RxAgentAI
GenAI-powered
Conversational Search
Engine For Research Scholars
Project Overview
The RXAgentAI project seeks to develop a GenAI-powered conversational search engine that aims to be of help to research scholars. RXAgentAI is an advanced chatbot that makes research more fruitful by supporting efficient and precise information retrieval and analysis. It utilizes sophisticated natural language processing to understand complex queries and has access to extensive databases and online resources for supplying real-time information. RXAgentAI stands out as a must-have tool for researchers from various disciplines because it saves time, and reduces the effort required leading to better quality of research undertaken.
The Background & Objective
A drug discovery and development service provider approached Evalogical with the need to design and develop a GenAI-powered chatbot like Chat-GPT for research scholars in the medical and pharmaceutical industry, that can make research more fruitful by supporting efficient and precise information retrieval and analysis. The client wanted a clean user experience (UX), efficient data processing, a well-trained model, and advanced natural language processing (NLP) capabilities.
- Enhance Information Access Provide accurate and timely responses catering to healthcare and pharmaceutical queries.
- Domain-Specific Training Train the ChatGPT model using pharmaceutical, and healthcare industry-specific data.
The Development
01
Data Collection & Preparation
- Sources: Website content, PDFs, research papers, reports, FAQs, knowledge bases, and customer support records.
- Process: Cleaned and ensured consistency across the dataset.
- Tools & Libraries: Python, PyPDF2, PyTorch.
02
Model Selection & Customization
- Choosing the Model: Evaluated requirements and selected GPT-4.
- Customization: Preprocessed data, set up training environment, and fine-tuned the model using domain-specific data.
03
Model Evaluation & Optimization
- Evaluation: Tested with a set of questions and expected answers.
- Optimization: Adjusted hyperparameters and collected user feedback for continuous improvement.
04
Deployment & Integration
- Deployment: Used AWS to deploy the model and set up an API endpoint.
- Frontend Integration: Created a chatbot in Next.js to accept user input and handle real-time communication with the backend.
UI/UX Development & Customization
01
Subject-Specific Chatbots
- Customized User Journeys: Multi-path interface for selecting research-specific chatbots, ensuring relevant information.
- Dynamic Interface: Adapts to the chosen subject, providing relevant options and a seamless user experience.
02
Transparency in Algorithmic Processes
- Visual Algorithm Insights: Displays steps behind data loading for user understanding.
- Interactive Process Visualization: Users can see and interact with the AI's data collection and analysis steps.
03
Enhanced Media Integration
- Rich Media Support: Supports images, videos, and links within the chat for a richer research experience.
- Contextual Content Delivery: Delivers media based on user queries and research direction.
04
User-Friendly Interface Design
- Simplified Navigation: Clean, intuitive interface for easy navigation.
- Accessible Features: Key features are prominently displayed and easily accessible.
05
Engaging Animations and Interactions
- Interactive Design Elements: Engaging animations and interactive elements for better understanding.
- Responsive Feedback: Immediate feedback to user interactions for an engaging experience.
06
Comprehensive User Testing and Iteration
- Iterative Design Process: User-centered design with extensive testing and iterative refinements.
- Feedback-Driven Enhancements: User feedback informed key enhancements for improved experience.
Tech Stack
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