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.

app/db/db.py

@@ -0,0 +1,42 @@
+import os
+from contextlib import contextmanager
+from typing import Generator
+
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session, sessionmaker
+
+from schema import Base
+
+# Database configuration
+DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///investors.db")
+
+# Create engine
+engine = create_engine(DATABASE_URL, echo=False)
+
+# Create session factory
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+
+def init_database():
+    """Initialize the database by creating all tables"""
+    Base.metadata.create_all(bind=engine)
+    print("Database initialized successfully!")
+
+
+@contextmanager
+def get_session() -> Generator[Session, None, None]:
+    """Get a database session with automatic cleanup"""
+    session = SessionLocal()
+    try:
+        yield session
+        session.commit()
+    except Exception as e:
+        session.rollback()
+        raise e
+    finally:
+        session.close()
+
+
+def get_session_sync() -> Session:
+    """Get a database session for synchronous operations"""
+    return SessionLocal()