Build a Local CSV Query Assistant Using Gradio and LangChain

Author(s): Vikram Bhat

Originally published on Towards AI.

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In this blog, we’ll walk through creating an interactive Gradio application that allows users to upload a CSV file and query its data using a conversational AI model powered by LangChain’s create_pandas_dataframe_agent and Ollama's Llama 3.2. This guide will focus on building a local application where the user can upload CSVs, ask questions about the data, and receive answers in real-time.

You can find the complete code for this application in the GitHub repository.

Gradio is a powerful alternative to Streamlit, offering many new features that make building machine learning applications easy. Gradio excels with simple interfaces and impressive integration capabilities. Some standout features include native support for various data types (such as images, audio, and text), dynamic UI updates, and easy integration with popular libraries like TensorFlow, PyTorch, and LangChain.

In this tutorial, we leverage LangChain’s experimental create_pandas_dataframe_agent, which allows us to analyze simple CSVs without the need to implement complex Retrieval-Augmented Generation (RAG) systems. This makes it ideal for users who want to quickly query CSV data in a conversational manner without the overhead of building a full-fledged RAG system.

Additionally, Ollama enables us to run the entire system locally, using… Read the full blog for free on Medium.

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Published via Towards AI