Building a Financial Report Retrieval System with LlamaIndex and Gemini 2.0

Author(s): Adi Insights and Innovations

Originally published on Towards AI.

Building a Financial Report Retrieval System with LlamaIndex and Gemini 2.0

Financial reports are critical for assessing a company’s health. They span hundreds of pages, making it difficult to extract specific insights efficiently. Analysts and investors spend hours sifting through balance sheets, income statements and footnotes just to answer simple questions such as — What was the company’s revenue in 2024? With recent advancements in LLM models and vector search technologies, we can automate financial report analysis using LlamaIndex and related frameworks. This blog post explores how we can use LlamaIndex, ChromaDB, Gemini2.0, and Ollama to build a robust financial RAG system that answers queries from lengthy reports with precision.

This article discusses the importance of financial report retrieval systems and outlines their capabilities in automating the analysis process. It details the setup and implementation of such a system using advanced technologies like LlamaIndex and Gemini. Key features include preprocessing financial documents for efficient indexing, using vector databases for improved data retrieval, and conducting natural language queries for actionable insights. Additionally, the article explains how to build a hybrid approach that combines cloud and local models, optimizing the querying process while emphasizing the versatility of the system across different domains.

Read the full blog for free on Medium.

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Published via Towards AI