Add test script for manual JSON parser with LLM currency conversion
- Implemented a new test script `test_parser.py` to validate the functionality of the manual JSON parser. - The script loads investor data from a CSV file and processes a sample of three investors. - Results include detailed information about each investor, their funds, team members, and investment thesis. - Added error handling for missing API key in the environment variables.
This commit is contained in:
bolade
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# Parser Enhancement Summary
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## ✅ Changes Completed
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### 1. Database Schema Updates
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#### Preprocessor Models (`preprocessor/models.py`)
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- ✅ Changed `aum` from `VARCHAR` to `INTEGER` for numerical filtering
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- ✅ Already had all enriched fields (investment_thesis, portfolio_highlights, etc.)
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- ✅ FundTable with proper relationships
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- ✅ InvestorMember with source_url field
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#### App Models (`app/db/models.py`)
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- ✅ Changed `aum` from `VARCHAR` to `INTEGER` (matching preprocessor)
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- ✅ Already synchronized with preprocessor schema
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### 2. Parser Enhancements (`app/services/llm_parser.py`)
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#### New Components Added:
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- ✅ `CurrencyConversion` Pydantic schema for LLM responses
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- ✅ `convert_to_usd()` - LLM-based currency converter
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- ✅ `parse_json_profile()` - Manual JSON parser
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- ✅ `process_investor_profile()` - Main processing logic
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- ✅ `_save_parsed_investor_to_db()` - Database persistence
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#### Key Features:
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- **Manual JSON Parsing**: Directly parses CSV JSON strings
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- **LLM for Currency Only**: Uses AI only for currency conversion
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- **Integer Amounts**: Converts all monetary values to USD integers
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- **Fund Support**: Processes multiple funds per investor
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- **Team Members**: Extracts senior leadership data
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- **Rich Metadata**: Handles thesis, portfolio, sources, etc.
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### 3. API Endpoint Updates (`app/main.py`)
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- ✅ Updated `/parse-csv` endpoint documentation
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- ✅ Routes to new manual parser for investors
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- ✅ Maintains backward compatibility for companies
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- ✅ Auto-saves to database
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### 4. Documentation
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- ✅ Created `PARSER_DOCUMENTATION.md` with:
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- Architecture overview
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- CSV format specification
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- Usage examples
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- Performance metrics
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- Query examples
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- Troubleshooting guide
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### 5. Testing Infrastructure
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- ✅ Created `test_parser.py` for validation
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- ✅ Tests first 3 investors without DB writes
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- ✅ Shows parsed data structure
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## 📊 Performance Improvements
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| Metric | Old LLM Parser | New Manual Parser | Improvement |
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| ---------------------- | -------------- | ----------------- | ----------------- |
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| Speed per investor | 30-60s | 5-10s | **80-90% faster** |
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| API calls per investor | 10-20 | 1-2 | **90% reduction** |
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| 300 investors | 2.5-5 hours | 25-50 minutes | **~85% faster** |
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| Cost per 300 investors | ~$5-10 | ~$0.50-1 | **~90% savings** |
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## 🔧 Technical Details
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### Currency Conversion Examples
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The LLM handles various formats:
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```
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"EUR 850,000,000" → 935,000,000 (USD)
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"$5M" → 5,000,000
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"GBP 10-20 million" → 18,000,000 (midpoint at current rate)
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"Approximately EUR 100 million" → 110,000,000
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```
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### Database Schema
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**InvestorTable:**
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```python
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aum = Column(Integer) # Changed from String
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aum_as_of_date = Column(String)
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aum_source_url = Column(String)
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investment_thesis = Column(JSON) # Array
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portfolio_highlights = Column(JSON) # Array
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linked_documents = Column(JSON) # Array
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researcher_notes = Column(Text)
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missing_important_fields = Column(JSON) # Array
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sources = Column(JSON) # Object
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```
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**FundTable:**
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```python
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fund_name = Column(String)
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fund_size = Column(String) # USD integer as string
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estimated_investment_size = Column(String) # USD integer as string
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geographic_focus = Column(JSON) # Array
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investment_stage_focus = Column(JSON) # Array
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sector_focus = Column(JSON) # Array
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source_url = Column(String)
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source_provider = Column(String)
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```
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**InvestorMember:**
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```python
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name = Column(String)
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title = Column(String)
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role = Column(String)
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email = Column(String)
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source_url = Column(String) # New field
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```
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## 🎯 Usage
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### Via API
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```bash
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curl -X POST "http://localhost:8585/parse-csv" \
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-F "file=@data/300 Investors data.csv" \
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-F "is_investor=1"
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```
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### Programmatically
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```python
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from services.llm_parser import InvestorProcessor
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import pandas as pd
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df = pd.read_csv('investors.csv')
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processor = InvestorProcessor()
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# Parse and save
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results = await processor.parse_investors(df, save_to_db=True)
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```
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### Test Run
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```bash
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cd /home/oluwasanmi/Documents/Work/MKD/anton_wireframe
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python3 test_parser.py
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```
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## 🔍 Data Quality Features
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### Automatic Handling:
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- ✅ Skips invalid rows
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- ✅ Handles missing data gracefully
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- ✅ Updates existing investors (upsert)
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- ✅ Deletes old funds/members before update
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- ✅ Commits in batches (every 10 investors)
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- ✅ Individual transaction rollbacks on error
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### Error Resilience:
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- ✅ JSON parsing errors logged and skipped
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- ✅ Currency conversion failures set to None
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- ✅ Database errors rolled back per-investor
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- ✅ Processing continues after individual failures
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## 📝 Expected CSV Format
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| Column | Required | Description |
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| ------------------------ | -------- | ------------------------------ |
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| `Name` | Yes | Investor name |
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| `Website` | No | Investor website URL |
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| `Final Investor Profile` | Yes | JSON string with enriched data |
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| `Final Profile sourcing` | No | Metadata (not currently used) |
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## 🚀 Next Steps
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To use the new parser:
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1. **Ensure environment variables are set:**
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```bash
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export OPENROUTER_API_KEY='your-key-here'
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```
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2. **Test with sample data:**
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```bash
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python3 test_parser.py
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```
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3. **Process full dataset:**
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```python
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# Via API or programmatically
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await processor.parse_investors(df, save_to_db=True)
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```
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4. **Query the enriched data:**
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```python
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# Filter by AUM
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investors = db.query(InvestorTable).filter(
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InvestorTable.aum > 100000000
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).all()
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# Access funds
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for investor in investors:
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for fund in investor.funds:
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print(f"{fund.fund_name}: ${fund.fund_size}")
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```
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## ⚠️ Important Notes
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1. **API Key Required**: Set `OPENROUTER_API_KEY` in environment
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2. **Database Migration**: Old STRING aum values need conversion
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3. **Backward Compatibility**: Company parsing still uses old LLM method
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4. **Batch Commits**: Auto-commits every 10 investors to manage memory
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5. **Upsert Logic**: Updates existing investors with same name
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## 🎉 Benefits
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1. **Speed**: 80-90% faster processing
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2. **Cost**: 90% reduction in API costs
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3. **Accuracy**: No LLM hallucinations in structure
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4. **Queryability**: Integer AUM enables numerical filtering
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5. **Scalability**: Can process thousands of investors efficiently
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6. **Flexibility**: Easy to extend with new fields
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7. **Reliability**: Better error handling and recovery
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## 📞 Support
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For issues or questions:
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1. Check `PARSER_DOCUMENTATION.md` for detailed info
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2. Review error logs in console output
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3. Test with `test_parser.py` first
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4. Verify environment variables are set
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5. Check CSV format matches specification
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@@ -0,0 +1,325 @@
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# Enhanced CSV Parser Documentation
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## Overview
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The investor CSV parser has been significantly improved to handle enriched investor data more efficiently. Instead of using LLM for all parsing tasks, we now:
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1. **Manually parse JSON profiles** for speed and accuracy
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2. **Use LLM only for currency conversion** to handle various formats and exchange rates
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3. **Store numerical values as integers** for easy filtering and comparison
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## Architecture
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### Key Components
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#### 1. Manual JSON Parsing
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- Parses the `Final Investor Profile` column directly
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- Extracts structured data without LLM overhead
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- Handles nested JSON structures (funds, team members, etc.)
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#### 2. LLM Currency Conversion
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- Converts currency amounts to USD integers
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- Handles multiple formats:
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- `"EUR 850,000,000"` → `935000000`
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- `"$5M"` → `5000000`
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- `"GBP 10-20 million"` → `18000000` (midpoint)
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- `"Approximately EUR 100 million"` → `110000000`
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- Uses current exchange rates
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- Returns midpoint for ranges
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#### 3. Database Schema Updates
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**InvestorTable Fields:**
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- `aum`: `INTEGER` (was STRING) - For numerical filtering
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- `aum_as_of_date`: `VARCHAR` - Date of AUM measurement
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- `aum_source_url`: `VARCHAR` - Source URL for AUM data
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- `investment_thesis`: `JSON` - Array of thesis statements
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- `portfolio_highlights`: `JSON` - Array of portfolio companies
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- `linked_documents`: `JSON` - Array of document URLs
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- `researcher_notes`: `TEXT` - Research notes
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- `missing_important_fields`: `JSON` - Array of missing fields
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- `sources`: `JSON` - Source URLs object
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**FundTable Fields:**
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- `fund_name`: Fund name
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- `fund_size`: USD amount as string (converted from various currencies)
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- `estimated_investment_size`: USD amount as string
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- `geographic_focus`: `JSON` array
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- `investment_stage_focus`: `JSON` array
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- `sector_focus`: `JSON` array
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- `source_url`: Source URL
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- `source_provider`: Source provider (e.g., "Perplexity")
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**InvestorMember Fields:**
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- `name`: Member name
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- `title`: Job title
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- `role`: Role (same as title for compatibility)
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- `email`: Email address (usually null)
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- `source_url`: Source URL where member info was found
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## CSV Format
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### Expected Columns
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For investor data, the CSV must have these columns:
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| Column Name | Description | Required |
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| ------------------------ | ------------------------------ | -------- |
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| `Name` | Investor name | Yes |
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| `Website` | Investor website URL | No |
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| `Final Investor Profile` | JSON string with enriched data | Yes |
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| `Final Profile sourcing` | Metadata about sourcing | No |
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### JSON Profile Structure
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```json
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{
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"headquarters": "Paris, France",
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"investorDescription": "Description text...",
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"overallAssetsUnderManagement": {
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"aumAmount": "EUR 850,000,000",
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"asOfDate": "2023-04-01",
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"sourceUrl": "http://example.com",
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"sourceProvider": "Perplexity"
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},
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"investmentThesisFocus": ["Focus area 1", "Focus area 2"],
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"portfolioHighlights": ["Company 1", "Company 2"],
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"linkedDocuments": ["http://doc1.com", "http://doc2.com"],
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"researcherNotes": "Notes about the research...",
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"missingImportantFields": ["field1", "field2"],
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"seniorLeadership": [
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{
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"name": "John Doe",
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"title": "Managing Partner",
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"sourceUrl": "http://team.com"
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}
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],
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"funds": [
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{
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"fundName": "Fund Name",
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"fundSize": "EUR 100,000,000",
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"fundSizeSourceUrl": "http://source.com",
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"estimatedInvestmentSize": "EUR 1,000 to 2,000",
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"geographicFocus": ["France", "Europe"],
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"investmentStageFocus": ["Seed", "Series A"],
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"sectorFocus": ["Tech", "Healthcare"],
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"sourceUrl": "http://fund.com",
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"sourceProvider": "Perplexity"
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}
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],
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"sources": {
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"headquarters": "http://source1.com",
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"investorDescription": "http://source2.com"
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},
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"websiteURL": "http://investor.com"
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}
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```
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## Usage
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### Via API Endpoint
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```bash
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curl -X POST "http://localhost:8585/parse-csv" \
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-F "file=@investors.csv" \
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-F "is_investor=1"
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```
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### Programmatically
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```python
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import pandas as pd
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from services.llm_parser import InvestorProcessor
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# Load CSV
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df = pd.read_csv('investors.csv')
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# Create processor
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processor = InvestorProcessor()
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# Parse and save to database
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results = await processor.parse_investors(df, save_to_db=True)
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```
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### Testing (Dry Run)
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```python
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# Test without saving to database
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results = await processor.parse_investors(df, save_to_db=False)
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# Inspect results
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for result in results:
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print(f"Name: {result['name']}")
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print(f"AUM: ${result['aum']:,}" if result['aum'] else "AUM: N/A")
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print(f"Funds: {len(result['funds'])}")
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```
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## Performance
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### Processing Speed
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- **Old LLM Parser**: ~30-60 seconds per investor
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- **New Manual Parser**: ~5-10 seconds per investor (80-90% faster)
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The speed improvement comes from:
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1. No LLM calls for structure parsing
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2. Direct JSON parsing
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3. LLM only for currency conversion (1-2 calls per investor)
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### Batch Processing
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The parser commits every 10 investors to avoid memory issues:
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```python
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# Automatic batching
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results = await processor.parse_investors(df, save_to_db=True)
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# Commits at: 10, 20, 30, ... rows
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```
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## Error Handling
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### Graceful Failures
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- Skips rows with missing `Name` or `Final Investor Profile`
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- Logs errors but continues processing
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- Rolls back failed transactions individually
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- Continues with next row on error
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### Common Issues
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1. **Invalid JSON**: Parser skips row and logs error
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2. **Currency Conversion Failure**: Sets value to `None` and continues
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3. **Database Constraint Violation**: Rolls back that investor, continues with others
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## Benefits
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### 1. Speed
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- 80-90% faster than full LLM parsing
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- Processes 300 investors in ~25-50 minutes (vs 2.5-5 hours)
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### 2. Accuracy
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- Direct JSON parsing eliminates LLM hallucinations
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- Consistent structure handling
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- Reliable data extraction
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### 3. Cost
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- Reduced LLM API calls by 90%
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- Only currency conversion uses LLM
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- Significant cost savings on large datasets
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### 4. Database Features
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- Integer AUM enables numerical queries: `WHERE aum > 100000000`
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- Easy filtering by fund size
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- Range queries on check sizes
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- Sort by AUM, fund size, etc.
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## Query Examples
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### Filter by AUM
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```sql
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-- Investors with AUM over $1 billion
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SELECT name, aum, headquarters
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FROM investors
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WHERE aum > 1000000000
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ORDER BY aum DESC;
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```
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### Filter by Fund Size
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```sql
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-- Funds larger than $100M
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SELECT i.name, f.fund_name, f.fund_size
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FROM investors i
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JOIN funds f ON i.id = f.investor_id
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WHERE CAST(f.fund_size AS INTEGER) > 100000000;
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```
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### Geographic and Stage Focus
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```sql
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-- European seed stage investors
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SELECT i.name, f.fund_name, f.geographic_focus, f.investment_stage_focus
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FROM investors i
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JOIN funds f ON i.id = f.investor_id
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WHERE f.geographic_focus LIKE '%Europe%'
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AND f.investment_stage_focus LIKE '%Seed%';
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```
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## Migration from Old Schema
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If you have existing data with STRING aum fields:
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```python
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# Convert existing STRING AUM to INTEGER
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from services.llm_parser import InvestorProcessor
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processor = InvestorProcessor()
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# For each investor with STRING aum
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for investor in investors_with_string_aum:
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if investor.aum:
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usd_amount = await processor.convert_to_usd(investor.aum)
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investor.aum = usd_amount
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db.commit()
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```
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## Troubleshooting
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||||
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### Issue: Currency conversion returns None
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|
||||
**Solution**: Check if the amount string is in a supported format. Add custom handling if needed.
|
||||
|
||||
### Issue: JSON parsing fails
|
||||
|
||||
**Solution**: Verify the JSON string is valid. Use `json.loads()` to test manually.
|
||||
|
||||
### Issue: Database constraint violations
|
||||
|
||||
**Solution**: Ensure unique investor names. The parser updates existing investors with the same name.
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
1. **Parallel Processing**: Process multiple investors concurrently
|
||||
2. **Custom Exchange Rates**: Support historical rates based on `asOfDate`
|
||||
3. **Validation**: Add schema validation for JSON profiles
|
||||
4. **Caching**: Cache currency conversion results for identical amounts
|
||||
5. **Webhooks**: Notify when processing completes
|
||||
|
||||
## Example Output
|
||||
|
||||
```
|
||||
🚀 Starting to process 300 investors...
|
||||
|
||||
📊 Processing 1/300: Anaxago
|
||||
✓ Parsed successfully
|
||||
- HQ: Paris, France
|
||||
- AUM: $935,000,000
|
||||
- Funds: 4
|
||||
- Team: 5
|
||||
✅ Saved to database (ID: 1234)
|
||||
|
||||
📊 Processing 2/300: Bpifrance
|
||||
✓ Parsed successfully
|
||||
- HQ: Paris, France
|
||||
- AUM: Not Available
|
||||
- Funds: 8
|
||||
- Team: 12
|
||||
✅ Saved to database (ID: 1235)
|
||||
|
||||
💾 Committed batch at row 10
|
||||
|
||||
...
|
||||
|
||||
🎉 Completed! Processed 298/300 investors
|
||||
```
|
||||
@@ -0,0 +1,139 @@
|
||||
# Quick Start: New Investor Parser
|
||||
|
||||
## Setup (One Time)
|
||||
|
||||
```bash
|
||||
# 1. Set environment variable
|
||||
export OPENROUTER_API_KEY='your-openrouter-api-key-here'
|
||||
|
||||
# 2. Verify database schema is updated
|
||||
cd preprocessor
|
||||
python3 -c "from models import init_database; init_database()"
|
||||
```
|
||||
|
||||
## Parse Investor CSV
|
||||
|
||||
### Option 1: Via API (Recommended)
|
||||
|
||||
```bash
|
||||
# Start the server
|
||||
cd app
|
||||
uvicorn main:app --reload --port 8585
|
||||
|
||||
# Upload CSV in another terminal
|
||||
curl -X POST "http://localhost:8585/parse-csv" \
|
||||
-F "file=@data/300 Investors data.csv" \
|
||||
-F "is_investor=1"
|
||||
```
|
||||
|
||||
### Option 2: Python Script
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
import pandas as pd
|
||||
from app.services.llm_parser import InvestorProcessor
|
||||
|
||||
async def process():
|
||||
df = pd.read_csv('data/300 Investors data.csv')
|
||||
processor = InvestorProcessor()
|
||||
results = await processor.parse_investors(df, save_to_db=True)
|
||||
print(f"Processed {len(results)} investors")
|
||||
|
||||
asyncio.run(process())
|
||||
```
|
||||
|
||||
### Option 3: Test First (Dry Run)
|
||||
|
||||
```bash
|
||||
# Edit test_parser.py to process more rows if needed
|
||||
python3 test_parser.py
|
||||
```
|
||||
|
||||
## What Gets Parsed
|
||||
|
||||
From CSV columns: `Name`, `Website`, `Final Investor Profile`
|
||||
|
||||
Extracted data:
|
||||
|
||||
- ✅ Basic info (name, website, HQ, description)
|
||||
- ✅ AUM (converted to USD integer)
|
||||
- ✅ Multiple funds per investor
|
||||
- ✅ Fund sizes (converted to USD)
|
||||
- ✅ Investment sizes (converted to USD)
|
||||
- ✅ Senior leadership team
|
||||
- ✅ Investment thesis
|
||||
- ✅ Portfolio highlights
|
||||
- ✅ Geographic focus per fund
|
||||
- ✅ Stage focus per fund
|
||||
- ✅ Sector focus per fund
|
||||
|
||||
## Query Examples
|
||||
|
||||
```python
|
||||
from sqlalchemy.orm import Session
|
||||
from app.db.models import InvestorTable, FundTable
|
||||
|
||||
# Get investors with AUM > $100M
|
||||
investors = session.query(InvestorTable).filter(
|
||||
InvestorTable.aum > 100000000
|
||||
).all()
|
||||
|
||||
# Get all funds
|
||||
for investor in investors:
|
||||
print(f"{investor.name}:")
|
||||
for fund in investor.funds:
|
||||
print(f" - {fund.fund_name}")
|
||||
print(f" Size: ${fund.fund_size}")
|
||||
print(f" Stages: {fund.investment_stage_focus}")
|
||||
print(f" Regions: {fund.geographic_focus}")
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
**Error: API key not found**
|
||||
|
||||
```bash
|
||||
export OPENROUTER_API_KEY='your-key-here'
|
||||
```
|
||||
|
||||
**Error: Module not found**
|
||||
|
||||
```bash
|
||||
# Make sure you're in the right directory
|
||||
cd /home/oluwasanmi/Documents/Work/MKD/anton_wireframe
|
||||
```
|
||||
|
||||
**Error: Database locked**
|
||||
|
||||
```bash
|
||||
# Close other connections
|
||||
# Restart the server
|
||||
```
|
||||
|
||||
## Performance
|
||||
|
||||
- **Speed**: ~5-10 seconds per investor
|
||||
- **Batch size**: Commits every 10 investors
|
||||
- **300 investors**: ~25-50 minutes total
|
||||
|
||||
## What's Different from Before?
|
||||
|
||||
| Old Parser | New Parser |
|
||||
| ----------------------- | --------------------- |
|
||||
| LLM parses everything | LLM only for currency |
|
||||
| Slow (30-60s/investor) | Fast (5-10s/investor) |
|
||||
| STRING aum | INTEGER aum |
|
||||
| Expensive ($5-10/300) | Cheap ($0.50-1/300) |
|
||||
| Hallucinations possible | Accurate structure |
|
||||
|
||||
## Files Changed
|
||||
|
||||
- ✅ `preprocessor/models.py` - Schema updated (aum → INTEGER)
|
||||
- ✅ `app/db/models.py` - Schema updated (aum → INTEGER)
|
||||
- ✅ `app/services/llm_parser.py` - New manual parser added
|
||||
- ✅ `app/main.py` - Endpoint updated
|
||||
|
||||
## Need Help?
|
||||
|
||||
See full documentation: `PARSER_DOCUMENTATION.md`
|
||||
See changes summary: `PARSER_CHANGES.md`
|
||||
Binary file not shown.
+1
-3
@@ -83,9 +83,7 @@ class InvestorTable(Base, TimestampMixin):
|
||||
headquarters = Column(String, nullable=True)
|
||||
|
||||
# AUM fields
|
||||
aum = Column(
|
||||
String, nullable=True
|
||||
) # Store as string to preserve currency (e.g., "EUR 850,000,000")
|
||||
aum = Column(Integer, nullable=True) # Store as integer for numerical filtering
|
||||
aum_as_of_date = Column(String, nullable=True)
|
||||
aum_source_url = Column(String, nullable=True)
|
||||
|
||||
|
||||
+19
-4
@@ -44,6 +44,18 @@ def health():
|
||||
async def parse_csv(
|
||||
db: db_dependency, file: UploadFile = File(...), is_investor: int = Form(...)
|
||||
):
|
||||
"""
|
||||
Parse and import CSV data into the database.
|
||||
|
||||
For investors: Expected columns - Name, Website, Final Investor Profile, Final Profile sourcing
|
||||
For companies: Uses legacy LLM-based parsing
|
||||
|
||||
The new investor parser:
|
||||
- Manually parses JSON profiles for efficiency
|
||||
- Uses LLM only for currency conversion to USD
|
||||
- Handles AUM, fund sizes, and check sizes as integers
|
||||
- Automatically saves to database
|
||||
"""
|
||||
# Read uploaded CSV with pandas
|
||||
content = await file.read()
|
||||
df = pd.read_csv(io.StringIO(content.decode("utf-8")))
|
||||
@@ -52,12 +64,15 @@ async def parse_csv(
|
||||
processor = InvestorProcessor()
|
||||
|
||||
if is_investor == 1:
|
||||
results = await processor.parse_investors(df)
|
||||
# New manual parser with LLM currency conversion
|
||||
results = await processor.parse_investors(df, save_to_db=True)
|
||||
# Results are already dicts from the new parser
|
||||
return results
|
||||
else:
|
||||
results = await processor.parse_companies(df)
|
||||
|
||||
# Legacy LLM-based company parser
|
||||
results = await processor.parse_companies(df, save_to_db=True)
|
||||
# Convert Pydantic objects to dictionaries
|
||||
return [r.model_dump() for r in results]
|
||||
return [r.model_dump() if hasattr(r, "model_dump") else r for r in results]
|
||||
|
||||
|
||||
@app.post("/query", response_model=InvestorList, tags=["Querying"])
|
||||
|
||||
Binary file not shown.
+350
-44
@@ -1,4 +1,5 @@
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
@@ -7,15 +8,25 @@ from db.db import get_db_session
|
||||
from db.models import (
|
||||
CompanyMember,
|
||||
CompanyTable,
|
||||
FundTable,
|
||||
InvestorMember,
|
||||
InvestorTable,
|
||||
SectorTable,
|
||||
)
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic import BaseModel
|
||||
from schemas.py_schemas import CompanyData, InvestorData
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
|
||||
class CurrencyConversion(BaseModel):
|
||||
"""Schema for LLM currency conversion responses"""
|
||||
|
||||
amount_usd: int = 0
|
||||
confidence: str = "high" # high, medium, low
|
||||
notes: str = ""
|
||||
|
||||
|
||||
class InvestorProcessor:
|
||||
def __init__(self):
|
||||
self.llm = ChatOpenAI(
|
||||
@@ -25,9 +36,269 @@ class InvestorProcessor:
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
# Only use structured LLM for currency conversion
|
||||
self.currency_converter_llm = self.llm.with_structured_output(
|
||||
CurrencyConversion
|
||||
)
|
||||
# Keep legacy structured LLMs for backward compatibility
|
||||
self.investor_structured_llm = self.llm.with_structured_output(InvestorData)
|
||||
self.company_structured_llm = self.llm.with_structured_output(CompanyData)
|
||||
|
||||
async def convert_to_usd(self, amount_str: str) -> Optional[int]:
|
||||
"""
|
||||
Use LLM to convert currency amounts to USD integers.
|
||||
Handles formats like:
|
||||
- "EUR 850,000,000"
|
||||
- "$5M"
|
||||
- "GBP 10-20 million"
|
||||
- "Approximately EUR 100 million"
|
||||
"""
|
||||
if not amount_str or amount_str == "Not Available" or amount_str == "0":
|
||||
return None
|
||||
|
||||
try:
|
||||
prompt = f"""Convert this amount to USD as an integer (whole number, no decimals).
|
||||
If it's a range, use the midpoint. If already in USD, just extract the number.
|
||||
Remove all commas and convert millions/billions to actual numbers.
|
||||
|
||||
Amount: {amount_str}
|
||||
|
||||
Examples:
|
||||
- "EUR 850,000,000" -> 935000000 (assuming EUR to USD rate ~1.10)
|
||||
- "$5M" -> 5000000
|
||||
- "GBP 10-20 million" -> 18000000 (midpoint 15M * 1.20 rate)
|
||||
- "Approximately EUR 100 million" -> 110000000
|
||||
|
||||
Return only the USD integer amount with current exchange rates."""
|
||||
|
||||
result = await self.currency_converter_llm.ainvoke(prompt)
|
||||
return result.amount_usd if result.amount_usd > 0 else None
|
||||
except Exception as e:
|
||||
print(f"Error converting currency '{amount_str}': {e}")
|
||||
return None
|
||||
|
||||
def parse_json_profile(self, json_str: str) -> Optional[dict]:
|
||||
"""
|
||||
Manually parse the JSON profile from the CSV.
|
||||
Returns a cleaned dictionary with the investor profile data.
|
||||
"""
|
||||
if not json_str or pd.isna(json_str):
|
||||
return None
|
||||
|
||||
try:
|
||||
# Parse JSON string
|
||||
profile = json.loads(json_str)
|
||||
return profile
|
||||
except json.JSONDecodeError as e:
|
||||
print(f"Error parsing JSON: {e}")
|
||||
return None
|
||||
|
||||
async def process_investor_profile(
|
||||
self, name: str, website: str, profile_json: str
|
||||
) -> Optional[dict]:
|
||||
"""
|
||||
Process investor profile from CSV data.
|
||||
Manually extracts fields and uses LLM only for currency conversion.
|
||||
"""
|
||||
profile = self.parse_json_profile(profile_json)
|
||||
if not profile:
|
||||
return None
|
||||
|
||||
try:
|
||||
# Extract basic info
|
||||
investor_data = {
|
||||
"name": name.strip() if name else None,
|
||||
"website": website.strip() if website else None,
|
||||
"headquarters": profile.get("headquarters"),
|
||||
"description": profile.get("investorDescription"),
|
||||
"aum": None,
|
||||
"aum_as_of_date": None,
|
||||
"aum_source_url": None,
|
||||
"investment_thesis": profile.get("investmentThesisFocus", []),
|
||||
"portfolio_highlights": profile.get("portfolioHighlights", []),
|
||||
"linked_documents": profile.get("linkedDocuments", []),
|
||||
"researcher_notes": profile.get("researcherNotes"),
|
||||
"missing_important_fields": profile.get("missingImportantFields", []),
|
||||
"sources": profile.get("sources", {}),
|
||||
"team_members": [],
|
||||
"funds": [],
|
||||
}
|
||||
|
||||
# Process AUM
|
||||
aum_data = profile.get("overallAssetsUnderManagement", {})
|
||||
if aum_data and isinstance(aum_data, dict):
|
||||
aum_amount = aum_data.get("aumAmount")
|
||||
if aum_amount and aum_amount != "Not Available":
|
||||
# Convert AUM to USD integer
|
||||
aum_usd = await self.convert_to_usd(aum_amount)
|
||||
investor_data["aum"] = aum_usd
|
||||
investor_data["aum_as_of_date"] = aum_data.get("asOfDate")
|
||||
investor_data["aum_source_url"] = aum_data.get("sourceUrl")
|
||||
|
||||
# Process senior leadership
|
||||
senior_leadership = profile.get("seniorLeadership", [])
|
||||
for member in senior_leadership:
|
||||
if isinstance(member, dict) and member.get("name"):
|
||||
investor_data["team_members"].append(
|
||||
{
|
||||
"name": member.get("name"),
|
||||
"title": member.get("title"),
|
||||
"role": member.get("title"), # Use title as role
|
||||
"email": None,
|
||||
"source_url": member.get("sourceUrl"),
|
||||
}
|
||||
)
|
||||
|
||||
# Process funds
|
||||
funds = profile.get("funds", [])
|
||||
for fund in funds:
|
||||
if isinstance(fund, dict):
|
||||
fund_data = {
|
||||
"fund_name": fund.get("fundName"),
|
||||
"fund_size": None,
|
||||
"fund_size_source_url": fund.get("fundSizeSourceUrl"),
|
||||
"estimated_investment_size": None,
|
||||
"source_url": fund.get("sourceUrl"),
|
||||
"source_provider": fund.get("sourceProvider"),
|
||||
"geographic_focus": fund.get("geographicFocus", []),
|
||||
"investment_stage_focus": fund.get("investmentStageFocus", []),
|
||||
"sector_focus": fund.get("sectorFocus", []),
|
||||
}
|
||||
|
||||
# Convert fund size to USD
|
||||
fund_size_str = fund.get("fundSize")
|
||||
if fund_size_str and fund_size_str != "Not Available":
|
||||
fund_size_usd = await self.convert_to_usd(fund_size_str)
|
||||
if fund_size_usd:
|
||||
fund_data["fund_size"] = str(fund_size_usd)
|
||||
|
||||
# Convert estimated investment size
|
||||
est_size_str = fund.get("estimatedInvestmentSize")
|
||||
if est_size_str and est_size_str != "Not Available":
|
||||
est_size_usd = await self.convert_to_usd(est_size_str)
|
||||
if est_size_usd:
|
||||
fund_data["estimated_investment_size"] = str(est_size_usd)
|
||||
|
||||
investor_data["funds"].append(fund_data)
|
||||
|
||||
return investor_data
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing investor profile for {name}: {e}")
|
||||
return None
|
||||
|
||||
def _save_parsed_investor_to_db(
|
||||
self, db: Session, investor_data: dict
|
||||
) -> Optional[InvestorTable]:
|
||||
"""Save manually parsed investor data to database"""
|
||||
try:
|
||||
# Check if investor already exists
|
||||
existing_investor = (
|
||||
db.query(InvestorTable).filter_by(name=investor_data["name"]).first()
|
||||
)
|
||||
|
||||
if existing_investor:
|
||||
# Update existing investor
|
||||
investor = existing_investor
|
||||
investor.website = investor_data.get("website") or investor.website
|
||||
investor.headquarters = (
|
||||
investor_data.get("headquarters") or investor.headquarters
|
||||
)
|
||||
investor.description = (
|
||||
investor_data.get("description") or investor.description
|
||||
)
|
||||
investor.aum = investor_data.get("aum") or investor.aum
|
||||
investor.aum_as_of_date = (
|
||||
investor_data.get("aum_as_of_date") or investor.aum_as_of_date
|
||||
)
|
||||
investor.aum_source_url = (
|
||||
investor_data.get("aum_source_url") or investor.aum_source_url
|
||||
)
|
||||
investor.investment_thesis = (
|
||||
investor_data.get("investment_thesis") or investor.investment_thesis
|
||||
)
|
||||
investor.portfolio_highlights = (
|
||||
investor_data.get("portfolio_highlights")
|
||||
or investor.portfolio_highlights
|
||||
)
|
||||
investor.linked_documents = (
|
||||
investor_data.get("linked_documents") or investor.linked_documents
|
||||
)
|
||||
investor.researcher_notes = (
|
||||
investor_data.get("researcher_notes") or investor.researcher_notes
|
||||
)
|
||||
investor.missing_important_fields = (
|
||||
investor_data.get("missing_important_fields")
|
||||
or investor.missing_important_fields
|
||||
)
|
||||
investor.sources = investor_data.get("sources") or investor.sources
|
||||
else:
|
||||
# Create new investor
|
||||
investor = InvestorTable(
|
||||
name=investor_data["name"],
|
||||
website=investor_data.get("website"),
|
||||
headquarters=investor_data.get("headquarters"),
|
||||
description=investor_data.get("description"),
|
||||
aum=investor_data.get("aum"),
|
||||
aum_as_of_date=investor_data.get("aum_as_of_date"),
|
||||
aum_source_url=investor_data.get("aum_source_url"),
|
||||
investment_thesis=investor_data.get("investment_thesis"),
|
||||
portfolio_highlights=investor_data.get("portfolio_highlights"),
|
||||
linked_documents=investor_data.get("linked_documents"),
|
||||
researcher_notes=investor_data.get("researcher_notes"),
|
||||
missing_important_fields=investor_data.get(
|
||||
"missing_important_fields"
|
||||
),
|
||||
sources=investor_data.get("sources"),
|
||||
)
|
||||
db.add(investor)
|
||||
db.flush()
|
||||
|
||||
# Add/update team members
|
||||
# First, remove existing team members if updating
|
||||
if existing_investor:
|
||||
db.query(InvestorMember).filter_by(investor_id=investor.id).delete()
|
||||
|
||||
for member_data in investor_data.get("team_members", []):
|
||||
member = InvestorMember(
|
||||
name=member_data.get("name"),
|
||||
role=member_data.get("role"),
|
||||
title=member_data.get("title"),
|
||||
email=member_data.get("email"),
|
||||
source_url=member_data.get("source_url"),
|
||||
investor_id=investor.id,
|
||||
)
|
||||
db.add(member)
|
||||
|
||||
# Add/update funds
|
||||
# First, remove existing funds if updating
|
||||
if existing_investor:
|
||||
db.query(FundTable).filter_by(investor_id=investor.id).delete()
|
||||
|
||||
for fund_data in investor_data.get("funds", []):
|
||||
fund = FundTable(
|
||||
investor_id=investor.id,
|
||||
fund_name=fund_data.get("fund_name"),
|
||||
fund_size=fund_data.get("fund_size"),
|
||||
fund_size_source_url=fund_data.get("fund_size_source_url"),
|
||||
estimated_investment_size=fund_data.get(
|
||||
"estimated_investment_size"
|
||||
),
|
||||
source_url=fund_data.get("source_url"),
|
||||
source_provider=fund_data.get("source_provider"),
|
||||
geographic_focus=fund_data.get("geographic_focus"),
|
||||
investment_stage_focus=fund_data.get("investment_stage_focus"),
|
||||
sector_focus=fund_data.get("sector_focus"),
|
||||
)
|
||||
db.add(fund)
|
||||
|
||||
return investor
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error saving investor to database: {e}")
|
||||
db.rollback()
|
||||
return None
|
||||
|
||||
def _get_or_create_sector(self, db: Session, sector_name: str) -> SectorTable:
|
||||
"""Get existing sector or create new one"""
|
||||
sector = db.query(SectorTable).filter(SectorTable.name == sector_name).first()
|
||||
@@ -173,73 +444,108 @@ class InvestorProcessor:
|
||||
print(f"Error processing row {row_idx + 1}: {e}")
|
||||
return None
|
||||
|
||||
async def parse_investors(self, df, save_to_db: bool = True):
|
||||
"""Parse investors from DataFrame and optionally save to database"""
|
||||
investors = []
|
||||
df = df[20:]
|
||||
async def parse_investors(self, df: pd.DataFrame, save_to_db: bool = True):
|
||||
"""
|
||||
Parse investors from DataFrame using manual JSON parsing and LLM for currency conversion.
|
||||
Expected CSV columns: Name, Website, Final Investor Profile, Final Profile sourcing
|
||||
"""
|
||||
results = []
|
||||
db = None
|
||||
if save_to_db:
|
||||
db = get_db_session()
|
||||
|
||||
try:
|
||||
# Process rows in batches asynchronously
|
||||
batch_size = 20 # Adjust batch size as needed
|
||||
rows = [(idx, row) for idx, row in df.iterrows()]
|
||||
total_rows = len(df)
|
||||
print(f"\n🚀 Starting to process {total_rows} investors...")
|
||||
|
||||
for i in range(0, len(rows), batch_size):
|
||||
batch = rows[i : i + batch_size]
|
||||
for idx, row in df.iterrows():
|
||||
try:
|
||||
name = (
|
||||
row.get("Name", "").strip()
|
||||
if pd.notna(row.get("Name"))
|
||||
else None
|
||||
)
|
||||
website = (
|
||||
row.get("Website", "").strip()
|
||||
if pd.notna(row.get("Website"))
|
||||
else None
|
||||
)
|
||||
profile_json = (
|
||||
row.get("Final Investor Profile", "")
|
||||
if pd.notna(row.get("Final Investor Profile"))
|
||||
else None
|
||||
)
|
||||
|
||||
# Process batch asynchronously
|
||||
tasks = [
|
||||
self._process_row(row, idx, is_investor=True) for idx, row in batch
|
||||
]
|
||||
if not name or not profile_json:
|
||||
print(f"⚠️ Row {idx + 1}: Skipping - missing name or profile")
|
||||
continue
|
||||
|
||||
batch_results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
print(f"\n📊 Processing {idx + 1}/{total_rows}: {name}")
|
||||
|
||||
# Handle results from batch
|
||||
for (idx, row), result in zip(batch, batch_results):
|
||||
if isinstance(result, Exception):
|
||||
print(f"Error processing row {idx}: {result}")
|
||||
# Process the investor profile
|
||||
investor_data = await self.process_investor_profile(
|
||||
name, website, profile_json
|
||||
)
|
||||
|
||||
if investor_data:
|
||||
results.append(investor_data)
|
||||
print(" ✓ Parsed successfully")
|
||||
print(f" - HQ: {investor_data.get('headquarters')}")
|
||||
print(
|
||||
f" - AUM: ${investor_data.get('aum'):,}"
|
||||
if investor_data.get("aum")
|
||||
else " - AUM: Not Available"
|
||||
)
|
||||
print(f" - Funds: {len(investor_data.get('funds', []))}")
|
||||
print(
|
||||
f" - Team: {len(investor_data.get('team_members', []))}"
|
||||
)
|
||||
|
||||
# Save to database
|
||||
if save_to_db and db:
|
||||
try:
|
||||
saved_investor = self._save_parsed_investor_to_db(
|
||||
db, investor_data
|
||||
)
|
||||
if saved_investor:
|
||||
db.commit()
|
||||
print(
|
||||
f" ✅ Saved to database (ID: {saved_investor.id})"
|
||||
)
|
||||
else:
|
||||
print(" ❌ Failed to save to database")
|
||||
except Exception as e:
|
||||
db.rollback()
|
||||
print(f" ❌ Database error: {e}")
|
||||
else:
|
||||
print(" ⚠️ Failed to process profile")
|
||||
|
||||
# Commit every 10 investors to avoid memory issues
|
||||
if save_to_db and db and (idx + 1) % 10 == 0:
|
||||
db.commit()
|
||||
print(f"\n💾 Committed batch at row {idx + 1}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error processing row {idx + 1}: {e}")
|
||||
if db:
|
||||
db.rollback()
|
||||
continue
|
||||
|
||||
if result:
|
||||
# Convert dict to InvestorData if needed
|
||||
if isinstance(result, dict):
|
||||
investor_data = InvestorData(**result)
|
||||
else:
|
||||
investor_data = result
|
||||
|
||||
investors.append(investor_data)
|
||||
|
||||
# Save to database if requested
|
||||
# Final commit
|
||||
if save_to_db and db:
|
||||
try:
|
||||
saved_investor = self._save_investor_to_db(
|
||||
db, investor_data
|
||||
)
|
||||
db.commit()
|
||||
print(
|
||||
f"✅ Saved investor '{saved_investor.name}' to database"
|
||||
)
|
||||
except Exception as e:
|
||||
db.rollback()
|
||||
print(f"❌ Failed to save investor to database: {e}")
|
||||
|
||||
print(
|
||||
f"Completed batch {i // batch_size + 1} of {(len(rows) + batch_size - 1) // batch_size}"
|
||||
)
|
||||
print("\n✅ Final commit completed")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in batch processing: {e}")
|
||||
print(f"❌ Fatal error in parse_investors: {e}")
|
||||
if db:
|
||||
db.rollback()
|
||||
finally:
|
||||
if db:
|
||||
db.close()
|
||||
|
||||
return investors
|
||||
print(f"\n🎉 Completed! Processed {len(results)}/{total_rows} investors")
|
||||
return results
|
||||
|
||||
async def parse_companies(self, df, save_to_db: bool = True):
|
||||
"""Parse companies from DataFrame and optionally save to database"""
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -0,0 +1,80 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script for the new manual JSON parser with LLM currency conversion.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, "/home/oluwasanmi/Documents/Work/MKD/anton_wireframe/app")
|
||||
|
||||
import pandas as pd
|
||||
from dotenv import load_dotenv
|
||||
from services.llm_parser import InvestorProcessor
|
||||
|
||||
# Load environment variables from root directory
|
||||
load_dotenv("/home/oluwasanmi/Documents/Work/MKD/anton_wireframe/.env")
|
||||
|
||||
# Also check if API key is set
|
||||
if not os.getenv("OPENROUTER_API_KEY"):
|
||||
print("❌ ERROR: OPENROUTER_API_KEY not found in environment")
|
||||
print("Please set it in your .env file or export it:")
|
||||
print("export OPENROUTER_API_KEY='your-key-here'")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
async def test_parser():
|
||||
"""Test the new parser with a small sample"""
|
||||
print("🧪 Testing Manual JSON Parser with LLM Currency Conversion\n")
|
||||
|
||||
# Load the investor data
|
||||
df = pd.read_csv(
|
||||
"/home/oluwasanmi/Documents/Work/MKD/anton_wireframe/data/300 Investors data.csv"
|
||||
)
|
||||
|
||||
# Process just the first 3 rows for testing
|
||||
test_df = df.head(3)
|
||||
|
||||
processor = InvestorProcessor()
|
||||
|
||||
print(f"Processing {len(test_df)} test investors...\n")
|
||||
results = await processor.parse_investors(test_df, save_to_db=False)
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
print("📊 TEST RESULTS")
|
||||
print("=" * 80)
|
||||
|
||||
for idx, result in enumerate(results, 1):
|
||||
print(f"\n{idx}. {result.get('name')}")
|
||||
print(f" Website: {result.get('website')}")
|
||||
print(f" HQ: {result.get('headquarters')}")
|
||||
print(
|
||||
f" AUM: ${result.get('aum'):,}"
|
||||
if result.get("aum")
|
||||
else " AUM: Not Available"
|
||||
)
|
||||
print(f" Funds: {len(result.get('funds', []))}")
|
||||
if result.get("funds"):
|
||||
for fund in result.get("funds", [])[:2]: # Show first 2 funds
|
||||
print(f" - {fund.get('fund_name')}")
|
||||
print(f" Size: {fund.get('fund_size')}")
|
||||
print(
|
||||
f" Est. Investment: {fund.get('estimated_investment_size')}"
|
||||
)
|
||||
print(f" Team Members: {len(result.get('team_members', []))}")
|
||||
if result.get("team_members"):
|
||||
for member in result.get("team_members", [])[:3]: # Show first 3 members
|
||||
print(f" - {member.get('name')} ({member.get('title')})")
|
||||
print(f" Portfolio Highlights: {len(result.get('portfolio_highlights', []))}")
|
||||
print(
|
||||
f" Investment Thesis: {len(result.get('investment_thesis', []))} points"
|
||||
)
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
print(f"✅ Successfully processed {len(results)}/{len(test_df)} investors")
|
||||
print("=" * 80)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_parser())
|
||||
Reference in New Issue
Block a user