Enabling Seamless Data Exploration Using Large Language Models
Author(s):
Shaik Abdul Shafi, A Durga Praveen Kumar, M Chikith Rishi, N Thanuja, K Likith
Keywords:
Data Analysis, Data visualization, Langchain, LLM, Streamlit, sqlite3, Lida, Pandas
Abstract
In today’s data-centric landscape, the demand for accessible and intuitive data analysis tools is more significant than ever. This project addresses the critical challenge of democratizing data analysis by developing a user-friendly platform that harnesses the power of advanced language models and innovative vectorization techniques. By breaking down the complexities associated with querying both structured and unstructured data, the project aims to empower individuals across various technical backgrounds. The system provides a seamless and inclusive experience, allowing users to interact naturally with data through simple, conversational language. The core innovation lies in transforming intricate data structures into intuitive natural language interfaces, enabling effortless querying of datasets. Leveraging large language models, the system translates complex user queries into actionable commands, facilitating in-depth data analysis. The study encompasses the development of a novel system architecture that integrates Langchain API with a diverse set of technologies including Streamlit, SQLite3, Lida, SQLDB, and Pandas. Through the integration of Langchain's LLM API, we seek to decipher complex user queries with precision and efficiency. Furthermore, the project encompasses the utilization of Streamlit for creating interactive web applications, SQLite3 for database management, Pandas for data manipulation, and SQLDB for query execution, LIDA for data visualization. The proposed system aims to streamline the data analysis workflow and empower users with giving and extracting actionable insights from their datasets. Our research delves into the extraction of insights from uploaded files, such as CSV files, by harnessing the power of Langchain's LLM API. By employing techniques such as vectorization and tokenization, coupled with Langchain's capabilities, we aim to extract valuable insights from diverse datasets, thereby empowering users to glean actionable intelligence from their data. The project's objective is not only to simplify data analysis but also to foster a culture of collaborative decision-making. By providing universal accessibility and democratizing data-driven insights, this project signifies a p
Article Details
Unique Paper ID: 163972

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 18 - 24
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