bolade
bbf6af58f0
- 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.
43 lines
1.0 KiB
Python
43 lines
1.0 KiB
Python
import os
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from contextlib import contextmanager
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from typing import Generator
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from sqlalchemy import create_engine
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from sqlalchemy.orm import Session, sessionmaker
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from schema import Base
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# Database configuration
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DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///investors.db")
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# Create engine
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engine = create_engine(DATABASE_URL, echo=False)
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# Create session factory
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SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
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def init_database():
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"""Initialize the database by creating all tables"""
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Base.metadata.create_all(bind=engine)
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print("Database initialized successfully!")
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@contextmanager
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def get_session() -> Generator[Session, None, None]:
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"""Get a database session with automatic cleanup"""
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session = SessionLocal()
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try:
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yield session
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session.commit()
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except Exception as e:
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session.rollback()
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raise e
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finally:
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session.close()
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def get_session_sync() -> Session:
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"""Get a database session for synchronous operations"""
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return SessionLocal()
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