Data Query & Visualisation using LLM Agents from Scratch

Author(s): Akash Modi

Originally published on Towards AI.

Building a simple data analysis & plotting engine using LLaMa and LangChain Agentic Framework with Structured Data

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In this article, we will build a tool from scratch that enables querying from various structured datasets, including tables, CSVs, and DataFrames. This tool is designed to perform data analysis and generate charts/graphs for better visualization and understanding of the provided data. This tool uses llama-3.2–90b-text and LangChains Agentic framework to automatically understand the type of query and respond accordingly with an text or chart. We will be using GROQ as the free LLM inferencing platform to consume LlaMa 3.2.

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To fully grasp the concepts in this article, a basic understanding of LLMs and agents is recommended. You can refer to the articles below to refresh and strengthen your knowledge of these topics.

LLMs — How do LLM Works?Agents — Agents from scratch

We are going to use the Financial Tabular dataset from Kaggle.

Financial Statements.csv

This dataset is a compilation of information extracted from the 10-K annual reports and balance sheets of various companies. It features longitudinal or panel data covering the years 2009 to 2022. The companies are categorized according to their stock classifications. Here is a… Read the full blog for free on Medium.

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Published via Towards AI