PromptX: An AI-Powered Personal Assistant

This paper introduces PromptX as an advanced AI- powered personal assistant framework which addresses complex challenges in multimodal task automation. The system addresses essential problems through its combination of Large Language Models (LLMs) and specialized agents and privacy-preserving mechanisms to achieve cross-modal alignment and dynamic tool routing and transparent decision-making. The architecture uses Gemini for intent analysis and LangChain for workflow orchestration and Qdrant for document indexing and OpenAI’s API for fallback reasoning to provide a unified solution for email management and file operations and web automation and document Q&A. The proposed framework introduces new methods for tool optimization and user trust enhancement and multi-agent collaboration which push the current state-of-the-art in AI assistants.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-05-05
DOI
https://doi.org/10.22214/ijraset.2025.69750
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

PromptX: An AI-Powered Personal Assistant

Varun Dixit
International Journal for Research in Applied Science and Engineering Technology
Context-Aware Activity Recognition Systems
article

PromptX: An AI-Powered Personal Assistant

Varun Dixit
article en

Abstract

This paper introduces PromptX as an advanced AI- powered personal assistant framework which addresses complex challenges in multimodal task automation. The system addresses essential problems through its combination of Large Language Models (LLMs) and specialized agents and privacy-preserving mechanisms to achieve cross-modal alignment and dynamic tool routing and transparent decision-making. The architecture uses Gemini for intent analysis and LangChain for workflow orchestration and Qdrant for document indexing and OpenAI’s API for fallback reasoning to provide a unified solution for email management and file operations and web automation and document Q&A. The proposed framework introduces new methods for tool optimization and user trust enhancement and multi-agent collaboration which push the current state-of-the-art in AI assistants.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(5)
Openalex Percentile: Top 92%
Context-Aware Activity Recognition Systems
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