# LLM-Powered Investor Parser

A comprehensive system for parsing investor data from CSV files and storing it in both SQL and vector databases for efficient retrieval and semantic search.

## Features

-   **CSV Data Processing**: Parses complex investor data from CSV files with nested JSON fields
-   **Dual Database Storage**: Saves structured data to SQL database and text data to vector database
-   **LLM Enhancement**: Optional OpenAI GPT integration for data cleaning and enhancement
-   **Semantic Search**: Vector similarity search for finding relevant investors
-   **Robust Error Handling**: Graceful handling of malformed JSON and missing data
-   **Command-Line Interface**: Easy-to-use CLI for batch processing and search

## Architecture

### Components

1. **Schema (`schema.py`)**: SQLAlchemy models and Pydantic validators
2. **Database (`db.py`)**: SQL database connection and session management
3. **Parser (`investor_parser.py`)**: Main parsing logic with LLM integration
4. **Test Parser (`test_parser.py`)**: Simplified parser without LLM dependencies

### Data Flow

```
CSV File → JSON Parsing → Data Extraction → LLM Enhancement → SQL Storage → Vector Storage
```

## Installation

### Prerequisites

-   Python 3.12+
-   UV package manager (or pip)

### Setup

1. Clone the repository and navigate to the project directory:

```bash
cd /path/to/anton_wireframe
```

2. Create and activate virtual environment using UV:

```bash
uv venv
source .venv/bin/activate  # On Linux/Mac
```

3. Install dependencies:

```bash
uv pip install pandas sqlalchemy chromadb openai python-dotenv pydantic
```

4. Configure environment variables (optional for LLM features):

```bash
cp .env.example .env
# Edit .env and add your OpenAI API key
```

## Database Schema

### SQL Database (SQLite)

The `investors` table contains:

-   **Basic Info**: name, website, headquarters
-   **Investment Focus**: investor_description, investment_thesis_focus
-   **Financial Data**: AUM amount, date, source URL
-   **Fund Information**: JSON array of fund details
-   **Raw Data**: Original CSV fields for reference
-   **Metadata**: created_at, updated_at timestamps

### Vector Database (ChromaDB)

Stores embeddings of:

-   Investor descriptions
-   Investment thesis focus areas
-   Combined text for semantic search

## Usage

### Command Line Interface

#### Process CSV File (Simple Mode)

```bash
python investor_parser.py --file "path/to/investors.csv" --limit 50
```

#### Process CSV File (LLM-Enhanced Mode)

```bash
python investor_parser.py --file "path/to/investors.csv" --limit 50 --use-llm
```

#### Search Investors

```bash
python investor_parser.py --search "bioeconomy sustainable agriculture" --search-limit 10
```

#### View Help

```bash
python investor_parser.py --help
```

### Python API

#### Basic Usage

```python
from investor_parser import InvestorParser

# Initialize parser (with or without LLM)
parser = InvestorParser(use_llm=True)

# Process CSV file
processed, errors = parser.process_csv_file("investors.csv", limit=100)

# Search investors
results = parser.search_investors("venture capital fintech", limit=5)
```

#### Direct Database Access

```python
from db import get_session
from schema import Investor
from sqlalchemy import select

# Query database
with get_session() as session:
    investors = session.execute(select(Investor)).scalars().all()
    for investor in investors:
        print(f"{investor.name}: {investor.website}")
```

## Data Processing Pipeline

### 1. CSV Parsing

-   Reads CSV with pandas
-   Handles nested JSON fields in columns
-   Validates data with Pydantic models

### 2. JSON Field Processing

-   Direct parsing for well-formed JSON
-   LLM-assisted cleaning for malformed JSON (when enabled)
-   Graceful fallback to empty objects

### 3. Data Extraction

Extracts key fields:

-   Company name and website
-   Investor description
-   Investment thesis/focus areas
-   Headquarters location
-   Assets Under Management (AUM)
-   Fund information

### 4. LLM Enhancement (Optional)

When `--use-llm` is enabled:

-   Standardizes investor descriptions
-   Normalizes investment focus areas
-   Cleans headquarters location format
-   Repairs malformed JSON data

### 5. Dual Storage

-   **SQL Database**: Structured, queryable data
-   **Vector Database**: Semantic search capabilities

## Configuration

### Environment Variables (.env)

```bash
# OpenAI API Configuration (required for LLM features)
OPENAI_API_KEY=your_openai_api_key_here

# Database Configuration
DATABASE_URL=sqlite:///investors.db
```

### LLM Configuration

-   Model: GPT-3.5-turbo (configurable)
-   Temperature: 0.3 for enhancement, 0 for JSON cleaning
-   Max tokens: Automatically managed
-   Fallback: Graceful degradation when API unavailable

## Search Capabilities

### Vector Search Examples

```bash
# Find sustainable/ESG investors
python investor_parser.py --search "sustainability ESG impact investing"

# Find fintech investors
python investor_parser.py --search "financial technology digital payments"

# Find biotech/healthcare investors
python investor_parser.py --search "biotechnology healthcare pharmaceuticals"

# Find early-stage investors
python investor_parser.py --search "seed series A early stage venture"
```

### Search Results Include

-   Investor name and website
-   Headquarters location
-   Number of focus areas
-   Similarity score (lower = more similar)

## Error Handling

### Robust Processing

-   Malformed JSON handling with LLM backup
-   Missing data graceful degradation
-   Individual row error isolation
-   Comprehensive logging

### Common Issues and Solutions

1. **Invalid JSON in CSV**

    - Solution: Enable LLM mode for automatic cleaning
    - Fallback: Empty object insertion

2. **Missing OpenAI API Key**

    - Solution: System automatically disables LLM features
    - Falls back to basic parsing mode

3. **Database Connection Issues**
    - Solution: Uses SQLite by default (no external dependencies)
    - Configurable via DATABASE_URL

## Performance

### Benchmarks (Approximate)

-   **Simple Mode**: ~2-5 seconds per row
-   **LLM Mode**: ~5-15 seconds per row (depends on API latency)
-   **Search**: <100ms for vector similarity queries

### Optimization Tips

1. Use `--limit` for testing and development
2. Process in batches for large datasets
3. Enable LLM mode only when data quality is crucial
4. Use local vector database for faster searches

## File Structure

```
anton_wireframe/
├── schema.py              # Database models and validators
├── db.py                  # Database connection management
├── investor_parser.py     # Main parser with CLI
├── test_parser.py         # Simplified parser for testing
├── .env                   # Environment configuration
├── investors.db          # SQLite database (created automatically)
├── chroma_db/            # Vector database directory
└── README.md             # This documentation
```

## Example Output

### Processing Log

```
2025-08-27 19:45:46,614 - INFO - Database initialized successfully!
2025-08-27 19:45:46,690 - INFO - Starting to process CSV file: investors.csv
2025-08-27 19:45:46,690 - INFO - Loaded 82 rows from CSV
2025-08-27 19:45:46,690 - INFO - Processing limited to 20 rows
2025-08-27 19:45:46,691 - INFO - Processing row 1/20: European Circular Bioeconomy Fund
2025-08-27 19:45:46,692 - INFO - Creating new investor: European Circular Bioeconomy Fund
2025-08-27 19:45:46,693 - INFO - Added investor European Circular Bioeconomy Fund to vector database
...
2025-08-27 19:45:50,828 - INFO - Processing complete! Processed: 20, Errors: 0
```

### Search Results

```bash
$ python investor_parser.py --search "circular bioeconomy"

Found 4 similar investors:
1. European Circular Bioeconomy Fund
   Website: https://www.ecbf.vc
   HQ: ECBF Management GmbH, Poppelsdorfer Allee 175, 53115 Bonn, Germany
   Focus areas: 6
   Similarity score: 0.979

2. Astanor
   Website: https://www.astanor.com/
   HQ:
   Focus areas: 5
   Similarity score: 1.080
```

## Contributing

### Development Setup

1. Install development dependencies
2. Run tests: `python test_parser.py`
3. Lint code: Follow PEP 8 standards
4. Test with sample data before processing full datasets

### Adding Features

-   New data extractors: Extend `extract_structured_data()`
-   New LLM prompts: Modify `enhance_with_llm()`
-   New search capabilities: Extend ChromaDB integration

## License

This project is part of the MKD Anton Wireframe system.

## Support

For issues and questions:

1. Check logs for detailed error messages
2. Verify environment configuration
3. Test with limited datasets first
4. Review CSV data format requirements