Young Financial Advisor

This project introduces a state-of-the-art AI application designed to provide personalized financial feedback to users through an intuitive and user-friendly interface. Leveraging advanced language models (LLMs) and a robust technological stack, the application assists users in navigating various financial scenarios, including debt management, retirement planning, post-graduation education decisions, and specific financial goals such as home buying and investment strategies. By collecting user-specific information, the AI delivers tailored advice and predictions to help users make informed financial decisions and take appropriate next steps.

The application is built using LangChain for managing language models, while MySQL and SQLAlchemy handle the database and data collection. The frontend is developed with Streamlit and FastAPI, ensuring a seamless user experience. The database schema supports efficient insertion and deletion operations, enabling dynamic and responsive data management.

The backend incorporates a Neo4j knowledge graph and agentic workflows, utilizing agents and tools to process and analyze financial data. LangChain facilitates the integration of computational and data collection subgraphs, enhancing the application's ability to compute precise financial outcomes and deliver actionable insights.

Through a multi-agent workflow within a graph structure, this AI financial chatbot not only computes accurate financial results but also interprets them effectively, providing users with clear and actionable financial advice. This innovative approach ensures that users receive comprehensive and reliable financial guidance tailored to their unique circumstances and goals.