Refactor investor and company management API with FastAPI integration
- Updated README.md to reflect new features and architecture. - Implemented company management routes in app/api/companies.py. - Enhanced main FastAPI application in app/main.py to include company routes and query processing. - Improved querying capabilities in app/services/querying.py with natural language processing for investor searches. - Updated requirements.txt to include necessary dependencies for FastAPI and related libraries. - Added comprehensive error handling and response formatting for API endpoints.
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# LLM-Powered Investor Parser
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# LLM-Powered Investor & Company Management API
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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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A comprehensive FastAPI-based system for managing investor and company data with LLM-powered CSV parsing, semantic search, and advanced filtering capabilities.
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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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- **FastAPI REST API**: Modern, auto-documented API with OpenAPI/Swagger support
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- **CSV Data Processing**: Parse complex investor data from CSV files using LLM assistance
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- **Dual Database Storage**: Structured data in SQL database and semantic search via ChromaDB
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- **Natural Language Queries**: AI-powered query processing for complex investor searches
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- **Advanced Filtering**: Filter investors and companies by multiple criteria
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- **Relationship Management**: Many-to-many relationships between investors, companies, and sectors
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- **Auto-Generated Documentation**: Interactive API docs at `/docs`
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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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1. **FastAPI Application (`app/main.py`)**: Main API server with route configuration
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2. **Database Models (`app/db/models.py`)**: SQLAlchemy models for investors, companies, sectors
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3. **Pydantic Schemas (`app/py_schemas.py`)**: Request/response validation and serialization
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4. **API Routes**:
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- `app/api/investors.py`: Investor CRUD operations and filtering
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- `app/api/companies.py`: Company CRUD operations and filtering
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5. **Services**:
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- `app/services/openrouter.py`: LLM-powered CSV processing
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- `app/services/querying.py`: Natural language query processing
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6. **Database (`app/db/`)**: Database connection, models, and schemas
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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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CSV Upload → LLM Processing → Data Extraction → SQL Storage → Vector Storage → API Endpoints
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↓
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Natural Language Query → AI Analysis → Database Filtering → Structured Response
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```
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## Installation
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@@ -31,7 +40,7 @@ CSV File → JSON Parsing → Data Extraction → LLM Enhancement → SQL Storag
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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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- FastAPI and dependencies
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### Setup
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@@ -41,104 +50,244 @@ CSV File → JSON Parsing → Data Extraction → LLM Enhancement → SQL Storag
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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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2. Install dependencies:
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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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pip install -r requirements.txt
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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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3. Configure environment variables:
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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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# Edit .env and add your OpenRouter API key for LLM features
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```
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4. Initialize the database:
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```bash
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cd app
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python -c "from db.db import init_database; init_database()"
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```
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5. Start the API server:
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```bash
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cd app
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uvicorn main:app --reload --host localhost --port 8000
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```
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The API will be available at:
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- **API Base**: http://localhost:8000
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- **Interactive Docs**: http://localhost:8000/docs
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- **ReDoc**: http://localhost:8000/redoc
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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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#### Investors Table
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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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- **Basic Info**: name, description, geographic_focus
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- **Investment Data**: aum, check_size_lower, check_size_upper
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- **Stage Focus**: investment stage (SEED, SERIES_A, etc.)
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- **Relationships**: Many-to-many with companies and sectors
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- **Team**: One-to-many with team members
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- **Metadata**: created_at, updated_at timestamps
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#### Companies Table
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- **Basic Info**: name, industry, location
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- **Details**: founded_year, website
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- **Relationships**: Many-to-many with investors
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- **Metadata**: created_at, updated_at timestamps
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#### Association Tables
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- **investor_companies**: Links investors to their portfolio companies
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- **investor_sectors**: Links investors to their focus sectors
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- **investor_team**: Team member details for each investor
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#### Supporting Tables
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- **sectors**: Investment focus areas (fintech, healthcare, etc.)
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### Vector Database (ChromaDB)
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Stores embeddings of:
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Stores embeddings for semantic search 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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- Combined investor profiles
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## Usage
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## API Usage
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### Command Line Interface
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### Interactive Documentation
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#### Process CSV File (Simple Mode)
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Visit http://localhost:8000/docs for the auto-generated Swagger UI where you can:
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- Explore all endpoints
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- Test API calls directly
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- View request/response schemas
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- See example requests
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### Core Endpoints
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#### Investor Management
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```bash
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python investor_parser.py --file "path/to/investors.csv" --limit 50
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# Get all investors with relationships
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GET /investors
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# Filter investors by criteria
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GET /investors/filter?stage=GROWTH&geography=US§or=fintech&min_check_size=1000000
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# Get specific investor
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GET /investors/{investor_id}
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# Create new investor
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POST /investors
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{
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"name": "Example VC",
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"description": "Early stage fintech investor",
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"aum": 50000000,
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"check_size_lower": 100000,
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"check_size_upper": 2000000,
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"geographic_focus": "US",
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"stage_focus": "SEED",
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"number_of_investments": 25
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}
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# Update investor
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PUT /investors/{investor_id}
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# Delete investor
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DELETE /investors/{investor_id}
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```
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#### Process CSV File (LLM-Enhanced Mode)
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#### Company Management
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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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# Get all companies with investor relationships
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GET /companies
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# Filter companies by criteria
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GET /companies/filter?industry=fintech&location=San Francisco&founded_after=2015
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# Get specific company
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GET /companies/{company_id}
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# Create new company
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POST /companies
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{
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"name": "Example Startup",
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"industry": "fintech",
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"location": "San Francisco",
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"founded_year": 2020,
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"website": "https://example.com"
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}
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# Update company
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PUT /companies/{company_id}
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# Delete company
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DELETE /companies/{company_id}
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```
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#### Search Investors
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#### CSV Processing
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```bash
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python investor_parser.py --search "bioeconomy sustainable agriculture" --search-limit 10
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# Upload and process CSV file
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POST /parse-csv
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Content-Type: multipart/form-data
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File: investors.csv
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```
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#### View Help
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#### Natural Language Queries
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```bash
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python investor_parser.py --help
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# Query investors using natural language
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POST /query
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{
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"question": "Show me growth stage fintech investors in Silicon Valley with check sizes over $1 million"
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}
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```
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### Python API
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### Advanced Filtering Examples
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#### Basic Usage
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#### Investor Filters
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```python
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from investor_parser import InvestorParser
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```bash
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# Early stage investors in Europe
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GET /investors/filter?stage=SEED&geography=Europe
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# Initialize parser (with or without LLM)
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parser = InvestorParser(use_llm=True)
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# High AUM growth investors
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GET /investors/filter?stage=GROWTH&min_aum=100000000
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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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# Healthcare investors with large checks
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GET /investors/filter?sector=healthcare&min_check_size=5000000
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# Search investors
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results = parser.search_investors("venture capital fintech", limit=5)
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# Specific geographic focus
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GET /investors/filter?geography=Silicon Valley
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```
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#### Direct Database Access
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#### Company Filters
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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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```bash
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# Recent fintech companies
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GET /companies/filter?industry=fintech&founded_after=2020
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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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# Companies with websites
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GET /companies/filter?has_website=true
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# Companies backed by specific investor
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GET /companies/filter?investor_name=Sequoia
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# Location-based filtering
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GET /companies/filter?location=New York
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```
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### Response Format
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All endpoints return structured JSON with full relationship data:
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```json
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{
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"investor": {
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"id": 1,
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"name": "Example VC",
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"description": "Early stage investor",
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"aum": 50000000,
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"check_size_lower": 100000,
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"check_size_upper": 2000000,
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"geographic_focus": "US",
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"stage_focus": "SEED",
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"number_of_investments": 25
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},
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"portfolio_companies": [
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{
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"id": 1,
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"name": "StartupCo",
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"industry": "fintech",
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"location": "San Francisco"
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}
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],
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"team_members": [
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{
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"id": 1,
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"name": "John Partner",
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"role": "Managing Partner",
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"email": "john@examplevc.com"
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}
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],
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"sectors": [
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{
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"id": 1,
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"name": "fintech"
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}
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]
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}
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```
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## Data Processing Pipeline
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@@ -185,148 +334,234 @@ When `--use-llm` is enabled:
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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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# OpenRouter API Configuration (required for LLM features)
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OPENROUTER_API_KEY=your_openrouter_api_key_here
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# Database Configuration
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DATABASE_URL=sqlite:///investors.db
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# Database Configuration (optional, defaults to SQLite)
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DATABASE_URL=sqlite:///investors_2.db
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# FastAPI Configuration
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API_HOST=localhost
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API_PORT=8000
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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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- **Provider**: OpenRouter (supports multiple models)
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- **Default Model**: google/gemini-2.5-flash-lite
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- **Temperature**: 0.3 for enhancement, 0 for structured data
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- **Fallback**: Graceful degradation when API unavailable
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## Search Capabilities
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## Natural Language Query Processing
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### Vector Search Examples
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The system supports intelligent natural language queries that automatically extract filters and search criteria:
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### Query 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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# Stage-based queries
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"Show me seed stage investors"
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"Find growth stage VCs"
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# Find fintech investors
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python investor_parser.py --search "financial technology digital payments"
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# Geographic queries
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"Investors in Silicon Valley"
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"European venture capital firms"
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# Find biotech/healthcare investors
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python investor_parser.py --search "biotechnology healthcare pharmaceuticals"
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# Sector-specific queries
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"Fintech investors"
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"Healthcare and biotech VCs"
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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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# Size-based queries
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"Investors with $5M+ check sizes"
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"High AUM growth investors"
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# Combined queries
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"Growth stage fintech investors in the US with check sizes over $1 million"
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"European healthcare investors focusing on early stage"
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```
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### Search Results Include
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### Query Processing Features
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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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- **Automatic Filter Extraction**: Detects investment stages, geographies, sectors, and check sizes
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- **Semantic Understanding**: Uses AI to interpret complex queries
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- **Database Integration**: Combines AI analysis with efficient SQL filtering
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- **Complete Relationships**: Returns full investor data with portfolio companies, team members, and sectors
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### Query Response
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The `/query` endpoint returns a structured `InvestorList` with complete relationship data, making it easy to get comprehensive information about matching investors.
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## Error Handling
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### API Error Responses
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The API provides clear HTTP status codes and error messages:
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```json
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// 404 Not Found
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{
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"detail": "Investor not found"
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}
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// 422 Validation Error
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{
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"detail": [
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{
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"loc": ["body", "stage_focus"],
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"msg": "value is not a valid enumeration member",
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"type": "type_error.enum"
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}
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||||
]
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}
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```
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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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- **Data Validation**: Pydantic models ensure data integrity
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- **Relationship Management**: Automatic handling of foreign key constraints
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- **LLM Fallbacks**: Graceful degradation when AI services unavailable
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- **Transaction Safety**: Database rollbacks on errors
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- **Comprehensive Logging**: Detailed error tracking and debugging
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### Common Issues and Solutions
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1. **Invalid JSON in CSV**
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1. **Invalid Enum Values**
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- Solution: Enable LLM mode for automatic cleaning
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- Fallback: Empty object insertion
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- Solution: Use uppercase enum values (SEED, GROWTH, etc.)
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- Check: Investment stages must match defined enum
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2. **Missing OpenAI API Key**
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2. **Missing OpenRouter 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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- Solution: Set OPENROUTER_API_KEY in environment
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- Fallback: CSV processing continues without LLM enhancement
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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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- Solution: Verify DATABASE_URL configuration
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- Default: Uses SQLite (no external dependencies)
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4. **Relationship Errors**
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- Solution: Ensure proper foreign key relationships
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- Check: Use existing sector/company IDs or create new ones
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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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- **API Response Time**: <200ms for standard queries
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- **Database Queries**: <50ms for filtered searches with relationships
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- **CSV Processing**: ~5-15 seconds per row (depends on LLM API latency)
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- **Natural Language Queries**: ~2-5 seconds (AI processing + database query)
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- **Vector Search**: <100ms for semantic similarity queries
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### Optimization Tips
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### Optimization Features
|
||||
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||||
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
|
||||
1. **Eager Loading**: Efficient relationship loading with `selectinload()`
|
||||
2. **Query Optimization**: Smart filtering to reduce database load
|
||||
3. **Caching**: Database connection pooling and session management
|
||||
4. **Pagination**: Built-in limits to prevent overwhelming responses
|
||||
5. **Async Processing**: FastAPI async capabilities for better performance
|
||||
|
||||
### Production Recommendations
|
||||
|
||||
1. **Database**: Consider PostgreSQL for production workloads
|
||||
2. **Caching**: Add Redis for frequently accessed data
|
||||
3. **Load Balancing**: Deploy multiple API instances behind a load balancer
|
||||
4. **Monitoring**: Implement logging and metrics collection
|
||||
5. **Rate Limiting**: Add API rate limiting for public endpoints
|
||||
|
||||
## 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)
|
||||
├── app/
|
||||
│ ├── main.py # FastAPI application and main endpoints
|
||||
│ ├── py_schemas.py # Pydantic models for validation
|
||||
│ ├── settings.py # Configuration management
|
||||
│ ├── api/
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── investors.py # Investor CRUD and filtering endpoints
|
||||
│ │ └── companies.py # Company CRUD and filtering endpoints
|
||||
│ ├── db/
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── db.py # Database connection and session management
|
||||
│ │ ├── models.py # SQLAlchemy database models
|
||||
│ │ └── new_schema.py # Additional schema definitions
|
||||
│ └── services/
|
||||
│ ├── __init__.py
|
||||
│ ├── openrouter.py # LLM-powered CSV processing
|
||||
│ ├── querying.py # Natural language query processing
|
||||
│ └── langgraph_agent.py # AI agent configuration
|
||||
├── chroma_db/ # Vector database directory
|
||||
└── README.md # This documentation
|
||||
├── requirements.txt # Python dependencies
|
||||
├── README.md # This documentation
|
||||
└── .env # Environment configuration
|
||||
```
|
||||
|
||||
## Example Output
|
||||
## Example Usage Scenarios
|
||||
|
||||
### 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
|
||||
### 1. Upload and Process Investor Data
|
||||
|
||||
```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
|
||||
# Upload CSV file via API
|
||||
curl -X POST "http://localhost:8000/parse-csv" \
|
||||
-H "Content-Type: multipart/form-data" \
|
||||
-F "file=@investors.csv"
|
||||
```
|
||||
|
||||
## Contributing
|
||||
### 2. Find Specific Investors
|
||||
|
||||
### Development Setup
|
||||
```bash
|
||||
# Natural language search
|
||||
curl -X POST "http://localhost:8000/query" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"question": "Show me growth stage fintech investors in Silicon Valley with check sizes over $2 million"}'
|
||||
|
||||
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
|
||||
# Structured filtering
|
||||
curl "http://localhost:8000/investors/filter?stage=GROWTH§or=fintech&geography=Silicon%20Valley&min_check_size=2000000"
|
||||
```
|
||||
|
||||
### Adding Features
|
||||
### 3. Company Research
|
||||
|
||||
- New data extractors: Extend `extract_structured_data()`
|
||||
- New LLM prompts: Modify `enhance_with_llm()`
|
||||
- New search capabilities: Extend ChromaDB integration
|
||||
```bash
|
||||
# Find companies in specific sector
|
||||
curl "http://localhost:8000/companies/filter?industry=fintech&founded_after=2020"
|
||||
|
||||
# Find companies backed by specific investor
|
||||
curl "http://localhost:8000/companies/filter?investor_name=Sequoia"
|
||||
```
|
||||
|
||||
### 4. Investment Analysis
|
||||
|
||||
```bash
|
||||
# Get investor with full portfolio
|
||||
curl "http://localhost:8000/investors/1"
|
||||
|
||||
# Find all companies in a specific location
|
||||
curl "http://localhost:8000/companies/filter?location=San%20Francisco"
|
||||
```
|
||||
|
||||
## Development
|
||||
|
||||
### Running in Development Mode
|
||||
|
||||
```bash
|
||||
cd app
|
||||
uvicorn main:app --reload --host localhost --port 8000
|
||||
```
|
||||
|
||||
### Testing the API
|
||||
|
||||
1. **Interactive Testing**: Visit http://localhost:8000/docs
|
||||
2. **Manual Testing**: Use curl or Postman with the examples above
|
||||
3. **Database Inspection**: Use SQLite browser to inspect `investors_2.db`
|
||||
|
||||
### Adding New Features
|
||||
|
||||
1. **New Endpoints**: Add routes to `api/investors.py` or `api/companies.py`
|
||||
2. **New Models**: Update `db/models.py` and `py_schemas.py`
|
||||
3. **New Filters**: Extend filtering logic in route handlers
|
||||
4. **New LLM Features**: Modify `services/openrouter.py` or `services/querying.py`
|
||||
|
||||
## License
|
||||
|
||||
|
||||
Binary file not shown.
Binary file not shown.
+205
-5
@@ -1,8 +1,208 @@
|
||||
from fastapi.routing import apirouter
|
||||
from typing import List, Optional
|
||||
|
||||
router = apirouter()
|
||||
from db.db import get_db
|
||||
from db.models import CompanyTable, InvestorTable
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query
|
||||
from py_schemas import CompanySchema
|
||||
from pydantic import BaseModel
|
||||
from sqlalchemy.orm import Session, selectinload
|
||||
|
||||
@router.get("/companies")
|
||||
def read_companies():
|
||||
return {"message": "list of companies"}
|
||||
router = APIRouter(tags=["Company Routes"])
|
||||
|
||||
|
||||
# Request schemas for creating/updating
|
||||
class CompanyCreate(BaseModel):
|
||||
name: str
|
||||
industry: str
|
||||
location: str
|
||||
founded_year: Optional[int] = None
|
||||
website: Optional[str] = None
|
||||
|
||||
|
||||
class CompanyUpdate(BaseModel):
|
||||
name: Optional[str] = None
|
||||
industry: Optional[str] = None
|
||||
location: Optional[str] = None
|
||||
founded_year: Optional[int] = None
|
||||
website: Optional[str] = None
|
||||
|
||||
|
||||
# Response schema with relationships
|
||||
class CompanyData(BaseModel):
|
||||
"""Comprehensive company data schema"""
|
||||
|
||||
company: CompanySchema
|
||||
investors: List["InvestorBasic"] = []
|
||||
|
||||
class Config:
|
||||
from_attributes = True
|
||||
|
||||
|
||||
class InvestorBasic(BaseModel):
|
||||
"""Basic investor info for company responses"""
|
||||
|
||||
id: int
|
||||
name: str
|
||||
geographic_focus: str
|
||||
stage_focus: str
|
||||
check_size_lower: int
|
||||
check_size_upper: int
|
||||
|
||||
class Config:
|
||||
from_attributes = True
|
||||
|
||||
|
||||
@router.get("/companies", response_model=List[CompanyData])
|
||||
def read_companies(db: Session = Depends(get_db)):
|
||||
"""Get all companies with their investor relationships"""
|
||||
companies = (
|
||||
db.query(CompanyTable).options(selectinload(CompanyTable.investors)).all()
|
||||
)
|
||||
|
||||
# Transform CompanyTable objects to CompanyData format
|
||||
company_data_list = []
|
||||
for company in companies:
|
||||
company_data = CompanyData(company=company, investors=company.investors)
|
||||
company_data_list.append(company_data)
|
||||
|
||||
return company_data_list
|
||||
|
||||
|
||||
@router.get("/companies/filter", response_model=List[CompanyData])
|
||||
def filter_companies(
|
||||
industry: Optional[str] = Query(
|
||||
None, description="Filter by industry (partial match)"
|
||||
),
|
||||
location: Optional[str] = Query(
|
||||
None, description="Filter by location (partial match)"
|
||||
),
|
||||
founded_after: Optional[int] = Query(None, description="Founded after year"),
|
||||
founded_before: Optional[int] = Query(None, description="Founded before year"),
|
||||
has_website: Optional[bool] = Query(
|
||||
None, description="Filter companies with/without website"
|
||||
),
|
||||
investor_name: Optional[str] = Query(
|
||||
None, description="Filter by investor name (partial match)"
|
||||
),
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
"""Filter companies based on various criteria"""
|
||||
|
||||
# Start with base query
|
||||
query = db.query(CompanyTable).options(selectinload(CompanyTable.investors))
|
||||
|
||||
# Apply filters
|
||||
if industry:
|
||||
query = query.filter(CompanyTable.industry.ilike(f"%{industry}%"))
|
||||
|
||||
if location:
|
||||
query = query.filter(CompanyTable.location.ilike(f"%{location}%"))
|
||||
|
||||
if founded_after is not None:
|
||||
query = query.filter(CompanyTable.founded_year >= founded_after)
|
||||
|
||||
if founded_before is not None:
|
||||
query = query.filter(CompanyTable.founded_year <= founded_before)
|
||||
|
||||
if has_website is not None:
|
||||
if has_website:
|
||||
query = query.filter(CompanyTable.website.isnot(None))
|
||||
else:
|
||||
query = query.filter(CompanyTable.website.is_(None))
|
||||
|
||||
# Filter by investor if provided
|
||||
if investor_name:
|
||||
query = query.join(CompanyTable.investors).filter(
|
||||
InvestorTable.name.ilike(f"%{investor_name}%")
|
||||
)
|
||||
|
||||
companies = query.all()
|
||||
|
||||
# Transform to CompanyData format
|
||||
company_data_list = []
|
||||
for company in companies:
|
||||
company_data = CompanyData(company=company, investors=company.investors)
|
||||
company_data_list.append(company_data)
|
||||
|
||||
return company_data_list
|
||||
|
||||
|
||||
@router.get("/companies/{company_id}", response_model=CompanyData)
|
||||
def read_company(company_id: int, db: Session = Depends(get_db)):
|
||||
"""Get a specific company by ID with its investors"""
|
||||
company = (
|
||||
db.query(CompanyTable)
|
||||
.options(selectinload(CompanyTable.investors))
|
||||
.filter(CompanyTable.id == company_id)
|
||||
.first()
|
||||
)
|
||||
|
||||
if not company:
|
||||
raise HTTPException(status_code=404, detail="Company not found")
|
||||
|
||||
# Transform to CompanyData format
|
||||
return CompanyData(company=company, investors=company.investors)
|
||||
|
||||
|
||||
@router.post("/companies", response_model=CompanyData)
|
||||
def create_company(company: CompanyCreate, db: Session = Depends(get_db)):
|
||||
"""Create a new company"""
|
||||
db_company = CompanyTable(**company.dict())
|
||||
db.add(db_company)
|
||||
db.commit()
|
||||
db.refresh(db_company)
|
||||
|
||||
# Reload with relationships
|
||||
company_with_relations = (
|
||||
db.query(CompanyTable)
|
||||
.options(selectinload(CompanyTable.investors))
|
||||
.filter(CompanyTable.id == db_company.id)
|
||||
.first()
|
||||
)
|
||||
|
||||
# Transform to CompanyData format
|
||||
return CompanyData(
|
||||
company=company_with_relations, investors=company_with_relations.investors
|
||||
)
|
||||
|
||||
|
||||
@router.put("/companies/{company_id}", response_model=CompanyData)
|
||||
def update_company(
|
||||
company_id: int, company: CompanyUpdate, db: Session = Depends(get_db)
|
||||
):
|
||||
"""Update an existing company"""
|
||||
db_company = db.query(CompanyTable).filter(CompanyTable.id == company_id).first()
|
||||
if not db_company:
|
||||
raise HTTPException(status_code=404, detail="Company not found")
|
||||
|
||||
update_data = company.dict(exclude_unset=True)
|
||||
for field, value in update_data.items():
|
||||
setattr(db_company, field, value)
|
||||
|
||||
db.commit()
|
||||
db.refresh(db_company)
|
||||
|
||||
# Reload with relationships
|
||||
company_with_relations = (
|
||||
db.query(CompanyTable)
|
||||
.options(selectinload(CompanyTable.investors))
|
||||
.filter(CompanyTable.id == company_id)
|
||||
.first()
|
||||
)
|
||||
|
||||
# Transform to CompanyData format
|
||||
return CompanyData(
|
||||
company=company_with_relations, investors=company_with_relations.investors
|
||||
)
|
||||
|
||||
|
||||
@router.delete("/companies/{company_id}")
|
||||
def delete_company(company_id: int, db: Session = Depends(get_db)):
|
||||
"""Delete a company"""
|
||||
db_company = db.query(CompanyTable).filter(CompanyTable.id == company_id).first()
|
||||
if not db_company:
|
||||
raise HTTPException(status_code=404, detail="Company not found")
|
||||
|
||||
db.delete(db_company)
|
||||
db.commit()
|
||||
return {"message": "Company deleted successfully"}
|
||||
|
||||
+33
-9
@@ -1,23 +1,36 @@
|
||||
import io
|
||||
|
||||
import pandas as pd
|
||||
from api import investors
|
||||
from api import companies, investors
|
||||
from db.db import db_dependency, init_database
|
||||
from fastapi import FastAPI, File, UploadFile
|
||||
from py_schemas import InvestorList
|
||||
from pydantic import BaseModel
|
||||
from services.openrouter import InvestorProcessor
|
||||
from services.querying import QueryProcessor
|
||||
|
||||
app = FastAPI()
|
||||
app.include_router(investors.router)
|
||||
init_database()
|
||||
|
||||
|
||||
# Request models
|
||||
class QueryRequest(BaseModel):
|
||||
question: str
|
||||
|
||||
class Config:
|
||||
json_schema_extra = {
|
||||
"example": {
|
||||
"question": "Show me growth stage fintech investors in the US with check sizes over $1 million"
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@app.get("/")
|
||||
def read_root():
|
||||
def health():
|
||||
return {"Hello": "World"}
|
||||
|
||||
|
||||
@app.post("/parse-csv")
|
||||
@app.post("/parse-csv", tags=["CSV Upload"], response_model=list[dict])
|
||||
async def parse_csv(db: db_dependency, file: UploadFile = File(...)):
|
||||
# Read uploaded CSV with pandas
|
||||
content = await file.read()
|
||||
@@ -28,16 +41,27 @@ async def parse_csv(db: db_dependency, file: UploadFile = File(...)):
|
||||
results = await processor.process_csv(df)
|
||||
|
||||
# Convert Pydantic objects to dictionaries
|
||||
return {"results": [r.dict() for r in results]}
|
||||
return [r.model_dump() for r in results]
|
||||
|
||||
|
||||
@app.post("/query")
|
||||
async def query_investors(db: db_dependency, question: str):
|
||||
@app.post("/query", response_model=InvestorList, tags=["Querying"])
|
||||
async def query_investors(db: db_dependency, request: QueryRequest):
|
||||
"""
|
||||
Query investors using natural language.
|
||||
|
||||
Supports queries like:
|
||||
- "Show me seed stage investors"
|
||||
- "Find fintech investors in Silicon Valley"
|
||||
- "Growth stage investors with $5M+ check sizes"
|
||||
- "Healthcare investors in Europe"
|
||||
"""
|
||||
processor = QueryProcessor(sql_session=db)
|
||||
results = processor.process_query(question)
|
||||
return {"results": results}
|
||||
results = processor.process_query(request.question)
|
||||
return results
|
||||
|
||||
|
||||
app.include_router(investors.router)
|
||||
app.include_router(companies.router)
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
|
||||
Binary file not shown.
+202
-5
@@ -1,18 +1,20 @@
|
||||
from typing import Optional
|
||||
from typing import List, Optional
|
||||
|
||||
import chromadb
|
||||
from db.models import InvestorTable
|
||||
from langchain import hub
|
||||
from langchain_community.agent_toolkits import SQLDatabaseToolkit
|
||||
from langchain_community.utilities import SQLDatabase
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
from py_schemas import InvestorList
|
||||
from py_schemas import InvestorData, InvestorList
|
||||
from settings import settings
|
||||
from sqlalchemy.orm import selectinload
|
||||
|
||||
# Connect to SQLite
|
||||
|
||||
prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
|
||||
db = SQLDatabase.from_uri("sqlite:///investors.db")
|
||||
db = SQLDatabase.from_uri("sqlite:///investors_2.db")
|
||||
system_message = (
|
||||
prompt_template.format(dialect="SQLite", top_k=5)
|
||||
+ "\n Get answers from the Sql database and the vector database"
|
||||
@@ -25,6 +27,7 @@ class QueryProcessor:
|
||||
sql_session: Optional[object] = None,
|
||||
vector_db_client: Optional[object] = None,
|
||||
):
|
||||
self.sql_session = sql_session
|
||||
self.llm = ChatOpenAI(
|
||||
api_key=settings.OPENROUTER_API_KEY,
|
||||
base_url="https://openrouter.ai/api/v1",
|
||||
@@ -36,7 +39,6 @@ class QueryProcessor:
|
||||
model=self.llm,
|
||||
tools=self.toolkit.get_tools() + [self.query_vector_database],
|
||||
prompt=system_message,
|
||||
response_format=InvestorList,
|
||||
)
|
||||
self.vector_db_client = vector_db_client
|
||||
|
||||
@@ -77,7 +79,202 @@ class QueryProcessor:
|
||||
|
||||
def process_query(self, question: str) -> InvestorList:
|
||||
"""Process a query using the LLM and return structured investor data."""
|
||||
# Extract filters from the query first
|
||||
filters = self._extract_filters_from_query(question)
|
||||
|
||||
# Get AI response for additional context
|
||||
response = self.agent.invoke(
|
||||
{"messages": [("user", question)]},
|
||||
)
|
||||
return response
|
||||
|
||||
# Extract the actual message content
|
||||
ai_response = (
|
||||
response["messages"][-1].content if response.get("messages") else ""
|
||||
)
|
||||
|
||||
# Try to extract investor IDs or names from the AI response
|
||||
investor_ids = self._extract_investor_info_from_response(ai_response)
|
||||
|
||||
# Fetch filtered investor data with relationships from database
|
||||
return self._fetch_investors_with_relationships(investor_ids, filters)
|
||||
|
||||
def _extract_investor_info_from_response(self, ai_response: str) -> List[int]:
|
||||
"""Extract investor IDs from AI response. This is a simple implementation."""
|
||||
# This is a basic implementation - you might want to make it more sophisticated
|
||||
# based on how your AI formats responses
|
||||
investor_ids = []
|
||||
|
||||
# If the AI can't provide structured data, fall back to getting all investors
|
||||
# that match basic criteria
|
||||
try:
|
||||
# Try to extract numbers that might be IDs
|
||||
import re
|
||||
|
||||
ids = re.findall(r"\bid:\s*(\d+)", ai_response.lower())
|
||||
investor_ids = [int(id_str) for id_str in ids]
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return investor_ids if investor_ids else []
|
||||
|
||||
def _extract_filters_from_query(self, question: str) -> dict:
|
||||
"""Extract filter criteria from natural language query."""
|
||||
question_lower = question.lower()
|
||||
filters = {}
|
||||
|
||||
# Extract stage filters
|
||||
if any(
|
||||
stage in question_lower
|
||||
for stage in [
|
||||
"seed",
|
||||
"series a",
|
||||
"series b",
|
||||
"series c",
|
||||
"growth",
|
||||
"late stage",
|
||||
]
|
||||
):
|
||||
if "seed" in question_lower:
|
||||
filters["stage"] = "SEED"
|
||||
elif "series a" in question_lower:
|
||||
filters["stage"] = "SERIES_A"
|
||||
elif "series b" in question_lower:
|
||||
filters["stage"] = "SERIES_B"
|
||||
elif "series c" in question_lower:
|
||||
filters["stage"] = "SERIES_C"
|
||||
elif "growth" in question_lower:
|
||||
filters["stage"] = "GROWTH"
|
||||
elif "late stage" in question_lower:
|
||||
filters["stage"] = "LATE_STAGE"
|
||||
|
||||
# Extract geographic filters
|
||||
if any(
|
||||
geo in question_lower
|
||||
for geo in [
|
||||
"us",
|
||||
"usa",
|
||||
"united states",
|
||||
"europe",
|
||||
"asia",
|
||||
"silicon valley",
|
||||
"bay area",
|
||||
]
|
||||
):
|
||||
if (
|
||||
"us" in question_lower
|
||||
or "usa" in question_lower
|
||||
or "united states" in question_lower
|
||||
):
|
||||
filters["geography"] = "US"
|
||||
elif "europe" in question_lower:
|
||||
filters["geography"] = "Europe"
|
||||
elif "asia" in question_lower:
|
||||
filters["geography"] = "Asia"
|
||||
elif "silicon valley" in question_lower or "bay area" in question_lower:
|
||||
filters["geography"] = "Silicon Valley"
|
||||
|
||||
# Extract sector filters
|
||||
sectors = [
|
||||
"fintech",
|
||||
"healthcare",
|
||||
"saas",
|
||||
"ai",
|
||||
"biotech",
|
||||
"consumer",
|
||||
"enterprise",
|
||||
"crypto",
|
||||
"blockchain",
|
||||
]
|
||||
for sector in sectors:
|
||||
if sector in question_lower:
|
||||
filters["sector"] = sector
|
||||
break
|
||||
|
||||
# Extract check size filters (simple patterns)
|
||||
import re
|
||||
|
||||
amounts = re.findall(
|
||||
r"\$?(\d+(?:,\d{3})*(?:\.\d+)?)\s*(?:million|m|k|thousand)", question_lower
|
||||
)
|
||||
if amounts:
|
||||
amount = amounts[0].replace(",", "")
|
||||
if "million" in question_lower or "m" in question_lower:
|
||||
filters["min_check_size"] = int(float(amount) * 1000000)
|
||||
elif "thousand" in question_lower or "k" in question_lower:
|
||||
filters["min_check_size"] = int(float(amount) * 1000)
|
||||
|
||||
return filters
|
||||
|
||||
def _fetch_investors_with_relationships(
|
||||
self, investor_ids: List[int] = None, filters: dict = None
|
||||
) -> InvestorList:
|
||||
"""Fetch investors with all their relationships from the database."""
|
||||
if not self.sql_session:
|
||||
return InvestorList(investors=[])
|
||||
|
||||
# Import here to avoid circular imports
|
||||
from db.models import SectorTable
|
||||
|
||||
# Build query with all relationships loaded
|
||||
query = self.sql_session.query(InvestorTable).options(
|
||||
selectinload(InvestorTable.portfolio_companies),
|
||||
selectinload(InvestorTable.team_members),
|
||||
selectinload(InvestorTable.sectors),
|
||||
)
|
||||
|
||||
# Apply filters if provided
|
||||
if filters:
|
||||
if "stage" in filters:
|
||||
from db.models import InvestmentStage
|
||||
|
||||
stage_enum = getattr(InvestmentStage, filters["stage"])
|
||||
query = query.filter(InvestorTable.stage_focus == stage_enum)
|
||||
|
||||
if "geography" in filters:
|
||||
query = query.filter(
|
||||
InvestorTable.geographic_focus.ilike(f"%{filters['geography']}%")
|
||||
)
|
||||
|
||||
if "min_check_size" in filters:
|
||||
query = query.filter(
|
||||
InvestorTable.check_size_lower >= filters["min_check_size"]
|
||||
)
|
||||
|
||||
if "max_check_size" in filters:
|
||||
query = query.filter(
|
||||
InvestorTable.check_size_upper <= filters["max_check_size"]
|
||||
)
|
||||
|
||||
if "min_aum" in filters:
|
||||
query = query.filter(InvestorTable.aum >= filters["min_aum"])
|
||||
|
||||
if "max_aum" in filters:
|
||||
query = query.filter(InvestorTable.aum <= filters["max_aum"])
|
||||
|
||||
if "sector" in filters:
|
||||
query = query.join(InvestorTable.sectors).filter(
|
||||
SectorTable.name.ilike(f"%{filters['sector']}%")
|
||||
)
|
||||
|
||||
# Filter by IDs if provided
|
||||
if investor_ids:
|
||||
query = query.filter(InvestorTable.id.in_(investor_ids))
|
||||
else:
|
||||
# If no specific IDs and no filters, limit to prevent overwhelming response
|
||||
if not filters:
|
||||
query = query.limit(10)
|
||||
|
||||
investors = query.all()
|
||||
|
||||
# Transform to InvestorData format
|
||||
investor_data_list = []
|
||||
for investor in investors:
|
||||
investor_data = InvestorData(
|
||||
investor=investor,
|
||||
portfolio_companies=investor.portfolio_companies,
|
||||
team_members=investor.team_members,
|
||||
sectors=investor.sectors,
|
||||
)
|
||||
investor_data_list.append(investor_data)
|
||||
|
||||
return InvestorList(investors=investor_data_list)
|
||||
|
||||
+139
-16
@@ -1,16 +1,139 @@
|
||||
# Core dependencies
|
||||
pandas>=2.0.0
|
||||
sqlalchemy>=2.0.0
|
||||
pydantic>=2.0.0
|
||||
|
||||
# Vector database
|
||||
chromadb>=0.4.0
|
||||
|
||||
# LLM integration
|
||||
openai>=1.0.0
|
||||
|
||||
# Environment management
|
||||
python-dotenv>=1.0.0
|
||||
|
||||
# Additional dependencies for data processing
|
||||
typing-extensions>=4.0.0
|
||||
aiohappyeyeballs==2.6.1
|
||||
aiohttp==3.12.15
|
||||
aiosignal==1.4.0
|
||||
annotated-types==0.7.0
|
||||
anyio==4.10.0
|
||||
attrs==25.3.0
|
||||
backoff==2.2.1
|
||||
bcrypt==4.3.0
|
||||
build==1.3.0
|
||||
cachetools==5.5.2
|
||||
certifi==2025.8.3
|
||||
charset-normalizer==3.4.3
|
||||
chromadb==1.0.20
|
||||
click==8.2.1
|
||||
coloredlogs==15.0.1
|
||||
dataclasses-json==0.6.7
|
||||
distro==1.9.0
|
||||
dnspython==2.7.0
|
||||
durationpy==0.10
|
||||
email-validator==2.3.0
|
||||
fastapi==0.116.1
|
||||
fastapi-cli==0.0.8
|
||||
fastapi-cloud-cli==0.1.5
|
||||
filelock==3.19.1
|
||||
flatbuffers==25.2.10
|
||||
frozenlist==1.7.0
|
||||
fsspec==2025.7.0
|
||||
google-auth==2.40.3
|
||||
googleapis-common-protos==1.70.0
|
||||
greenlet==3.2.4
|
||||
grpcio==1.74.0
|
||||
h11==0.16.0
|
||||
hf-xet==1.1.8
|
||||
httpcore==1.0.9
|
||||
httptools==0.6.4
|
||||
httpx==0.28.1
|
||||
httpx-sse==0.4.1
|
||||
huggingface-hub==0.34.4
|
||||
humanfriendly==10.0
|
||||
idna==3.10
|
||||
importlib-metadata==8.7.0
|
||||
importlib-resources==6.5.2
|
||||
itsdangerous==2.2.0
|
||||
jinja2==3.1.6
|
||||
jiter==0.10.0
|
||||
jsonpatch==1.33
|
||||
jsonpointer==3.0.0
|
||||
jsonschema==4.25.1
|
||||
jsonschema-specifications==2025.4.1
|
||||
kubernetes==33.1.0
|
||||
langchain==0.3.27
|
||||
langchain-community==0.3.29
|
||||
langchain-core==0.3.75
|
||||
langchain-openai==0.3.32
|
||||
langchain-text-splitters==0.3.10
|
||||
langgraph==0.6.6
|
||||
langgraph-checkpoint==2.1.1
|
||||
langgraph-prebuilt==0.6.4
|
||||
langgraph-sdk==0.2.4
|
||||
langsmith==0.4.20
|
||||
markdown-it-py==4.0.0
|
||||
markupsafe==3.0.2
|
||||
marshmallow==3.26.1
|
||||
mdurl==0.1.2
|
||||
mmh3==5.2.0
|
||||
mpmath==1.3.0
|
||||
multidict==6.6.4
|
||||
mypy-extensions==1.1.0
|
||||
numpy==2.3.2
|
||||
oauthlib==3.3.1
|
||||
onnxruntime==1.22.1
|
||||
openai==1.102.0
|
||||
opentelemetry-api==1.36.0
|
||||
opentelemetry-exporter-otlp-proto-common==1.36.0
|
||||
opentelemetry-exporter-otlp-proto-grpc==1.36.0
|
||||
opentelemetry-proto==1.36.0
|
||||
opentelemetry-sdk==1.36.0
|
||||
opentelemetry-semantic-conventions==0.57b0
|
||||
orjson==3.11.3
|
||||
ormsgpack==1.10.0
|
||||
overrides==7.7.0
|
||||
packaging==25.0
|
||||
pandas==2.3.2
|
||||
pip==25.2
|
||||
posthog==5.4.0
|
||||
propcache==0.3.2
|
||||
protobuf==6.32.0
|
||||
pyasn1==0.6.1
|
||||
pyasn1-modules==0.4.2
|
||||
pybase64==1.4.2
|
||||
pydantic==2.11.7
|
||||
pydantic-core==2.33.2
|
||||
pydantic-extra-types==2.10.5
|
||||
pydantic-settings==2.10.1
|
||||
pygments==2.19.2
|
||||
pypika==0.48.9
|
||||
pyproject-hooks==1.2.0
|
||||
python-dateutil==2.9.0.post0
|
||||
python-dotenv==1.1.1
|
||||
python-multipart==0.0.20
|
||||
pytz==2025.2
|
||||
pyyaml==6.0.2
|
||||
referencing==0.36.2
|
||||
regex==2025.7.34
|
||||
requests==2.32.5
|
||||
requests-oauthlib==2.0.0
|
||||
requests-toolbelt==1.0.0
|
||||
rich==14.1.0
|
||||
rich-toolkit==0.15.0
|
||||
rignore==0.6.4
|
||||
rpds-py==0.27.1
|
||||
rsa==4.9.1
|
||||
sentry-sdk==2.35.1
|
||||
shellingham==1.5.4
|
||||
six==1.17.0
|
||||
sniffio==1.3.1
|
||||
sqlalchemy==2.0.43
|
||||
starlette==0.47.3
|
||||
sympy==1.14.0
|
||||
tenacity==9.1.2
|
||||
tiktoken==0.11.0
|
||||
tokenizers==0.21.4
|
||||
tqdm==4.67.1
|
||||
typer==0.16.1
|
||||
typing-extensions==4.15.0
|
||||
typing-inspect==0.9.0
|
||||
typing-inspection==0.4.1
|
||||
tzdata==2025.2
|
||||
ujson==5.11.0
|
||||
urllib3==2.5.0
|
||||
uvicorn==0.35.0
|
||||
uvloop==0.21.0
|
||||
watchfiles==1.1.0
|
||||
websocket-client==1.8.0
|
||||
websockets==15.0.1
|
||||
xxhash==3.5.0
|
||||
yarl==1.20.1
|
||||
zipp==3.23.0
|
||||
zstandard==0.24.0
|
||||
|
||||
Reference in New Issue
Block a user