Base code
This commit is contained in:
Kunthawat Greethong
166
docs/ALwrity Researcher/COMPLETE_IMPLEMENTATION_SUMMARY.md
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166
docs/ALwrity Researcher/COMPLETE_IMPLEMENTATION_SUMMARY.md
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# Complete Research Persona Enhancement Implementation Summary
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## Date: 2025-12-31
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---
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## 🎉 **All Phases Complete**
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### **Phase 1: High Impact, Low Effort** ✅
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1. ✅ Extract `content_type` → Generate content-type-specific presets
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2. ✅ Extract `writing_style.complexity` → Map to research depth
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3. ✅ Extract `crawl_result` topics → Use for suggested_keywords
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### **Phase 2: Medium Impact, Medium Effort** ✅
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1. ✅ Extract `style_patterns` → Generate pattern-based research angles
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2. ✅ Extract `content_characteristics.vocabulary` → Sophisticated keyword expansion
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3. ✅ Extract `style_guidelines` → Query enhancement rules
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### **Phase 3: High Impact, High Effort** ✅
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1. ✅ Full crawl_result analysis → Topic extraction, theme identification
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2. ✅ Complete writing style mapping → All research preferences
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3. ✅ Content strategy intelligence → Comprehensive preset generation
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### **UI Indicators** ✅
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1. ✅ PersonalizationIndicator component
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2. ✅ PersonalizationBadge component
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3. ✅ Indicators in key UI locations
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4. ✅ Tooltips explaining personalization
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---
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## 📊 **Complete Feature Matrix**
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| Feature | Phase | Status | Impact |
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|---------|-------|--------|--------|
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| Content-Type Presets | 1 | ✅ | High |
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| Complexity → Research Depth | 1 | ✅ | High |
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| Crawl Topics → Keywords | 1 | ✅ | High |
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| Pattern-Based Angles | 2 | ✅ | Medium |
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| Vocabulary Expansions | 2 | ✅ | Medium |
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| Guideline Query Rules | 2 | ✅ | Medium |
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| Full Crawl Analysis | 3 | ✅ | High |
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| Complete Style Mapping | 3 | ✅ | High |
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| Theme Extraction | 3 | ✅ | High |
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| UI Indicators | UI | ✅ | High |
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---
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## 🔧 **Technical Implementation**
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### **Backend Changes**:
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**File**: `backend/services/research/research_persona_prompt_builder.py`
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**Added Methods**:
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1. `_extract_topics_from_crawl()` - Phase 1
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2. `_extract_keywords_from_crawl()` - Phase 1
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3. `_extract_writing_patterns()` - Phase 2
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4. `_extract_style_guidelines()` - Phase 2
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5. `_analyze_crawl_result_comprehensive()` - Phase 3
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6. `_map_writing_style_comprehensive()` - Phase 3
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7. `_extract_content_themes()` - Phase 3
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**Enhanced Prompt Sections**:
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- Phase 1: Website Analysis Intelligence
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- Phase 2: Writing Patterns & Style Intelligence
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- Phase 3: Comprehensive Analysis & Mapping
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- Enhanced all generation requirements with phase-specific instructions
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### **Frontend Changes**:
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**New Components**:
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1. `PersonalizationIndicator.tsx` - Info icon with tooltip
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2. `PersonalizationBadge.tsx` - Badge-style indicator
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**Modified Components**:
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1. `ResearchInput.tsx` - Added indicators and persona data
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2. `ResearchAngles.tsx` - Added persona indicator
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3. `ResearchControlsBar.tsx` - Added persona indicator
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4. `TargetAudience.tsx` - Added persona indicator
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5. `ResearchTest.tsx` - Added indicator to presets header
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---
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## 🎯 **User Experience Improvements**
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### **Before**:
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- Generic presets for all users
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- No indication of personalization
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- Users unaware of AI-powered features
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- Generic placeholders
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### **After**:
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- ✅ Personalized presets based on content types and themes
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- ✅ Clear indicators showing what's personalized
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- ✅ Tooltips explaining personalization sources
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- ✅ Personalized placeholders from research persona
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- ✅ Research angles from writing patterns
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- ✅ Keyword expansions matching vocabulary level
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- ✅ Query enhancement from style guidelines
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---
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## 📱 **UI Indicator Locations**
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1. **Research Topic & Keywords** - Shows when placeholders are personalized
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2. **Research Angles** - Shows when angles are from writing patterns
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3. **Quick Start Presets** - Shows when presets are personalized
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4. **Industry Dropdown** - Shows when industry is from persona
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5. **Target Audience** - Shows when audience is from persona
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---
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## 🧪 **Testing Checklist**
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### **Phase 1 Testing**:
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- [ ] Content-type-specific presets appear
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- [ ] Research depth matches writing complexity
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- [ ] Keywords include extracted topics
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### **Phase 2 Testing**:
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- [ ] Research angles match writing patterns
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- [ ] Keyword expansions match vocabulary level
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- [ ] Query rules match style guidelines
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### **Phase 3 Testing**:
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- [ ] Presets use content themes
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- [ ] All research preferences mapped from style
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- [ ] Content categories reflected in presets
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### **UI Indicator Testing**:
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- [ ] Indicators appear when persona exists
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- [ ] Tooltips show correct information
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- [ ] Indicators are unobtrusive but visible
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- [ ] Mobile responsiveness works
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---
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## 📝 **Next Steps for User**
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1. **Test Research Persona Generation**:
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- Generate new persona to see Phase 1-3 enhancements
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- Verify presets match content types
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- Check research angles match patterns
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2. **Test UI Indicators**:
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- Hover over indicators to see tooltips
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- Verify indicators appear when persona exists
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- Check all personalization sources are clear
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3. **Validate Personalization**:
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- Compare presets before/after persona generation
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- Verify placeholders are personalized
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- Check research angles are relevant
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---
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## ✅ **Implementation Complete**
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All phases implemented and ready for testing. The research persona now provides:
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- **Hyper-personalization** based on complete website analysis
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- **Transparent UI** showing what's personalized and why
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- **Intelligent defaults** matching user's writing style
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- **Content-aware** presets and research angles
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**Status**: Ready for User Testing 🚀
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168
docs/ALwrity Researcher/ENHANCED_GROUNDING_UI_IMPLEMENTATION.md
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docs/ALwrity Researcher/ENHANCED_GROUNDING_UI_IMPLEMENTATION.md
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# Enhanced Google Grounding UI Implementation
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## 🎯 **Objective**
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Based on the rich terminal logs analysis, enhance the ResearchResults UI to display comprehensive Google grounding metadata including inline citations, source indices, and detailed traceability.
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## 📊 **Terminal Logs Analysis**
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From the logs, we identified these rich data structures:
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### **Sources Data:**
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- **17 sources** with index, title, URL, and type
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- **Index mapping**: Each source has a unique index (0-16)
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- **Type classification**: All sources marked as 'web' type
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- **Domain variety**: precedenceresearch.com, mordorintelligence.com, fortunebusinessinsights.com, etc.
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### **Citations Data:**
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- **45+ inline citations** with detailed information
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- **Source mapping**: Each citation references specific source indices
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- **Text segments**: Exact text that was grounded from sources
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- **Position tracking**: Start and end indices for each citation
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- **Reference labels**: "Source 1", "Source 2", etc.
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### **Example Citation from Logs:**
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```json
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{
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"type": "inline",
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"start_index": 419,
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"end_index": 615,
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"text": "The global medical devices market was valued at $640.45 billion in 2024...",
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"source_indices": [0],
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"reference": "Source 1"
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}
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```
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## ✅ **What Was Implemented**
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### 1. **Enhanced Backend Models**
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- ✅ **ResearchSource**: Added `index` and `source_type` fields
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- ✅ **Citation**: New model for inline citations with position tracking
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- ✅ **GroundingMetadata**: Added `citations` array to capture all citation data
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### 2. **Backend Service Enhancements**
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- ✅ **Source Extraction**: Enhanced to capture index and type from raw data
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- ✅ **Citation Extraction**: New method to parse inline citations from logs
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- ✅ **Data Mapping**: Proper mapping of citations to source indices
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### 3. **Frontend Interface Updates**
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- ✅ **TypeScript Interfaces**: Added Citation interface and updated existing ones
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- ✅ **Type Safety**: Maintained full type safety across the application
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### 4. **Enhanced UI Components**
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#### **🔍 Enhanced Sources Display:**
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- **Source Index Badges**: Shows #1, #2, #3, etc. for easy reference
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- **Type Indicators**: Shows 'web' type with color-coded badges
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- **Improved Layout**: Better organization with badges and titles
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- **Visual Hierarchy**: Clear distinction between index, type, and title
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#### **📝 New Inline Citations Section:**
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- **Citation Cards**: Each citation displayed in its own card
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- **Source Mapping**: Shows which sources (S1, S2, etc.) each citation references
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- **Text Display**: Full citation text in italicized format
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- **Position Tracking**: Shows start-end indices for each citation
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- **Reference Labels**: Displays "Source 1", "Source 2" references
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- **Type Indicators**: Shows citation type (inline, etc.)
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#### **🎯 Enhanced Grounding Supports:**
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- **Chunk References**: Shows which grounding chunks are referenced
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- **Confidence Scores**: Multiple confidence scores with individual indicators
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- **Segment Text**: Displays the exact text that was grounded
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## 🎨 **UI Features Implemented**
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### **Source Index System:**
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```
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#1 [web] precedenceresearch.com
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#2 [web] mordorintelligence.com
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#3 [web] fortunebusinessinsights.com
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```
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### **Citation Display:**
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```
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[inline] Source 1 [S1]
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"The global medical devices market was valued at $640.45 billion in 2024..."
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Position: 419-615
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```
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### **Source Mapping:**
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- **S1, S2, S3...**: Direct mapping to source indices
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- **Color-coded badges**: Blue for source references
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- **Visual connection**: Easy to trace citations back to sources
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## 📊 **Data Displayed from Logs**
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### **From Terminal Logs (Real Data):**
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- **17 Sources**: All with indices 0-16 and 'web' type
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- **45+ Citations**: Each with source mapping and position data
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- **Rich Text Segments**: Market data, statistics, and insights
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- **Source References**: Clear mapping from citations to sources
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### **Example Real Citations:**
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1. **Market Size**: "$640.45 billion in 2024" → Source 1
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2. **Growth Rate**: "CAGR of 6% from 2025 to 2034" → Source 1
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3. **AI Market**: "USD 9.81 billion in 2022" → Source 6
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4. **Telemedicine**: "USD 590.9 billion by 2032" → Source 6
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## 🔧 **Technical Implementation**
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### **Backend Data Flow:**
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```
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Raw Logs → _extract_sources_from_grounding() → Enhanced ResearchSource
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Raw Logs → _extract_grounding_metadata() → Citations Array
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```
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### **Frontend Data Flow:**
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```
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Enhanced BlogResearchResponse → ResearchResults → Enhanced UI Components
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```
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### **Key Features:**
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- ✅ **Source Indexing**: Clear #1, #2, #3 numbering system
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- ✅ **Citation Mapping**: Direct S1, S2, S3 references to sources
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- ✅ **Position Tracking**: Exact text positions for each citation
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- ✅ **Type Classification**: Source types and citation types
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- ✅ **Visual Hierarchy**: Color-coded badges and clear organization
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## 🚀 **User Experience**
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### **Before:**
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- ❌ No source indexing or numbering
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- ❌ No inline citations display
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- ❌ No citation-to-source mapping
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- ❌ Limited traceability of grounded content
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### **After:**
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- ✅ **Complete Source Indexing**: Easy reference with #1, #2, #3
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- ✅ **Inline Citations**: See exactly what text was grounded
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- ✅ **Source Mapping**: Direct connection between citations and sources
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- ✅ **Position Tracking**: Know exactly where each citation appears
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- ✅ **Professional Display**: Clean, organized, and easy to understand
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## 📁 **Files Modified**
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### **Backend:**
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- `backend/models/blog_models.py` - Enhanced models with index, type, and citations
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- `backend/services/blog_writer/research/research_service.py` - Enhanced extraction methods
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### **Frontend:**
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- `frontend/src/services/blogWriterApi.ts` - Added Citation interface and enhanced types
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- `frontend/src/components/BlogWriter/ResearchResults.tsx` - Enhanced UI with citations and indexing
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## 🎉 **Result**
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The ResearchResults component now provides **enterprise-grade transparency** with:
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- 🔢 **Source Indexing**: Clear numbering system for easy reference
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- 📝 **Inline Citations**: See exactly what text was grounded from which sources
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- 🔗 **Source Mapping**: Direct traceability from citations to sources
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- 📊 **Position Tracking**: Know exactly where each citation appears in the content
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- 🎨 **Professional UI**: Clean, organized display of complex grounding data
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### **Real Data from Logs:**
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- **17 sources** with clear indexing
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- **45+ citations** with source mapping
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- **Rich market data** with proper attribution
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- **Complete traceability** from citation to source
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Users now have **complete visibility** into the Google grounding process with **professional-grade transparency** and **easy source verification**! 🎉
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297
docs/ALwrity Researcher/FIRST_TIME_USER_EXPERIENCE_ANALYSIS.md
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297
docs/ALwrity Researcher/FIRST_TIME_USER_EXPERIENCE_ANALYSIS.md
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# First-Time User Experience Analysis & Preset Integration
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## Review Date: 2025-12-30
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---
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## 🎯 **What First-Time Users See**
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### **Current Experience:**
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1. **Page Loads** → Research page appears
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2. **Modal Blocks Page** → "Generate Research Persona" modal appears immediately
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3. **User Must Choose:**
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- **Option A**: Click "Generate Persona" → Wait 30-60 seconds → Get personalized presets
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- **Option B**: Click "Skip for Now" → Use generic sample presets
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### **What's Visible:**
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- ✅ **Quick Start Presets** section (left panel)
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- ✅ **Research Wizard** (main content area)
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- ❌ **Modal blocks everything** until user interacts
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---
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## 🔌 **How Quick Start Presets Are Wired**
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### **Preset Generation Flow:**
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```
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Page Load
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↓
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Check for Research Persona
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↓
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┌─────────────────────────────────────┐
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│ CASE 1: Persona Exists │
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│ └─ Has recommended_presets? │
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│ ├─ YES → Use AI presets ✅ │
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│ └─ NO → Use rule-based presets │
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└─────────────────────────────────────┘
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↓
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┌─────────────────────────────────────┐
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│ CASE 2: No Persona │
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│ └─ Use rule-based presets │
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│ └─ Show modal to generate persona │
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└─────────────────────────────────────┘
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```
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### **Preset Types & Persona Integration:**
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#### **1. AI-Generated Presets** (Best - Full Personalization)
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**Source**: `research_persona.recommended_presets`
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**When Used**: Persona exists AND has `recommended_presets` array
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**✅ Benefits from Research Persona:**
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- **Full Config**: Complete `ResearchConfig` with all Exa/Tavily options
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- **Personalized Keywords**: Based on industry, audience, interests
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- **Industry-Specific**: Uses `default_industry` and `default_target_audience`
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- **Provider Optimization**:
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- `suggested_exa_category`
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- `suggested_exa_domains` (3-5 most relevant)
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- `suggested_exa_search_type`
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- `suggested_tavily_*` options
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- **Research Mode**: Uses `default_research_mode`
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- **Research Angles**: Uses `research_angles` for preset names/keywords
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- **Competitor Data**: Can create competitive analysis presets
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**Example**:
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```json
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{
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"name": "Content Marketing Competitive Analysis",
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"keywords": "Research top content marketing platforms, tools, and strategies used by leading B2B SaaS companies",
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"industry": "Content Marketing",
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"target_audience": "Marketing professionals and content creators",
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"research_mode": "comprehensive",
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"config": {
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"mode": "comprehensive",
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"provider": "exa",
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"max_sources": 20,
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"exa_category": "company",
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"exa_search_type": "neural",
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"exa_include_domains": ["contentmarketinginstitute.com", "hubspot.com", "marketo.com"],
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"include_competitors": true,
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"include_trends": true,
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"include_statistics": true
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},
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"description": "Analyze competitive landscape and identify top content marketing tools and strategies"
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}
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```
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#### **2. Rule-Based Presets** (Good - Partial Personalization)
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**Source**: `generatePersonaPresets(persona_defaults)`
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**When Used**: Persona exists but has no `recommended_presets`
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**✅ Benefits from Research Persona:**
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- **Industry**: Uses `persona_defaults.industry`
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- **Audience**: Uses `persona_defaults.target_audience`
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- **Exa Category**: Uses `persona_defaults.suggested_exa_category`
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- **Exa Domains**: Uses `persona_defaults.suggested_domains`
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- **Provider Settings**: Uses Exa search type and domains
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- ⚠️ **Limited**: Only 3 generic presets with template keywords
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**Example**:
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```javascript
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{
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name: "Content Marketing Trends",
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keywords: "Research latest trends and innovations in Content Marketing", // Template-based
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industry: "Content Marketing", // From persona
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targetAudience: "Professionals and content consumers", // From persona
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config: {
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exa_category: "company", // From persona
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exa_include_domains: ["contentmarketinginstitute.com", ...], // From persona
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exa_search_type: "neural" // From persona
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}
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}
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```
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#### **3. Sample Presets** (No Personalization)
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**Source**: Hardcoded `samplePresets` array
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**When Used**: No persona exists or persona has no industry
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||||
**❌ No Benefits from Research Persona:**
|
||||
- Generic presets (AI Marketing Tools, Small Business SEO, etc.)
|
||||
- Same for all users
|
||||
- Not personalized
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Improvements Made**
|
||||
|
||||
### **1. Enhanced Persona Generation Prompt**
|
||||
|
||||
**Added**:
|
||||
- ✅ **Competitor Analysis Integration**: Prompt now includes competitor data
|
||||
- ✅ **Research Angles Usage**: Instructions to use `research_angles` for preset names/keywords
|
||||
- ✅ **Better Preset Instructions**: More detailed guidelines for creating actionable presets
|
||||
- ✅ **Competitive Presets**: Instructions to create competitive analysis presets if competitor data exists
|
||||
|
||||
**Enhanced Sections**:
|
||||
1. **Research Angles**: Now includes competitive landscape angles
|
||||
2. **Recommended Presets**:
|
||||
- More specific keyword requirements
|
||||
- Use research_angles for inspiration
|
||||
- Create competitive presets if competitor data exists
|
||||
- Better config instructions with all provider options
|
||||
|
||||
### **2. Competitor Data Collection**
|
||||
|
||||
**Added**:
|
||||
- ✅ `_collect_onboarding_data()` now retrieves competitor analysis
|
||||
- ✅ Competitor data included in persona generation prompt
|
||||
- ✅ Enables creation of competitive analysis presets
|
||||
|
||||
---
|
||||
|
||||
## 🎨 **UX Improvements Needed**
|
||||
|
||||
### **Issue 1: Blocking Modal**
|
||||
|
||||
**Problem**: Modal blocks entire page, user can't see value immediately
|
||||
|
||||
**Proposed Solution**:
|
||||
- Convert to **non-blocking banner** at top of page
|
||||
- Show presets immediately (even if generic)
|
||||
- Allow user to start researching right away
|
||||
- Persona generation becomes optional enhancement
|
||||
|
||||
### **Issue 2: No Preview of Personalized Presets**
|
||||
|
||||
**Problem**: User doesn't know what they're getting
|
||||
|
||||
**Proposed Solution**:
|
||||
- Show preview examples in modal/banner
|
||||
- "After generation, you'll see presets like: [examples]"
|
||||
- Visual comparison: Generic vs. Personalized
|
||||
|
||||
### **Issue 3: Generic Presets Initially**
|
||||
|
||||
**Problem**: Shows sample presets until persona generates
|
||||
|
||||
**Proposed Solution**:
|
||||
- Show presets immediately based on `persona_defaults` (from core persona)
|
||||
- Even without research persona, use industry/audience from onboarding
|
||||
- Progressive enhancement: Generic → Rule-based → AI-generated
|
||||
|
||||
### **Issue 4: Unclear Value Proposition**
|
||||
|
||||
**Problem**: User doesn't understand why persona is needed
|
||||
|
||||
**Proposed Solution**:
|
||||
- Better explanation in modal/banner
|
||||
- Show concrete examples
|
||||
- Explain what changes after generation
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Preset Integration Summary**
|
||||
|
||||
### **✅ How Presets Currently Benefit:**
|
||||
|
||||
| Preset Type | Persona Integration | Benefits |
|
||||
|------------|---------------------|----------|
|
||||
| **AI-Generated** | ✅ Full | All persona fields, competitor data, research angles |
|
||||
| **Rule-Based** | ✅ Partial | Industry, audience, Exa options |
|
||||
| **Sample** | ❌ None | Generic for all users |
|
||||
|
||||
### **✅ Improvements Made:**
|
||||
|
||||
1. **Competitor Data**: Now included in persona generation
|
||||
2. **Research Angles**: Used for preset inspiration
|
||||
3. **Better Instructions**: More detailed preset generation guidelines
|
||||
4. **Competitive Presets**: Can create competitive analysis presets
|
||||
|
||||
### **⚠️ Remaining Gaps:**
|
||||
|
||||
1. **Modal Blocks Action**: User must interact before seeing value
|
||||
2. **No Preview**: Can't see personalized presets before generating
|
||||
3. **Generic Initially**: Shows sample presets until persona generates
|
||||
|
||||
---
|
||||
|
||||
## 🚀 **Recommended Next Steps**
|
||||
|
||||
### **Phase 1: Quick UX Wins** (High Impact)
|
||||
1. ✅ Make modal non-blocking (banner instead)
|
||||
2. ✅ Show presets immediately based on `persona_defaults`
|
||||
3. ✅ Add visual indicators for personalized presets
|
||||
|
||||
### **Phase 2: Enhanced Personalization** (Already Done)
|
||||
1. ✅ Use competitor data in persona generation
|
||||
2. ✅ Use research angles for preset inspiration
|
||||
3. ✅ Enhanced preset generation instructions
|
||||
|
||||
### **Phase 3: Advanced Features** (Future)
|
||||
1. Preset preview in modal
|
||||
2. Preset analytics
|
||||
3. Custom preset creation
|
||||
4. Preset templates library
|
||||
|
||||
---
|
||||
|
||||
## 📝 **Key Findings**
|
||||
|
||||
### **✅ What's Working:**
|
||||
- Presets DO benefit from research persona (when it exists)
|
||||
- AI-generated presets are fully personalized
|
||||
- Rule-based presets use industry/audience from persona
|
||||
- Data retrieval is working correctly
|
||||
|
||||
### **⚠️ What Needs Improvement:**
|
||||
- First-time UX (blocking modal)
|
||||
- No preview of personalized presets
|
||||
- Generic presets shown initially
|
||||
- Better explanation of value
|
||||
|
||||
### **✅ Improvements Implemented:**
|
||||
- Enhanced persona generation prompt
|
||||
- Competitor data integration
|
||||
- Better preset generation instructions
|
||||
- Research angles usage
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Answer to User Questions**
|
||||
|
||||
### **Q: What do first-time users expect to see?**
|
||||
**A**: Users expect to:
|
||||
- See the research interface immediately
|
||||
- Understand what the page does
|
||||
- Start researching without barriers
|
||||
- See relevant presets for their industry
|
||||
- Get better experience after persona generation
|
||||
|
||||
### **Q: How are Quick Start presets wired?**
|
||||
**A**:
|
||||
- **AI Presets**: Use `research_persona.recommended_presets` (full personalization)
|
||||
- **Rule-Based**: Use `persona_defaults` to generate industry-specific presets
|
||||
- **Sample**: Generic fallback if no persona
|
||||
|
||||
**✅ Presets DO benefit from research persona** - they use industry, audience, Exa options, and competitor data.
|
||||
|
||||
### **Q: Room for improving research persona?**
|
||||
**A**: Yes! Improvements made:
|
||||
- ✅ Added competitor data to generation
|
||||
- ✅ Enhanced preset generation instructions
|
||||
- ✅ Use research angles for preset inspiration
|
||||
- ✅ Better keyword requirements (specific, actionable)
|
||||
- ✅ Competitive preset creation
|
||||
|
||||
---
|
||||
|
||||
## 📋 **Implementation Status**
|
||||
|
||||
- ✅ Enhanced persona generation prompt
|
||||
- ✅ Competitor data collection
|
||||
- ✅ Better preset generation instructions
|
||||
- ⏳ Non-blocking modal (recommended for Phase 1)
|
||||
- ⏳ Preset preview (recommended for Phase 1)
|
||||
669
docs/ALwrity Researcher/PHASE1_IMPLEMENTATION_REVIEW.md
Normal file
669
docs/ALwrity Researcher/PHASE1_IMPLEMENTATION_REVIEW.md
Normal file
@@ -0,0 +1,669 @@
|
||||
# Phase 1 Implementation Review & Gap Analysis
|
||||
|
||||
**Date**: 2025-01-29
|
||||
**Status**: ✅ Phase 1 Complete - Ready for End-User Testing
|
||||
|
||||
---
|
||||
|
||||
## 📊 Gap Status Summary
|
||||
|
||||
| Gap | Status | Implementation Details |
|
||||
|-----|--------|----------------------|
|
||||
| **1. Persona-Aware Defaults Integration** | ✅ **COMPLETE** | Frontend fetches and applies defaults on wizard load |
|
||||
| **2. Research Persona Integration** | ✅ **COMPLETE** | Backend enriches context with persona data |
|
||||
| **3. Provider Auto-Selection (Exa First)** | ✅ **COMPLETE** | Exa → Tavily → Google for all modes |
|
||||
| **4. Visual Status Indicators** | ✅ **COMPLETE** | Provider chips show actual availability |
|
||||
| **5. Domain Suggestions Auto-Population** | ✅ **VERIFIED** | Industry change triggers domain suggestions |
|
||||
| **6. AI Query Enhancement** | ❌ **NOT STARTED** | Phase 2 feature |
|
||||
| **7. Smart Preset Generation** | ❌ **NOT STARTED** | Phase 2 feature (depends on research persona) |
|
||||
| **8. Date Range & Source Type Filtering** | ❌ **NOT STARTED** | Phase 2 feature |
|
||||
|
||||
**Completion Rate**: 5/8 gaps addressed (62.5%)
|
||||
|
||||
---
|
||||
|
||||
## ✅ Implemented Features
|
||||
|
||||
### 1. Persona-Aware Defaults Integration ✅
|
||||
|
||||
**What Was Implemented:**
|
||||
- `getResearchConfig()` now fetches both provider availability AND persona defaults in parallel
|
||||
- `ResearchInput.tsx` applies persona defaults on component mount:
|
||||
- Industry auto-fills if currently "General"
|
||||
- Target audience auto-fills if currently "General"
|
||||
- Exa domains auto-populate if Exa is available and domains not already set
|
||||
- Exa category auto-applies if not already set
|
||||
|
||||
**Files Modified:**
|
||||
- `frontend/src/api/researchConfig.ts` - Fetches persona defaults
|
||||
- `frontend/src/components/Research/steps/ResearchInput.tsx` - Applies defaults (lines 85-114)
|
||||
|
||||
**How It Works:**
|
||||
1. Wizard loads → `getResearchConfig()` called
|
||||
2. API fetches `/api/research/persona-defaults` in parallel with provider status
|
||||
3. If fields are "General" (default), persona defaults are applied
|
||||
4. User can still override any auto-filled values
|
||||
|
||||
**Testing Notes:**
|
||||
- ✅ Works for new users (fields start as "General")
|
||||
- ⚠️ May not apply if localStorage has saved state with non-General values (intentional - respects user choices)
|
||||
- ✅ Graceful fallback if persona API fails
|
||||
|
||||
---
|
||||
|
||||
### 2. Research Persona Integration ✅
|
||||
|
||||
**What Was Implemented:**
|
||||
- `ResearchEngine` now fetches and uses research persona during research execution
|
||||
- Persona data enriches the research context:
|
||||
- Industry and target audience (if not set)
|
||||
- Suggested Exa domains (if not set)
|
||||
- Suggested Exa category (if not set)
|
||||
- Uses cached persona (7-day TTL) - no expensive LLM calls during research
|
||||
|
||||
**Files Modified:**
|
||||
- `backend/services/research/core/research_engine.py`:
|
||||
- Added `_get_research_persona()` method (lines 88-114)
|
||||
- Added `_enrich_context_with_persona()` method (lines 116-152)
|
||||
- Integrated into `research()` method (lines 171-177)
|
||||
|
||||
**How It Works:**
|
||||
1. User executes research → `ResearchEngine.research()` called
|
||||
2. Engine fetches cached research persona for user (if available)
|
||||
3. Persona data enriches the `ResearchContext`:
|
||||
- Only applies if fields are not already set
|
||||
- User-provided values always take precedence
|
||||
4. Enriched context passed to `ParameterOptimizer`
|
||||
5. Optimizer uses persona data for better parameter selection
|
||||
|
||||
**Testing Notes:**
|
||||
- ✅ Only loads cached persona (fast, no LLM calls)
|
||||
- ✅ Graceful fallback if persona not available
|
||||
- ✅ User overrides are respected
|
||||
- ⚠️ Requires user to have completed onboarding and have research persona generated
|
||||
|
||||
---
|
||||
|
||||
### 3. Provider Auto-Selection (Exa First) ✅
|
||||
|
||||
**What Was Implemented:**
|
||||
- **Frontend**: Auto-selects Exa → Tavily → Google for ALL modes (including basic)
|
||||
- **Backend**: `ParameterOptimizer` always prefers Exa → Tavily → Google
|
||||
- Removed mode-based provider selection logic
|
||||
|
||||
**Files Modified:**
|
||||
- `frontend/src/components/Research/steps/ResearchInput.tsx` (lines 154-191)
|
||||
- `backend/services/research/core/parameter_optimizer.py` (lines 176-224)
|
||||
|
||||
**Priority Order:**
|
||||
1. **Exa** (Primary) - Neural semantic search, best for all content types
|
||||
2. **Tavily** (Secondary) - AI-powered search, good for real-time/news
|
||||
3. **Google** (Fallback) - Gemini grounding, used when others unavailable
|
||||
|
||||
**Testing Notes:**
|
||||
- ✅ Exa selected when available (regardless of mode)
|
||||
- ✅ Falls back to Tavily if Exa unavailable
|
||||
- ✅ Falls back to Google if both unavailable
|
||||
- ✅ User can still manually override provider
|
||||
|
||||
---
|
||||
|
||||
### 4. Visual Status Indicators ✅
|
||||
|
||||
**What Was Implemented:**
|
||||
- `ProviderChips` component shows actual provider availability
|
||||
- Status dots: Green = configured, Red = not configured
|
||||
- Reordered to show priority: Exa → Tavily → Google
|
||||
- Updated tooltips to indicate provider roles
|
||||
|
||||
**Files Modified:**
|
||||
- `frontend/src/components/Research/steps/components/ProviderChips.tsx`
|
||||
|
||||
**Visual Changes:**
|
||||
- Exa shown first (primary provider)
|
||||
- Tavily shown second (secondary provider)
|
||||
- Google shown third (fallback provider)
|
||||
- Status dots reflect actual API key configuration
|
||||
|
||||
**Testing Notes:**
|
||||
- ✅ Status indicators reflect real API key status
|
||||
- ✅ Tooltips explain provider roles
|
||||
- ✅ No longer tied to "advanced mode" toggle
|
||||
|
||||
---
|
||||
|
||||
### 5. Domain Suggestions Auto-Population ✅
|
||||
|
||||
**What Was Implemented:**
|
||||
- Industry change triggers domain suggestions (already existed)
|
||||
- Persona defaults also provide domain suggestions
|
||||
- Works for both Exa and Tavily providers
|
||||
|
||||
**Files Modified:**
|
||||
- `frontend/src/components/Research/steps/ResearchInput.tsx` (lines 193-225)
|
||||
- Uses existing `getIndustryDomainSuggestions()` utility
|
||||
|
||||
**How It Works:**
|
||||
1. User selects industry → `useEffect` triggers
|
||||
2. `getIndustryDomainSuggestions(industry)` called
|
||||
3. Domains auto-populate in Exa config if Exa available
|
||||
4. Persona defaults also provide domains on initial load
|
||||
|
||||
**Testing Notes:**
|
||||
- ✅ Industry change triggers domain suggestions
|
||||
- ✅ Persona defaults provide domains on load
|
||||
- ✅ Works for both Exa and Tavily
|
||||
- ⚠️ Domains only auto-populate for Exa (Tavily domains need manual transfer)
|
||||
|
||||
---
|
||||
|
||||
## ❌ Remaining Gaps (Phase 2)
|
||||
|
||||
### 6. AI Query Enhancement ❌
|
||||
|
||||
**Status**: Not Started
|
||||
**Priority**: High
|
||||
**Dependencies**: Research persona (✅ now available)
|
||||
|
||||
**What's Needed:**
|
||||
- Backend service to enhance vague user queries
|
||||
- Endpoint: `/api/research/enhance-query`
|
||||
- Frontend "Enhance Query" button
|
||||
- Uses research persona's `query_enhancement_rules`
|
||||
|
||||
**Implementation Plan:**
|
||||
1. Create `backend/services/research/core/query_enhancer.py`
|
||||
2. Add `/api/research/enhance-query` endpoint
|
||||
3. Add UI button in `ResearchInput.tsx`
|
||||
4. Integrate with research persona rules
|
||||
|
||||
---
|
||||
|
||||
### 7. Smart Preset Generation ❌
|
||||
|
||||
**Status**: Not Started
|
||||
**Priority**: Medium
|
||||
**Dependencies**: Research persona (✅ now available)
|
||||
|
||||
**What's Needed:**
|
||||
- Generate presets from research persona
|
||||
- Use persona's `recommended_presets` field
|
||||
- Display in frontend wizard
|
||||
- Learn from successful research patterns
|
||||
|
||||
**Implementation Plan:**
|
||||
1. Use research persona's `recommended_presets` field
|
||||
2. Display presets in `ResearchInput.tsx`
|
||||
3. Add preset generation service (future)
|
||||
4. Track successful research patterns (future)
|
||||
|
||||
---
|
||||
|
||||
### 8. Date Range & Source Type Filtering ❌
|
||||
|
||||
**Status**: Not Started
|
||||
**Priority**: Medium
|
||||
|
||||
**What's Needed:**
|
||||
- Add date range controls to frontend
|
||||
- Add source type checkboxes
|
||||
- Pass to Research Engine API
|
||||
- Integrate with providers (Tavily supports time_range)
|
||||
|
||||
**Implementation Plan:**
|
||||
1. Add `date_range` and `source_types` to `ResearchContext`
|
||||
2. Add UI controls (collapsible section or advanced mode)
|
||||
3. Update `ResearchEngine` to pass to providers
|
||||
4. Test with Tavily time_range parameter
|
||||
|
||||
---
|
||||
|
||||
## 🧪 End-User Testing Checklist
|
||||
|
||||
### Test Scenario 1: New User (No Onboarding)
|
||||
- [ ] Open Research Wizard
|
||||
- [ ] Verify fields start as "General"
|
||||
- [ ] Verify provider auto-selects to Exa (if available)
|
||||
- [ ] Verify status indicators show correct provider availability
|
||||
- [ ] Enter keywords and execute research
|
||||
- [ ] Verify research completes successfully
|
||||
|
||||
### Test Scenario 2: User with Onboarding (Persona Available)
|
||||
- [ ] Open Research Wizard
|
||||
- [ ] Verify industry auto-fills from persona defaults
|
||||
- [ ] Verify target audience auto-fills from persona defaults
|
||||
- [ ] Verify Exa domains auto-populate (if Exa available)
|
||||
- [ ] Verify Exa category auto-applies
|
||||
- [ ] Execute research
|
||||
- [ ] Verify backend logs show persona enrichment
|
||||
- [ ] Verify research uses persona-suggested domains/category
|
||||
|
||||
### Test Scenario 3: Provider Availability
|
||||
- [ ] Test with Exa available → Should select Exa
|
||||
- [ ] Test with only Tavily available → Should select Tavily
|
||||
- [ ] Test with only Google available → Should select Google
|
||||
- [ ] Verify status chips show correct colors (green/red)
|
||||
- [ ] Verify tooltips explain provider roles
|
||||
|
||||
### Test Scenario 4: Provider Fallback
|
||||
- [ ] Configure only Exa → Execute research → Verify Exa used
|
||||
- [ ] Disable Exa, enable Tavily → Execute research → Verify Tavily used
|
||||
- [ ] Disable both, enable Google → Execute research → Verify Google used
|
||||
|
||||
### Test Scenario 5: User Overrides
|
||||
- [ ] Auto-fill persona defaults
|
||||
- [ ] Manually change industry → Verify override works
|
||||
- [ ] Manually change provider → Verify override works
|
||||
- [ ] Execute research → Verify user values are respected
|
||||
|
||||
### Test Scenario 6: Domain Suggestions
|
||||
- [ ] Select "Healthcare" industry → Verify domains auto-populate
|
||||
- [ ] Select "Technology" industry → Verify domains change
|
||||
- [ ] Verify domains appear in Exa options
|
||||
- [ ] Execute research → Verify domains are used in search
|
||||
|
||||
---
|
||||
|
||||
## 📋 Next Implementation Items (Phase 2)
|
||||
|
||||
### Priority 1: High-Value Features
|
||||
|
||||
**1. AI Query Enhancement** (High Priority)
|
||||
- **Impact**: Transforms vague inputs into actionable queries
|
||||
- **Effort**: Medium (2-3 days)
|
||||
- **Dependencies**: ✅ Research persona available
|
||||
- **Files to Create/Modify**:
|
||||
- `backend/services/research/core/query_enhancer.py` (NEW)
|
||||
- `backend/api/research/router.py` (add endpoint)
|
||||
- `frontend/src/components/Research/steps/ResearchInput.tsx` (add button)
|
||||
|
||||
**2. Research Persona Presets Display** (Medium Priority)
|
||||
- **Impact**: Shows personalized presets from research persona
|
||||
- **Effort**: Low (1 day)
|
||||
- **Dependencies**: ✅ Research persona available
|
||||
- **Files to Modify**:
|
||||
- `frontend/src/components/Research/steps/ResearchInput.tsx` (display presets)
|
||||
- Use `research_persona.recommended_presets` field
|
||||
|
||||
### Priority 2: Enhanced Filtering
|
||||
|
||||
**3. Date Range & Source Type Filtering** (Medium Priority)
|
||||
- **Impact**: Better control over research scope
|
||||
- **Effort**: Medium (2 days)
|
||||
- **Dependencies**: None
|
||||
- **Files to Modify**:
|
||||
- `backend/services/research/core/research_context.py` (add fields)
|
||||
- `backend/services/research/core/research_engine.py` (pass to providers)
|
||||
- `frontend/src/components/Research/steps/ResearchInput.tsx` (add UI)
|
||||
|
||||
### Priority 3: Advanced Features
|
||||
|
||||
**4. Smart Preset Generation** (Low Priority)
|
||||
- **Impact**: AI-generated presets based on research history
|
||||
- **Effort**: High (3-4 days)
|
||||
- **Dependencies**: Research history tracking
|
||||
- **Files to Create/Modify**:
|
||||
- `backend/services/research/core/preset_generator.py` (NEW)
|
||||
- Research history tracking service (NEW)
|
||||
|
||||
---
|
||||
|
||||
## 🔍 Known Issues & Limitations
|
||||
|
||||
### 1. Persona Defaults Timing
|
||||
- **Issue**: Persona defaults only apply if fields are "General"
|
||||
- **Impact**: If localStorage has saved state, defaults may not apply
|
||||
- **Workaround**: Clear localStorage or manually reset to "General"
|
||||
- **Future Fix**: Add "Reset to Persona Defaults" button
|
||||
|
||||
### 2. Domain Suggestions Provider-Specific
|
||||
- **Issue**: Domain suggestions only auto-populate for Exa
|
||||
- **Impact**: Tavily domains need manual entry
|
||||
- **Future Fix**: Auto-populate for both providers
|
||||
|
||||
### 3. Research Persona Cache
|
||||
- **Issue**: Persona only loaded if cached (7-day TTL)
|
||||
- **Impact**: New users or expired cache won't get persona benefits
|
||||
- **Workaround**: Persona generation happens during onboarding or scheduled task
|
||||
- **Future Fix**: Auto-generate on-demand if cache expired
|
||||
|
||||
### 4. Query Enhancement Not Available
|
||||
- **Issue**: No way to enhance vague queries
|
||||
- **Impact**: Users must manually refine queries
|
||||
- **Future Fix**: Implement AI query enhancement (Phase 2)
|
||||
|
||||
---
|
||||
|
||||
## 📈 Success Metrics
|
||||
|
||||
### Phase 1 Goals (Current)
|
||||
- ✅ Persona defaults auto-apply for onboarded users
|
||||
- ✅ Research persona enriches backend research
|
||||
- ✅ Exa preferred for all research modes
|
||||
- ✅ Provider status clearly visible
|
||||
|
||||
### Phase 2 Goals (Next)
|
||||
- ⏳ AI query enhancement reduces query refinement time
|
||||
- ⏳ Smart presets increase research efficiency
|
||||
- ⏳ Date range filtering improves result relevance
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Recommendations for Testing
|
||||
|
||||
1. **Test with Real User Accounts**:
|
||||
- New user (no onboarding)
|
||||
- User with completed onboarding
|
||||
- User with research persona generated
|
||||
|
||||
2. **Test Provider Scenarios**:
|
||||
- All providers available
|
||||
- Only Exa available
|
||||
- Only Tavily available
|
||||
- Only Google available
|
||||
|
||||
3. **Test Persona Integration**:
|
||||
- Verify persona defaults apply on wizard load
|
||||
- Verify backend persona enrichment works
|
||||
- Check backend logs for persona application
|
||||
|
||||
4. **Test Edge Cases**:
|
||||
- localStorage with saved state
|
||||
- Network errors during config fetch
|
||||
- Missing research persona
|
||||
- Provider API failures
|
||||
|
||||
---
|
||||
|
||||
## 📝 Summary
|
||||
|
||||
**Phase 1 Implementation**: ✅ **COMPLETE**
|
||||
|
||||
**Key Achievements**:
|
||||
- Persona-aware defaults integrated (frontend + backend)
|
||||
- Research persona enriches research context
|
||||
- Exa-first provider selection for all modes
|
||||
- Visual status indicators working correctly
|
||||
- Domain suggestions auto-populate
|
||||
|
||||
**Ready for Testing**: ✅ Yes
|
||||
|
||||
**Next Steps**:
|
||||
1. End-user testing (current focus)
|
||||
2. Phase 2: AI Query Enhancement
|
||||
3. Phase 2: Research Persona Presets Display
|
||||
4. Phase 2: Date Range & Source Type Filtering
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Phase 2 Implementation Plan (User-Clarified Requirements)
|
||||
|
||||
### Understanding the Flow
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────┐
|
||||
│ USER JOURNEY │
|
||||
├─────────────────────────────────────────────────────────────────────┤
|
||||
│ 1. User signs up → MUST complete onboarding (mandatory) │
|
||||
│ └── Creates: Core Persona, Blog Persona, (opt) Social Personas │
|
||||
│ │
|
||||
│ 2. User accesses Dashboard/Tools (only after onboarding) │
|
||||
│ │
|
||||
│ 3. User visits Researcher (first time) │
|
||||
│ └── Research Persona does NOT exist yet │
|
||||
│ └── System GENERATES Research Persona from Core Persona │
|
||||
│ └── Stores in onboarding database │
|
||||
│ │
|
||||
│ 4. User visits Researcher (subsequent times) │
|
||||
│ └── Research Persona loaded from cache/database │
|
||||
│ └── NO fallback to "General" - always use persona │
|
||||
└─────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Key User Requirements
|
||||
|
||||
1. **Onboarding is mandatory** - Users cannot access tools without completing onboarding
|
||||
2. **Core persona always exists** - After onboarding, core persona + blog persona are guaranteed
|
||||
3. **Research persona generated on first use** - NOT during onboarding
|
||||
4. **Never fallback to "General"** - Always use persona data for hyper-personalization
|
||||
5. **Pre-fill Exa/Tavily options** - Make research easier for non-technical users
|
||||
6. **AI analysis personalized** - Use persona to customize research result presentation
|
||||
|
||||
---
|
||||
|
||||
### Phase 2 Changes Required
|
||||
|
||||
#### 1. Backend - Generate Research Persona on First Visit
|
||||
|
||||
**File**: `backend/services/research/core/research_engine.py`
|
||||
|
||||
**Current Code (Phase 1)**:
|
||||
```python
|
||||
persona = persona_service.get_cached_only(user_id) # Never generates
|
||||
```
|
||||
|
||||
**Phase 2 Change**:
|
||||
```python
|
||||
persona = persona_service.get_or_generate(user_id) # Generates if missing
|
||||
```
|
||||
|
||||
**Impact**:
|
||||
- First-time users get research persona generated automatically
|
||||
- Subsequent users get cached persona (7-day TTL)
|
||||
- LLM API call cost on first research execution
|
||||
|
||||
---
|
||||
|
||||
#### 2. Backend - `/api/research/persona-defaults` Enhancement
|
||||
|
||||
**File**: `backend/api/research_config.py`
|
||||
|
||||
**Current Behavior**:
|
||||
- Uses core persona from onboarding
|
||||
- Falls back to "General" if not found
|
||||
|
||||
**Phase 2 Change**:
|
||||
1. Check if research persona exists
|
||||
2. If yes → Use research persona fields
|
||||
3. If no → Use core persona fields (never "General")
|
||||
4. Optionally trigger research persona generation in background
|
||||
|
||||
**Why**: Research persona has better defaults (suggested_exa_domains, suggested_exa_category, research_angles) than core persona.
|
||||
|
||||
---
|
||||
|
||||
#### 3. Frontend - Ensure Persona Always Loaded
|
||||
|
||||
**File**: `frontend/src/components/Research/steps/ResearchInput.tsx`
|
||||
|
||||
**Current Behavior**:
|
||||
- Applies persona defaults if fields are "General"
|
||||
- Falls back to "General" if persona API fails
|
||||
|
||||
**Phase 2 Change**:
|
||||
1. Remove fallback to "General"
|
||||
2. Show loading state until persona is loaded
|
||||
3. If persona fails, show error with retry option
|
||||
4. Never proceed with "General" values
|
||||
|
||||
---
|
||||
|
||||
#### 4. Frontend - First Visit Detection
|
||||
|
||||
**File**: `frontend/src/components/Research/ResearchWizard.tsx` or `useResearchWizard.ts`
|
||||
|
||||
**Phase 2 Addition**:
|
||||
1. Check if research persona exists on mount
|
||||
2. If not → Show "Generating your personalized research settings..." loading state
|
||||
3. Call `/api/research/research-persona` to trigger generation
|
||||
4. Once complete → Load persona defaults into wizard
|
||||
|
||||
---
|
||||
|
||||
#### 5. Remove All "General" Fallbacks
|
||||
|
||||
**Files to Update**:
|
||||
- `ResearchInput.tsx` - Remove "General" default values
|
||||
- `useResearchWizard.ts` - Remove "General" from `defaultState`
|
||||
- `researchConfig.ts` - Remove empty fallback for `PersonaDefaults`
|
||||
- `research_engine.py` - Remove context creation without personalization
|
||||
|
||||
**Why**: User explicitly stated "no fallback to General" - always use persona data.
|
||||
|
||||
---
|
||||
|
||||
### Implementation Order
|
||||
|
||||
#### Step 1: Backend - Enable Research Persona Generation on First Use
|
||||
```
|
||||
File: backend/services/research/core/research_engine.py
|
||||
Change: get_cached_only() → get_or_generate()
|
||||
Risk: LLM API cost on first research
|
||||
Mitigation: Rate limiting already in place
|
||||
```
|
||||
|
||||
#### Step 2: Backend - Enhance Persona Defaults Endpoint
|
||||
```
|
||||
File: backend/api/research_config.py
|
||||
Change: Use research persona fields if available
|
||||
Why: Research persona has richer defaults
|
||||
```
|
||||
|
||||
#### Step 3: Frontend - First Visit Research Persona Generation Flow
|
||||
```
|
||||
Files: ResearchWizard.tsx, useResearchWizard.ts
|
||||
Change: Add generation flow for first-time users
|
||||
UX: Show friendly loading state during generation
|
||||
```
|
||||
|
||||
#### Step 4: Remove "General" Fallbacks
|
||||
```
|
||||
Files: Multiple frontend and backend files
|
||||
Change: Replace "General" with persona-derived values
|
||||
Why: Hyper-personalization requirement
|
||||
```
|
||||
|
||||
#### Step 5: Pre-fill Advanced Exa/Tavily Options
|
||||
```
|
||||
Files: ResearchInput.tsx, ExaOptions.tsx, TavilyOptions.tsx
|
||||
Change: Auto-populate from research persona
|
||||
Why: Simplify UI for non-technical users
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Testing Checklist for Phase 2
|
||||
|
||||
#### Test Scenario 1: First-Time Researcher User
|
||||
- [ ] User completes onboarding (has core persona, blog persona)
|
||||
- [ ] User visits Researcher for first time
|
||||
- [ ] Shows "Generating personalized research settings..." loading
|
||||
- [ ] Research persona is generated (check backend logs)
|
||||
- [ ] Wizard fields auto-populate with persona data (NOT "General")
|
||||
- [ ] Execute research → verify persona enrichment in backend
|
||||
|
||||
#### Test Scenario 2: Returning Researcher User
|
||||
- [ ] User with existing research persona visits Researcher
|
||||
- [ ] Persona loaded from cache (no generation)
|
||||
- [ ] Wizard fields auto-populate correctly
|
||||
- [ ] Execute research → verify cached persona used
|
||||
|
||||
#### Test Scenario 3: Expired Cache
|
||||
- [ ] User with expired research persona (>7 days) visits Researcher
|
||||
- [ ] Persona is regenerated (check backend logs)
|
||||
- [ ] New persona used for research
|
||||
|
||||
#### Test Scenario 4: No "General" Values
|
||||
- [ ] Verify industry is never "General"
|
||||
- [ ] Verify target audience is never "General"
|
||||
- [ ] Verify Exa domains/category are always populated
|
||||
- [ ] Verify Tavily options are pre-filled
|
||||
|
||||
---
|
||||
|
||||
### API Flow Diagram
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────┐
|
||||
│ PHASE 2 API FLOW │
|
||||
├─────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ User Opens Researcher │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ GET /api/research/persona-defaults │ │
|
||||
│ │ + GET /api/research/providers/status │
|
||||
│ └─────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ Backend checks research persona │ │
|
||||
│ │ exists in cache/database? │ │
|
||||
│ └─────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ┌────┴────┐ │
|
||||
│ YES NO │
|
||||
│ │ │ │
|
||||
│ ▼ ▼ │
|
||||
│ ┌──────┐ ┌───────────────────────────┐ │
|
||||
│ │Return│ │ Generate research persona │ │
|
||||
│ │cached│ │ from core persona (LLM) │ │
|
||||
│ │data │ │ Save to database │ │
|
||||
│ └──────┘ │ Return generated data │ │
|
||||
│ │ └───────────────────────────┘ │
|
||||
│ │ │ │
|
||||
│ └────┬─────┘ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ Frontend receives persona defaults │ │
|
||||
│ │ (industry, audience, domains, etc.) │ │
|
||||
│ └─────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ Auto-populate wizard fields │ │
|
||||
│ │ (NO "General" values) │ │
|
||||
│ └─────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ User Executes Research │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ POST /api/research/start │ │
|
||||
│ │ (ResearchEngine.research()) │ │
|
||||
│ └─────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ Backend enriches context with │ │
|
||||
│ │ research persona (cached) │ │
|
||||
│ │ → AI optimizes Exa/Tavily params │ │
|
||||
│ │ → Executes research │ │
|
||||
│ │ → AI analyzes results (personalized)│ │
|
||||
│ └─────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ Return personalized research results│ │
|
||||
│ └─────────────────────────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Benefits of Phase 2
|
||||
|
||||
1. **Zero Configuration for Users**: Research works out-of-box with personalized settings
|
||||
2. **Hyper-Personalization**: Every research is tailored to user's industry and audience
|
||||
3. **No Technical Complexity**: Exa/Tavily options pre-filled, hidden from users
|
||||
4. **Consistent Experience**: No "General" fallbacks - always meaningful defaults
|
||||
5. **AI-Optimized Results**: Research output digestible and relevant to user's needs
|
||||
|
||||
---
|
||||
|
||||
**Document Version**: 1.1
|
||||
**Last Updated**: 2025-01-29
|
||||
**Phase 2 Status**: Ready for Implementation
|
||||
136
docs/ALwrity Researcher/PHASE1_IMPLEMENTATION_SUMMARY.md
Normal file
136
docs/ALwrity Researcher/PHASE1_IMPLEMENTATION_SUMMARY.md
Normal file
@@ -0,0 +1,136 @@
|
||||
# Phase 1 Implementation Summary: Research Persona Enhancements
|
||||
|
||||
## Date: 2025-12-31
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Phase 1 Implementation Complete**
|
||||
|
||||
### **What Was Implemented:**
|
||||
|
||||
#### **1. Content Type → Preset Generation** ✅
|
||||
|
||||
**Enhancement**: Generate presets based on actual content types from website analysis
|
||||
|
||||
**Changes Made**:
|
||||
- Extract `content_type` from website analysis (primary_type, secondary_types, purpose)
|
||||
- Added instructions to generate content-type-specific presets:
|
||||
- Blog → "Blog Topic Research" preset
|
||||
- Article → "Article Research" preset
|
||||
- Case Study → "Case Study Research" preset
|
||||
- Tutorial → "Tutorial Research" preset
|
||||
- Thought Leadership → "Thought Leadership Research" preset
|
||||
- Education → "Educational Content Research" preset
|
||||
- Preset names now include content type when relevant
|
||||
- Research mode selection considers content_type.purpose
|
||||
|
||||
**Impact**: Presets now match user's actual content creation needs
|
||||
|
||||
---
|
||||
|
||||
#### **2. Writing Style Complexity → Research Depth** ✅
|
||||
|
||||
**Enhancement**: Map writing style complexity to research depth preferences
|
||||
|
||||
**Changes Made**:
|
||||
- Extract `writing_style.complexity` from website analysis
|
||||
- Added mapping logic:
|
||||
- `complexity == "high"` → `default_research_mode = "comprehensive"`
|
||||
- `complexity == "medium"` → `default_research_mode = "targeted"`
|
||||
- `complexity == "low"` → `default_research_mode = "basic"`
|
||||
- Fallback to `research_preferences.research_depth` if complexity not available
|
||||
|
||||
**Impact**: Research depth now matches user's writing sophistication level
|
||||
|
||||
---
|
||||
|
||||
#### **3. Crawl Result Topics → Suggested Keywords** ✅
|
||||
|
||||
**Enhancement**: Extract topics and keywords from actual website content
|
||||
|
||||
**Changes Made**:
|
||||
- Added `_extract_topics_from_crawl()` method:
|
||||
- Extracts from topics, headings, titles, sections, metadata
|
||||
- Returns top 15 unique topics
|
||||
- Added `_extract_keywords_from_crawl()` method:
|
||||
- Extracts from keywords, metadata, tags, content frequency
|
||||
- Returns top 20 unique keywords
|
||||
- Updated prompt to prioritize extracted keywords:
|
||||
- First use extracted_keywords (top 8-10)
|
||||
- Then supplement with industry/interests keywords
|
||||
- Total: 8-12 keywords, with 50%+ from extracted_keywords
|
||||
|
||||
**Impact**: Keywords now reflect user's actual website content topics
|
||||
|
||||
---
|
||||
|
||||
## 📋 **Code Changes**
|
||||
|
||||
### **File Modified**: `backend/services/research/research_persona_prompt_builder.py`
|
||||
|
||||
**Added**:
|
||||
1. Extraction of `writing_style`, `content_type`, `crawl_result` from website analysis
|
||||
2. `_extract_topics_from_crawl()` method
|
||||
3. `_extract_keywords_from_crawl()` method
|
||||
4. Enhanced prompt instructions for:
|
||||
- Content-type-based preset generation
|
||||
- Complexity-based research depth mapping
|
||||
- Extracted keywords prioritization
|
||||
|
||||
**Prompt Enhancements**:
|
||||
- Added "PHASE 1: WEBSITE ANALYSIS INTELLIGENCE" section
|
||||
- Enhanced "DEFAULT VALUES" section with complexity mapping
|
||||
- Enhanced "KEYWORD INTELLIGENCE" section with extracted keywords priority
|
||||
- Enhanced "RECOMMENDED PRESETS" section with content-type-specific generation
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Expected Benefits**
|
||||
|
||||
1. **More Accurate Presets**: Based on actual content types (blog, tutorial, case study, etc.)
|
||||
2. **Aligned Research Depth**: Matches writing complexity (high complexity → comprehensive research)
|
||||
3. **Relevant Keywords**: Uses actual website topics instead of generic industry keywords
|
||||
4. **Better Personalization**: Research persona reflects user's actual content strategy
|
||||
|
||||
---
|
||||
|
||||
## 🧪 **Testing Recommendations**
|
||||
|
||||
1. **Test with Different Content Types**:
|
||||
- User with blog content → Should see "Blog Topic Research" preset
|
||||
- User with tutorial content → Should see "Tutorial Research" preset
|
||||
- User with case study content → Should see "Case Study Research" preset
|
||||
|
||||
2. **Test Complexity Mapping**:
|
||||
- High complexity writing → Should get "comprehensive" research mode
|
||||
- Low complexity writing → Should get "basic" research mode
|
||||
|
||||
3. **Test Keyword Extraction**:
|
||||
- User with crawl_result → Should see extracted keywords in suggested_keywords
|
||||
- User without crawl_result → Should fall back to industry keywords
|
||||
|
||||
---
|
||||
|
||||
## 📝 **Next Steps (Phase 2 & 3)**
|
||||
|
||||
### **Phase 2: Medium Impact, Medium Effort**
|
||||
- Extract `style_patterns` → Generate pattern-based research angles
|
||||
- Extract `content_characteristics.vocabulary` → Sophisticated keyword expansion
|
||||
- Extract `style_guidelines` → Query enhancement rules
|
||||
|
||||
### **Phase 3: High Impact, High Effort**
|
||||
- Full crawl_result analysis → Topic extraction, theme identification
|
||||
- Complete writing style mapping → All research preferences
|
||||
- Content strategy intelligence → Comprehensive preset generation
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Implementation Status**
|
||||
|
||||
- ✅ Content type extraction and preset generation
|
||||
- ✅ Writing style complexity mapping to research depth
|
||||
- ✅ Crawl result topic/keyword extraction
|
||||
- ✅ Enhanced prompt instructions
|
||||
- ✅ Helper methods for data extraction
|
||||
|
||||
**Status**: Phase 1 Complete - Ready for Testing
|
||||
195
docs/ALwrity Researcher/PHASE2_IMPLEMENTATION_SUMMARY.md
Normal file
195
docs/ALwrity Researcher/PHASE2_IMPLEMENTATION_SUMMARY.md
Normal file
@@ -0,0 +1,195 @@
|
||||
# Phase 2 Implementation Summary: Writing Patterns & Style Intelligence
|
||||
|
||||
## Date: 2025-12-31
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Phase 2 Implementation Complete**
|
||||
|
||||
### **What Was Implemented:**
|
||||
|
||||
#### **1. Style Patterns → Research Angles** ✅
|
||||
|
||||
**Enhancement**: Generate research angles from actual writing patterns
|
||||
|
||||
**Changes Made**:
|
||||
- Added `_extract_writing_patterns()` method to extract patterns from `style_patterns`
|
||||
- Extracts from multiple sources:
|
||||
- `patterns`, `common_patterns`, `writing_patterns`
|
||||
- `content_structure.patterns`
|
||||
- `analysis.identified_patterns`
|
||||
- Updated prompt to use extracted patterns for research angles:
|
||||
- "comparison" → "Compare {topic} solutions and alternatives"
|
||||
- "how-to" / "tutorial" → "Step-by-step guide to {topic} implementation"
|
||||
- "case-study" → "Real-world {topic} case studies and success stories"
|
||||
- "trend-analysis" → "Latest {topic} trends and future predictions"
|
||||
- "best-practices" → "{topic} best practices and industry standards"
|
||||
- "review" / "evaluation" → "{topic} review and evaluation criteria"
|
||||
- "problem-solving" → "{topic} problem-solving strategies and solutions"
|
||||
|
||||
**Impact**: Research angles now match user's actual writing patterns and content structure
|
||||
|
||||
---
|
||||
|
||||
#### **2. Vocabulary Level → Keyword Expansion Sophistication** ✅
|
||||
|
||||
**Enhancement**: Create keyword expansion patterns matching user's vocabulary level
|
||||
|
||||
**Changes Made**:
|
||||
- Extract `vocabulary_level` from `content_characteristics`
|
||||
- Added vocabulary-based expansion logic:
|
||||
- **Advanced**: Technical, sophisticated terminology
|
||||
- Example: "AI" → ["machine learning algorithms", "neural network architectures", "deep learning frameworks"]
|
||||
- **Medium**: Balanced, professional terminology
|
||||
- Example: "AI" → ["artificial intelligence", "automated systems", "smart technology"]
|
||||
- **Simple**: Accessible, beginner-friendly terminology
|
||||
- Example: "AI" → ["smart technology", "automated tools", "helpful software"]
|
||||
- Updated prompt to generate expansions at appropriate complexity level
|
||||
|
||||
**Impact**: Keyword expansions now match user's writing sophistication and audience level
|
||||
|
||||
---
|
||||
|
||||
#### **3. Style Guidelines → Query Enhancement Rules** ✅
|
||||
|
||||
**Enhancement**: Create query enhancement rules from style guidelines
|
||||
|
||||
**Changes Made**:
|
||||
- Added `_extract_style_guidelines()` method to extract guidelines from `style_guidelines`
|
||||
- Extracts from multiple sources:
|
||||
- `guidelines`, `recommendations`, `best_practices`
|
||||
- `tone_recommendations`, `structure_guidelines`
|
||||
- `vocabulary_suggestions`, `engagement_tips`
|
||||
- `audience_considerations`, `seo_optimization`, `conversion_optimization`
|
||||
- Updated prompt to create enhancement rules from guidelines:
|
||||
- "Use specific examples" → "Research: {query} with specific examples and case studies"
|
||||
- "Include data points" / "statistics" → "Research: {query} including statistics, metrics, and data analysis"
|
||||
- "Reference industry standards" → "Research: {query} with industry benchmarks and best practices"
|
||||
- "Cite authoritative sources" → "Research: {query} from authoritative sources and expert opinions"
|
||||
- "Provide actionable insights" → "Research: {query} with actionable strategies and implementation steps"
|
||||
- "Compare alternatives" → "Research: Compare {query} alternatives and evaluate options"
|
||||
|
||||
**Impact**: Query enhancement rules now align with user's writing style and content guidelines
|
||||
|
||||
---
|
||||
|
||||
## 📋 **Code Changes**
|
||||
|
||||
### **File Modified**: `backend/services/research/research_persona_prompt_builder.py`
|
||||
|
||||
**Added**:
|
||||
1. Extraction of `style_patterns`, `content_characteristics`, `style_guidelines` from website analysis
|
||||
2. `_extract_writing_patterns()` method (extracts up to 10 patterns)
|
||||
3. `_extract_style_guidelines()` method (extracts up to 15 guidelines)
|
||||
4. Vocabulary level extraction and usage
|
||||
5. Enhanced prompt instructions for:
|
||||
- Pattern-based research angles
|
||||
- Vocabulary-sophisticated keyword expansion
|
||||
- Guideline-based query enhancement rules
|
||||
|
||||
**Prompt Enhancements**:
|
||||
- Added "PHASE 2: WRITING PATTERNS & STYLE INTELLIGENCE" section
|
||||
- Enhanced "KEYWORD INTELLIGENCE" section with vocabulary-based expansion
|
||||
- Enhanced "RESEARCH ANGLES" section with pattern-based generation
|
||||
- Enhanced "QUERY ENHANCEMENT" section with guideline-based rules
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Expected Benefits**
|
||||
|
||||
1. **Pattern-Aligned Research Angles**: Research angles match user's actual writing patterns
|
||||
2. **Vocabulary-Appropriate Expansions**: Keyword expansions match user's sophistication level
|
||||
3. **Guideline-Based Query Enhancement**: Query rules follow user's style guidelines
|
||||
4. **Better Content Alignment**: Research persona reflects user's writing style and preferences
|
||||
|
||||
---
|
||||
|
||||
## 🔍 **Pattern Extraction Logic**
|
||||
|
||||
### **Writing Patterns Extracted From**:
|
||||
- `style_patterns.patterns`
|
||||
- `style_patterns.common_patterns`
|
||||
- `style_patterns.writing_patterns`
|
||||
- `style_patterns.content_structure.patterns`
|
||||
- `style_patterns.analysis.identified_patterns`
|
||||
|
||||
### **Pattern Normalization**:
|
||||
- Converted to lowercase
|
||||
- Replaced underscores and spaces with hyphens
|
||||
- Removed duplicates
|
||||
- Limited to 10 most relevant patterns
|
||||
|
||||
---
|
||||
|
||||
## 📚 **Guideline Extraction Logic**
|
||||
|
||||
### **Style Guidelines Extracted From**:
|
||||
- `style_guidelines.guidelines`
|
||||
- `style_guidelines.recommendations`
|
||||
- `style_guidelines.best_practices`
|
||||
- `style_guidelines.tone_recommendations`
|
||||
- `style_guidelines.structure_guidelines`
|
||||
- `style_guidelines.vocabulary_suggestions`
|
||||
- `style_guidelines.engagement_tips`
|
||||
- `style_guidelines.audience_considerations`
|
||||
- `style_guidelines.seo_optimization`
|
||||
- `style_guidelines.conversion_optimization`
|
||||
|
||||
### **Guideline Normalization**:
|
||||
- Removed duplicates (case-insensitive)
|
||||
- Filtered out very short guidelines (< 5 characters)
|
||||
- Limited to 15 most relevant guidelines
|
||||
|
||||
---
|
||||
|
||||
## 🧪 **Testing Recommendations**
|
||||
|
||||
1. **Test Pattern Extraction**:
|
||||
- User with "comparison" pattern → Should see "Compare {topic} solutions" angle
|
||||
- User with "how-to" pattern → Should see "Step-by-step guide" angle
|
||||
- User with "case-study" pattern → Should see "Real-world case studies" angle
|
||||
|
||||
2. **Test Vocabulary Mapping**:
|
||||
- Advanced vocabulary → Should get sophisticated keyword expansions
|
||||
- Simple vocabulary → Should get accessible keyword expansions
|
||||
- Medium vocabulary → Should get balanced keyword expansions
|
||||
|
||||
3. **Test Guideline Extraction**:
|
||||
- User with "Use specific examples" guideline → Should see enhancement rule for examples
|
||||
- User with "Include data points" guideline → Should see enhancement rule for statistics
|
||||
- User with "Reference industry standards" guideline → Should see enhancement rule for benchmarks
|
||||
|
||||
---
|
||||
|
||||
## 📝 **Next Steps (Phase 3)**
|
||||
|
||||
### **Phase 3: High Impact, High Effort**
|
||||
- Full crawl_result analysis → Topic extraction, theme identification
|
||||
- Complete writing style mapping → All research preferences
|
||||
- Content strategy intelligence → Comprehensive preset generation
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Implementation Status**
|
||||
|
||||
- ✅ Style patterns extraction and research angle generation
|
||||
- ✅ Vocabulary level extraction and sophisticated keyword expansion
|
||||
- ✅ Style guidelines extraction and query enhancement rules
|
||||
- ✅ Enhanced prompt instructions for all Phase 2 features
|
||||
- ✅ Helper methods for pattern and guideline extraction
|
||||
|
||||
**Status**: Phase 2 Complete - Ready for Testing
|
||||
|
||||
---
|
||||
|
||||
## 🔄 **Combined Phase 1 + Phase 2 Benefits**
|
||||
|
||||
With both phases implemented, the research persona now:
|
||||
1. ✅ Generates presets based on actual content types
|
||||
2. ✅ Maps research depth to writing complexity
|
||||
3. ✅ Uses extracted keywords from website content
|
||||
4. ✅ Creates research angles from writing patterns
|
||||
5. ✅ Generates vocabulary-appropriate keyword expansions
|
||||
6. ✅ Creates query enhancement rules from style guidelines
|
||||
|
||||
**Result**: Highly personalized research persona that reflects user's actual content strategy, writing style, and preferences.
|
||||
@@ -0,0 +1,274 @@
|
||||
# Phase 3 Implementation & UI Indicators Summary
|
||||
|
||||
## Date: 2025-12-31
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Phase 3 Implementation Complete**
|
||||
|
||||
### **What Was Implemented:**
|
||||
|
||||
#### **1. Full Crawl Analysis** ✅
|
||||
|
||||
**Enhancement**: Comprehensive analysis of crawl_result to extract content intelligence
|
||||
|
||||
**Changes Made**:
|
||||
- Added `_analyze_crawl_result_comprehensive()` method
|
||||
- Extracts:
|
||||
- **Content Categories**: From content_structure.categories
|
||||
- **Main Topics**: From headings (filtered and categorized)
|
||||
- **Content Density**: Based on word count (high/medium/low)
|
||||
- **Content Focus**: Key phrases from description
|
||||
- **Key Phrases**: From metadata keywords
|
||||
- **Semantic Clusters**: Related topics from links
|
||||
- Used for:
|
||||
- Preset generation based on actual content categories
|
||||
- Theme-based preset creation
|
||||
- Content-aware research configuration
|
||||
|
||||
**Impact**: Presets now reflect user's actual website content structure and categories
|
||||
|
||||
---
|
||||
|
||||
#### **2. Complete Writing Style Mapping** ✅
|
||||
|
||||
**Enhancement**: Comprehensive mapping of writing style to all research preferences
|
||||
|
||||
**Changes Made**:
|
||||
- Added `_map_writing_style_comprehensive()` method
|
||||
- Maps:
|
||||
- **Complexity** → Research depth preference, data richness, include statistics/expert quotes
|
||||
- **Tone** → Provider preference (academic → exa, news → tavily)
|
||||
- **Engagement Level** → Include trends preference
|
||||
- **Vocabulary Level** → Data richness, include statistics
|
||||
- Returns comprehensive mapping object used throughout persona generation
|
||||
|
||||
**Impact**: All research preferences now aligned with user's complete writing style profile
|
||||
|
||||
---
|
||||
|
||||
#### **3. Content Themes Extraction** ✅
|
||||
|
||||
**Enhancement**: Extract content themes from crawl result and topics
|
||||
|
||||
**Changes Made**:
|
||||
- Added `_extract_content_themes()` method
|
||||
- Extracts themes from:
|
||||
- Extracted topics (from Phase 1)
|
||||
- Main content keywords (frequency-based)
|
||||
- Metadata categories
|
||||
- Used for:
|
||||
- Theme-based preset generation
|
||||
- Content-aware keyword suggestions
|
||||
- Research angle inspiration
|
||||
|
||||
**Impact**: Research persona reflects user's actual content themes and focus areas
|
||||
|
||||
---
|
||||
|
||||
#### **4. Enhanced Preset Generation** ✅
|
||||
|
||||
**Enhancement**: Use content themes and crawl analysis for preset generation
|
||||
|
||||
**Changes Made**:
|
||||
- Updated prompt to use `content_themes` for preset generation
|
||||
- Create at least one preset per major theme (up to 3 themes)
|
||||
- Use `crawl_analysis.content_categories` and `main_topics` for preset keywords
|
||||
- Presets now match user's actual website content categories
|
||||
|
||||
**Impact**: Presets are highly relevant to user's actual content strategy
|
||||
|
||||
---
|
||||
|
||||
## 🎨 **UI Indicators Implementation**
|
||||
|
||||
### **What Was Added:**
|
||||
|
||||
#### **1. PersonalizationIndicator Component** ✅
|
||||
|
||||
**New Component**: `frontend/src/components/Research/steps/components/PersonalizationIndicator.tsx`
|
||||
|
||||
**Features**:
|
||||
- Info icon with tooltip showing personalization source
|
||||
- Different types: `placeholder`, `keywords`, `presets`, `angles`, `provider`, `mode`
|
||||
- Customizable source text
|
||||
- Only shows when persona exists
|
||||
- Uses Material-UI Tooltip and AutoAwesome icon
|
||||
|
||||
**Usage**:
|
||||
```tsx
|
||||
<PersonalizationIndicator
|
||||
type="placeholder"
|
||||
hasPersona={!!researchPersona}
|
||||
source="from your research persona"
|
||||
/>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### **2. PersonalizationBadge Component** ✅
|
||||
|
||||
**New Component**: Badge-style indicator for inline personalization labels
|
||||
|
||||
**Features**:
|
||||
- Compact badge with sparkle icon
|
||||
- Tooltip explaining personalization
|
||||
- Can be used inline with text
|
||||
|
||||
---
|
||||
|
||||
#### **3. UI Integration Points** ✅
|
||||
|
||||
**Added Indicators To**:
|
||||
|
||||
1. **Research Topic & Keywords Label**
|
||||
- Shows indicator when placeholders are personalized
|
||||
- Tooltip: "Personalized Placeholders - customized based on your research persona"
|
||||
|
||||
2. **Research Angles Section**
|
||||
- Shows indicator when angles are from writing patterns
|
||||
- Tooltip: "Personalized Research Angles - derived from your writing patterns"
|
||||
|
||||
3. **Quick Start Presets Header**
|
||||
- Shows indicator when presets are personalized
|
||||
- Tooltip: "Personalized Presets - customized based on your content types and website topics"
|
||||
|
||||
4. **Industry Dropdown** (via ResearchControlsBar)
|
||||
- Shows indicator when industry is from persona
|
||||
- Tooltip: "Personalized Keywords - extracted from your website content"
|
||||
|
||||
5. **Target Audience Field**
|
||||
- Shows indicator when audience is from persona
|
||||
- Tooltip: "Personalized Keywords - from your research persona"
|
||||
|
||||
---
|
||||
|
||||
## 📋 **Code Changes**
|
||||
|
||||
### **Backend Files Modified**:
|
||||
|
||||
1. **`backend/services/research/research_persona_prompt_builder.py`**
|
||||
- Added `_analyze_crawl_result_comprehensive()` method
|
||||
- Added `_map_writing_style_comprehensive()` method
|
||||
- Added `_extract_content_themes()` method
|
||||
- Enhanced prompt with Phase 3 instructions
|
||||
- Added "PHASE 3: COMPREHENSIVE ANALYSIS & MAPPING" section
|
||||
|
||||
### **Frontend Files Modified**:
|
||||
|
||||
1. **`frontend/src/components/Research/steps/components/PersonalizationIndicator.tsx`** (NEW)
|
||||
- PersonalizationIndicator component
|
||||
- PersonalizationBadge component
|
||||
- Tooltip definitions for all personalization types
|
||||
|
||||
2. **`frontend/src/components/Research/steps/ResearchInput.tsx`**
|
||||
- Added PersonalizationIndicator import
|
||||
- Added indicator to "Research Topic & Keywords" label
|
||||
- Passed `hasPersona` prop to ResearchAngles
|
||||
|
||||
3. **`frontend/src/components/Research/steps/components/ResearchAngles.tsx`**
|
||||
- Added `hasPersona` prop
|
||||
- Added PersonalizationIndicator to header
|
||||
|
||||
4. **`frontend/src/components/Research/steps/components/ResearchControlsBar.tsx`**
|
||||
- Added `hasPersona` prop
|
||||
- Added PersonalizationIndicator next to Industry dropdown
|
||||
|
||||
5. **`frontend/src/components/Research/steps/components/TargetAudience.tsx`**
|
||||
- Added `hasPersona` prop
|
||||
- Added PersonalizationIndicator to label
|
||||
|
||||
6. **`frontend/src/pages/ResearchTest.tsx`**
|
||||
- Added Tooltip and AutoAwesome imports
|
||||
- Added indicator to "Quick Start Presets" header
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Expected Benefits**
|
||||
|
||||
### **Phase 3 Benefits**:
|
||||
1. **Content-Aware Presets**: Based on actual website content categories and themes
|
||||
2. **Complete Style Mapping**: All research preferences aligned with writing style
|
||||
3. **Theme-Based Research**: Research angles and presets match content themes
|
||||
4. **Comprehensive Intelligence**: Full utilization of website analysis data
|
||||
|
||||
### **UI Indicator Benefits**:
|
||||
1. **User Awareness**: Users understand what's personalized and why
|
||||
2. **Transparency**: Clear indication of personalization sources
|
||||
3. **Trust Building**: Shows the system is learning from their data
|
||||
4. **Educational**: Tooltips explain the value of personalization
|
||||
|
||||
---
|
||||
|
||||
## 🎨 **UI Indicator Design**
|
||||
|
||||
### **Visual Design**:
|
||||
- **Icon**: AutoAwesome (✨) from Material-UI
|
||||
- **Color**: Sky blue (#0ea5e9) to match research theme
|
||||
- **Size**: Small (14-16px) to be unobtrusive
|
||||
- **Placement**: Next to relevant labels/headers
|
||||
- **Tooltip**: Rich, informative content explaining personalization
|
||||
|
||||
### **Tooltip Content Structure**:
|
||||
1. **Title**: "Personalized [Feature]"
|
||||
2. **Description**: What is personalized and how
|
||||
3. **Source**: "✨ Personalized from [source]"
|
||||
|
||||
---
|
||||
|
||||
## 🧪 **Testing Recommendations**
|
||||
|
||||
### **Phase 3 Testing**:
|
||||
1. **Crawl Analysis**: Verify content categories and themes are extracted
|
||||
2. **Style Mapping**: Verify all preferences are mapped from writing style
|
||||
3. **Theme-Based Presets**: Verify presets match content themes
|
||||
|
||||
### **UI Indicator Testing**:
|
||||
1. **Visibility**: Indicators only show when persona exists
|
||||
2. **Tooltips**: Hover to see personalization explanations
|
||||
3. **Placement**: Indicators appear next to relevant fields
|
||||
4. **Responsiveness**: Tooltips work on mobile/desktop
|
||||
|
||||
---
|
||||
|
||||
## 📝 **Complete Implementation Summary**
|
||||
|
||||
### **All Phases Complete**:
|
||||
|
||||
✅ **Phase 1**: Content type presets, complexity mapping, crawl topics
|
||||
✅ **Phase 2**: Style patterns angles, vocabulary expansions, guideline rules
|
||||
✅ **Phase 3**: Full crawl analysis, complete style mapping, theme extraction
|
||||
✅ **UI Indicators**: Personalization visibility and transparency
|
||||
|
||||
### **Combined Benefits**:
|
||||
|
||||
The research persona now:
|
||||
1. ✅ Generates presets based on actual content types and themes
|
||||
2. ✅ Maps research depth to writing complexity comprehensively
|
||||
3. ✅ Uses extracted keywords from website content
|
||||
4. ✅ Creates research angles from writing patterns
|
||||
5. ✅ Generates vocabulary-appropriate keyword expansions
|
||||
6. ✅ Creates query enhancement rules from style guidelines
|
||||
7. ✅ Uses content themes for preset generation
|
||||
8. ✅ Maps all research preferences from complete writing style
|
||||
9. ✅ Shows users what's personalized and why (UI indicators)
|
||||
|
||||
**Result**: Highly personalized, transparent research experience that reflects user's actual content strategy, writing style, and preferences, with clear UI indicators showing the personalization magic behind the scenes.
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Implementation Status**
|
||||
|
||||
- ✅ Phase 3: Full crawl analysis
|
||||
- ✅ Phase 3: Complete writing style mapping
|
||||
- ✅ Phase 3: Content themes extraction
|
||||
- ✅ Phase 3: Enhanced preset generation
|
||||
- ✅ UI: PersonalizationIndicator component
|
||||
- ✅ UI: PersonalizationBadge component
|
||||
- ✅ UI: Indicators in ResearchInput
|
||||
- ✅ UI: Indicators in ResearchAngles
|
||||
- ✅ UI: Indicators in ResearchControlsBar
|
||||
- ✅ UI: Indicators in TargetAudience
|
||||
- ✅ UI: Indicators in ResearchTest presets
|
||||
|
||||
**Status**: Phase 3 + UI Indicators Complete - Ready for Testing
|
||||
@@ -0,0 +1,202 @@
|
||||
# Research Input Placeholder Personalization Implementation
|
||||
|
||||
## Date: 2025-12-31
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Validation: Research Persona Storage**
|
||||
|
||||
**Status**: ✅ **Confirmed - Research persona is successfully stored in database**
|
||||
|
||||
**Validation Results**:
|
||||
- PersonaData record exists with ID: 1
|
||||
- Research persona field is populated (not None)
|
||||
- Generated at: 2025-12-31 11:47:49
|
||||
- Contains all expected fields:
|
||||
- `default_industry`: "Content Marketing"
|
||||
- `default_target_audience`: (populated)
|
||||
- `research_angles`: Array of research angles
|
||||
- `recommended_presets`: Array of personalized presets
|
||||
- `suggested_keywords`: Array of suggested keywords
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Implementation: Personalized Placeholders**
|
||||
|
||||
### **What Was Changed:**
|
||||
|
||||
#### **1. Enhanced Placeholder Function** (`placeholders.ts`)
|
||||
|
||||
**Added**:
|
||||
- ✅ `PersonaPlaceholderData` interface to type persona data
|
||||
- ✅ Enhanced `getIndustryPlaceholders()` to accept optional persona data
|
||||
- ✅ Logic to generate placeholders from:
|
||||
- **Research Angles**: First 3 angles formatted as research queries
|
||||
- **Recommended Presets**: First 2 presets with their keywords and descriptions
|
||||
- ✅ Fallback to industry defaults if persona data is unavailable
|
||||
|
||||
**How It Works**:
|
||||
```typescript
|
||||
// If research persona exists:
|
||||
1. Extract first 3 research_angles → Format as placeholders
|
||||
2. Extract first 2 recommended_presets → Use keywords + descriptions
|
||||
3. Combine with 2 industry defaults as backup
|
||||
4. Return personalized placeholders array
|
||||
|
||||
// If no persona:
|
||||
1. Fall back to industry-specific defaults
|
||||
```
|
||||
|
||||
#### **2. Updated ResearchInput Component** (`ResearchInput.tsx`)
|
||||
|
||||
**Added**:
|
||||
- ✅ `researchPersona` state to store persona data
|
||||
- ✅ Logic to extract persona data from `config.research_persona`
|
||||
- ✅ Pass persona data to `getIndustryPlaceholders()` function
|
||||
|
||||
**Flow**:
|
||||
```
|
||||
Component Mount
|
||||
↓
|
||||
Load Research Config
|
||||
↓
|
||||
Check if research_persona exists
|
||||
↓
|
||||
Extract research_angles and recommended_presets
|
||||
↓
|
||||
Store in researchPersona state
|
||||
↓
|
||||
Pass to getIndustryPlaceholders(industry, personaData)
|
||||
↓
|
||||
Display personalized placeholders
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Placeholder Generation Logic**
|
||||
|
||||
### **Priority Order:**
|
||||
|
||||
1. **Research Angles** (if available)
|
||||
- Format: `"Research: {angle}"` or use angle as-is if it contains `{topic}` placeholder
|
||||
- Example: `"Research: Compare {topic} tools"` → `"Research: Compare Content Marketing tools"`
|
||||
- Adds helpful description: "This will help you: Discover relevant insights..."
|
||||
|
||||
2. **Recommended Presets** (if available)
|
||||
- Uses preset keywords directly
|
||||
- Includes preset description if available
|
||||
- Example: Uses actual preset keywords from persona
|
||||
|
||||
3. **Industry Defaults** (fallback)
|
||||
- Uses original industry-specific placeholders
|
||||
- Only used if no persona data or as backup
|
||||
|
||||
### **Example Output:**
|
||||
|
||||
**With Research Persona**:
|
||||
```
|
||||
Research: Compare Content Marketing tools
|
||||
|
||||
💡 This will help you:
|
||||
• Discover relevant insights and data
|
||||
• Find authoritative sources and experts
|
||||
• Get comprehensive analysis tailored to your needs
|
||||
|
||||
---
|
||||
|
||||
Research latest content marketing automation platforms for B2B SaaS companies
|
||||
|
||||
💡 Analyze competitive landscape and identify top content marketing tools and strategies
|
||||
```
|
||||
|
||||
**Without Research Persona** (fallback):
|
||||
```
|
||||
Research: Latest AI advancements in your industry
|
||||
|
||||
💡 What you'll get:
|
||||
• Recent breakthroughs and innovations
|
||||
• Key companies and technologies
|
||||
• Expert insights and market trends
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔧 **Technical Details**
|
||||
|
||||
### **Files Modified:**
|
||||
|
||||
1. **`frontend/src/components/Research/steps/utils/placeholders.ts`**
|
||||
- Added `PersonaPlaceholderData` interface
|
||||
- Enhanced `getIndustryPlaceholders()` function
|
||||
- Added `getIndustryDefaults()` helper function
|
||||
|
||||
2. **`frontend/src/components/Research/steps/ResearchInput.tsx`**
|
||||
- Added `researchPersona` state
|
||||
- Updated config loading to extract and store persona data
|
||||
- Updated placeholder generation to pass persona data
|
||||
|
||||
### **Data Flow:**
|
||||
|
||||
```
|
||||
Backend API
|
||||
↓
|
||||
getResearchConfig()
|
||||
↓
|
||||
config.research_persona
|
||||
↓
|
||||
Extract: research_angles, recommended_presets
|
||||
↓
|
||||
Store in researchPersona state
|
||||
↓
|
||||
getIndustryPlaceholders(industry, researchPersona)
|
||||
↓
|
||||
Generate personalized placeholders
|
||||
↓
|
||||
Display in textarea (rotates every 4 seconds)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Benefits**
|
||||
|
||||
1. **Hyper-Personalization**: Placeholders are now based on user's actual research persona
|
||||
2. **Relevant Examples**: Users see research angles and presets that match their industry/audience
|
||||
3. **Better UX**: More actionable placeholder text that guides users
|
||||
4. **Progressive Enhancement**: Falls back gracefully if persona data unavailable
|
||||
|
||||
---
|
||||
|
||||
## 🧪 **Testing**
|
||||
|
||||
**To Test**:
|
||||
1. Generate research persona (if not already generated)
|
||||
2. Navigate to Research page
|
||||
3. Check textarea placeholders - should show:
|
||||
- Research angles formatted as queries
|
||||
- Recommended preset keywords
|
||||
- Personalized descriptions
|
||||
|
||||
**Expected Behavior**:
|
||||
- Placeholders rotate every 4 seconds
|
||||
- Show personalized content from research persona
|
||||
- Fall back to industry defaults if persona unavailable
|
||||
|
||||
---
|
||||
|
||||
## 📝 **Next Steps** (Optional)
|
||||
|
||||
1. **Add Visual Indicator**: Show badge when placeholders are personalized
|
||||
2. **User Feedback**: Allow users to rate placeholder helpfulness
|
||||
3. **Dynamic Updates**: Update placeholders when persona is refreshed
|
||||
4. **A/B Testing**: Compare personalized vs. generic placeholder effectiveness
|
||||
|
||||
---
|
||||
|
||||
## 🎉 **Summary**
|
||||
|
||||
✅ Research persona storage validated
|
||||
✅ Placeholders now use research_angles and recommended_presets
|
||||
✅ Personalized experience for users with research persona
|
||||
✅ Graceful fallback for users without persona
|
||||
|
||||
The research input placeholders are now fully personalized based on the user's research persona, providing a more relevant and helpful experience for content creators.
|
||||
495
docs/ALwrity Researcher/RESEARCH_AI_HYPERPERSONALIZATION.md
Normal file
495
docs/ALwrity Researcher/RESEARCH_AI_HYPERPERSONALIZATION.md
Normal file
@@ -0,0 +1,495 @@
|
||||
# Research Phase - AI Hyperpersonalization Guide
|
||||
|
||||
## Overview
|
||||
This document outlines all research inputs, prompts, and configuration options that can be intelligently personalized using AI and user persona data. The goal is to make research effortless for beginners while maintaining full control for power users.
|
||||
|
||||
---
|
||||
|
||||
## 1. User Inputs (Current)
|
||||
|
||||
### 1.1 Primary Research Input
|
||||
**Field**: `keywords` (textarea)
|
||||
**Current Format**: Array of strings
|
||||
**User Input Types**:
|
||||
- Full sentences/paragraphs (e.g., "Research latest AI advancements in healthcare")
|
||||
- Comma-separated keywords (e.g., "AI, healthcare, diagnostics")
|
||||
- URLs (e.g., "https://techcrunch.com/2024/ai-trends")
|
||||
- Mixed formats
|
||||
|
||||
**AI Personalization Opportunity**:
|
||||
- Parse user intent and generate optimized search queries
|
||||
- Expand keywords based on industry and audience
|
||||
- Suggest related topics from persona interests
|
||||
- Rewrite vague inputs into specific, actionable research queries
|
||||
|
||||
---
|
||||
|
||||
### 1.2 Industry Selection
|
||||
**Field**: `industry` (dropdown)
|
||||
**Options**: General, Technology, Business, Marketing, Finance, Healthcare, Education, Real Estate, Entertainment, Food & Beverage, Travel, Fashion, Sports, Science, Law, Other
|
||||
|
||||
**Current Default**: "General"
|
||||
|
||||
**AI Personalization Opportunity**:
|
||||
- Auto-detect from persona's `core_persona.industry` or `core_persona.profession`
|
||||
- Suggest related industries based on research topic
|
||||
- Use onboarding data: `business_info.industry`, `business_info.niche`
|
||||
|
||||
---
|
||||
|
||||
### 1.3 Target Audience
|
||||
**Field**: `targetAudience` (text input)
|
||||
**Current Default**: "General"
|
||||
|
||||
**AI Personalization Opportunity**:
|
||||
- Pull from persona's `core_persona.target_audience`
|
||||
- Suggest audience based on research topic
|
||||
- Use demographic data: `core_persona.demographics`, `core_persona.psychographics`
|
||||
|
||||
---
|
||||
|
||||
### 1.4 Research Mode
|
||||
**Field**: `researchMode` (dropdown)
|
||||
**Options**:
|
||||
- `basic` - Quick insights (10 sources, fast)
|
||||
- `comprehensive` - In-depth analysis (15-25 sources, thorough)
|
||||
- `targeted` - Specific focus (12 sources, precise)
|
||||
|
||||
**Current Default**: "basic"
|
||||
|
||||
**AI Personalization Opportunity**:
|
||||
- Infer from query complexity (word count, specificity)
|
||||
- Match to user's persona complexity/expertise level
|
||||
- Suggest based on content type (blog, whitepaper, social post)
|
||||
|
||||
---
|
||||
|
||||
### 1.5 Search Provider
|
||||
**Field**: `config.provider` (dropdown)
|
||||
**Options**:
|
||||
- `google` - Google Search grounding (broad, general)
|
||||
- `exa` - Exa Neural Search (semantic, deep)
|
||||
|
||||
**Current Default**: "google"
|
||||
|
||||
**AI Personalization Opportunity**:
|
||||
- Academic topics → Exa (research papers)
|
||||
- News/trends → Google (real-time)
|
||||
- Technical deep-dive → Exa (neural semantic search)
|
||||
- Match to persona's writing style (technical vs. casual)
|
||||
|
||||
---
|
||||
|
||||
## 2. Advanced Configuration (ResearchConfig)
|
||||
|
||||
### 2.1 Common Options (Both Providers)
|
||||
|
||||
#### `max_sources` (number)
|
||||
- **Default**: 10 (basic), 15 (comprehensive), 12 (targeted)
|
||||
- **Range**: 5-30
|
||||
- **AI Suggestion**: More sources for complex topics, fewer for news updates
|
||||
|
||||
#### `include_statistics` (boolean)
|
||||
- **Default**: true
|
||||
- **AI Suggestion**: Enable for data-driven industries (Finance, Healthcare, Technology)
|
||||
|
||||
#### `include_expert_quotes` (boolean)
|
||||
- **Default**: true
|
||||
- **AI Suggestion**: Enable for thought leadership content
|
||||
|
||||
#### `include_competitors` (boolean)
|
||||
- **Default**: true
|
||||
- **AI Suggestion**: Enable for business/marketing topics
|
||||
|
||||
#### `include_trends` (boolean)
|
||||
- **Default**: true
|
||||
- **AI Suggestion**: Enable for forward-looking content
|
||||
|
||||
---
|
||||
|
||||
### 2.2 Exa-Specific Options
|
||||
|
||||
#### `exa_category` (string)
|
||||
**Options**:
|
||||
- '' (All Categories)
|
||||
- 'company' - Company Profiles
|
||||
- 'research paper' - Research Papers
|
||||
- 'news' - News Articles
|
||||
- 'linkedin profile' - LinkedIn Profiles
|
||||
- 'github' - GitHub Repos
|
||||
- 'tweet' - Tweets
|
||||
- 'movie', 'song', 'personal site', 'pdf', 'financial report'
|
||||
|
||||
**AI Personalization**:
|
||||
```typescript
|
||||
const aiSuggestExaCategory = (topic: string, industry: string) => {
|
||||
if (topic.includes('academic') || topic.includes('study')) return 'research paper';
|
||||
if (industry === 'Finance') return 'financial report';
|
||||
if (topic.includes('company') || topic.includes('startup')) return 'company';
|
||||
if (topic.includes('breaking') || topic.includes('latest')) return 'news';
|
||||
if (topic.includes('developer') || topic.includes('code')) return 'github';
|
||||
return '';
|
||||
};
|
||||
```
|
||||
|
||||
#### `exa_search_type` (string)
|
||||
**Options**: 'auto', 'keyword', 'neural'
|
||||
**Default**: 'auto'
|
||||
|
||||
**AI Personalization**:
|
||||
- `keyword` - For precise technical terms, product names
|
||||
- `neural` - For conceptual, semantic queries
|
||||
- `auto` - Let Exa decide (usually best)
|
||||
|
||||
#### `exa_include_domains` (string[])
|
||||
**Example**: `['pubmed.gov', 'nejm.org', 'thelancet.com']`
|
||||
|
||||
**AI Personalization by Industry**:
|
||||
```typescript
|
||||
const domainSuggestions = {
|
||||
Healthcare: ['pubmed.gov', 'nejm.org', 'thelancet.com', 'nih.gov'],
|
||||
Technology: ['techcrunch.com', 'wired.com', 'arstechnica.com', 'theverge.com'],
|
||||
Finance: ['wsj.com', 'bloomberg.com', 'ft.com', 'reuters.com'],
|
||||
Science: ['nature.com', 'sciencemag.org', 'cell.com', 'pnas.org'],
|
||||
Business: ['hbr.org', 'forbes.com', 'businessinsider.com', 'mckinsey.com']
|
||||
};
|
||||
```
|
||||
|
||||
#### `exa_exclude_domains` (string[])
|
||||
**Example**: `['spam.com', 'ads.com']`
|
||||
|
||||
**AI Personalization**:
|
||||
- Auto-exclude low-quality domains
|
||||
- Exclude competitor domains if requested
|
||||
- Exclude domains based on persona's dislikes
|
||||
|
||||
---
|
||||
|
||||
## 3. Persona Data Integration
|
||||
|
||||
### 3.1 Available Persona Fields (from Onboarding)
|
||||
|
||||
#### Core Persona
|
||||
```typescript
|
||||
interface CorePersona {
|
||||
// Demographics
|
||||
age_range?: string;
|
||||
gender?: string;
|
||||
location?: string;
|
||||
education_level?: string;
|
||||
income_level?: string;
|
||||
occupation?: string;
|
||||
industry?: string;
|
||||
company_size?: string;
|
||||
|
||||
// Psychographics
|
||||
interests?: string[];
|
||||
values?: string[];
|
||||
pain_points?: string[];
|
||||
goals?: string[];
|
||||
challenges?: string[];
|
||||
|
||||
// Behavioral
|
||||
content_preferences?: string[];
|
||||
learning_style?: string;
|
||||
decision_making_style?: string;
|
||||
preferred_platforms?: string[];
|
||||
|
||||
// Content Context
|
||||
target_audience?: string;
|
||||
writing_tone?: string;
|
||||
expertise_level?: string;
|
||||
}
|
||||
```
|
||||
|
||||
#### Business Info (from onboarding)
|
||||
```typescript
|
||||
interface BusinessInfo {
|
||||
industry: string;
|
||||
niche: string;
|
||||
target_audience: string;
|
||||
content_goals: string[];
|
||||
primary_platform: string;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. AI-Powered Suggestions (Implementation Roadmap)
|
||||
|
||||
### Phase 1: Rule-Based Intelligence (Current)
|
||||
✅ Intelligent input parsing (sentences, keywords, URLs)
|
||||
✅ Preset templates with full configuration
|
||||
✅ Visual feedback on input type
|
||||
|
||||
### Phase 2: Persona-Aware Defaults (Next)
|
||||
🔄 Auto-fill industry from persona
|
||||
🔄 Auto-fill target audience from persona
|
||||
🔄 Suggest research mode based on topic complexity
|
||||
🔄 Suggest provider based on topic type
|
||||
🔄 Suggest Exa category based on industry
|
||||
🔄 Suggest domains based on industry
|
||||
|
||||
### Phase 3: AI Query Enhancement (Future)
|
||||
🔮 Generate optimal search queries from vague inputs
|
||||
🔮 Expand keywords semantically
|
||||
🔮 Suggest related research angles
|
||||
🔮 Predict best configuration for user's goal
|
||||
|
||||
---
|
||||
|
||||
## 5. Backend Research Prompt Templates
|
||||
|
||||
### 5.1 Basic Research Prompt
|
||||
```python
|
||||
def build_basic_research_prompt(topic: str, industry: str, target_audience: str) -> str:
|
||||
return f"""You are a professional blog content strategist researching for a {industry} blog targeting {target_audience}.
|
||||
|
||||
Research Topic: "{topic}"
|
||||
|
||||
Provide analysis in this EXACT format:
|
||||
|
||||
## CURRENT TRENDS (2024-2025)
|
||||
- [Trend 1 with specific data and source URL]
|
||||
- [Trend 2 with specific data and source URL]
|
||||
- [Trend 3 with specific data and source URL]
|
||||
|
||||
## KEY STATISTICS
|
||||
- [Statistic 1: specific number/percentage with source URL]
|
||||
- [Statistic 2: specific number/percentage with source URL]
|
||||
... (5 total)
|
||||
|
||||
## PRIMARY KEYWORDS
|
||||
1. "{topic}" (main keyword)
|
||||
2. [Variation 1]
|
||||
3. [Variation 2]
|
||||
|
||||
## SECONDARY KEYWORDS
|
||||
[5 related keywords for blog content]
|
||||
|
||||
## CONTENT ANGLES (Top 5)
|
||||
1. [Angle 1: specific unique approach]
|
||||
...
|
||||
|
||||
REQUIREMENTS:
|
||||
- Cite EVERY claim with authoritative source URLs
|
||||
- Use 2024-2025 data when available
|
||||
- Include specific numbers, dates, examples
|
||||
- Focus on actionable blog insights for {target_audience}"""
|
||||
```
|
||||
|
||||
### 5.2 Comprehensive Research Prompt
|
||||
```python
|
||||
def build_comprehensive_research_prompt(topic: str, industry: str, target_audience: str, config: ResearchConfig) -> str:
|
||||
sections = []
|
||||
|
||||
sections.append(f"""You are an expert research analyst for {industry} content targeting {target_audience}.
|
||||
|
||||
Research Topic: "{topic}"
|
||||
|
||||
Conduct comprehensive research and provide:""")
|
||||
|
||||
if config.include_trends:
|
||||
sections.append("""
|
||||
## TREND ANALYSIS
|
||||
- Emerging trends (2024-2025) with adoption rates
|
||||
- Historical context and evolution
|
||||
- Future projections from industry experts""")
|
||||
|
||||
if config.include_statistics:
|
||||
sections.append("""
|
||||
## DATA & STATISTICS
|
||||
- Market size, growth rates, key metrics
|
||||
- Demographic data and user behavior
|
||||
- Comparative statistics across segments
|
||||
(Minimum 10 statistics with sources)""")
|
||||
|
||||
if config.include_expert_quotes:
|
||||
sections.append("""
|
||||
## EXPERT INSIGHTS
|
||||
- Quotes from industry leaders with credentials
|
||||
- Research findings from institutions
|
||||
- Case studies and success stories""")
|
||||
|
||||
if config.include_competitors:
|
||||
sections.append("""
|
||||
## COMPETITIVE LANDSCAPE
|
||||
- Key players and market share
|
||||
- Differentiating factors
|
||||
- Best practices and innovations""")
|
||||
|
||||
return "\n".join(sections)
|
||||
```
|
||||
|
||||
### 5.3 Targeted Research Prompt
|
||||
```python
|
||||
def build_targeted_research_prompt(topic: str, industry: str, target_audience: str, config: ResearchConfig) -> str:
|
||||
return f"""You are a specialized researcher for {industry} focusing on {target_audience}.
|
||||
|
||||
Research Topic: "{topic}"
|
||||
|
||||
Provide TARGETED, ACTIONABLE insights:
|
||||
|
||||
## CORE FINDINGS
|
||||
- 3-5 most critical insights
|
||||
- Each with specific data points and authoritative sources
|
||||
- Direct relevance to {target_audience}'s needs
|
||||
|
||||
## IMPLEMENTATION GUIDANCE
|
||||
- Practical steps and recommendations
|
||||
- Tools, resources, platforms
|
||||
- Expected outcomes and metrics
|
||||
|
||||
## EVIDENCE BASE
|
||||
- Recent studies (2024-2025)
|
||||
- Industry reports and whitepapers
|
||||
- Expert consensus
|
||||
|
||||
CONSTRAINTS:
|
||||
- Maximum {config.max_sources} sources
|
||||
- Focus on depth over breadth
|
||||
- Prioritize actionable over theoretical"""
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. AI Personalization API Design (Proposed)
|
||||
|
||||
### Endpoint: `/api/research/ai-suggestions`
|
||||
|
||||
#### Request
|
||||
```typescript
|
||||
interface AISuggestionRequest {
|
||||
user_input: string; // Raw user input
|
||||
user_id?: string; // For persona access
|
||||
context?: {
|
||||
previous_research?: string[];
|
||||
content_type?: 'blog' | 'whitepaper' | 'social' | 'email';
|
||||
};
|
||||
}
|
||||
```
|
||||
|
||||
#### Response
|
||||
```typescript
|
||||
interface AISuggestionResponse {
|
||||
enhanced_query: string; // Optimized research query
|
||||
suggested_config: ResearchConfig; // Recommended configuration
|
||||
keywords: string[]; // Extracted/expanded keywords
|
||||
industry: string; // Detected industry
|
||||
target_audience: string; // Suggested audience
|
||||
reasoning: string; // Why these suggestions
|
||||
alternative_angles: string[]; // Other research directions
|
||||
}
|
||||
```
|
||||
|
||||
### Implementation Steps
|
||||
1. **Fetch persona data** from onboarding
|
||||
2. **Parse user input** (detect intent, entities, complexity)
|
||||
3. **Apply persona context** (industry, audience, preferences)
|
||||
4. **Generate suggestions** using LLM with persona-aware prompt
|
||||
5. **Return structured config** ready to apply
|
||||
|
||||
---
|
||||
|
||||
## 7. Example AI Enhancement Flow
|
||||
|
||||
### User Input (Vague)
|
||||
```
|
||||
"write something about AI"
|
||||
```
|
||||
|
||||
### AI Analysis
|
||||
- **Intent Detection**: User wants to create content about AI
|
||||
- **Persona Context**:
|
||||
- Industry: Healthcare (from onboarding)
|
||||
- Audience: Medical professionals
|
||||
- Expertise: Intermediate
|
||||
- **Complexity**: Low (very vague)
|
||||
|
||||
### AI Enhanced Output
|
||||
```typescript
|
||||
{
|
||||
enhanced_query: "Research: AI-powered diagnostic tools and clinical decision support systems in healthcare",
|
||||
suggested_config: {
|
||||
mode: 'comprehensive',
|
||||
provider: 'exa',
|
||||
max_sources: 20,
|
||||
include_statistics: true,
|
||||
include_expert_quotes: true,
|
||||
exa_category: 'research paper',
|
||||
exa_search_type: 'neural',
|
||||
exa_include_domains: ['pubmed.gov', 'nejm.org', 'nih.gov']
|
||||
},
|
||||
keywords: [
|
||||
"AI diagnostic tools",
|
||||
"clinical decision support",
|
||||
"medical AI applications",
|
||||
"healthcare automation",
|
||||
"patient outcomes AI"
|
||||
],
|
||||
industry: "Healthcare",
|
||||
target_audience: "Medical professionals and healthcare administrators",
|
||||
reasoning: "Based on your healthcare focus and medical professional audience from your profile, I've tailored this research to explore AI diagnostic tools with clinical evidence and expert insights.",
|
||||
alternative_angles: [
|
||||
"AI ethics in medical decision-making",
|
||||
"Cost-benefit analysis of AI diagnostic systems",
|
||||
"Training medical staff on AI tools"
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 8. Testing Scenarios
|
||||
|
||||
### Scenario 1: Beginner User
|
||||
- **Profile**: New blogger, general audience
|
||||
- **Input**: "best marketing tools"
|
||||
- **AI Should**: Suggest basic mode, Google search, expand to "top marketing automation tools for small businesses"
|
||||
|
||||
### Scenario 2: Technical Expert
|
||||
- **Profile**: Data scientist, technical audience
|
||||
- **Input**: "transformer architectures"
|
||||
- **AI Should**: Suggest comprehensive mode, Exa neural, include research papers, arxiv.org domains
|
||||
|
||||
### Scenario 3: Business Professional
|
||||
- **Profile**: CMO, C-suite audience
|
||||
- **Input**: "ROI of content marketing"
|
||||
- **AI Should**: Suggest targeted mode, include statistics & competitors, focus on HBR, McKinsey sources
|
||||
|
||||
---
|
||||
|
||||
## 9. Implementation Priority
|
||||
|
||||
### High Priority (Week 1)
|
||||
1. ✅ Fix preset click behavior
|
||||
2. ✅ Show Exa options for all modes
|
||||
3. 🔄 Create persona fetch API endpoint
|
||||
4. 🔄 Add persona-aware default suggestions
|
||||
|
||||
### Medium Priority (Week 2)
|
||||
5. AI query enhancement endpoint
|
||||
6. Smart preset generation from persona
|
||||
7. Industry-specific domain suggestions
|
||||
|
||||
### Low Priority (Week 3+)
|
||||
8. Learning from user research history
|
||||
9. Collaborative filtering (similar users' successful configs)
|
||||
10. A/B testing AI suggestions
|
||||
|
||||
---
|
||||
|
||||
## 10. Success Metrics
|
||||
|
||||
- **User Engagement**: % of users who modify AI suggestions
|
||||
- **Research Quality**: User ratings of research results
|
||||
- **Time Saved**: Reduction in research configuration time
|
||||
- **Adoption Rate**: % of users using presets vs. manual config
|
||||
- **Accuracy**: % of AI suggestions that match user intent
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
By leveraging persona data and AI, we can transform research from a complex configuration task into a simple, one-click experience for beginners while maintaining full customization for power users. The key is intelligent defaults that "just work" based on who the user is and what they're trying to achieve.
|
||||
|
||||
335
docs/ALwrity Researcher/RESEARCH_COMPONENT_INTEGRATION.md
Normal file
335
docs/ALwrity Researcher/RESEARCH_COMPONENT_INTEGRATION.md
Normal file
@@ -0,0 +1,335 @@
|
||||
# Research Component Integration Guide
|
||||
|
||||
## Overview
|
||||
|
||||
The modular Research component has been implemented as a standalone, testable wizard that can be integrated into the blog writer or used independently. This document outlines the architecture, usage, and integration steps.
|
||||
|
||||
## Architecture
|
||||
|
||||
### Backend Strategy Pattern
|
||||
|
||||
The research service now supports multiple research modes through a strategy pattern:
|
||||
|
||||
```python
|
||||
# Research modes
|
||||
- Basic: Quick keyword-focused analysis
|
||||
- Comprehensive: Full analysis with all components
|
||||
- Targeted: Customizable components based on config
|
||||
|
||||
# Strategy implementation
|
||||
backend/services/blog_writer/research/research_strategies.py
|
||||
- ResearchStrategy (base class)
|
||||
- BasicResearchStrategy
|
||||
- ComprehensiveResearchStrategy
|
||||
- TargetedResearchStrategy
|
||||
```
|
||||
|
||||
### Frontend Component Structure
|
||||
|
||||
```
|
||||
frontend/src/components/Research/
|
||||
├── index.tsx # Main exports
|
||||
├── ResearchWizard.tsx # Main wizard container
|
||||
├── steps/
|
||||
│ ├── StepKeyword.tsx # Step 1: Keyword input
|
||||
│ ├── StepOptions.tsx # Step 2: Mode selection
|
||||
│ ├── StepProgress.tsx # Step 3: Progress display
|
||||
│ └── StepResults.tsx # Step 4: Results display
|
||||
├── hooks/
|
||||
│ ├── useResearchWizard.ts # Wizard state management
|
||||
│ └── useResearchExecution.ts # API calls and polling
|
||||
├── types/
|
||||
│ └── research.types.ts # TypeScript interfaces
|
||||
└── utils/
|
||||
└── researchUtils.ts # Utility functions
|
||||
```
|
||||
|
||||
## Test Page
|
||||
|
||||
A dedicated test page is available at `/research-test` for testing the research wizard independently.
|
||||
|
||||
**Features:**
|
||||
- Quick preset keywords for testing
|
||||
- Debug panel with JSON export
|
||||
- Performance metrics display
|
||||
- Cache state visualization
|
||||
|
||||
## Usage
|
||||
|
||||
### Standalone Usage
|
||||
|
||||
```typescript
|
||||
import { ResearchWizard } from '../components/Research';
|
||||
|
||||
<ResearchWizard
|
||||
onComplete={(results) => {
|
||||
console.log('Research complete:', results);
|
||||
}}
|
||||
onCancel={() => {
|
||||
console.log('Cancelled');
|
||||
}}
|
||||
initialKeywords={['AI', 'marketing']}
|
||||
initialIndustry="Technology"
|
||||
/>
|
||||
```
|
||||
|
||||
### Integration with Blog Writer
|
||||
|
||||
The component is designed to be easily integrated into the BlogWriter research phase:
|
||||
|
||||
**Current Implementation:**
|
||||
- Uses CopilotKit sidebar for research input
|
||||
- Displays results in `ResearchResults` component
|
||||
- Manual fallback via `ManualResearchForm`
|
||||
|
||||
**Proposed Integration:**
|
||||
Replace the CopilotKit/manual form with the wizard:
|
||||
|
||||
```typescript
|
||||
// In BlogWriter.tsx
|
||||
{currentPhase === 'research' && (
|
||||
<ResearchWizard
|
||||
onComplete={(results) => setResearch(results)}
|
||||
onCancel={() => navigate('blog-writer')}
|
||||
/>
|
||||
)}
|
||||
```
|
||||
|
||||
## Backend API Changes
|
||||
|
||||
### New Models
|
||||
|
||||
The `BlogResearchRequest` model now supports:
|
||||
|
||||
```python
|
||||
class BlogResearchRequest(BaseModel):
|
||||
keywords: List[str]
|
||||
topic: Optional[str] = None
|
||||
industry: Optional[str] = None
|
||||
target_audience: Optional[str] = None
|
||||
tone: Optional[str] = None
|
||||
word_count_target: Optional[int] = 1500
|
||||
persona: Optional[PersonaInfo] = None
|
||||
research_mode: Optional[ResearchMode] = ResearchMode.BASIC # NEW
|
||||
config: Optional[ResearchConfig] = None # NEW
|
||||
```
|
||||
|
||||
### Backward Compatibility
|
||||
|
||||
The API remains backward compatible:
|
||||
- If `research_mode` is not provided, defaults to `BASIC`
|
||||
- If `config` is not provided, defaults to standard configuration
|
||||
- Existing requests continue to work unchanged
|
||||
|
||||
## Research Modes
|
||||
|
||||
### Basic Mode
|
||||
- Quick keyword analysis
|
||||
- Primary & secondary keywords
|
||||
- Current trends overview
|
||||
- Top 5 content angles
|
||||
- Key statistics
|
||||
|
||||
### Comprehensive Mode
|
||||
- All basic features plus:
|
||||
- Expert quotes & opinions
|
||||
- Competitor analysis
|
||||
- Market forecasts
|
||||
- Best practices & case studies
|
||||
- Content gaps identification
|
||||
|
||||
### Targeted Mode
|
||||
- Selectable components:
|
||||
- Statistics
|
||||
- Expert quotes
|
||||
- Competitors
|
||||
- Trends
|
||||
- Always includes: Keywords & content angles
|
||||
|
||||
## Configuration Options
|
||||
|
||||
### ResearchConfig Model
|
||||
|
||||
```python
|
||||
class ResearchConfig(BaseModel):
|
||||
mode: ResearchMode = ResearchMode.BASIC
|
||||
date_range: Optional[DateRange] = None
|
||||
source_types: List[SourceType] = []
|
||||
max_sources: int = 10
|
||||
include_statistics: bool = True
|
||||
include_expert_quotes: bool = True
|
||||
include_competitors: bool = True
|
||||
include_trends: bool = True
|
||||
```
|
||||
|
||||
### Date Range Options
|
||||
- `last_week`
|
||||
- `last_month`
|
||||
- `last_3_months`
|
||||
- `last_6_months`
|
||||
- `last_year`
|
||||
- `all_time`
|
||||
|
||||
### Source Types
|
||||
- `web` - Web articles
|
||||
- `academic` - Academic papers
|
||||
- `news` - News articles
|
||||
- `industry` - Industry reports
|
||||
- `expert` - Expert opinions
|
||||
|
||||
## Caching
|
||||
|
||||
The research component uses the existing cache infrastructure:
|
||||
- Cache keys include research mode
|
||||
- Cache is shared across basic/comprehensive/targeted modes
|
||||
- Cache invalidation handled automatically
|
||||
|
||||
## Testing
|
||||
|
||||
### Test the Wizard
|
||||
|
||||
1. Navigate to `/research-test`
|
||||
2. Use quick presets or enter custom keywords
|
||||
3. Select research mode
|
||||
4. Monitor progress
|
||||
5. Review results
|
||||
6. Export JSON for analysis
|
||||
|
||||
### Integration Testing
|
||||
|
||||
To test integration with BlogWriter:
|
||||
|
||||
1. Start backend: `python start_alwrity_backend.py`
|
||||
2. Navigate to `/blog-writer` (current implementation)
|
||||
3. Or navigate to `/research-test` (new wizard)
|
||||
4. Compare results and UI
|
||||
|
||||
## Migration Path
|
||||
|
||||
### Phase 1: Parallel Testing (Current)
|
||||
- `/research-test` - New wizard available
|
||||
- `/blog-writer` - Current implementation unchanged
|
||||
- Users can test both
|
||||
|
||||
### Phase 2: Integration
|
||||
1. Add wizard as option in BlogWriter
|
||||
2. A/B test user preference
|
||||
3. Monitor performance metrics
|
||||
|
||||
### Phase 3: Replacement (Optional)
|
||||
1. Replace CopilotKit/manual form with wizard
|
||||
2. Remove old implementation
|
||||
3. Update documentation
|
||||
|
||||
## API Endpoints
|
||||
|
||||
All existing endpoints remain unchanged:
|
||||
|
||||
```
|
||||
POST /api/blog/research/start
|
||||
- Supports new research_mode and config parameters
|
||||
- Backward compatible with existing requests
|
||||
|
||||
GET /api/blog/research/status/{task_id}
|
||||
- No changes required
|
||||
```
|
||||
|
||||
## Benefits
|
||||
|
||||
1. **Modularity**: Component works standalone
|
||||
2. **Testability**: Dedicated test page for experimentation
|
||||
3. **Backward Compatibility**: Existing functionality unchanged
|
||||
4. **Progressive Enhancement**: Can add features incrementally
|
||||
5. **Reusability**: Can be used in other parts of the app
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
Potential future improvements:
|
||||
|
||||
1. **Multi-stage Research**: Sequential research with refinement
|
||||
2. **Source Quality Validation**: Advanced credibility scoring
|
||||
3. **Interactive Query Builder**: Dynamic search refinement
|
||||
4. **Advanced Prompting**: Few-shot examples, reasoning chains
|
||||
5. **Custom Strategy Plugins**: User-defined research strategies
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Research Results Not Showing
|
||||
|
||||
Check:
|
||||
1. Backend logs for API errors
|
||||
2. Network tab for failed requests
|
||||
3. Browser console for JavaScript errors
|
||||
4. Verify user authentication
|
||||
|
||||
### Cache Issues
|
||||
|
||||
Clear cache:
|
||||
```typescript
|
||||
import { researchCache } from '../services/researchCache';
|
||||
researchCache.clearCache();
|
||||
```
|
||||
|
||||
### Type Errors
|
||||
|
||||
Ensure all imports are correct:
|
||||
```typescript
|
||||
import {
|
||||
ResearchWizard,
|
||||
useResearchWizard,
|
||||
WizardState
|
||||
} from '../components/Research';
|
||||
|
||||
import {
|
||||
BlogResearchRequest,
|
||||
BlogResearchResponse,
|
||||
ResearchMode,
|
||||
ResearchConfig
|
||||
} from '../services/blogWriterApi';
|
||||
```
|
||||
|
||||
## Examples
|
||||
|
||||
### Basic Integration
|
||||
|
||||
```typescript
|
||||
import { ResearchWizard } from './components/Research';
|
||||
import { BlogResearchResponse } from './services/blogWriterApi';
|
||||
|
||||
const MyComponent: React.FC = () => {
|
||||
const [results, setResults] = useState<BlogResearchResponse | null>(null);
|
||||
|
||||
return (
|
||||
<ResearchWizard
|
||||
onComplete={(res) => setResults(res)}
|
||||
onCancel={() => console.log('Cancelled')}
|
||||
/>
|
||||
);
|
||||
};
|
||||
```
|
||||
|
||||
### Advanced Integration with Custom Config
|
||||
|
||||
```typescript
|
||||
const request: BlogResearchRequest = {
|
||||
keywords: ['AI', 'automation'],
|
||||
industry: 'Technology',
|
||||
research_mode: 'targeted',
|
||||
config: {
|
||||
mode: 'targeted',
|
||||
include_statistics: true,
|
||||
include_competitors: true,
|
||||
include_trends: false,
|
||||
max_sources: 20,
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
## Support
|
||||
|
||||
For issues or questions:
|
||||
1. Check this documentation
|
||||
2. Review test page examples
|
||||
3. Inspect backend logs
|
||||
4. Check frontend console
|
||||
|
||||
130
docs/ALwrity Researcher/RESEARCH_IMPROVEMENTS_SUMMARY.md
Normal file
130
docs/ALwrity Researcher/RESEARCH_IMPROVEMENTS_SUMMARY.md
Normal file
@@ -0,0 +1,130 @@
|
||||
# Research Phase Improvements Summary
|
||||
|
||||
## Key Changes
|
||||
|
||||
### 1. Provider Auto-Selection ✅
|
||||
- **Removed** manual provider dropdown from UI
|
||||
- **Auto-selects** provider based on Research Depth:
|
||||
- `Basic` → Google Search (fast)
|
||||
- `Comprehensive` → Exa Neural (if available, else Google)
|
||||
- `Targeted` → Exa Neural (if available, else Google)
|
||||
- Transparent to user, intelligent fallback
|
||||
|
||||
### 2. Visual Status Indicators ✅
|
||||
- Red/green dots show API key status: `Research Depth [🟢 Google 🟢 Exa]`
|
||||
- Real-time availability check via `/api/research/provider-availability`
|
||||
- Tooltips show configuration status
|
||||
|
||||
### 3. Persona-Aware Defaults ✅
|
||||
- **Auto-fills** from onboarding data:
|
||||
- Industry → From `business_info` or `core_persona`
|
||||
- Target Audience → From persona data
|
||||
- Exa Domains → Industry-specific sources (e.g., Healthcare: pubmed.gov, nejm.org)
|
||||
- Exa Category → Industry-appropriate (e.g., Finance: financial report)
|
||||
- Endpoint: `/api/research/persona-defaults`
|
||||
|
||||
### 4. Fixed Issues ✅
|
||||
- **Preset clicks** now properly update all fields and clear localStorage
|
||||
- **Exa options** visible for all modes when Exa provider selected
|
||||
- **State management** prioritizes initial props over cached state
|
||||
|
||||
---
|
||||
|
||||
## New API Endpoints
|
||||
|
||||
| Endpoint | Purpose | Returns |
|
||||
|----------|---------|---------|
|
||||
| `GET /api/research/provider-availability` | Check API key status | `{google_available, exa_available, key_status}` |
|
||||
| `GET /api/research/persona-defaults` | Get user defaults | `{industry, target_audience, suggested_domains, exa_category}` |
|
||||
| `GET /api/research/config` | Combined config | Both availability + defaults |
|
||||
|
||||
---
|
||||
|
||||
## Provider Selection Logic
|
||||
|
||||
```typescript
|
||||
Basic: Always Google
|
||||
Comprehensive/Targeted: Exa (if available) → Google (fallback)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Domain & Category Suggestions
|
||||
|
||||
**By Industry**:
|
||||
- Healthcare → pubmed.gov, nejm.org + `research paper`
|
||||
- Technology → techcrunch.com, wired.com + `company`
|
||||
- Finance → wsj.com, bloomberg.com + `financial report`
|
||||
- Science → nature.com, sciencemag.org + `research paper`
|
||||
|
||||
---
|
||||
|
||||
## Quick Test Guide
|
||||
|
||||
1. **Provider Auto-Selection**: Change research depth → provider updates automatically
|
||||
2. **Status Indicators**: Check dots match API key configuration
|
||||
3. **Persona Defaults**: New users see industry/audience pre-filled
|
||||
4. **Preset Clicks**: Click preset → all fields update instantly
|
||||
5. **Exa Visibility**: Select Comprehensive → Exa options appear (if available)
|
||||
|
||||
---
|
||||
|
||||
## Files Changed
|
||||
|
||||
**Frontend**:
|
||||
- `frontend/src/components/Research/steps/ResearchInput.tsx` - Auto-selection, status UI
|
||||
- `frontend/src/components/Research/hooks/useResearchWizard.ts` - State management
|
||||
- `frontend/src/pages/ResearchTest.tsx` - Enhanced presets
|
||||
- `frontend/src/api/researchConfig.ts` - New API client
|
||||
|
||||
**Backend**:
|
||||
- `backend/api/research_config.py` - New endpoints
|
||||
- `backend/app.py` - Router registration
|
||||
|
||||
**Documentation**:
|
||||
- `docs/RESEARCH_AI_HYPERPERSONALIZATION.md` - Complete AI personalization guide
|
||||
- `docs/RESEARCH_IMPROVEMENTS_SUMMARY.md` - This summary
|
||||
|
||||
---
|
||||
|
||||
## Before vs After
|
||||
|
||||
| Before | After |
|
||||
|--------|-------|
|
||||
| Manual provider selection | Auto-selected by depth |
|
||||
| No API key visibility | Red/green status dots |
|
||||
| Generic "General" defaults | Persona-aware pre-fills |
|
||||
| Broken preset clicks | Instant preset application |
|
||||
| Exa hidden in Basic | Exa always accessible |
|
||||
|
||||
---
|
||||
|
||||
## Next Steps (Phase 2)
|
||||
|
||||
1. **AI Query Enhancement** - Transform vague inputs into actionable queries
|
||||
2. **Smart Presets** - Generate presets from persona + AI
|
||||
3. **Learning** - Track successful patterns, suggest optimizations
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
- **Immediate**: Reduced clicks, better UX, working presets
|
||||
- **Track**: Time to research start, preset adoption rate, Exa usage %
|
||||
- **Goal**: 30% faster research setup, higher user satisfaction
|
||||
|
||||
---
|
||||
|
||||
## Reused from Documentation
|
||||
|
||||
From `RESEARCH_AI_HYPERPERSONALIZATION.md`:
|
||||
- Domain suggestion maps (8 industries)
|
||||
- Exa category mappings (8 industries)
|
||||
- Provider selection rules
|
||||
- Persona data structure
|
||||
- API design patterns
|
||||
|
||||
---
|
||||
|
||||
**Status**: All changes complete and tested. Foundation ready for AI enhancement (Phase 2).
|
||||
|
||||
303
docs/ALwrity Researcher/RESEARCH_PAGE_UX_IMPROVEMENTS.md
Normal file
303
docs/ALwrity Researcher/RESEARCH_PAGE_UX_IMPROVEMENTS.md
Normal file
@@ -0,0 +1,303 @@
|
||||
# Research Page UX Improvements & Preset Integration Analysis
|
||||
|
||||
## Review Date: 2025-12-30
|
||||
|
||||
## Current First-Time User Experience
|
||||
|
||||
### **What Users See on First Visit:**
|
||||
|
||||
1. **Research Page Loads** → Shows "Quick Start Presets" section
|
||||
2. **Modal Appears Immediately** → "Generate Research Persona" modal
|
||||
3. **User Options:**
|
||||
- **Generate Persona** (30-60 seconds) → Gets personalized presets
|
||||
- **Skip for Now** → Uses generic sample presets
|
||||
|
||||
### **Current Flow:**
|
||||
|
||||
```
|
||||
First Visit
|
||||
↓
|
||||
Modal: "Generate Research Persona?"
|
||||
↓
|
||||
[User clicks "Generate Persona"]
|
||||
↓
|
||||
Loading... (30-60 seconds)
|
||||
↓
|
||||
Persona Generated ✅
|
||||
↓
|
||||
Presets Updated with AI-generated presets
|
||||
↓
|
||||
User can start researching
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔍 **Current Preset System Analysis**
|
||||
|
||||
### **How Presets Are Generated:**
|
||||
|
||||
#### **1. AI-Generated Presets** (Best Experience)
|
||||
**Source**: `research_persona.recommended_presets`
|
||||
**When Used**: If research persona exists AND has `recommended_presets`
|
||||
|
||||
**Benefits from Research Persona:**
|
||||
- ✅ **Full Config**: Complete `ResearchConfig` object with all Exa/Tavily options
|
||||
- ✅ **Personalized Keywords**: Based on user's industry, audience, interests
|
||||
- ✅ **Industry-Specific**: Uses `default_industry` and `default_target_audience`
|
||||
- ✅ **Provider Optimization**: Uses `suggested_exa_category`, `suggested_exa_domains`, `suggested_exa_search_type`
|
||||
- ✅ **Research Mode**: Uses `default_research_mode`
|
||||
- ✅ **Smart Defaults**: All provider-specific settings from persona
|
||||
|
||||
**Example AI Preset:**
|
||||
```json
|
||||
{
|
||||
"name": "Content Marketing Trends",
|
||||
"keywords": "Research latest content marketing automation tools and AI-powered content strategies",
|
||||
"industry": "Content Marketing",
|
||||
"target_audience": "Marketing professionals and content creators",
|
||||
"research_mode": "comprehensive",
|
||||
"config": {
|
||||
"mode": "comprehensive",
|
||||
"provider": "exa",
|
||||
"max_sources": 20,
|
||||
"exa_category": "company",
|
||||
"exa_search_type": "neural",
|
||||
"exa_include_domains": ["contentmarketinginstitute.com", "hubspot.com"],
|
||||
"include_statistics": true,
|
||||
"include_expert_quotes": true,
|
||||
"include_competitors": true,
|
||||
"include_trends": true
|
||||
},
|
||||
"description": "Discover latest trends in content marketing automation"
|
||||
}
|
||||
```
|
||||
|
||||
#### **2. Rule-Based Presets** (Fallback)
|
||||
**Source**: `generatePersonaPresets(persona_defaults)`
|
||||
**When Used**: If persona exists but has no `recommended_presets`
|
||||
|
||||
**Benefits from Research Persona:**
|
||||
- ✅ **Industry**: Uses `persona_defaults.industry`
|
||||
- ✅ **Audience**: Uses `persona_defaults.target_audience`
|
||||
- ✅ **Exa Category**: Uses `persona_defaults.suggested_exa_category`
|
||||
- ✅ **Exa Domains**: Uses `persona_defaults.suggested_domains`
|
||||
- ⚠️ **Limited**: Only generates 3 generic presets with template keywords
|
||||
|
||||
**Example Rule-Based Preset:**
|
||||
```javascript
|
||||
{
|
||||
name: "Content Marketing Trends",
|
||||
keywords: "Research latest trends and innovations in Content Marketing",
|
||||
industry: "Content Marketing",
|
||||
targetAudience: "Professionals and content consumers",
|
||||
researchMode: "comprehensive",
|
||||
config: {
|
||||
mode: "comprehensive",
|
||||
provider: "exa",
|
||||
exa_category: "company",
|
||||
exa_search_type: "neural",
|
||||
exa_include_domains: ["contentmarketinginstitute.com", ...]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### **3. Sample Presets** (No Personalization)
|
||||
**Source**: Hardcoded `samplePresets` array
|
||||
**When Used**: If no persona exists or persona has no industry
|
||||
|
||||
**No Benefits from Research Persona:**
|
||||
- ❌ Generic presets (AI Marketing Tools, Small Business SEO, etc.)
|
||||
- ❌ Not personalized to user
|
||||
- ❌ Same for all users
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **What First-Time Users Expect**
|
||||
|
||||
### **User Expectations:**
|
||||
|
||||
1. **Immediate Value**: See something useful right away, not a modal
|
||||
2. **Clear Purpose**: Understand what the page does
|
||||
3. **Quick Start**: Be able to start researching without barriers
|
||||
4. **Personalization**: See relevant presets for their industry
|
||||
5. **Progressive Enhancement**: Get better experience after persona generation
|
||||
|
||||
### **Current Issues:**
|
||||
|
||||
1. ❌ **Modal Blocks Action**: User must interact with modal before seeing value
|
||||
2. ❌ **Unclear Benefits**: User doesn't know what they're getting
|
||||
3. ❌ **Generic Presets Initially**: Shows sample presets until persona generates
|
||||
4. ❌ **No Preview**: Can't see what personalized presets look like
|
||||
5. ❌ **No Context**: User doesn't understand why persona is needed
|
||||
|
||||
---
|
||||
|
||||
## 💡 **Proposed UX Improvements**
|
||||
|
||||
### **Improvement 1: Non-Blocking Modal with Preview**
|
||||
|
||||
**Current**: Modal blocks entire page
|
||||
**Proposed**:
|
||||
- Show presets immediately (even if generic)
|
||||
- Modal appears as a **banner/notification** at top, not blocking
|
||||
- Show preview of what personalized presets will look like
|
||||
- Allow user to start researching immediately with generic presets
|
||||
|
||||
**Benefits**:
|
||||
- ✅ User can start immediately
|
||||
- ✅ Persona generation is optional enhancement
|
||||
- ✅ Less friction for first-time users
|
||||
|
||||
### **Improvement 2: Enhanced Persona Generation Prompt**
|
||||
|
||||
**Current Issues**:
|
||||
- Prompt doesn't emphasize creating **actionable, specific presets**
|
||||
- Doesn't use competitor analysis data
|
||||
- Doesn't leverage research angles for preset names
|
||||
|
||||
**Proposed Enhancements**:
|
||||
1. **Use Competitor Analysis**: Include competitor data in prompt to create competitive research presets
|
||||
2. **Leverage Research Angles**: Use `research_angles` to create preset names and keywords
|
||||
3. **More Specific Instructions**: Emphasize creating presets that user would actually want to use
|
||||
4. **Industry-Specific Examples**: Include examples based on user's industry
|
||||
|
||||
### **Improvement 3: Progressive Enhancement Flow**
|
||||
|
||||
**Proposed Flow**:
|
||||
```
|
||||
First Visit
|
||||
↓
|
||||
Show Generic Presets Immediately ✅
|
||||
↓
|
||||
Banner: "Personalize your research experience" (non-blocking)
|
||||
↓
|
||||
[User can click preset and start researching]
|
||||
OR
|
||||
[User clicks "Generate Persona" in banner]
|
||||
↓
|
||||
Background Generation (doesn't block)
|
||||
↓
|
||||
Presets Update Automatically When Ready
|
||||
↓
|
||||
Notification: "Your personalized presets are ready!"
|
||||
```
|
||||
|
||||
### **Improvement 4: Better Preset Generation**
|
||||
|
||||
**Enhancements**:
|
||||
1. **Use Research Angles**: Create presets from `research_angles` field
|
||||
2. **Competitor-Focused Presets**: If competitor data exists, create competitive analysis presets
|
||||
3. **Query Enhancement Integration**: Use `query_enhancement_rules` to create better preset keywords
|
||||
4. **Industry-Specific Templates**: Use industry to select preset templates
|
||||
|
||||
### **Improvement 5: Visual Indicators**
|
||||
|
||||
**Add**:
|
||||
- Badge on presets: "AI Personalized" vs "Generic"
|
||||
- Tooltip explaining what personalized presets include
|
||||
- Progress indicator during persona generation
|
||||
- Success animation when presets update
|
||||
|
||||
---
|
||||
|
||||
## 🔧 **Technical Improvements Needed**
|
||||
|
||||
### **1. Enhanced Prompt for Recommended Presets**
|
||||
|
||||
**Current Prompt Section** (Line 115-124):
|
||||
```
|
||||
6. RECOMMENDED PRESETS:
|
||||
- "recommended_presets": Generate 3-5 personalized research preset templates...
|
||||
```
|
||||
|
||||
**Proposed Enhancement**:
|
||||
- Include competitor analysis data in prompt
|
||||
- Use research_angles to inspire preset names
|
||||
- Add examples of good vs. bad presets
|
||||
- Emphasize actionability and specificity
|
||||
|
||||
### **2. Preset Generation Logic**
|
||||
|
||||
**Current**:
|
||||
- AI generates presets OR rule-based fallback
|
||||
- No use of competitor data
|
||||
- No use of research angles
|
||||
|
||||
**Proposed**:
|
||||
- Use `research_angles` to create preset names/keywords
|
||||
- Use competitor data to create competitive analysis presets
|
||||
- Use `query_enhancement_rules` to improve preset keywords
|
||||
- Create presets that match user's content goals
|
||||
|
||||
### **3. Frontend UX Enhancements**
|
||||
|
||||
**Current**:
|
||||
- Modal blocks entire page
|
||||
- No preview of personalized presets
|
||||
- No indication of what's personalized
|
||||
|
||||
**Proposed**:
|
||||
- Non-blocking banner/notification
|
||||
- Show preview of personalized presets
|
||||
- Visual indicators for personalized vs. generic
|
||||
- Progressive enhancement flow
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Preset Integration Summary**
|
||||
|
||||
### **✅ How Presets Currently Benefit from Research Persona:**
|
||||
|
||||
1. **AI-Generated Presets** (Best):
|
||||
- Full config with all provider options
|
||||
- Personalized keywords
|
||||
- Industry-specific settings
|
||||
- Uses all persona fields
|
||||
|
||||
2. **Rule-Based Presets** (Good):
|
||||
- Industry and audience
|
||||
- Exa category and domains
|
||||
- Provider settings
|
||||
- Limited personalization
|
||||
|
||||
3. **Sample Presets** (None):
|
||||
- No personalization
|
||||
- Generic for all users
|
||||
|
||||
### **⚠️ Gaps:**
|
||||
|
||||
1. **Competitor Data Not Used**: Competitor analysis exists but not used in preset generation
|
||||
2. **Research Angles Not Used**: `research_angles` field exists but not leveraged
|
||||
3. **Query Enhancement Not Used**: `query_enhancement_rules` not applied to presets
|
||||
4. **No Preview**: User can't see what personalized presets look like before generating
|
||||
|
||||
---
|
||||
|
||||
## 🚀 **Recommended Implementation Priority**
|
||||
|
||||
### **Phase 1: Quick Wins** (High Impact, Low Effort)
|
||||
1. ✅ Make modal non-blocking (banner instead)
|
||||
2. ✅ Show generic presets immediately
|
||||
3. ✅ Add visual indicators for personalized presets
|
||||
4. ✅ Improve persona generation prompt for better presets
|
||||
|
||||
### **Phase 2: Enhanced Personalization** (Medium Effort)
|
||||
1. ✅ Use research_angles in preset generation
|
||||
2. ✅ Use competitor data for competitive presets
|
||||
3. ✅ Use query_enhancement_rules for better keywords
|
||||
4. ✅ Add preset preview in modal
|
||||
|
||||
### **Phase 3: Advanced Features** (Future)
|
||||
1. ✅ Preset analytics (which presets are used most)
|
||||
2. ✅ User feedback on presets
|
||||
3. ✅ Custom preset creation
|
||||
4. ✅ Preset templates library
|
||||
|
||||
---
|
||||
|
||||
## 📝 **Next Steps**
|
||||
|
||||
1. **Review and approve** this improvement plan
|
||||
2. **Implement Phase 1** improvements
|
||||
3. **Test with users** to validate UX improvements
|
||||
4. **Iterate** based on feedback
|
||||
@@ -0,0 +1,251 @@
|
||||
# Research Persona Data Retrieval Review
|
||||
|
||||
## Review Date: 2025-12-30
|
||||
|
||||
## Summary
|
||||
|
||||
After fixing the competitor analysis bug, we reviewed the research persona generation to ensure it correctly retrieves and uses onboarding data. This document outlines findings and fixes.
|
||||
|
||||
---
|
||||
|
||||
## ✅ **What's Working Correctly**
|
||||
|
||||
### 1. **Database Retrieval Pattern**
|
||||
- ✅ `OnboardingDatabaseService.get_persona_data()` correctly uses `user_id` (Clerk ID) to find session
|
||||
- ✅ Queries `PersonaData` table using `session.id` (database session ID) - **CORRECT**
|
||||
- ✅ Returns data in expected format: `{'corePersona': ..., 'platformPersonas': ..., ...}`
|
||||
|
||||
### 2. **Data Collection Flow**
|
||||
- ✅ `ResearchPersonaService._collect_onboarding_data()` correctly calls:
|
||||
- `get_website_analysis(user_id, db)`
|
||||
- `get_persona_data(user_id, db)`
|
||||
- `get_research_preferences(user_id, db)`
|
||||
- ✅ All three data sources are successfully retrieved
|
||||
|
||||
### 3. **Session Lookup**
|
||||
- ✅ Uses `OnboardingSession.user_id == user_id` (Clerk ID) - **CORRECT**
|
||||
- ✅ No parameter confusion like the competitor analysis bug
|
||||
|
||||
---
|
||||
|
||||
## 🐛 **Issues Found & Fixed**
|
||||
|
||||
### **Issue 1: Prompt Builder Key Mismatch**
|
||||
|
||||
**Problem**:
|
||||
- Prompt builder was looking for `persona_data.get("core_persona")` (snake_case)
|
||||
- But database service returns `persona_data.get("corePersona")` (camelCase)
|
||||
- The `_collect_onboarding_data()` method correctly handles both, but prompt builder didn't
|
||||
|
||||
**Fix Applied**:
|
||||
```python
|
||||
# Before:
|
||||
core_persona = persona_data.get("core_persona", {}) or {}
|
||||
|
||||
# After:
|
||||
core_persona = persona_data.get("corePersona") or persona_data.get("core_persona") or {}
|
||||
```
|
||||
|
||||
**File**: `backend/services/research/research_persona_prompt_builder.py:26`
|
||||
|
||||
---
|
||||
|
||||
### **Issue 2: Core Persona Structure Mismatch**
|
||||
|
||||
**Problem**:
|
||||
- Code expects `core_persona.industry` and `core_persona.target_audience` to exist
|
||||
- Actual structure is:
|
||||
```json
|
||||
{
|
||||
"identity": {
|
||||
"persona_name": "...",
|
||||
"archetype": "...",
|
||||
"core_belief": "...",
|
||||
"brand_voice_description": "..."
|
||||
},
|
||||
"linguistic_fingerprint": {...},
|
||||
"stylistic_constraints": {...},
|
||||
"tonal_range": {...}
|
||||
}
|
||||
```
|
||||
- **No `industry` or `target_audience` fields exist in core persona**
|
||||
|
||||
**Current Behavior** (Working as Designed):
|
||||
- Code correctly falls back to `website_analysis.target_audience.industry_focus`
|
||||
- If not found, infers from `research_preferences.content_types`
|
||||
- If still not found, uses intelligent defaults
|
||||
|
||||
**Status**: ✅ **Working correctly** - The fallback logic handles missing fields properly.
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Actual Data Structure**
|
||||
|
||||
### **Core Persona Structure** (from database):
|
||||
```json
|
||||
{
|
||||
"identity": {
|
||||
"persona_name": "The Clarity Architect",
|
||||
"archetype": "The Sage",
|
||||
"core_belief": "...",
|
||||
"brand_voice_description": "..."
|
||||
},
|
||||
"linguistic_fingerprint": {
|
||||
"sentence_metrics": {...},
|
||||
"lexical_features": {...},
|
||||
...
|
||||
},
|
||||
"stylistic_constraints": {...},
|
||||
"tonal_range": {...}
|
||||
}
|
||||
```
|
||||
|
||||
### **Where Industry/Audience Actually Come From**:
|
||||
|
||||
1. **Primary Source**: `website_analysis.target_audience.industry_focus`
|
||||
2. **Secondary Source**: `research_preferences.content_types` (inferred)
|
||||
3. **Fallback**: Intelligent defaults based on content types
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Verification Tests**
|
||||
|
||||
### **Test 1: Persona Data Retrieval**
|
||||
```python
|
||||
persona_data = service.get_persona_data(user_id, db)
|
||||
# Result: ✅ Successfully retrieved
|
||||
# Keys: ['corePersona', 'platformPersonas', 'qualityMetrics', 'selectedPlatforms']
|
||||
```
|
||||
|
||||
### **Test 2: Website Analysis Retrieval**
|
||||
```python
|
||||
website_analysis = service.get_website_analysis(user_id, db)
|
||||
# Result: ✅ Successfully retrieved
|
||||
# Keys: ['id', 'website_url', 'writing_style', 'content_characteristics', ...]
|
||||
```
|
||||
|
||||
### **Test 3: Research Preferences Retrieval**
|
||||
```python
|
||||
research_prefs = service.get_research_preferences(user_id, db)
|
||||
# Result: ✅ Successfully retrieved
|
||||
# Keys: ['id', 'session_id', 'research_depth', 'content_types', ...]
|
||||
```
|
||||
|
||||
### **Test 4: Onboarding Data Collection**
|
||||
```python
|
||||
onboarding_data = service._collect_onboarding_data(user_id)
|
||||
# Result: ✅ Successfully collected all data sources
|
||||
# Keys: ['website_analysis', 'persona_data', 'research_preferences', 'business_info']
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔍 **Data Flow Verification**
|
||||
|
||||
### **Step 1: Database Retrieval** ✅
|
||||
```
|
||||
user_id (Clerk ID)
|
||||
→ OnboardingSession.user_id == user_id
|
||||
→ session.id (database ID)
|
||||
→ PersonaData.session_id == session.id
|
||||
→ Returns persona data
|
||||
```
|
||||
|
||||
### **Step 2: Data Collection** ✅
|
||||
```
|
||||
ResearchPersonaService._collect_onboarding_data()
|
||||
→ get_website_analysis(user_id, db) ✅
|
||||
→ get_persona_data(user_id, db) ✅
|
||||
→ get_research_preferences(user_id, db) ✅
|
||||
→ Constructs business_info with fallbacks ✅
|
||||
```
|
||||
|
||||
### **Step 3: Prompt Building** ✅ (Fixed)
|
||||
```
|
||||
ResearchPersonaPromptBuilder.build_research_persona_prompt()
|
||||
→ Extracts core_persona (now handles both camelCase and snake_case) ✅
|
||||
→ Includes all onboarding data in prompt ✅
|
||||
```
|
||||
|
||||
### **Step 4: LLM Generation** ✅
|
||||
```
|
||||
llm_text_gen(prompt, json_struct=ResearchPersona.schema())
|
||||
→ Generates structured ResearchPersona ✅
|
||||
→ Validates against Pydantic model ✅
|
||||
```
|
||||
|
||||
### **Step 5: Database Storage** ✅
|
||||
```
|
||||
ResearchPersonaService.save_research_persona()
|
||||
→ Updates PersonaData.research_persona ✅
|
||||
→ Sets PersonaData.research_persona_generated_at ✅
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📝 **Key Differences from Competitor Analysis Bug**
|
||||
|
||||
### **Competitor Analysis Bug** (Fixed):
|
||||
- ❌ Used `session_id` parameter that was actually `user_id` (Clerk ID)
|
||||
- ❌ Tried to query `OnboardingSession.id == session_id` (string vs integer)
|
||||
- ❌ Tried to save to non-existent `session.step_data` field
|
||||
|
||||
### **Persona Data Retrieval** (Working Correctly):
|
||||
- ✅ Uses `user_id` parameter correctly
|
||||
- ✅ Queries `OnboardingSession.user_id == user_id` (correct)
|
||||
- ✅ Queries `PersonaData.session_id == session.id` (correct)
|
||||
- ✅ Saves to correct `PersonaData.research_persona` field
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Recommendations**
|
||||
|
||||
### **1. Industry/Audience Extraction Enhancement** (Future)
|
||||
Consider extracting industry/audience from:
|
||||
- `core_persona.identity.brand_voice_description` (via NLP analysis)
|
||||
- `website_analysis.content_characteristics` (patterns suggest industry)
|
||||
- `research_preferences` (more structured industry field)
|
||||
|
||||
### **2. Data Validation** (Future)
|
||||
Add validation to ensure:
|
||||
- Core persona has expected structure
|
||||
- Website analysis has target_audience data
|
||||
- Research preferences have content_types
|
||||
|
||||
### **3. Logging Enhancement** (Future)
|
||||
Add detailed logging for:
|
||||
- What data sources were used
|
||||
- Which fallbacks were triggered
|
||||
- What fields were inferred vs. extracted
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Conclusion**
|
||||
|
||||
**Status**: ✅ **Persona data retrieval is working correctly**
|
||||
|
||||
The research persona generation:
|
||||
1. ✅ Correctly retrieves persona data from database using Clerk user_id
|
||||
2. ✅ Successfully collects all onboarding data sources
|
||||
3. ✅ Properly handles missing fields with intelligent fallbacks
|
||||
4. ✅ Fixed prompt builder key mismatch issue
|
||||
|
||||
**No critical bugs found** - The system is functioning as designed with proper fallback logic for missing industry/audience data.
|
||||
|
||||
---
|
||||
|
||||
## **Files Modified**
|
||||
|
||||
1. `backend/services/research/research_persona_prompt_builder.py`
|
||||
- Fixed: Handle both `corePersona` (camelCase) and `core_persona` (snake_case)
|
||||
|
||||
---
|
||||
|
||||
## **Test Results**
|
||||
|
||||
All data retrieval tests pass:
|
||||
- ✅ Persona data retrieval: **Working**
|
||||
- ✅ Website analysis retrieval: **Working**
|
||||
- ✅ Research preferences retrieval: **Working**
|
||||
- ✅ Onboarding data collection: **Working**
|
||||
- ✅ Prompt building: **Fixed and Working**
|
||||
238
docs/ALwrity Researcher/RESEARCH_PERSONA_DATA_SOURCES.md
Normal file
238
docs/ALwrity Researcher/RESEARCH_PERSONA_DATA_SOURCES.md
Normal file
@@ -0,0 +1,238 @@
|
||||
# Research Persona Data Sources & Generated Fields
|
||||
|
||||
## Overview
|
||||
|
||||
The Research Persona is an AI-generated profile that provides hyper-personalized research defaults, suggestions, and configurations based on a user's onboarding data. This document details what data is used to generate the persona and what fields are produced.
|
||||
|
||||
---
|
||||
|
||||
## Data Sources Used for Generation
|
||||
|
||||
### 1. **Website Analysis** (`website_analysis`)
|
||||
**Source**: Onboarding Step 2 - Website Analysis
|
||||
**Location**: `WebsiteAnalysis` table in database
|
||||
**Key Fields Used**:
|
||||
- `website_url`: User's website URL
|
||||
- `writing_style`: Tone, voice, complexity, engagement level
|
||||
- `content_characteristics`: Sentence structure, vocabulary, paragraph organization
|
||||
- `target_audience`: Demographics, expertise level, industry focus
|
||||
- `content_type`: Primary type, secondary types, purpose
|
||||
- `recommended_settings`: Writing tone, target audience, content type
|
||||
- `style_patterns`: Writing patterns analysis
|
||||
- `style_guidelines`: Generated guidelines
|
||||
|
||||
**Usage**: Extracts industry focus, target audience, content preferences, and writing style patterns to inform research defaults.
|
||||
|
||||
### 2. **Core Persona** (`core_persona`)
|
||||
**Source**: Onboarding Step 4 - Persona Generation
|
||||
**Location**: `PersonaData.core_persona` JSON field
|
||||
**Key Fields Used**:
|
||||
- `industry`: User's primary industry
|
||||
- `target_audience`: Detailed audience description
|
||||
- `interests`: User's content interests and focus areas
|
||||
- `pain_points`: Challenges and needs
|
||||
- `content_goals`: What the user wants to achieve with content
|
||||
|
||||
**Usage**: Primary source for industry, audience, and content strategy insights.
|
||||
|
||||
### 3. **Research Preferences** (`research_preferences`)
|
||||
**Source**: Onboarding Step 3 - Research Preferences
|
||||
**Location**: `ResearchPreferences` table
|
||||
**Key Fields Used**:
|
||||
- `research_depth`: "standard", "comprehensive", "basic"
|
||||
- `content_types`: Array of content types (e.g., ["blog", "social", "video"])
|
||||
- `auto_research`: Whether to auto-enable research
|
||||
- `factual_content`: Preference for factual vs. opinion-based content
|
||||
- `writing_style`: Inherited from website analysis
|
||||
- `content_characteristics`: Inherited from website analysis
|
||||
- `target_audience`: Inherited from website analysis
|
||||
|
||||
**Usage**: Determines default research mode, provider preferences, and content type focus.
|
||||
|
||||
### 4. **Business Information** (`business_info`)
|
||||
**Source**: Constructed from persona data and website analysis
|
||||
**Key Fields Used**:
|
||||
- `industry`: Extracted from `core_persona.industry` or `website_analysis.target_audience.industry_focus`
|
||||
- `target_audience`: Extracted from `core_persona.target_audience` or `website_analysis.target_audience.demographics`
|
||||
|
||||
**Usage**: Fallback and inference source when core persona data is minimal.
|
||||
|
||||
### 5. **Competitor Analysis** (Future Enhancement)
|
||||
**Source**: Onboarding Step 3 - Competitor Discovery
|
||||
**Location**: `CompetitorAnalysis` table
|
||||
**Status**: Currently not used in persona generation, but available for future enhancements
|
||||
|
||||
**Potential Usage**: Could inform industry context, competitive landscape insights, and domain suggestions.
|
||||
|
||||
---
|
||||
|
||||
## Generated Research Persona Fields
|
||||
|
||||
### **1. Smart Defaults**
|
||||
|
||||
| Field | Type | Description | Source Priority |
|
||||
|-------|------|-------------|-----------------|
|
||||
| `default_industry` | string | User's primary industry | 1. core_persona.industry<br>2. business_info.industry<br>3. website_analysis.target_audience.industry_focus<br>4. Inferred from content_types |
|
||||
| `default_target_audience` | string | Detailed audience description | 1. core_persona.target_audience<br>2. website_analysis.target_audience<br>3. business_info.target_audience<br>4. Default: "Professionals and content consumers" |
|
||||
| `default_research_mode` | string | "basic" \| "comprehensive" \| "targeted" | Based on research_preferences.research_depth and content_type preferences |
|
||||
| `default_provider` | string | "exa" \| "tavily" \| "google" | Based on user's typical research needs:<br>- Academic/research: "exa"<br>- News/current events: "tavily"<br>- General business: "exa"<br>- Default: "exa" |
|
||||
|
||||
### **2. Keyword Intelligence**
|
||||
|
||||
| Field | Type | Description | Generation Logic |
|
||||
|-------|------|-------------|------------------|
|
||||
| `suggested_keywords` | string[] | 8-12 relevant keywords | Generated from:<br>- User's industry<br>- Core persona interests<br>- Content goals<br>- Research preferences |
|
||||
| `keyword_expansion_patterns` | Dict<string, string[]> | Mapping of keywords to expanded terms | 10-15 patterns like:<br>`{"AI": ["healthcare AI", "medical AI"], "tools": ["medical devices"]}`<br>Focuses on industry-specific terminology |
|
||||
|
||||
### **3. Exa Provider Optimization**
|
||||
|
||||
| Field | Type | Description | Generation Logic |
|
||||
|-------|------|-------------|------------------|
|
||||
| `suggested_exa_domains` | string[] | 4-6 authoritative domains | Industry-specific authoritative sources:<br>- Healthcare: ["pubmed.gov", "nejm.org"]<br>- Finance: ["sec.gov", "bloomberg.com"]<br>- Tech: ["github.com", "stackoverflow.com"] |
|
||||
| `suggested_exa_category` | string? | Exa content category | Based on industry:<br>- Healthcare/Science: "research paper"<br>- Finance: "financial report"<br>- Tech/Business: "company" or "news"<br>- Social/Marketing: "tweet" or "linkedin profile"<br>- Default: null (all categories) |
|
||||
| `suggested_exa_search_type` | string? | Exa search algorithm | Based on content needs:<br>- Academic/research: "neural"<br>- Current news/trends: "fast"<br>- General research: "auto"<br>- Code/technical: "neural" |
|
||||
|
||||
### **4. Tavily Provider Optimization**
|
||||
|
||||
| Field | Type | Description | Generation Logic |
|
||||
|-------|------|-------------|------------------|
|
||||
| `suggested_tavily_topic` | string? | "general" \| "news" \| "finance" | Based on content type:<br>- Financial content: "finance"<br>- News/current events: "news"<br>- General research: "general" |
|
||||
| `suggested_tavily_search_depth` | string? | "basic" \| "advanced" \| "fast" \| "ultra-fast" | Based on research needs:<br>- Quick overview: "basic"<br>- In-depth analysis: "advanced"<br>- Breaking news: "fast" |
|
||||
| `suggested_tavily_include_answer` | string? | "false" \| "basic" \| "advanced" | Based on query type:<br>- Factual queries: "advanced"<br>- Research summaries: "basic"<br>- Custom content: "false" |
|
||||
| `suggested_tavily_time_range` | string? | "day" \| "week" \| "month" \| "year" \| null | Based on recency needs:<br>- Breaking news: "day"<br>- Recent developments: "week"<br>- Industry analysis: "month"<br>- Historical: null |
|
||||
| `suggested_tavily_raw_content_format` | string? | "false" \| "markdown" \| "text" | Based on use case:<br>- Blog content: "markdown"<br>- Text extraction: "text"<br>- No raw content: "false" |
|
||||
|
||||
### **5. Provider Selection Logic**
|
||||
|
||||
| Field | Type | Description | Generation Logic |
|
||||
|-------|------|-------------|------------------|
|
||||
| `provider_recommendations` | Dict<string, string> | Use case → provider mapping | Example:<br>`{"trends": "tavily", "deep_research": "exa", "factual": "google", "news": "tavily", "academic": "exa"}` |
|
||||
|
||||
### **6. Research Intelligence**
|
||||
|
||||
| Field | Type | Description | Generation Logic |
|
||||
|-------|------|-------------|------------------|
|
||||
| `research_angles` | string[] | 5-8 alternative research angles | Generated from:<br>- User's pain points<br>- Industry trends<br>- Content goals<br>- Audience interests<br>Examples: "Compare {topic} tools", "{topic} ROI analysis" |
|
||||
| `query_enhancement_rules` | Dict<string, string> | Templates for improving vague queries | 5-8 enhancement patterns:<br>`{"vague_ai": "Research: AI applications in {industry} for {audience}", "vague_tools": "Compare top {industry} tools"}` |
|
||||
|
||||
### **7. Research Presets**
|
||||
|
||||
| Field | Type | Description | Generation Logic |
|
||||
|-------|------|-------------|------------------|
|
||||
| `recommended_presets` | ResearchPreset[] | 3-5 personalized preset templates | Each preset includes:<br>- `name`: Descriptive name<br>- `keywords`: Research query<br>- `industry`: User's industry<br>- `target_audience`: User's audience<br>- `research_mode`: "basic" \| "comprehensive" \| "targeted"<br>- `config`: Complete ResearchConfig object<br>- `description`: Brief explanation |
|
||||
|
||||
### **8. Research Preferences (Structured)**
|
||||
|
||||
| Field | Type | Description | Source |
|
||||
|-------|------|-------------|--------|
|
||||
| `research_preferences` | Dict<string, any> | Structured research preferences | Extracted from onboarding:<br>- `research_depth`: From research_preferences.research_depth<br>- `content_types`: From research_preferences.content_types<br>- `auto_research`: From research_preferences.auto_research<br>- `factual_content`: From research_preferences.factual_content |
|
||||
|
||||
### **9. Metadata**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `generated_at` | string? | ISO timestamp of generation |
|
||||
| `confidence_score` | float? | Confidence score 0-1 (higher = richer data) |
|
||||
| `version` | string? | Schema version (e.g., "1.0") |
|
||||
|
||||
---
|
||||
|
||||
## Data Collection Process
|
||||
|
||||
### Step 1: Collect Onboarding Data
|
||||
```python
|
||||
onboarding_data = {
|
||||
"website_analysis": get_website_analysis(user_id),
|
||||
"persona_data": get_persona_data(user_id),
|
||||
"research_preferences": get_research_preferences(user_id),
|
||||
"business_info": construct_business_info(persona_data, website_analysis)
|
||||
}
|
||||
```
|
||||
|
||||
### Step 2: Build AI Prompt
|
||||
The prompt includes:
|
||||
- All onboarding data (JSON formatted)
|
||||
- Detailed instructions for each field
|
||||
- Examples and use cases
|
||||
- Rules for handling minimal data scenarios
|
||||
|
||||
### Step 3: LLM Generation
|
||||
- Uses structured JSON response format
|
||||
- Validates against `ResearchPersona` Pydantic model
|
||||
- Adds metadata (generated_at, confidence_score)
|
||||
|
||||
### Step 4: Save to Database
|
||||
- Stored in `PersonaData.research_persona` JSON field
|
||||
- Cached with 7-day TTL
|
||||
- Timestamp stored in `PersonaData.research_persona_generated_at`
|
||||
|
||||
---
|
||||
|
||||
## Handling Minimal Data Scenarios
|
||||
|
||||
When onboarding data is incomplete, the AI uses intelligent inference:
|
||||
|
||||
1. **Industry Inference**:
|
||||
- From `content_types`: "blog" → "Content Marketing", "video" → "Video Content Creation"
|
||||
- From `website_analysis.content_characteristics`: Patterns suggest industry
|
||||
- Default: "Technology" or "Business Consulting"
|
||||
|
||||
2. **Target Audience Inference**:
|
||||
- From `writing_style`: Complexity level suggests audience
|
||||
- From `content_goals`: Purpose suggests audience
|
||||
- Default: "Professionals and content consumers"
|
||||
|
||||
3. **Provider Defaults**:
|
||||
- Always defaults to "exa" for content creators
|
||||
- Uses "tavily" only for news/current events focus
|
||||
|
||||
4. **Never Uses "General"**:
|
||||
- The prompt explicitly instructs to never use "General"
|
||||
- Always infers specific categories based on available context
|
||||
|
||||
---
|
||||
|
||||
## Frontend Display
|
||||
|
||||
### Currently Displayed Fields:
|
||||
✅ Default Settings (industry, audience, mode, provider)
|
||||
✅ Suggested Keywords
|
||||
✅ Research Angles
|
||||
✅ Recommended Presets
|
||||
✅ Metadata (generated_at, confidence_score, version)
|
||||
|
||||
### Recently Added Fields (Enhanced Display):
|
||||
✅ Keyword Expansion Patterns
|
||||
✅ Exa Provider Settings (domains, category, search_type)
|
||||
✅ Tavily Provider Settings (topic, depth, answer, time_range, format)
|
||||
✅ Provider Recommendations
|
||||
✅ Query Enhancement Rules
|
||||
✅ Research Preferences (structured)
|
||||
|
||||
---
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
1. **Competitor Analysis Integration**: Use competitor data to inform industry context and domain suggestions
|
||||
2. **Research History**: Learn from past research queries to improve suggestions
|
||||
3. **A/B Testing**: Test different persona generation strategies
|
||||
4. **User Feedback Loop**: Allow users to rate and improve persona suggestions
|
||||
5. **Multi-Industry Support**: Handle users with multiple industries/niches
|
||||
|
||||
---
|
||||
|
||||
## API Endpoints
|
||||
|
||||
- `GET /api/research/persona-defaults`: Get persona defaults (cached only)
|
||||
- `GET /api/research/research-persona`: Get or generate research persona
|
||||
- `POST /api/research/research-persona?force_refresh=true`: Force regenerate persona
|
||||
|
||||
---
|
||||
|
||||
## Related Files
|
||||
|
||||
- **Backend**: `backend/services/research/research_persona_service.py`
|
||||
- **Prompt Builder**: `backend/services/research/research_persona_prompt_builder.py`
|
||||
- **Models**: `backend/models/research_persona_models.py`
|
||||
- **API**: `backend/api/research_config.py`
|
||||
- **Frontend**: `frontend/src/pages/ResearchTest.tsx` (Persona Details Modal)
|
||||
346
docs/ALwrity Researcher/RESEARCH_WIZARD_IMPLEMENTATION.md
Normal file
346
docs/ALwrity Researcher/RESEARCH_WIZARD_IMPLEMENTATION.md
Normal file
@@ -0,0 +1,346 @@
|
||||
# Research Wizard Implementation Summary
|
||||
|
||||
## Implementation Complete
|
||||
|
||||
A modular, pluggable research component has been successfully implemented with wizard-based UI that can be tested independently and integrated into the blog writer.
|
||||
|
||||
---
|
||||
|
||||
## Backend Implementation
|
||||
|
||||
### 1. Research Models (blog_models.py)
|
||||
|
||||
**New Enums:**
|
||||
- `ResearchMode`: `BASIC`, `COMPREHENSIVE`, `TARGETED`
|
||||
- `SourceType`: `WEB`, `ACADEMIC`, `NEWS`, `INDUSTRY`, `EXPERT`
|
||||
- `DateRange`: `LAST_WEEK` through `ALL_TIME`
|
||||
|
||||
**New Models:**
|
||||
```python
|
||||
class ResearchConfig(BaseModel):
|
||||
mode: ResearchMode = ResearchMode.BASIC
|
||||
date_range: Optional[DateRange] = None
|
||||
source_types: List[SourceType] = []
|
||||
max_sources: int = 10
|
||||
include_statistics: bool = True
|
||||
include_expert_quotes: bool = True
|
||||
include_competitors: bool = True
|
||||
include_trends: bool = True
|
||||
```
|
||||
|
||||
**Enhanced BlogResearchRequest:**
|
||||
- Added `research_mode: Optional[ResearchMode]`
|
||||
- Added `config: Optional[ResearchConfig]`
|
||||
- **Backward compatible** - defaults to existing behavior
|
||||
|
||||
### 2. Strategy Pattern (research_strategies.py)
|
||||
|
||||
**New file:** `backend/services/blog_writer/research/research_strategies.py`
|
||||
|
||||
**Three Strategy Classes:**
|
||||
1. **BasicResearchStrategy**: Quick keyword-focused analysis
|
||||
2. **ComprehensiveResearchStrategy**: Full analysis with all components
|
||||
3. **TargetedResearchStrategy**: Customizable components based on config
|
||||
|
||||
**Factory Function:**
|
||||
```python
|
||||
get_strategy_for_mode(mode: ResearchMode) -> ResearchStrategy
|
||||
```
|
||||
|
||||
### 3. Service Integration (research_service.py)
|
||||
|
||||
**Key Changes:**
|
||||
- Imports strategy factory and models
|
||||
- Uses strategy pattern in both `research()` and `research_with_progress()` methods
|
||||
- Automatically selects strategy based on `research_mode`
|
||||
- Backward compatible - defaults to BASIC if not specified
|
||||
|
||||
**Line Changes:**
|
||||
```python
|
||||
# Lines 88-96: Determine research mode and get appropriate strategy
|
||||
research_mode = request.research_mode or ResearchMode.BASIC
|
||||
config = request.config or ResearchConfig(mode=research_mode)
|
||||
strategy = get_strategy_for_mode(research_mode)
|
||||
|
||||
logger.info(f"Using research mode: {research_mode.value}")
|
||||
|
||||
# Build research prompt based on strategy
|
||||
research_prompt = strategy.build_research_prompt(topic, industry, target_audience, config)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Frontend Implementation
|
||||
|
||||
### 4. Component Structure
|
||||
|
||||
**New Directory:** `frontend/src/components/Research/`
|
||||
|
||||
```
|
||||
Research/
|
||||
├── index.tsx # Main exports
|
||||
├── ResearchWizard.tsx # Main wizard container
|
||||
├── steps/
|
||||
│ ├── StepKeyword.tsx # Step 1: Keyword input
|
||||
│ ├── StepOptions.tsx # Step 2: Mode selection (3 cards)
|
||||
│ ├── StepProgress.tsx # Step 3: Progress display
|
||||
│ └── StepResults.tsx # Step 4: Results display
|
||||
├── hooks/
|
||||
│ ├── useResearchWizard.ts # Wizard state management
|
||||
│ └── useResearchExecution.ts # API calls and polling
|
||||
├── types/
|
||||
│ └── research.types.ts # TypeScript interfaces
|
||||
├── utils/
|
||||
│ └── researchUtils.ts # Utility functions
|
||||
└── integrations/
|
||||
└── BlogWriterAdapter.tsx # Blog writer integration adapter
|
||||
```
|
||||
|
||||
### 5. Wizard Components
|
||||
|
||||
**ResearchWizard.tsx:**
|
||||
- Main container with progress bar
|
||||
- Step indicators (Setup → Options → Research → Results)
|
||||
- Navigation footer with Back/Next buttons
|
||||
- Responsive layout
|
||||
|
||||
**StepKeyword.tsx:**
|
||||
- Keywords textarea
|
||||
- Industry dropdown (16 options)
|
||||
- Target audience input
|
||||
- Validation for keyword requirements
|
||||
|
||||
**StepOptions.tsx:**
|
||||
- Three mode cards (Basic, Comprehensive, Targeted)
|
||||
- Visual selection feedback
|
||||
- Feature lists per mode
|
||||
- Hover effects
|
||||
|
||||
**StepProgress.tsx:**
|
||||
- Real-time progress updates
|
||||
- Progress messages display
|
||||
- Cancel button
|
||||
- Auto-advance to results on completion
|
||||
|
||||
**StepResults.tsx:**
|
||||
- Displays research results using existing `ResearchResults` component
|
||||
- Export JSON button
|
||||
- Start new research button
|
||||
|
||||
### 6. Hooks
|
||||
|
||||
**useResearchWizard.ts:**
|
||||
- State management for wizard steps
|
||||
- localStorage persistence
|
||||
- Step navigation (next/back)
|
||||
- Validation per step
|
||||
- Reset functionality
|
||||
|
||||
**useResearchExecution.ts:**
|
||||
- Research execution via API
|
||||
- Cache checking
|
||||
- Polling integration
|
||||
- Error handling
|
||||
- Progress tracking
|
||||
|
||||
### 7. Test Page (ResearchTest.tsx)
|
||||
|
||||
**Location:** `frontend/src/pages/ResearchTest.tsx`
|
||||
**Route:** `/research-test`
|
||||
|
||||
**Features:**
|
||||
- Quick preset buttons (3 samples)
|
||||
- Debug panel with JSON export
|
||||
- Performance metrics display
|
||||
- Cache state visualization
|
||||
- Research statistics summary
|
||||
|
||||
**Sample Presets:**
|
||||
1. AI Marketing Tools
|
||||
2. Small Business SEO
|
||||
3. Content Strategy
|
||||
|
||||
### 8. Type Definitions
|
||||
|
||||
**research.types.ts:**
|
||||
- `WizardState`
|
||||
- `WizardStepProps`
|
||||
- `ResearchWizardProps`
|
||||
- `ModeCardInfo`
|
||||
|
||||
**blogWriterApi.ts:**
|
||||
- `ResearchMode` type union
|
||||
- `SourceType` type union
|
||||
- `DateRange` type union
|
||||
- `ResearchConfig` interface
|
||||
- Updated `BlogResearchRequest` interface
|
||||
|
||||
---
|
||||
|
||||
## Integration
|
||||
|
||||
### 9. Blog Writer API (blogWriterApi.ts)
|
||||
|
||||
**Enhanced Interface:**
|
||||
```typescript
|
||||
export interface BlogResearchRequest {
|
||||
keywords: string[];
|
||||
topic?: string;
|
||||
industry?: string;
|
||||
target_audience?: string;
|
||||
tone?: string;
|
||||
word_count_target?: number;
|
||||
persona?: PersonaInfo;
|
||||
research_mode?: ResearchMode; // NEW
|
||||
config?: ResearchConfig; // NEW
|
||||
}
|
||||
```
|
||||
|
||||
### 10. App Routing (App.tsx)
|
||||
|
||||
**New Route:**
|
||||
```typescript
|
||||
<Route path="/research-test" element={<ResearchTest />} />
|
||||
```
|
||||
|
||||
### 11. Integration Adapter
|
||||
|
||||
**BlogWriterAdapter.tsx:**
|
||||
- Wrapper component for easy integration
|
||||
- Usage examples included
|
||||
- Clean interface for BlogWriter
|
||||
|
||||
---
|
||||
|
||||
## Documentation
|
||||
|
||||
### 12. Integration Guide
|
||||
|
||||
**File:** `docs/RESEARCH_COMPONENT_INTEGRATION.md`
|
||||
|
||||
**Contents:**
|
||||
- Architecture overview
|
||||
- Usage examples
|
||||
- Backend API details
|
||||
- Research modes explained
|
||||
- Configuration options
|
||||
- Testing instructions
|
||||
- Migration path
|
||||
- Troubleshooting guide
|
||||
|
||||
---
|
||||
|
||||
## Key Features
|
||||
|
||||
### Research Modes
|
||||
|
||||
**Basic Mode:**
|
||||
- Quick keyword analysis
|
||||
- Primary & secondary keywords
|
||||
- Trends overview
|
||||
- Top 5 content angles
|
||||
- Key statistics
|
||||
|
||||
**Comprehensive Mode:**
|
||||
- All basic features
|
||||
- Expert quotes & opinions
|
||||
- Competitor analysis
|
||||
- Market forecasts
|
||||
- Best practices & case studies
|
||||
- Content gaps identification
|
||||
|
||||
**Targeted Mode:**
|
||||
- Selectable components
|
||||
- Customizable filters
|
||||
- Date range options
|
||||
- Source type filtering
|
||||
|
||||
### User Experience
|
||||
|
||||
1. **Step-by-step wizard** with clear progress
|
||||
2. **Visual mode selection** with cards
|
||||
3. **Real-time progress** with live updates
|
||||
4. **Comprehensive results** with export capability
|
||||
5. **Error handling** with retry options
|
||||
6. **Cache integration** for instant results
|
||||
|
||||
### Developer Experience
|
||||
|
||||
1. **Modular architecture** - standalone components
|
||||
2. **Type safety** - full TypeScript interfaces
|
||||
3. **Reusable hooks** - state and execution management
|
||||
4. **Test page** - isolated testing environment
|
||||
5. **Documentation** - comprehensive guides
|
||||
|
||||
---
|
||||
|
||||
## Testing
|
||||
|
||||
### Quick Test
|
||||
|
||||
1. Navigate to `http://localhost:3000/research-test`
|
||||
2. Click "AI Marketing Tools" preset
|
||||
3. Select "Comprehensive" mode
|
||||
4. Watch progress updates
|
||||
5. Review results with export
|
||||
|
||||
### Integration Test
|
||||
|
||||
1. Compare `/research-test` wizard UI
|
||||
2. Compare `/blog-writer` current UI
|
||||
3. Test both research workflows
|
||||
4. Verify caching works across both
|
||||
|
||||
---
|
||||
|
||||
## Backward Compatibility
|
||||
|
||||
- Existing API calls continue working
|
||||
- No breaking changes to BlogWriter
|
||||
- Optional parameters default to current behavior
|
||||
- Cache infrastructure shared
|
||||
- All existing features preserved
|
||||
|
||||
---
|
||||
|
||||
## File Summary
|
||||
|
||||
**Backend (4 files):**
|
||||
- Modified: `blog_models.py`, `research_service.py`
|
||||
- Created: `research_strategies.py`
|
||||
|
||||
**Frontend (13 files):**
|
||||
- Created: `ResearchWizard.tsx`, 4 step components, 2 hooks, types, utils, adapter, test page
|
||||
- Modified: `App.tsx`, `blogWriterApi.ts`
|
||||
|
||||
**Documentation (2 files):**
|
||||
- Created: `RESEARCH_COMPONENT_INTEGRATION.md`, `RESEARCH_WIZARD_IMPLEMENTATION.md`
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. ✅ **Test the wizard** at `/research-test`
|
||||
2. ✅ **Review integration guide** in docs
|
||||
3. ⏳ **Integrate into BlogWriter** using adapter (optional)
|
||||
4. ⏳ **Gather user feedback** on wizard vs CopilotKit UI
|
||||
5. ⏳ **Add more presets** if needed
|
||||
|
||||
---
|
||||
|
||||
## Benefits Delivered
|
||||
|
||||
- Modular & Pluggable: Standalone component
|
||||
- Testable: Dedicated test page
|
||||
- Backward Compatible: No breaking changes
|
||||
- Reusable: Can be used anywhere in the app
|
||||
- Extensible: Easy to add new modes or features
|
||||
- Documented: Comprehensive guides
|
||||
- Type Safe: Full TypeScript support
|
||||
- Production Ready: No linting errors
|
||||
|
||||
---
|
||||
|
||||
Implementation Date: Current Session
|
||||
Status: Complete & Ready for Testing
|
||||
|
||||
Reference in New Issue
Block a user