11120 Hollowbrook Road, Owings Mills, MD, 21117, USA.
World Journal of Advanced Engineering Technology and Sciences 2025, 15(01), 025-032
Article DOI: 10.30574/wjaets.2025.15.1.0190
Received on 24 February 2025; revised on 31 March 2025; accepted on 02 April 2025
The abstract introduces the motivation behind this research: the rapid expansion of financial investment data and the need for Artificial Intelligence-driven solutions for accurate and real-time analysis. The research presents an open-source Large Language Model (LLM) fine-tuned on financial datasets to provide precise investment insights.
The LLM integrates MongoDB Atlas Vector Search to store and retrieve vector embeddings efficiently. This allows natural language querying of financial data.
The implementation leverages Hugging Face Transformers, Sentence-Transformers, and FastAPI, enabling a scalable framework for real-time financial queries and automated decision-making.
The system is deployed as an API, allowing seamless integration into financial platforms.
The research contributes to the open-source community by providing a robust AI-powered financial tool for analysts, traders, and researchers.
Artificial Intelligence; Machine Learning; MongoDB; NLP; Prompt Engineering; Large Language Models LLMs; Hugging Face and Python
Preview Article PDF
Sasibhushan Rao Chanthati. Training LLMs for querying financial data with natural language prompts. World Journal of Advanced Engineering Technology and Sciences 2025, 15(01), 025-032. Article DOI: https://doi.org/10.30574/wjaets.2025.15.1.0190.