Unveiling Insights Through Question Answering Chatbot using LangChain

In today's world, various chatbots have been created, such as to converse with humans for customer care, ChatGPT to generate text for several purposes, or domain specific tasks. However, the chatbot introduced in this study differs because it provides a convenient way to search and interact with PDF documents. This bot is capable of answering questions about multiple PDF documents. This study aims to create a user centric and intelligent system that enhances information retrieval from PDF documents through natural language queries. This intelligent system is designed to significantly improve user experience by making the search and retrieval process more intuitive and accessible. In this paper, a chatbot is designed as a web application using Streamlit that allows users to chat with multiple PDF documents, extracting information from them and providing responses based on user queries. This study introduces a novel chatbot designed to enhance the process of information retrieval from PDF documents through natural language queries. The paper details the architecture of the chatbot, its implementation, and the potential applications of this technology in various fields, such as research, education, and document management. Overall, this study aims to advance the capabilities of chatbots in handling domain-specific tasks and transforming the way users interact with complex document collections.

Authors

Institutions

Publication Details

Journal
AIJR Proceedings
Published
2025-03-17
DOI
https://doi.org/10.21467/proceedings.178.26
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unveiling Insights Through Question Answering Chatbot using LangChain

Poonam Bansal, Ritika Kumari, Tanishca Soodan
AIJR Proceedings
AI in Service Interactions
article

Unveiling Insights Through Question Answering Chatbot using LangChain

Poonam Bansal, Ritika Kumari, Tanishca Soodan
article en

Abstract

In today's world, various chatbots have been created, such as to converse with humans for customer care, ChatGPT to generate text for several purposes, or domain specific tasks. However, the chatbot introduced in this study differs because it provides a convenient way to search and interact with PDF documents. This bot is capable of answering questions about multiple PDF documents. This study aims to create a user centric and intelligent system that enhances information retrieval from PDF documents through natural language queries. This intelligent system is designed to significantly improve user experience by making the search and retrieval process more intuitive and accessible. In this paper, a chatbot is designed as a web application using Streamlit that allows users to chat with multiple PDF documents, extracting information from them and providing responses based on user queries. This study introduces a novel chatbot designed to enhance the process of information retrieval from PDF documents through natural language queries. The paper details the architecture of the chatbot, its implementation, and the potential applications of this technology in various fields, such as research, education, and document management. Overall, this study aims to advance the capabilities of chatbots in handling domain-specific tasks and transforming the way users interact with complex document collections.

AIJR Proceedings
Indira Gandhi Delhi Technical University for Women (IN)
Openalex Percentile: Top 97%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.