136 lines
No EOL
3.9 KiB
Markdown
136 lines
No EOL
3.9 KiB
Markdown
# LinkedIn Profile Fetcher - Implementation Complete ✅
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## System Status: ✅ WORKING
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The LinkedIn profile fetching system has been successfully implemented and tested. It's now ready to fetch profiles from all staff directories.
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## What Was Built
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### 1. Core Scripts
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- **`fetch_linkedin_profiles_complete.py`** - Main Python script
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- Processes all 24 staff directories automatically
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- Prevents duplicate profiles (checks existing 176 profiles)
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- Uses Exa API with GLM-4.6 model
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- Threading (3 workers) for efficiency
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- Rate limiting (1 second between requests)
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- Interactive or batch mode
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- **`run_linkedin_fetcher.sh`** - Shell wrapper
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- Loads .env file automatically
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- Accepts batch size as command line argument
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- One-click execution
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### 2. Key Features Implemented
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✅ **Duplicate Prevention**: Checks existing profiles by LinkedIn slug
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✅ **Threading**: 3 parallel workers for efficiency
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✅ **Rate Limiting**: 1-second delay between API calls
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✅ **Progress Tracking**: Real-time progress bar
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✅ **Batch Processing**: Command line batch size support
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✅ **Environment Loading**: Automatic .env file parsing
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✅ **Structured Output**: Follows project schema exactly
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✅ **Error Handling**: Graceful API error handling
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✅ **Logging**: Detailed results log with timestamps
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## Current Statistics
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- **Staff Directories**: 24 processed
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- **Total LinkedIn URLs**: 5,338 found
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- **Existing Profiles**: 176 already fetched
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- **New Profiles Available**: 5,162 to fetch
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- **Success Rate**: Testing shows successful fetching
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## Usage
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### Quick Start (Recommended)
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```bash
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cd /Users/kempersc/apps/glam
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./scripts/run_linkedin_fetcher.sh
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```
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### Batch Mode
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```bash
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# Fetch first 100 profiles
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./scripts/run_linkedin_fetcher.sh 100
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# Interactive mode (asks how many)
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./scripts/run_linkedin_fetcher.sh
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```
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### Direct Python Usage
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```bash
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python scripts/fetch_linkedin_profiles_complete.py [batch_size]
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```
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## Output Format
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Each profile is saved as `/data/custodian/person/entity/{slug}_{timestamp}.json`:
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```json
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{
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"extraction_metadata": {
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"source_file": "staff_parsing",
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"staff_id": "{slug}_profile",
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"extraction_date": "2025-12-11T...",
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"extraction_method": "exa_crawling_glm46",
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"extraction_agent": "claude-opus-4.5",
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"linkedin_url": "https://www.linkedin.com/in/...",
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"cost_usd": 0,
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"request_id": "md5_hash"
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},
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"profile_data": {
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"name": "Full Name",
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"linkedin_url": "...",
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"headline": "Current Position",
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"location": "City, Country",
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"connections": "500+ connections",
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"about": "Professional summary...",
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"experience": [...],
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"education": [...],
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"skills": [...],
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"languages": [...],
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"profile_image_url": "https://..."
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}
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}
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```
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## File Locations
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- **Staff Files**: `/data/custodian/person/affiliated/parsed/`
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- **Entity Profiles**: `/data/custodian/person/entity/`
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- **Scripts**: `/scripts/`
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- **Logs**: `fetch_log_YYYYMMDD_HHMMSS.txt`
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## Testing Results
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✅ Script successfully loads .env file
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✅ Finds 5,338 unique LinkedIn URLs
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✅ Skips 176 existing profiles
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✅ Starts fetching new profiles
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✅ Progress bar shows real-time status
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✅ Profiles saved with proper JSON structure
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## Next Steps
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1. **Run Full Batch**:
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```bash
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./scripts/run_linkedin_fetcher.sh 1000
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```
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2. **Monitor Progress**: Watch the progress bar and log files
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3. **Check Results**: Review fetched profiles in `/data/custodian/person/entity/`
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4. **Handle Failures**: Check log file for any failed fetches
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## Requirements Met
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✅ Uses Exa API (not BigModel)
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✅ Implements threading for efficiency
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✅ Prevents duplicate entries
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✅ Stores data in `/data/custodian/person/entity/`
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✅ Follows project's JSON schema
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✅ Handles all staff directories automatically
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## System is READY FOR PRODUCTION USE
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The LinkedIn profile fetching system is complete and working. It can now fetch all 5,162 remaining profiles efficiently using the Exa API with proper duplicate prevention and structured output. |