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.
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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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### 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.
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### Issue: JSON parsing fails
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**Solution**: Verify the JSON string is valid. Use `json.loads()` to test manually.
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### Issue: Database constraint violations
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**Solution**: Ensure unique investor names. The parser updates existing investors with the same name.
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## Future Enhancements
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1. **Parallel Processing**: Process multiple investors concurrently
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2. **Custom Exchange Rates**: Support historical rates based on `asOfDate`
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3. **Validation**: Add schema validation for JSON profiles
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4. **Caching**: Cache currency conversion results for identical amounts
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5. **Webhooks**: Notify when processing completes
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## Example Output
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```
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🚀 Starting to process 300 investors...
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📊 Processing 1/300: Anaxago
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✓ Parsed successfully
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- HQ: Paris, France
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- AUM: $935,000,000
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- Funds: 4
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- Team: 5
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✅ Saved to database (ID: 1234)
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📊 Processing 2/300: Bpifrance
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✓ Parsed successfully
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- HQ: Paris, France
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- AUM: Not Available
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- Funds: 8
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- Team: 12
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✅ Saved to database (ID: 1235)
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💾 Committed batch at row 10
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...
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🎉 Completed! Processed 298/300 investors
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```
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