Building a Financial Report Retrieval System with LlamaIndex and Gemini 2.0

Author(s): Adi Insights and Innovations

Originally published on Towards AI.

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.

Understand the need for financial report retrieval systems for efficient analysis.Learn how to preprocess and vectorize financial reports using LlamaIndex.Explore ChromaDB for building a robust vector database for document retrieval.Implement query engines using Gemini 2.0 and Llama 3.2 for financial data analysis.Discover advanced query routing techniques using LlamaIndex for enhanced insights.

Financial reports contain critical insights about a company’s performance, including revenue, expenses, liabilities, and profitability. However, these reports are huge, lengthy, and full of technical jargon, making it extremely time consuming for analysts, investors, and executives to extract relevant information manually.

A financial Report Retrieval System can automate this process by enabling natural… Read the full blog for free on Medium.

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Published via Towards AI