212 lines
6.3 KiB
Markdown
212 lines
6.3 KiB
Markdown
# CreativeX Score Extraction - Implementation Summary
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## ✅ Implementation Complete
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All components have been successfully created, tested locally, and pushed to Bitbucket.
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## 📦 What Was Created
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### 1. Main Script
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**File:** `Python-Version/scripts/creativex_scoring_storing.py`
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- Monitors Box folder 350605024645 for PDF files
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- Uses LlamaExtract with agent "Creativex-Extract"
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- Extracts 4 fields: filename, ID, URL, score
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- Stores full JSON in database
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- Deletes files from Box after processing
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- Sends email notifications
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### 2. Database Components
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**Table:** `creativex_scores`
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- 10 columns including JSONB for full extraction data
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- 3 indexes for fast lookups (filename, box_file_id, status)
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- Successfully created and tested locally
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**New Methods in `database.py`:**
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- `store_creativex_score()` - Insert extraction data
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- `get_creativex_score_by_filename()` - Lookup for future DAM integration
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### 3. Configuration
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**Added to `config.yaml`:**
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```yaml
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creativex:
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llama_api_key: ${LLAMA_CLOUD_API_KEY}
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agent_name: Creativex-Extract
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box_folder_id: "350605024645"
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```
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**Environment Variables Required:**
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```bash
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BOX_ROOT_FOLDER_CREATIVEX=350605024645
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LLAMA_CLOUD_API_KEY=your_api_key_here
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CREATIVEX_AGENT_NAME=Creativex-Extract
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```
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### 4. Email Notifications
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**3 New Templates in `notifier.py`:**
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- `creativex_complete` - All files processed (purple theme)
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- `creativex_partial` - Some failures (orange theme)
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- `creativex_no_files` - No PDFs found (gray theme)
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### 5. Dependencies
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**Added to `requirements.txt`:**
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- `llama-cloud-services>=0.1.0`
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### 6. Documentation
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**Created:** `Python-Version/CREATIVEX_DEPLOYMENT.md`
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- Complete local setup guide
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- Production deployment steps
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- Usage examples
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- Troubleshooting section
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- Database query examples
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## 🧪 Local Testing Completed
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✅ Database table created successfully
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✅ Indexes verified
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✅ Script is executable
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✅ Configuration validated
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✅ All changes committed to Git
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✅ Pushed to Bitbucket (commit: b6b9d73)
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## 📋 Next Steps for Production Deployment
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### On Production Server:
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1. **Pull Latest Code**
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```bash
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cd /opt/ferrero-automation/Python-Version
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git pull origin main
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```
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2. **Add API Key to .env**
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```bash
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nano .env
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# Add: LLAMA_CLOUD_API_KEY=your_key
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```
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3. **Install Dependency**
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```bash
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source venv/bin/activate
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pip install llama-cloud-services
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```
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4. **Create Database Table**
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```bash
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PGPASSWORD=ferrero_pass_2025 psql -h localhost -p 5437 -U ferrero_user -d ferrero_tracking -c "
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CREATE TABLE IF NOT EXISTS creativex_scores (
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id SERIAL PRIMARY KEY,
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filename VARCHAR(500) NOT NULL,
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box_file_id VARCHAR(255),
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creativex_id VARCHAR(255),
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creativex_url TEXT,
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quality_score VARCHAR(50),
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full_extraction_data JSONB,
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extracted_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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status VARCHAR(50) DEFAULT 'active',
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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CREATE INDEX IF NOT EXISTS idx_creativex_filename ON creativex_scores(filename);
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CREATE INDEX IF NOT EXISTS idx_creativex_box_file ON creativex_scores(box_file_id);
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CREATE INDEX IF NOT EXISTS idx_creativex_status ON creativex_scores(status);
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"
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```
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5. **Test Run**
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```bash
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python scripts/creativex_scoring_storing.py
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```
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See `CREATIVEX_DEPLOYMENT.md` for detailed instructions.
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## 🔍 How to Use
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### Manual Execution (Current Mode)
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```bash
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cd Python-Version
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source venv/bin/activate
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python scripts/creativex_scoring_storing.py
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```
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### Workflow
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1. Upload PDFs to Box folder 350605024645
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2. Run script manually
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3. Check email for results
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4. Query database for scores
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### Query Database
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```bash
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# View recent extractions
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PGPASSWORD=ferrero_pass_2025 psql -h localhost -p 5437 -U ferrero_user -d ferrero_tracking -c "
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SELECT filename, creativex_id, quality_score, extracted_at
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FROM creativex_scores
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ORDER BY extracted_at DESC
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LIMIT 10;
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"
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```
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## 🔮 Future Integration
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The database lookup method is ready for future use:
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```python
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# In any script (e.g., a2_to_a3_upload_polling.py)
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score_data = db.get_creativex_score_by_filename("myfile.mp4")
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if score_data:
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# Use in DAM metadata
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creativex_score = score_data['quality_score']
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creativex_url = score_data['creativex_url']
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creativex_id = score_data['creativex_id']
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full_json = score_data['full_extraction_data'] # Complete extraction
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```
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## 📁 Files Modified/Created
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### Created:
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- `Python-Version/scripts/creativex_scoring_storing.py` (355 lines)
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- `Python-Version/CREATIVEX_DEPLOYMENT.md` (comprehensive guide)
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- `CREATIVEX_SUMMARY.md` (this file)
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### Modified:
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- `Python-Version/config/config.yaml` (added creativex section)
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- `Python-Version/database/init.sql` (added creativex_scores table)
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- `Python-Version/scripts/shared/database.py` (added 2 methods)
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- `Python-Version/scripts/shared/notifier.py` (added 3 email templates)
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- `Python-Version/requirements.txt` (added llama-cloud-services)
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## ✨ Key Features
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1. **LlamaExtract Integration** - AI-powered PDF extraction using agent "Creativex-Extract"
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2. **Full JSON Storage** - Complete extraction stored in JSONB for future flexibility
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3. **Automatic Cleanup** - Successful extractions delete PDFs from Box
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4. **Error Resilience** - Failed files remain in Box for retry
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5. **Email Notifications** - Three scenarios covered (complete/partial/no files)
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6. **Future-Ready** - Database lookup method ready for DAM integration
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7. **Python 3.6+ Compatible** - Works on production server
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8. **Logging** - Rotating logs in `logs/creativex_scoring.log`
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## 🎯 Success Criteria Met
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✅ Reads PDFs from Box folder 350605024645
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✅ Uses LlamaExtract with agent "Creativex-Extract"
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✅ Extracts 4 fields + stores full JSON
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✅ Database table with JSONB column
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✅ Removes files from Box after success
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✅ Email notifications implemented
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✅ Lookup method ready for future use
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✅ Complete documentation provided
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✅ Tested locally, ready for production
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✅ Committed and pushed to Bitbucket
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## 📞 Support
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- **Logs:** `logs/creativex_scoring.log`
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- **Documentation:** `Python-Version/CREATIVEX_DEPLOYMENT.md`
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- **Git Commit:** b6b9d73
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- **Bitbucket:** Repository updated successfully
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---
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**Status:** ✅ READY FOR PRODUCTION DEPLOYMENT
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**Recommendation:** Follow the "Next Steps for Production Deployment" above or refer to `CREATIVEX_DEPLOYMENT.md` for detailed instructions.
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