Introduction

Exploring the development of FinSight, an app that won the Streamlit LLM Hackathon, the article delves into the process of using large language models (LLMs) to simplify financial analysis by extracting and summarizing insights from company annual reports. The app's primary feature, the Annual Report Analyzer, employs a Retrieval Augmented Generation (RAG) approach to generate insights from the data in annual reports, with the use of tools like LlamaIndex for building a knowledge base and querying it through an LLM, specifically gpt-4. The system is designed to break down complex queries into sub-questions to retrieve detailed responses from a vector database, using FAISS for vector storage. Prompt engineering ensures clarity and quality in the generated insights, which cover sections such as Fiscal Year Highlights and Strategic Outlook. The article highlights ongoing developments to enhance FinSight's functionality, such as profession-specific insights and improved query modules for financial statements, emphasizing the app's value in saving time and aiding decision-making for finance professionals.

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