AI Researcher and Video Studio implementation complete
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docs/ALwrity Researcher/CURRENT_ARCHITECTURE_OVERVIEW.md
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# Current Research Engine Architecture Overview
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**Date**: 2025-01-29
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**Status**: Authoritative Architecture Documentation
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---
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## 📋 Overview
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This document provides a comprehensive overview of the current Research Engine architecture. This is the **single source of truth** for understanding how the research system works.
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**Note**: For detailed implementation rules and patterns, see `.cursor/rules/researcher-architecture.mdc`
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---
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## 🏗️ High-Level Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ USER INTERFACE │
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├─────────────────────────────────────────────────────────────────┤
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│ ResearchWizard (3 Steps) │
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│ ├── Step 1: ResearchInput (Input + Intent & Options) │
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│ ├── Step 2: StepProgress (Progress/Polling) │
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│ └── Step 3: StepResults (Tabbed Results Display) │
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└─────────────────────────────────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────────┐
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│ FRONTEND HOOKS │
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├─────────────────────────────────────────────────────────────────┤
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│ useIntentResearch │
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│ ├── analyzeIntent() → /api/research/intent/analyze │
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│ ├── confirmIntent() → Updates local state │
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│ └── executeResearch() → /api/research/intent/research │
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│ │
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│ useResearchExecution │
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│ ├── executeIntentResearch() → Intent-driven flow │
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│ └── executeTraditionalResearch() → Fallback flow │
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└─────────────────────────────────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────────┐
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│ API ENDPOINTS │
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├─────────────────────────────────────────────────────────────────┤
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│ POST /api/research/intent/analyze │
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│ └── UnifiedResearchAnalyzer.analyze() │
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│ │
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│ POST /api/research/intent/research │
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│ ├── ResearchEngine.research() │
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│ └── IntentAwareAnalyzer.analyze() │
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│ │
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│ POST /api/research/execute (Traditional - Fallback) │
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│ POST /api/research/start (Traditional - Async) │
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└─────────────────────────────────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────────┐
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│ BACKEND SERVICES │
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├─────────────────────────────────────────────────────────────────┤
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│ UnifiedResearchAnalyzer │
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│ ├── Intent Inference │
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│ ├── Query Generation │
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│ └── Parameter Optimization (Exa/Tavily) │
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│ │
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│ ResearchEngine │
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│ ├── Provider Selection (Exa → Tavily → Google) │
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│ ├── ExaService │
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│ ├── TavilyService │
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│ └── GoogleSearchService │
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│ │
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│ IntentAwareAnalyzer │
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│ └── Intent-Based Result Analysis │
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│ │
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│ ResearchPersonaService │
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│ └── Persona Generation/Retrieval │
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└─────────────────────────────────────────────────────────────────┘
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```
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---
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## 🔄 Data Flow
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### Intent-Driven Research Flow
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```
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1. User Input
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│
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▼
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2. Frontend: useIntentResearch.analyzeIntent()
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│
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▼
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3. API: POST /api/research/intent/analyze
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│
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▼
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4. Backend: UnifiedResearchAnalyzer.analyze()
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├── Fetches Research Persona (if enabled)
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├── Fetches Competitor Data (if enabled)
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├── Single LLM Call:
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│ ├── Intent Inference
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│ ├── Query Generation (4-8 queries)
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│ └── Parameter Optimization (Exa/Tavily)
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└── Returns: Intent + Queries + Optimized Config
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│
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▼
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5. Frontend: IntentConfirmationPanel
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├── Displays inferred intent (editable)
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├── Shows suggested queries (selectable)
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└── Shows AI-optimized settings with justifications
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│
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▼
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6. User Confirms Intent
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│
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▼
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7. Frontend: useIntentResearch.executeResearch()
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│
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▼
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8. API: POST /api/research/intent/research
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│
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▼
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9. Backend: ResearchEngine.research()
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├── Executes queries via Exa/Tavily/Google
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└── Returns raw results
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│
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▼
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10. Backend: IntentAwareAnalyzer.analyze()
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├── Analyzes raw results based on intent
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├── Extracts specific deliverables:
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│ ├── Statistics
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│ ├── Expert Quotes
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│ ├── Case Studies
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│ ├── Trends
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│ ├── Comparisons
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│ └── More...
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└── Returns: IntentDrivenResearchResult
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│
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▼
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11. Frontend: IntentResultsDisplay
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├── Summary Tab
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├── Deliverables Tab
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├── Sources Tab
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└── Analysis Tab
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```
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---
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## 📁 Component Structure
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### Backend Structure
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```
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backend/services/research/
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├── core/
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│ ├── research_engine.py # Main orchestrator
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│ ├── research_context.py # Unified input schema
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│ └── parameter_optimizer.py # DEPRECATED (use unified analyzer)
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│
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├── intent/
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│ ├── unified_research_analyzer.py # ⭐ Unified AI analyzer (intent + queries + params)
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│ ├── research_intent_inference.py # Legacy (use unified)
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│ ├── intent_query_generator.py # Legacy (use unified)
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│ ├── intent_aware_analyzer.py # Result analysis based on intent
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│ └── intent_prompt_builder.py # LLM prompt builders
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│
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├── research_persona_service.py # Research persona generation/retrieval
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├── research_persona_prompt_builder.py # Persona generation prompts
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├── exa_service.py # Exa API integration
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├── tavily_service.py # Tavily API integration
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└── google_search_service.py # Google/Gemini grounding
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```
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### Frontend Structure
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```
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frontend/src/components/Research/
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├── ResearchWizard.tsx # Main wizard orchestrator
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├── steps/
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│ ├── ResearchInput.tsx # Step 1: Input + Intent & Options
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│ ├── StepProgress.tsx # Step 2: Progress/polling
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│ ├── StepResults.tsx # Step 3: Results display
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│ ├── components/
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│ │ ├── ResearchInputHeader.tsx # Header with Advanced toggle
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│ │ ├── ResearchInputContainer.tsx # Main input with Intent & Options button
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│ │ ├── IntentConfirmationPanel.tsx # Intent display/edit panel
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│ │ ├── IntentResultsDisplay.tsx # Tabbed results (Summary, Deliverables, Sources, Analysis)
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│ │ ├── AdvancedOptionsSection.tsx # Exa/Tavily options
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│ │ ├── ProviderChips.tsx # Provider availability display
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│ │ └── ... (other components)
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│ ├── hooks/
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│ │ ├── useResearchConfig.ts # Config + persona loading
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│ │ ├── useKeywordExpansion.ts # Keyword expansion with persona
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│ │ └── useResearchAngles.ts # Research angles generation
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│ └── utils/
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│ ├── placeholders.ts # Personalized placeholders
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│ ├── industryDefaults.ts # Industry-specific defaults
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│ └── ...
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└── hooks/
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├── useResearchWizard.ts # Wizard state management
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├── useResearchExecution.ts # Research execution orchestration
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└── useIntentResearch.ts # Intent research flow
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```
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---
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## 🔑 Key Components
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### 1. UnifiedResearchAnalyzer
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**Purpose**: Single AI call for intent + queries + params
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**Location**: `backend/services/research/intent/unified_research_analyzer.py`
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**Key Features**:
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- Combines intent inference, query generation, and parameter optimization
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- Reduces LLM calls from 2-3 to 1 (50% reduction)
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- Provides justifications for all parameter decisions
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- Uses research persona for context
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**Input**:
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- `user_input`: string
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- `keywords`: List[str]
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- `research_persona`: ResearchPersona (optional)
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- `competitor_data`: List[Dict] (optional)
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- `industry`: string (optional)
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- `target_audience`: string (optional)
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- `user_id`: string (required for subscription checks)
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**Output**:
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- `intent`: ResearchIntent
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- `queries`: List[ResearchQuery] (4-8 queries)
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- `exa_config`: Dict with settings + justifications
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- `tavily_config`: Dict with settings + justifications
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- `recommended_provider`: str
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- `provider_justification`: str
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### 2. IntentAwareAnalyzer
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**Purpose**: Analyzes results based on user intent
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**Location**: `backend/services/research/intent/intent_aware_analyzer.py`
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**Key Features**:
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- Extracts specific deliverables based on intent
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- Structures results by deliverable type
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- Provides credibility scores for sources
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- Identifies gaps and follow-up queries
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**Input**:
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- `raw_results`: Dict (from Exa/Tavily/Google)
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- `intent`: ResearchIntent
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- `research_persona`: ResearchPersona (optional)
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- `user_id`: string (required for subscription checks)
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**Output**:
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- `IntentDrivenResearchResult` with:
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- Statistics, quotes, case studies, trends
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- Comparisons, best practices, step-by-step guides
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- Pros/cons, definitions, examples, predictions
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- Executive summary, key takeaways, suggested outline
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- Sources with credibility scores
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### 3. ResearchEngine
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**Purpose**: Orchestrates provider calls
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**Location**: `backend/services/research/core/research_engine.py`
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**Key Features**:
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- Provider priority: Exa → Tavily → Google
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- Handles provider availability
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- Manages async research tasks
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- Integrates with research persona
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**Provider Selection**:
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1. **Exa** (Primary): Semantic understanding, academic papers, competitor research
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2. **Tavily** (Secondary): Real-time news, trending topics, quick facts
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3. **Google** (Fallback): Basic factual queries via Gemini grounding
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### 4. ResearchPersonaService
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**Purpose**: Generates and retrieves research persona
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**Location**: `backend/services/research/research_persona_service.py`
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**Key Features**:
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- Generates persona from onboarding data (core persona, website analysis, competitor analysis)
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- Caches persona (7-day TTL)
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- Provides persona defaults for UI pre-filling
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**Persona Sources**:
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- Core persona (onboarding step 1)
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- Website analysis (onboarding step 2)
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- Competitor analysis (onboarding step 3)
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---
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## 🔌 API Endpoints
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### Intent-Driven Endpoints
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1. **POST `/api/research/intent/analyze`**
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- Analyzes user input to understand intent
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- Generates queries and optimizes parameters
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- Returns intent, queries, and optimized config
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2. **POST `/api/research/intent/research`**
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- Executes research based on confirmed intent
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- Returns structured deliverables
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### Traditional Endpoints (Fallback)
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3. **POST `/api/research/execute`**
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- Synchronous research execution
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- Returns traditional research results
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4. **POST `/api/research/start`**
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- Asynchronous research execution
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- Returns task_id for polling
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5. **GET `/api/research/status/{task_id}`**
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- Polls async research status
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- Returns progress and results
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### Configuration Endpoints
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6. **GET `/api/research/config`**
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- Returns provider availability + persona defaults
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7. **GET `/api/research/providers/status`**
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- Returns provider availability only
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8. **GET `/api/research/persona-defaults`**
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- Returns persona defaults only
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---
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## 🎯 Key Patterns
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### Pattern 1: Unified Analysis
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**Always use UnifiedResearchAnalyzer** for new intent-driven research:
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```python
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from services.research.intent.unified_research_analyzer import UnifiedResearchAnalyzer
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analyzer = UnifiedResearchAnalyzer()
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result = await analyzer.analyze(
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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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user_id=user_id, # Required
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)
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```
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### Pattern 2: Intent-Aware Analysis
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**Always analyze results based on intent**:
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```python
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from services.research.intent.intent_aware_analyzer import IntentAwareAnalyzer
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analyzer = IntentAwareAnalyzer()
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result = await analyzer.analyze(
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raw_results=raw_results,
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intent=research_intent,
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research_persona=research_persona,
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user_id=user_id, # Required
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)
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```
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### Pattern 3: Provider Selection
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**Priority order**: Exa → Tavily → Google
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```python
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if provider_availability.exa_available:
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provider = "exa"
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elif provider_availability.tavily_available:
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provider = "tavily"
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else:
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provider = "google"
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```
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### Pattern 4: Persona Integration
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**Always check for research persona**:
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```python
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from services.research.research_persona_service import ResearchPersonaService
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persona_service = ResearchPersonaService(db)
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research_persona = persona_service.get_or_generate(user_id)
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```
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### Pattern 5: Subscription Checks
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**Always pass user_id to LLM calls**:
|
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```python
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result = llm_text_gen(
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prompt=prompt,
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json_struct=schema,
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user_id=user_id # Required for subscription checks
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)
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```
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---
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## 🔄 Research Modes
|
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### Intent-Driven Research (Current - Recommended)
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**Flow**: Intent Analysis → Confirmation → Execution → Intent-Aware Analysis
|
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|
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**Benefits**:
|
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- Understands user goals before searching
|
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- Delivers exactly what users need
|
||||
- Structured deliverables
|
||||
- 50% reduction in LLM calls
|
||||
|
||||
**Use When**: User wants specific deliverables (statistics, quotes, case studies, etc.)
|
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|
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### Traditional Research (Fallback)
|
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|
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**Flow**: Direct Execution → Generic Analysis
|
||||
|
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**Benefits**:
|
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- Faster for simple queries
|
||||
- No intent analysis overhead
|
||||
|
||||
**Use When**: Simple factual queries or when intent analysis fails
|
||||
|
||||
---
|
||||
|
||||
## 📊 Data Models
|
||||
|
||||
### ResearchIntent
|
||||
|
||||
```python
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||||
class ResearchIntent:
|
||||
primary_question: str
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secondary_questions: List[str]
|
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purpose: ResearchPurpose # learn, create_content, make_decision, etc.
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||||
content_output: ContentOutput # blog, podcast, video, etc.
|
||||
expected_deliverables: List[ExpectedDeliverable]
|
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depth: ResearchDepthLevel # overview, detailed, expert
|
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focus_areas: List[str]
|
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perspective: Optional[str]
|
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time_sensitivity: str
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confidence: float
|
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confidence_reason: Optional[str]
|
||||
great_example: Optional[str]
|
||||
needs_clarification: bool
|
||||
clarifying_questions: List[str]
|
||||
```
|
||||
|
||||
### ResearchQuery
|
||||
|
||||
```python
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class ResearchQuery:
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query: str
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purpose: ExpectedDeliverable
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provider: str # "exa" | "tavily"
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||||
priority: int # 1-5
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||||
expected_results: str
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||||
justification: Optional[str]
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||||
```
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||||
|
||||
### IntentDrivenResearchResult
|
||||
|
||||
```python
|
||||
class IntentDrivenResearchResult:
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||||
primary_answer: str
|
||||
secondary_answers: Dict[str, str]
|
||||
statistics: List[StatisticWithCitation]
|
||||
expert_quotes: List[ExpertQuote]
|
||||
case_studies: List[CaseStudySummary]
|
||||
trends: List[TrendAnalysis]
|
||||
comparisons: List[ComparisonTable]
|
||||
best_practices: List[str]
|
||||
step_by_step: List[str]
|
||||
pros_cons: Optional[ProsCons]
|
||||
definitions: Dict[str, str]
|
||||
examples: List[str]
|
||||
predictions: List[str]
|
||||
executive_summary: str
|
||||
key_takeaways: List[str]
|
||||
suggested_outline: List[str]
|
||||
sources: List[SourceWithRelevance]
|
||||
confidence: float
|
||||
gaps_identified: List[str]
|
||||
follow_up_queries: List[str]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🎨 UI Components
|
||||
|
||||
### ResearchWizard
|
||||
|
||||
**Purpose**: Main wizard orchestrator
|
||||
|
||||
**Steps**:
|
||||
1. **ResearchInput**: Input + Intent & Options button
|
||||
2. **StepProgress**: Progress/polling for async research
|
||||
3. **StepResults**: Tabbed results display
|
||||
|
||||
### IntentConfirmationPanel
|
||||
|
||||
**Purpose**: Shows inferred intent and allows editing
|
||||
|
||||
**Features**:
|
||||
- Displays inferred intent (editable)
|
||||
- Shows suggested queries (selectable)
|
||||
- Displays AI-optimized settings with justifications
|
||||
- Advanced options for manual override
|
||||
|
||||
### IntentResultsDisplay
|
||||
|
||||
**Purpose**: Tabbed results display
|
||||
|
||||
**Tabs**:
|
||||
- **Summary**: AI-generated overview
|
||||
- **Deliverables**: Extracted statistics, quotes, case studies, etc.
|
||||
- **Sources**: Citations with credibility scores
|
||||
- **Analysis**: Deep insights based on intent
|
||||
|
||||
---
|
||||
|
||||
## 🔐 Security & Subscription
|
||||
|
||||
### Authentication
|
||||
|
||||
All endpoints require JWT authentication via `get_current_user` dependency.
|
||||
|
||||
### Subscription Checks
|
||||
|
||||
All LLM calls must pass `user_id` for subscription and pre-flight validation:
|
||||
|
||||
```python
|
||||
result = llm_text_gen(
|
||||
prompt=prompt,
|
||||
json_struct=schema,
|
||||
user_id=user_id # Required
|
||||
)
|
||||
```
|
||||
|
||||
### Rate Limiting
|
||||
|
||||
- Subject to subscription tier limits
|
||||
- Provider APIs (Exa/Tavily/Google) have their own rate limits
|
||||
|
||||
---
|
||||
|
||||
## 📈 Performance
|
||||
|
||||
### Intent Analysis
|
||||
|
||||
- **Typical Time**: 2-5 seconds
|
||||
- **LLM Calls**: 1 (unified analyzer)
|
||||
- **Caching**: Research persona cached (7-day TTL)
|
||||
|
||||
### Research Execution
|
||||
|
||||
- **Typical Time**: 10-30 seconds
|
||||
- **Depends On**: Provider, query count, result count
|
||||
- **Async Support**: Yes (via `/api/research/start`)
|
||||
|
||||
### Result Analysis
|
||||
|
||||
- **Typical Time**: 5-10 seconds
|
||||
- **LLM Calls**: 1 (intent-aware analyzer)
|
||||
|
||||
---
|
||||
|
||||
## 🔗 Integration Points
|
||||
|
||||
### Blog Writer Integration
|
||||
|
||||
Research Engine can be imported by Blog Writer:
|
||||
|
||||
```python
|
||||
from services.research.core.research_engine import ResearchEngine
|
||||
from services.research.core.research_context import ResearchContext
|
||||
|
||||
context = ResearchContext(
|
||||
query=blog_topic,
|
||||
keywords=blog_keywords,
|
||||
goal=ResearchGoal.FACTUAL,
|
||||
depth=ResearchDepth.COMPREHENSIVE,
|
||||
)
|
||||
|
||||
engine = ResearchEngine()
|
||||
result = await engine.research(context, user_id=user_id)
|
||||
```
|
||||
|
||||
### Frontend Integration
|
||||
|
||||
Research Wizard can be reused in other tools:
|
||||
|
||||
```tsx
|
||||
import { ResearchWizard } from '@/components/Research/ResearchWizard';
|
||||
|
||||
<ResearchWizard
|
||||
onComplete={(results) => {
|
||||
// Use results in blog/video generation
|
||||
}}
|
||||
initialKeywords={blogTopic}
|
||||
initialIndustry={userIndustry}
|
||||
/>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Related Documentation
|
||||
|
||||
- **Architecture Rules**: `.cursor/rules/researcher-architecture.mdc` (Authoritative)
|
||||
- **Intent-Driven Guide**: `INTENT_DRIVEN_RESEARCH_GUIDE.md`
|
||||
- **API Reference**: `INTENT_RESEARCH_API_REFERENCE.md`
|
||||
- **Documentation Review**: `DOCUMENTATION_REVIEW_AND_UPDATE_PLAN.md`
|
||||
|
||||
---
|
||||
|
||||
## ✅ Best Practices
|
||||
|
||||
1. **Always use UnifiedResearchAnalyzer** for new intent-driven research
|
||||
2. **Always pass user_id** to all LLM calls
|
||||
3. **Always use IntentAwareAnalyzer** for result analysis
|
||||
4. **Check provider availability** before using providers
|
||||
5. **Provide justifications** for all AI-driven settings
|
||||
6. **Allow user overrides** in Advanced Options
|
||||
7. **Never fallback to "General"** - always use persona defaults
|
||||
|
||||
---
|
||||
|
||||
**Status**: Authoritative Architecture Documentation - Single Source of Truth
|
||||
Reference in New Issue
Block a user