| Summary: | The project focuses on harnessing the capabilities of Large Language Models
(LLMs) to enhance the functionality and user experience of Robo-Advisor
applications. The primary objective is to develop a sophisticated system capable of
providing personalised portfolio recommendations and facilitating fund exploration
for investors. Through the integration of advanced natural language processing
techniques and the implementation of a Retriever-Augmented Generation (RAG)
architecture, the application aims to deliver tailored investment advice based on
individual risk profiles and investment preferences. Additionally, the system aims to
offer an intuitive interface for users to explore various investment options, analyse
performance metrics, and make informed decisions. To ensure users have access to
up-to-date information on their portfolios, a robust data pipeline has been set up to
continuously ingest and process market data daily.
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