AI Researcher and Video Studio implementation complete
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ajaysi
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docs/ALwrity Researcher/INTENT_DRIVEN_RESEARCH_GUIDE.md
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# Intent-Driven Research Guide
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**Date**: 2025-01-29
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**Status**: Current Architecture Documentation
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---
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## 📋 Overview
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Intent-driven research is the core innovation of the ALwrity Research Engine. Instead of generic keyword-based searches, the system **understands what users want to accomplish** before executing research, then delivers exactly what they need.
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### Key Innovation
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**Traditional Research**:
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```
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User Input → Search → Generic Results → User filters/analyzes
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```
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**Intent-Driven Research**:
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```
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User Input → AI Understands Intent → Targeted Queries → Intent-Aware Analysis → Structured Deliverables
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```
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---
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## 🎯 Core Concepts
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### 1. **Intent Inference**
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Before searching, the AI analyzes user input to understand:
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- **What question** needs answering
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- **What purpose** (learn, create content, make decision, etc.)
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- **What deliverables** are expected (statistics, quotes, case studies, etc.)
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- **What depth** is needed (overview, detailed, expert)
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### 2. **Unified Analysis**
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A single AI call performs:
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- Intent inference
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- Query generation (4-8 targeted queries)
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- Provider parameter optimization (Exa/Tavily settings with justifications)
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### 3. **Intent-Aware Result Analysis**
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Results are analyzed through the lens of user intent, extracting:
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- Specific deliverables (statistics, quotes, case studies)
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- Structured answers to user's questions
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- Relevant sources with credibility scores
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- Actionable insights
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---
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## 🔄 Research Flow
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### Step 1: Intent Analysis
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**User Action**: Enters keywords/topic and clicks "Intent & Options"
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**What Happens**:
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1. Frontend calls `/api/research/intent/analyze`
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2. `UnifiedResearchAnalyzer` performs single AI call:
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- Infers research intent
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- Generates 4-8 targeted queries
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- Optimizes Exa/Tavily parameters with justifications
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- Recommends best provider
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3. Returns `ResearchIntent`, `ResearchQuery[]`, and `OptimizedConfig`
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**User Sees**:
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- Inferred intent (editable)
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- Suggested queries (selectable)
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- AI-optimized provider settings with justifications
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- Recommended provider
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### Step 2: Intent Confirmation
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**User Action**: Reviews and optionally edits intent, then confirms
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**What Happens**:
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- User can edit:
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- Primary question
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- Purpose
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- Expected deliverables
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- Depth level
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- Content output type
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- User selects which queries to execute
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- User can override AI-optimized settings in Advanced Options
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### Step 3: Research Execution
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**User Action**: Clicks "Research" button
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**What Happens**:
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1. Frontend calls `/api/research/intent/research`
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2. Backend executes selected queries via Exa/Tavily/Google
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3. `IntentAwareAnalyzer` analyzes raw results based on intent
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4. Extracts specific deliverables:
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- Statistics with citations
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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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- Best practices
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- Step-by-step guides
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- Pros/cons
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- Definitions
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- Examples
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- Predictions
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### Step 4: Results Display
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**User Sees**: Tabbed results organized by deliverable type:
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- **Summary**: AI-generated overview
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- **Deliverables**: Extracted statistics, quotes, case studies, etc.
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- **Sources**: Citations with credibility scores
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- **Analysis**: Deep insights based on intent
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---
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## 🏗️ Architecture Components
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### Backend Components
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#### 1. UnifiedResearchAnalyzer
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**Location**: `backend/services/research/intent/unified_research_analyzer.py`
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**Purpose**: Single AI call for intent + queries + params
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**Key Method**:
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```python
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async def analyze(
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user_input: str,
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keywords: Optional[List[str]] = None,
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research_persona: Optional[ResearchPersona] = None,
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competitor_data: Optional[List[Dict]] = None,
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industry: Optional[str] = None,
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target_audience: Optional[str] = None,
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user_id: Optional[str] = None,
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) -> Dict[str, Any]
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```
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**Returns**:
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- `intent`: ResearchIntent object
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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 ("exa" | "tavily" | "google")
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- `provider_justification`: str
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**Benefits**:
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- 50% reduction in LLM calls (from 2-3 calls to 1)
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- Coherent reasoning across intent, queries, and params
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- User-friendly justifications for all settings
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#### 2. IntentAwareAnalyzer
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**Location**: `backend/services/research/intent/intent_aware_analyzer.py`
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**Purpose**: Analyzes raw results based on user intent
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**Key Method**:
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```python
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async def analyze(
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raw_results: Dict[str, Any],
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intent: ResearchIntent,
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research_persona: Optional[ResearchPersona] = None,
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user_id: Optional[str] = None,
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) -> IntentDrivenResearchResult
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```
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**Returns**: `IntentDrivenResearchResult` with:
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- `primary_answer`: str
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- `secondary_answers`: Dict[str, str]
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- `statistics`: List[StatisticWithCitation]
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- `expert_quotes`: List[ExpertQuote]
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- `case_studies`: List[CaseStudySummary]
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- `trends`: List[TrendAnalysis]
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- `comparisons`: List[ComparisonTable]
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- `best_practices`: List[str]
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- `step_by_step`: List[str]
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- `pros_cons`: ProsCons
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- `definitions`: Dict[str, str]
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- `examples`: List[str]
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- `predictions`: List[str]
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- `executive_summary`: str
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- `key_takeaways`: List[str]
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- `suggested_outline`: List[str]
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- `sources`: List[SourceWithRelevance]
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- `confidence`: float
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- `gaps_identified`: List[str]
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- `follow_up_queries`: List[str]
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#### 3. Research Engine
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**Location**: `backend/services/research/core/research_engine.py`
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**Purpose**: Orchestrates provider calls (Exa → Tavily → Google)
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**Provider Priority**:
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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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### Frontend Components
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#### 1. ResearchWizard
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**Location**: `frontend/src/components/Research/ResearchWizard.tsx`
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**Purpose**: Main wizard orchestrator (3 steps)
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**Steps**:
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1. `ResearchInput` - Input + Intent & Options button
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2. `StepProgress` - Progress/polling
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3. `StepResults` - Results display
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#### 2. ResearchInput
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**Location**: `frontend/src/components/Research/steps/ResearchInput.tsx`
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**Features**:
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- Keyword/topic input
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- "Intent & Options" button (enabled after 2+ words)
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- Industry and target audience selection
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- Advanced options toggle
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#### 3. IntentConfirmationPanel
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**Location**: `frontend/src/components/Research/steps/components/IntentConfirmationPanel.tsx`
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**Purpose**: Shows inferred intent and allows editing
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**Features**:
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- Displays inferred intent (editable)
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- Shows suggested queries (selectable)
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- Displays AI-optimized provider settings with justifications
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- Advanced options for manual override
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- "Research" button to execute
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#### 4. IntentResultsDisplay
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**Location**: `frontend/src/components/Research/steps/components/IntentResultsDisplay.tsx`
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**Purpose**: Tabbed results display
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**Tabs**:
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- **Summary**: AI-generated overview
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- **Deliverables**: Extracted statistics, quotes, case studies, etc.
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- **Sources**: Citations with credibility scores
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- **Analysis**: Deep insights based on intent
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#### 5. AdvancedOptionsSection
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**Location**: `frontend/src/components/Research/steps/components/AdvancedOptionsSection.tsx`
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**Purpose**: Shows AI-optimized Exa/Tavily settings with justifications
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**Features**:
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- Exa options (type, category, domains, date filters, etc.)
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- Tavily options (topic, search depth, time range, etc.)
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- Each setting shows AI justification in tooltip
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- User can override any setting
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### Frontend Hooks
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#### 1. useIntentResearch
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**Location**: `frontend/src/components/Research/hooks/useIntentResearch.ts`
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**Purpose**: Manages intent-driven research flow
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**Key Methods**:
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- `analyzeIntent(userInput: string)` - Analyzes user input
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- `confirmIntent(intent: ResearchIntent)` - Confirms/modifies intent
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- `executeResearch(selectedQueries?: ResearchQuery[])` - Executes research
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- `reset()` - Resets state
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**State**:
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- `userInput`: string
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- `intent`: ResearchIntent | null
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- `suggestedQueries`: ResearchQuery[]
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- `selectedQueries`: ResearchQuery[]
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- `isAnalyzing`: boolean
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- `isResearching`: boolean
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- `result`: IntentDrivenResearchResponse | null
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#### 2. useResearchExecution
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**Location**: `frontend/src/components/Research/hooks/useResearchExecution.ts`
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**Purpose**: Handles research execution and polling
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**Key Methods**:
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- `executeIntentResearch(state, queries)` - Executes intent-driven research
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- `executeTraditionalResearch(state)` - Executes traditional research (fallback)
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- `pollStatus(taskId)` - Polls async research status
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---
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## 📡 API Endpoints
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### 1. POST `/api/research/intent/analyze`
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**Purpose**: Analyze user input to understand research intent
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**Request**:
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```typescript
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{
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user_input: string;
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keywords?: string[];
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use_persona?: boolean; // Default: true
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use_competitor_data?: boolean; // Default: true
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}
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```
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**Response**:
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```typescript
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{
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success: boolean;
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intent: ResearchIntent;
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analysis_summary: string;
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suggested_queries: ResearchQuery[];
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suggested_keywords: string[];
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suggested_angles: string[];
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confidence_reason?: string;
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great_example?: string;
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optimized_config: {
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provider: string;
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provider_justification: string;
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exa_type: string;
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exa_type_justification: string;
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exa_category?: string;
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exa_category_justification?: string;
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// ... more Exa settings with justifications
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tavily_topic: string;
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tavily_topic_justification: string;
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tavily_search_depth: string;
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tavily_search_depth_justification: string;
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// ... more Tavily settings with justifications
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};
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recommended_provider: string;
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error_message?: string;
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}
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```
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**What It Does**:
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1. Fetches research persona (if `use_persona: true`)
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2. Fetches competitor data (if `use_competitor_data: true`)
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3. Calls `UnifiedResearchAnalyzer.analyze()`
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4. Returns intent, queries, and optimized config with justifications
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### 2. POST `/api/research/intent/research`
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**Purpose**: Execute research based on confirmed intent
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**Request**:
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```typescript
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{
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user_input: string;
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confirmed_intent?: ResearchIntent; // If not provided, infers from user_input
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selected_queries?: ResearchQuery[]; // If not provided, generates from intent
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max_sources?: number; // Default: 10
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include_domains?: string[];
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exclude_domains?: string[];
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skip_inference?: boolean; // Skip intent inference if intent provided
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}
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```
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**Response**:
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```typescript
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{
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success: boolean;
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primary_answer: string;
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secondary_answers: Dict<string, string>;
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statistics: StatisticWithCitation[];
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expert_quotes: ExpertQuote[];
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case_studies: CaseStudySummary[];
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trends: TrendAnalysis[];
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comparisons: ComparisonTable[];
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best_practices: string[];
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step_by_step: string[];
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pros_cons?: ProsCons;
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definitions: Dict<string, string>;
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examples: string[];
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predictions: string[];
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executive_summary: string;
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key_takeaways: string[];
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suggested_outline: string[];
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sources: SourceWithRelevance[];
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confidence: number;
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gaps_identified: string[];
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follow_up_queries: string[];
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intent?: ResearchIntent;
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error_message?: string;
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}
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```
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**What It Does**:
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1. Uses confirmed intent (or infers if not provided)
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2. Uses selected queries (or generates if not provided)
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3. Executes research via `ResearchEngine`
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4. Analyzes results via `IntentAwareAnalyzer`
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5. Returns structured deliverables
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---
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## 🎨 User Experience Flow
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### Example: User wants to research "AI marketing tools"
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#### Step 1: User Input
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```
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User enters: "AI marketing tools"
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Clicks: "Intent & Options" button
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```
|
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#### Step 2: Intent Analysis
|
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```
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AI infers:
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- Primary Question: "What are the best AI marketing tools available?"
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- Purpose: "make_decision"
|
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- Expected Deliverables: ["key_statistics", "case_studies", "comparisons", "best_practices"]
|
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- Depth: "detailed"
|
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- Content Output: "blog"
|
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|
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AI generates queries:
|
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1. "best AI marketing tools 2024 comparison" (priority: 5)
|
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2. "AI marketing tools statistics adoption rates" (priority: 4)
|
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3. "AI marketing tools case studies ROI" (priority: 4)
|
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4. "AI marketing automation platforms features" (priority: 3)
|
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|
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AI optimizes settings:
|
||||
- Provider: Exa (semantic understanding needed)
|
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- Exa Type: "neural" (for semantic matching)
|
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- Exa Category: "company" (tool providers)
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- Justification: "Neural search best for finding similar tools and comparisons"
|
||||
```
|
||||
|
||||
#### Step 3: User Confirmation
|
||||
```
|
||||
User sees:
|
||||
- Inferred intent (can edit)
|
||||
- 4 suggested queries (can select/deselect)
|
||||
- AI-optimized settings with justifications (can override)
|
||||
|
||||
User confirms and clicks "Research"
|
||||
```
|
||||
|
||||
#### Step 4: Research Execution
|
||||
```
|
||||
Backend:
|
||||
1. Executes 4 queries via Exa
|
||||
2. Gets raw results (sources, content)
|
||||
3. IntentAwareAnalyzer extracts:
|
||||
- Statistics: "78% of marketers use AI tools"
|
||||
- Case studies: "Company X increased ROI by 40%"
|
||||
- Comparisons: Tool comparison table
|
||||
- Best practices: "5 best practices for AI marketing"
|
||||
```
|
||||
|
||||
#### Step 5: Results Display
|
||||
```
|
||||
User sees tabbed results:
|
||||
- Summary: Overview of AI marketing tools landscape
|
||||
- Deliverables: Statistics, quotes, case studies, comparisons
|
||||
- Sources: Citations with credibility scores
|
||||
- Analysis: Deep insights and recommendations
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔑 Key Patterns
|
||||
|
||||
### Pattern 1: Always Use UnifiedResearchAnalyzer
|
||||
|
||||
**✅ Correct**:
|
||||
```python
|
||||
from services.research.intent.unified_research_analyzer import UnifiedResearchAnalyzer
|
||||
|
||||
analyzer = UnifiedResearchAnalyzer()
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||||
result = await analyzer.analyze(
|
||||
user_input=user_input,
|
||||
keywords=keywords,
|
||||
research_persona=research_persona,
|
||||
user_id=user_id,
|
||||
)
|
||||
```
|
||||
|
||||
**❌ Incorrect** (Legacy - Don't Use):
|
||||
```python
|
||||
# Don't use separate intent inference + query generation
|
||||
intent_service = ResearchIntentInference()
|
||||
query_generator = IntentQueryGenerator()
|
||||
# ... multiple LLM calls
|
||||
```
|
||||
|
||||
### Pattern 2: Always Pass user_id
|
||||
|
||||
**✅ Correct**:
|
||||
```python
|
||||
result = llm_text_gen(
|
||||
prompt=prompt,
|
||||
json_struct=schema,
|
||||
user_id=user_id # Required for subscription checks
|
||||
)
|
||||
```
|
||||
|
||||
**❌ Incorrect**:
|
||||
```python
|
||||
result = llm_text_gen(prompt=prompt, json_struct=schema) # Missing user_id
|
||||
```
|
||||
|
||||
### Pattern 3: Intent-Aware Result Analysis
|
||||
|
||||
**✅ Correct**:
|
||||
```python
|
||||
from services.research.intent.intent_aware_analyzer import IntentAwareAnalyzer
|
||||
|
||||
analyzer = IntentAwareAnalyzer()
|
||||
result = await analyzer.analyze(
|
||||
raw_results=raw_results,
|
||||
intent=research_intent,
|
||||
research_persona=research_persona,
|
||||
user_id=user_id,
|
||||
)
|
||||
```
|
||||
|
||||
**❌ Incorrect** (Generic Analysis):
|
||||
```python
|
||||
# Don't do generic analysis - always use intent
|
||||
summary = analyze_generic(raw_results) # Wrong approach
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Benefits
|
||||
|
||||
### 1. **50% Reduction in LLM Calls**
|
||||
- Old: 2-3 separate calls (intent + queries + params)
|
||||
- New: 1 unified call
|
||||
|
||||
### 2. **Better Results**
|
||||
- Intent-aware analysis extracts exactly what users need
|
||||
- Structured deliverables instead of generic summaries
|
||||
|
||||
### 3. **User-Friendly**
|
||||
- AI justifications explain why settings were chosen
|
||||
- Users can understand and override AI decisions
|
||||
|
||||
### 4. **Coherent Reasoning**
|
||||
- Single AI call ensures intent, queries, and params are aligned
|
||||
- No inconsistencies between intent and search strategy
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Integration Examples
|
||||
|
||||
### Frontend: Using useIntentResearch Hook
|
||||
|
||||
```typescript
|
||||
import { useIntentResearch } from '../hooks/useIntentResearch';
|
||||
|
||||
const MyComponent = () => {
|
||||
const {
|
||||
state,
|
||||
analyzeIntent,
|
||||
confirmIntent,
|
||||
executeResearch,
|
||||
isAnalyzing,
|
||||
isResearching,
|
||||
result,
|
||||
} = useIntentResearch({
|
||||
usePersona: true,
|
||||
useCompetitorData: true,
|
||||
maxSources: 10,
|
||||
});
|
||||
|
||||
const handleAnalyze = async () => {
|
||||
await analyzeIntent("AI marketing tools");
|
||||
};
|
||||
|
||||
const handleResearch = async () => {
|
||||
await executeResearch(state.selectedQueries);
|
||||
};
|
||||
|
||||
return (
|
||||
<div>
|
||||
<button onClick={handleAnalyze} disabled={isAnalyzing}>
|
||||
{isAnalyzing ? 'Analyzing...' : 'Intent & Options'}
|
||||
</button>
|
||||
{state.intent && (
|
||||
<IntentConfirmationPanel
|
||||
intentAnalysis={state.intent}
|
||||
onConfirm={confirmIntent}
|
||||
onExecute={handleResearch}
|
||||
/>
|
||||
)}
|
||||
{result && <IntentResultsDisplay result={result} />}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
```
|
||||
|
||||
### Backend: Using UnifiedResearchAnalyzer
|
||||
|
||||
```python
|
||||
from services.research.intent.unified_research_analyzer import UnifiedResearchAnalyzer
|
||||
|
||||
async def analyze_user_request(user_input: str, user_id: str):
|
||||
analyzer = UnifiedResearchAnalyzer()
|
||||
|
||||
result = await analyzer.analyze(
|
||||
user_input=user_input,
|
||||
keywords=extract_keywords(user_input),
|
||||
research_persona=get_research_persona(user_id),
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
return {
|
||||
"intent": result["intent"],
|
||||
"queries": result["queries"],
|
||||
"exa_config": result["exa_config"],
|
||||
"tavily_config": result["tavily_config"],
|
||||
"recommended_provider": result["recommended_provider"],
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Related Documentation
|
||||
|
||||
- **Architecture Rules**: `.cursor/rules/researcher-architecture.mdc` (Authoritative source)
|
||||
- **API Reference**: `INTENT_RESEARCH_API_REFERENCE.md`
|
||||
- **Architecture Overview**: `CURRENT_ARCHITECTURE_OVERVIEW.md`
|
||||
|
||||
---
|
||||
|
||||
## ✅ Best Practices
|
||||
|
||||
1. **Always use UnifiedResearchAnalyzer** for new intent-driven research
|
||||
2. **Always pass user_id** to all LLM calls for subscription checks
|
||||
3. **Always use IntentAwareAnalyzer** for result analysis
|
||||
4. **Provide justifications** for all AI-driven settings
|
||||
5. **Allow user overrides** in Advanced Options
|
||||
6. **Check provider availability** before suggesting/using providers
|
||||
|
||||
---
|
||||
|
||||
**Status**: Current Architecture - Use this as reference for intent-driven research implementation.
|
||||
Reference in New Issue
Block a user