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The Agentic AI Chatbot is an advanced artificial intelligence system designed to provide human- like, context-aware responses by leveraging LlamaIndex, a powerful indexing and retrieval framework. Chatbots have revolutionized digital communication, serving as virtual assistants, customer support agents, and research aids across multiple industries. However, traditional chatbot models often suffer from limited contextual understanding, inefficient data retrieval, and a lack of adaptability when handling dynamic, large-scale datasets. These limitations lead to slow, inaccurate, and generic responses, reducing their effectiveness in real-world applications. To address these challenges, this project introduces an Agentic AI Chatbot that enhances conventional chatbot functionalities by integrating LlamaIndex for improved indexing and data retrieval. LlamaIndex enables the chatbot to process both structured and unstructured data efficiently, allowing it to provide intelligent and highly relevant responses. Unlike traditional keyword-based models, which rely on simple rule-based mechanisms, this chatbot can contextually analyze queries, extract the most relevant information, and generate precise answers in real time.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-18
DOI
https://doi.org/10.5281/zenodo.15239748
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
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Equation genie

Zenodo (CERN European Organization for Nuclear Research)
AI in Service Interactions
article

Equation genie

article en

Abstract

The Agentic AI Chatbot is an advanced artificial intelligence system designed to provide human- like, context-aware responses by leveraging LlamaIndex, a powerful indexing and retrieval framework. Chatbots have revolutionized digital communication, serving as virtual assistants, customer support agents, and research aids across multiple industries. However, traditional chatbot models often suffer from limited contextual understanding, inefficient data retrieval, and a lack of adaptability when handling dynamic, large-scale datasets. These limitations lead to slow, inaccurate, and generic responses, reducing their effectiveness in real-world applications. To address these challenges, this project introduces an Agentic AI Chatbot that enhances conventional chatbot functionalities by integrating LlamaIndex for improved indexing and data retrieval. LlamaIndex enables the chatbot to process both structured and unstructured data efficiently, allowing it to provide intelligent and highly relevant responses. Unlike traditional keyword-based models, which rely on simple rule-based mechanisms, this chatbot can contextually analyze queries, extract the most relevant information, and generate precise answers in real time.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 76%
AI in Service Interactions
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Equation genie · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS