Implement LLM-powered Investor Parser with CSV processing, SQL and vector database integration
- Added FastAPI application with a simple root endpoint. - Developed LLMInvestorParser class for processing investor data from CSV files. - Integrated OpenAI API for LLM enhancements and JSON cleaning. - Implemented structured data extraction and saving to SQL database. - Added functionality to save investor descriptions to ChromaDB for vector similarity search. - Created command-line interface for processing files and searching investors. - Added schema definitions for Investor and related data models using SQLAlchemy and Pydantic. - Implemented logging for better traceability and error handling. - Included requirements.txt for dependency management.
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# LLM-Powered Investor Parser
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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.
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## Features
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- **CSV Data Processing**: Parses complex investor data from CSV files with nested JSON fields
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- **Dual Database Storage**: Saves structured data to SQL database and text data to vector database
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- **LLM Enhancement**: Optional OpenAI GPT integration for data cleaning and enhancement
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- **Semantic Search**: Vector similarity search for finding relevant investors
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- **Robust Error Handling**: Graceful handling of malformed JSON and missing data
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- **Command-Line Interface**: Easy-to-use CLI for batch processing and search
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## Architecture
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### Components
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1. **Schema (`schema.py`)**: SQLAlchemy models and Pydantic validators
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2. **Database (`db.py`)**: SQL database connection and session management
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3. **Parser (`investor_parser.py`)**: Main parsing logic with LLM integration
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4. **Test Parser (`test_parser.py`)**: Simplified parser without LLM dependencies
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### Data Flow
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```
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CSV File → JSON Parsing → Data Extraction → LLM Enhancement → SQL Storage → Vector Storage
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```
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## Installation
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### Prerequisites
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- Python 3.12+
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- UV package manager (or pip)
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### Setup
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1. Clone the repository and navigate to the project directory:
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```bash
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cd /path/to/anton_wireframe
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```
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2. Create and activate virtual environment using UV:
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```bash
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uv venv
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source .venv/bin/activate # On Linux/Mac
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```
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3. Install dependencies:
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```bash
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uv pip install pandas sqlalchemy chromadb openai python-dotenv pydantic
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```
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4. Configure environment variables (optional for LLM features):
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```bash
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cp .env.example .env
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# Edit .env and add your OpenAI API key
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```
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## Database Schema
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### SQL Database (SQLite)
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The `investors` table contains:
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- **Basic Info**: name, website, headquarters
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- **Investment Focus**: investor_description, investment_thesis_focus
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- **Financial Data**: AUM amount, date, source URL
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- **Fund Information**: JSON array of fund details
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- **Raw Data**: Original CSV fields for reference
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- **Metadata**: created_at, updated_at timestamps
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### Vector Database (ChromaDB)
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Stores embeddings of:
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- Investor descriptions
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- Investment thesis focus areas
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- Combined text for semantic search
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## Usage
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### Command Line Interface
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#### Process CSV File (Simple Mode)
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```bash
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python investor_parser.py --file "path/to/investors.csv" --limit 50
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```
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#### Process CSV File (LLM-Enhanced Mode)
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```bash
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python investor_parser.py --file "path/to/investors.csv" --limit 50 --use-llm
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```
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#### Search Investors
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```bash
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python investor_parser.py --search "bioeconomy sustainable agriculture" --search-limit 10
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```
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#### View Help
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```bash
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python investor_parser.py --help
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```
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### Python API
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#### Basic Usage
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```python
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from investor_parser import InvestorParser
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# Initialize parser (with or without LLM)
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parser = InvestorParser(use_llm=True)
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# Process CSV file
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processed, errors = parser.process_csv_file("investors.csv", limit=100)
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# Search investors
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results = parser.search_investors("venture capital fintech", limit=5)
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```
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#### Direct Database Access
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```python
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from db import get_session
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from schema import Investor
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from sqlalchemy import select
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# Query database
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with get_session() as session:
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investors = session.execute(select(Investor)).scalars().all()
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for investor in investors:
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print(f"{investor.name}: {investor.website}")
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```
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## Data Processing Pipeline
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### 1. CSV Parsing
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- Reads CSV with pandas
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- Handles nested JSON fields in columns
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- Validates data with Pydantic models
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### 2. JSON Field Processing
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- Direct parsing for well-formed JSON
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- LLM-assisted cleaning for malformed JSON (when enabled)
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- Graceful fallback to empty objects
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### 3. Data Extraction
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Extracts key fields:
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- Company name and website
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- Investor description
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- Investment thesis/focus areas
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- Headquarters location
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- Assets Under Management (AUM)
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- Fund information
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### 4. LLM Enhancement (Optional)
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When `--use-llm` is enabled:
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- Standardizes investor descriptions
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- Normalizes investment focus areas
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- Cleans headquarters location format
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- Repairs malformed JSON data
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### 5. Dual Storage
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- **SQL Database**: Structured, queryable data
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- **Vector Database**: Semantic search capabilities
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## Configuration
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### Environment Variables (.env)
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```bash
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# OpenAI API Configuration (required for LLM features)
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OPENAI_API_KEY=your_openai_api_key_here
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# Database Configuration
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DATABASE_URL=sqlite:///investors.db
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```
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### LLM Configuration
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- Model: GPT-3.5-turbo (configurable)
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- Temperature: 0.3 for enhancement, 0 for JSON cleaning
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- Max tokens: Automatically managed
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- Fallback: Graceful degradation when API unavailable
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## Search Capabilities
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### Vector Search Examples
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```bash
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# Find sustainable/ESG investors
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python investor_parser.py --search "sustainability ESG impact investing"
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# Find fintech investors
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python investor_parser.py --search "financial technology digital payments"
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# Find biotech/healthcare investors
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python investor_parser.py --search "biotechnology healthcare pharmaceuticals"
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# Find early-stage investors
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python investor_parser.py --search "seed series A early stage venture"
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```
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### Search Results Include
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- Investor name and website
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- Headquarters location
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- Number of focus areas
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- Similarity score (lower = more similar)
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## Error Handling
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### Robust Processing
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- Malformed JSON handling with LLM backup
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- Missing data graceful degradation
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- Individual row error isolation
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- Comprehensive logging
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### Common Issues and Solutions
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1. **Invalid JSON in CSV**
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- Solution: Enable LLM mode for automatic cleaning
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- Fallback: Empty object insertion
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2. **Missing OpenAI API Key**
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- Solution: System automatically disables LLM features
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- Falls back to basic parsing mode
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3. **Database Connection Issues**
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- Solution: Uses SQLite by default (no external dependencies)
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- Configurable via DATABASE_URL
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## Performance
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### Benchmarks (Approximate)
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- **Simple Mode**: ~2-5 seconds per row
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- **LLM Mode**: ~5-15 seconds per row (depends on API latency)
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- **Search**: <100ms for vector similarity queries
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### Optimization Tips
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1. Use `--limit` for testing and development
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2. Process in batches for large datasets
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3. Enable LLM mode only when data quality is crucial
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4. Use local vector database for faster searches
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## File Structure
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```
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anton_wireframe/
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├── schema.py # Database models and validators
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├── db.py # Database connection management
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├── investor_parser.py # Main parser with CLI
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├── test_parser.py # Simplified parser for testing
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├── .env # Environment configuration
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├── investors.db # SQLite database (created automatically)
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├── chroma_db/ # Vector database directory
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└── README.md # This documentation
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```
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## Example Output
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### Processing Log
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```
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2025-08-27 19:45:46,614 - INFO - Database initialized successfully!
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2025-08-27 19:45:46,690 - INFO - Starting to process CSV file: investors.csv
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2025-08-27 19:45:46,690 - INFO - Loaded 82 rows from CSV
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2025-08-27 19:45:46,690 - INFO - Processing limited to 20 rows
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2025-08-27 19:45:46,691 - INFO - Processing row 1/20: European Circular Bioeconomy Fund
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2025-08-27 19:45:46,692 - INFO - Creating new investor: European Circular Bioeconomy Fund
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2025-08-27 19:45:46,693 - INFO - Added investor European Circular Bioeconomy Fund to vector database
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...
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2025-08-27 19:45:50,828 - INFO - Processing complete! Processed: 20, Errors: 0
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```
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### Search Results
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```bash
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$ python investor_parser.py --search "circular bioeconomy"
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Found 4 similar investors:
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1. European Circular Bioeconomy Fund
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Website: https://www.ecbf.vc
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HQ: ECBF Management GmbH, Poppelsdorfer Allee 175, 53115 Bonn, Germany
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Focus areas: 6
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Similarity score: 0.979
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2. Astanor
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Website: https://www.astanor.com/
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HQ:
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Focus areas: 5
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Similarity score: 1.080
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```
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## Contributing
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### Development Setup
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1. Install development dependencies
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2. Run tests: `python test_parser.py`
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3. Lint code: Follow PEP 8 standards
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4. Test with sample data before processing full datasets
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### Adding Features
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- New data extractors: Extend `extract_structured_data()`
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- New LLM prompts: Modify `enhance_with_llm()`
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- New search capabilities: Extend ChromaDB integration
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## License
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This project is part of the MKD Anton Wireframe system.
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## Support
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For issues and questions:
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1. Check logs for detailed error messages
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2. Verify environment configuration
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3. Test with limited datasets first
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4. Review CSV data format requirements
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