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Kunthawat Greethong
2026-01-08 22:39:53 +07:00
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"""
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)