavily_time_range, ) @router.get("/providers/status", response_model=ProviderStatusResponse) async def get_provider_status(): """ Get status of available research providers. Returns availability and priority of Exa, Tavily, and Google providers. """ engine = ResearchEngine() return engine.get_provider_status() @router.post("/execute", response_model=ResearchResponse) async def execute_research( request: ResearchRequest, current_user: Dict[str, Any] = Depends(get_current_user), ): """ Execute research synchronously. For quick research needs. For longer research, use /start endpoint. """ try: if not current_user: raise HTTPException(status_code=401, detail="Authentication required") user_id = str(current_user.get('id', '')) if not user_id: raise HTTPException(status_code=401, detail="Invalid user ID in authentication token") logger.info(f"[Research API] Execute request: {request.query[:50]}...") engine = ResearchEngine() context = _convert_to_research_context(request, user_id) result = await engine.research(context) return ResearchResponse( success=result.success, sources=result.sources, keyword_analysis=result.keyword_analysis, competitor_analysis=result.competitor_analysis, suggested_angles=result.suggested_angles, provider_used=result.provider_used, search_queries=result.search_queries, error_message=result.error_message, error_code=result.error_code, ) except Exception as e: logger.error(f"[Research API] Execute failed: {e}") raise HTTPException(status_code=500, detail=str(e)) @router.post("/start", response_model=ResearchResponse) async def start_research( request: ResearchRequest, background_tasks: BackgroundTasks, current_user: Dict[str, Any] = Depends(get_current_user), ): """ Start research asynchronously. Returns a task_id that can be used to poll for status. Use this for comprehensive research that may take longer. """ try: if not current_user: raise HTTPException(status_code=401, detail="Authentication required") user_id = str(current_user.get('id', '')) if not user_id: raise HTTPException(status_code=401, detail="Invalid user ID in authentication token") logger.info(f"[Research API] Start async request: {request.query[:50]}...") task_id = str(uuid.uuid4()) # Initialize task _research_tasks[task_id] = { "status": "pending", "progress_messages": [], "result": None, "error": None, } # Start background task context = _convert_to_research_context(request, user_id) background_tasks.add_task(_run_research_task, task_id, context) return ResearchResponse( success=True, task_id=task_id, ) except Exception as e: logger.error(f"[Research API] Start failed: {e}") raise HTTPException(status_code=500, detail=str(e)) async def _run_research_task(task_id: str, context: ResearchContext): """Background task to run research.""" try: _research_tasks[task_id]["status"] = "running" def progress_callback(message: str): _research_tasks[task_id]["progress_messages"].append(message) engine = ResearchEngine() result = await engine.research(context, progress_callback=progress_callback) _research_tasks[task_id]["status"] = "completed" _research_tasks[task_id]["result"] = result except Exception as e: logger.error(f"[Research API] Task {task_id} failed: {e}") _research_tasks[task_id]["status"] = "failed" _research_tasks[task_id]["error"] = str(e) @router.get("/status/{task_id}") async def get_research_status(task_id: str): """ Get status of an async research task. Poll this endpoint to get progress updates and final results. """ if task_id not in _research_tasks: raise HTTPException(status_code=404, detail="Task not found") task = _research_tasks[task_id] response = { "task_id": task_id, "status": task["status"], "progress_messages": task["progress_messages"], } if task["status"] == "completed" and task["result"]: result = task["result"] response["result"] = { "success": result.success, "sources": result.sources, "keyword_analysis": result.keyword_analysis, "competitor_analysis": result.competitor_analysis, "suggested_angles": result.suggested_angles, "provider_used": result.provider_used, "search_queries": result.search_queries, } # Clean up completed task after returning # In production, use Redis or database for persistence elif task["status"] == "failed": response["error"] = task["error"] return response @router.delete("/status/{task_id}") async def cancel_research(task_id: str): """ Cancel a running research task. """ if task_id not in _research_tasks: raise HTTPException(status_code=404, detail="Task not found") task = _research_tasks[task_id] if task["status"] in ["pending", "running"]: task["status"] = "cancelled" return {"message": "Task cancelled", "task_id": task_id} return {"message": f"Task already {task['status']}", "task_id": task_id} # ============================================================================ # Intent-Driven Research Endpoints # ============================================================================ class AnalyzeIntentRequest(BaseModel): """Request to analyze user research intent.""" user_input: str = Field(..., description="User's keywords, question, or goal") keywords: List[str] = Field(default_factory=list, description="Extracted keywords") use_persona: bool = Field(True, description="Use research persona for context") use_competitor_data: bool = Field(True, description="Use competitor data for context") class AnalyzeIntentResponse(BaseModel): """Response from intent analysis.""" success: bool intent: Dict[str, Any] analysis_summary: str suggested_queries: List[Dict[str, Any]] suggested_keywords: List[str] suggested_angles: List[str] quick_options: List[Dict[str, Any]] error_message: Optional[str] = None class IntentDrivenResearchRequest(BaseModel): """Request for intent-driven research.""" # Intent from previous analyze step, or minimal input for auto-inference user_input: str = Field(..., description="User's original input") # Optional: Confirmed intent from UI (if user modified the inferred intent) confirmed_intent: Optional[Dict[str, Any]] = None # Optional: Specific queries to run (if user selected from suggested) selected_queries: Optional[List[Dict[str, Any]]] = None # Research configuration max_sources: int = Field(default=10, ge=1, le=25) include_domains: List[str] = Field(default_factory=list) exclude_domains: List[str] = Field(default_factory=list) # Skip intent inference (for re-runs with same intent) skip_inference: bool = False class IntentDrivenResearchResponse(BaseModel): """Response from intent-driven research.""" success: bool # Direct answers primary_answer: str = "" secondary_answers: Dict[str, str] = Field(default_factory=dict) # Deliverables statistics: List[Dict[str, Any]] = Field(default_factory=list) expert_quotes: List[Dict[str, Any]] = Field(default_factory=list) case_studies: List[Dict[str, Any]] = Field(default_factory=list) trends: List[Dict[str, Any]] = Field(default_factory=list) comparisons: List[Dict[str, Any]] = Field(default_factory=list) best_practices: List[str] = Field(default_factory=list) step_by_step: List[str] = Field(default_factory=list) pros_cons: Optional[Dict[str, Any]] = None definitions: Dict[str, str] = Field(default_factory=dict) examples: List[str] = Field(default_factory=list) predictions: List[str] = Field(default_factory=list) # Content-ready outputs executive_summary: str = "" key_takeaways: List[str] = Field(default_factory=list) suggested_outline: List[str] = Field(default_factory=list) # Sources and metadata sources: List[Dict[str, Any]] = Field(default_factory=list) confidence: float = 0.8 gaps_identified: List[str] = Field(default_factory=list) follow_up_queries: List[str] = Field(default_factory=list) # The inferred/confirmed intent intent: Optional[Dict[str, Any]] = None # Error handling error_message: Optional[str] = None @router.post("/intent/analyze", response_model=AnalyzeIntentResponse) async def analyze_research_intent( request: AnalyzeIntentRequest, current_user: Dict[str, Any] = Depends(get_current_user), ): """ Analyze user input to understand research intent. This endpoint uses AI to infer what the user really wants from their research: - What questions need answering - What deliverables they expect (statistics, quotes, case studies, etc.) - What depth and focus is appropriate The response includes quick options that can be shown in the UI for user confirmation. """ try: if not current_user: raise HTTPException(status_code=401, detail="Authentication required") user_id = str(current_user.get('id', '')) if not user_id: raise HTTPException(status_code=401, detail="Invalid user ID") logger.info(f"[Intent API] Analyzing intent for: {request.user_input[:50]}...") # Get research persona if requested research_persona = None competitor_data = None if request.use_persona or request.use_competitor_data: from services.research.research_persona_service import ResearchPersonaService from services.onboarding_service import OnboardingService from sqlalchemy.orm import Session # Get database session db = next(get_db()) try: persona_service = ResearchPersonaService(db) onboarding_service = OnboardingService() if request.use_persona: research_persona = persona_service.get_or_generate(user_id) if request.use_competitor_data: competitor_data = onboarding_service.get_competitor_analysis(user_id, db) finally: db.close() # Infer intent intent_service = ResearchIntentInference() response = await intent_service.infer_intent( user_input=request.user_input, keywords=request.keywords, research_persona=research_persona, competitor_data=competitor_data, industry=research_persona.default_industry if research_persona else None, target_audience=research_persona.default_target_audience if research_persona else None, ) # Generate targeted queries query_generator = IntentQueryGenerator() query_result = await query_generator.generate_queries( intent=response.intent, research_persona=research_persona, ) # Update response with queries response.suggested_queries = [q.dict() for q in query_result.get("queries", [])] response.suggested_keywords = query_result.get("enhanced_keywords", []) response.suggested_angles = query_result.get("research_angles", []) return AnalyzeIntentResponse( success=True, intent=response.intent.dict(), analysis_summary=response.analysis_summary, suggested_queries=response.suggested_queries, suggested_keywords=response.suggested_keywords, suggested_angles=response.suggested_angles, quick_options=response.quick_options, ) except Exception as e: logger.error(f"[Intent API] Analyze failed: {e}") return AnalyzeIntentResponse( success=False, intent={}, analysis_summary="", suggested_queries=[], suggested_keywords=[], suggested_angles=[], quick_options=[], error_message=str(e), ) @router.post("/intent/research", response_model=IntentDrivenResearchResponse) async def execute_intent_driven_research( request: IntentDrivenResearchRequest, current_user: Dict[str, Any] = Depends(get_current_user), ): """ Execute research based on user intent. This is the main endpoint for intent-driven research. It: 1. Uses the confirmed intent (or infers from user_input if not provided) 2. Generates targeted queries for each expected deliverable 3. Executes research using Exa/Tavily/Google 4. Analyzes results through the lens of user intent 5. Returns exactly what the user needs The response is organized by deliverable type (statistics, quotes, case studies, etc.) instead of generic search results. """ try: if not current_user: raise HTTPException(status_code=401, detail="Authentication required") user_id = str(current_user.get('id', '')) if not user_id: raise HTTPException(status_code=401, detail="Invalid user ID") logger.info(f"[Intent API] Executing intent-driven research for: {request.user_input[:50]}...") # Get database session db = next(get_db()) try: # Get research persona from services.research.research_persona_service import ResearchPersonaService persona_service = ResearchPersonaService(db) research_persona = persona_service.get_or_generate(user_id) # Determine intent if request.confirmed_intent: # Use confirmed intent from UI intent = ResearchIntent(**request.confirmed_intent) elif not request.skip_inference: # Infer intent from user input intent_service = ResearchIntentInference() intent_response = await intent_service.infer_intent( user_input=request.user_input, research_persona=research_persona, ) intent = intent_response.intent else: # Create basic intent from input intent = ResearchIntent( primary_question=f"What are the key insights about: {request.user_input}?", purpose="learn", content_output="general", expected_deliverables=["key_statistics", "best_practices", "examples"], depth="detailed", original_input=request.user_input, confidence=0.6, ) # Generate or use provided queries if request.selected_queries: queries = [ResearchQuery(**q) for q in request.selected_queries] else: query_generator = IntentQueryGenerator() query_result = await query_generator.generate_queries( intent=intent, research_persona=research_persona, ) queries = query_result.get("queries", []) # Execute research using the Research Engine engine = ResearchEngine(db_session=db) # Build context from intent personalization = ResearchPersonalizationContext( creator_id=user_id, industry=research_persona.default_industry if research_persona else None, target_audience=research_persona.default_target_audience if research_persona else None, ) # Use the highest priority query for the main search # (In a more advanced version, we could run multiple queries and merge) primary_query = queries[0] if queries else ResearchQuery( query=request.user_input, purpose=ExpectedDeliverable.KEY_STATISTICS, provider="exa", priority=5, expected_results="General research results", ) context = ResearchContext( query=primary_query.query, keywords=request.user_input.split()[:10], goal=_map_purpose_to_goal(intent.purpose), depth=_map_depth_to_engine_depth(intent.depth), provider_preference=_map_provider_to_preference(primary_query.provider), personalization=personalization, max_sources=request.max_sources, include_domains=request.include_domains, exclude_domains=request.exclude_domains, ) # Execute research raw_result = await engine.research(context) # Analyze results using intent-aware analyzer analyzer = IntentAwareAnalyzer() analyzed_result = await analyzer.analyze( raw_results={ "content": raw_result.raw_content or "", "sources": raw_result.sources, "grounding_metadata": raw_result.grounding_metadata, }, intent=intent, research_persona=research_persona, ) # Build response return IntentDrivenResearchResponse( success=True, primary_answer=analyzed_result.primary_answer, secondary_answers=analyzed_result.secondary_answers, statistics=[s.dict() for s in analyzed_result.statistics], expert_quotes=[q.dict() for q in analyzed_result.expert_quotes], case_studies=[cs.dict() for cs in analyzed_result.case_studies], trends=[t.dict() for t in analyzed_result.trends], comparisons=[c.dict() for c in analyzed_result.comparisons], best_practices=analyzed_result.best_practices, step_by_step=analyzed_result.step_by_step, pros_cons=analyzed_result.pros_cons.dict() if analyzed_result.pros_cons else None, definitions=analyzed_result.definitions, examples=analyzed_result.examples, predictions=analyzed_result.predictions, executive_summary=analyzed_result.executive_summary, key_takeaways=analyzed_result.key_takeaways, suggested_outline=analyzed_result.suggested_outline, sources=[s.dict() for s in analyzed_result.sources], confidence=analyzed_result.confidence, gaps_identified=analyzed_result.gaps_identified, follow_up_queries=analyzed_result.follow_up_queries, intent=intent.dict(), ) finally: db.close() except Exception as e: logger.error(f"[Intent API] Research failed: {e}") import traceback traceback.print_exc() return IntentDrivenResearchResponse( success=False, error_message=str(e), ) def _map_purpose_to_goal(purpose: str) -> ResearchGoal: """Map intent purpose to research goal.""" mapping = { "learn": ResearchGoal.EDUCATIONAL, "create_content": ResearchGoal.FACTUAL, "make_decision": ResearchGoal.FACTUAL, "compare": ResearchGoal.COMPETITIVE, "solve_problem": ResearchGoal.EDUCATIONAL, "find_data": ResearchGoal.FACTUAL, "explore_trends": ResearchGoal.TRENDING, "validate": ResearchGoal.FACTUAL, "generate_ideas": ResearchGoal.INSPIRATIONAL, } return mapping.get(purpose, ResearchGoal.FACTUAL) def _map_depth_to_engine_depth(depth: str) -> ResearchDepth: """Map intent depth to research engine depth.""" mapping = { "overview": ResearchDepth.QUICK, "detailed": ResearchDepth.STANDARD, "expert": ResearchDepth.COMPREHENSIVE, } return mapping.get(depth, ResearchDepth.STANDARD) def _map_provider_to_preference(provider: str) -> ProviderPreference: """Map query provider to engine preference.""" mapping = { "exa": ProviderPreference.EXA, "tavily": ProviderPreference.TAVILY, "google": ProviderPreference.GOOGLE, } return mapping.get(provider, ProviderPreference.AUTO)