This repository contains several projects focused on Generative AI (GenAI) applications. Each project showcases different implementations, features, and demos that highlight the capabilities of GenAI in various domains. Below is a detailed breakdown of each project.
| Project Name | Description | Demo Thumbnail | Watch the Demo |
|---|---|---|---|
| Chat-Groc-Demo | This project demonstrates the integration of a Generative AI chatbot with Groc, providing a conversational interface that leverages AI for interactive queries and responses. The chatbot is designed to handle various user queries efficiently, showcasing the potential of AI in enhancing user interactions. |  |
Watch the Demo |
| On-Device-AI-RAG-ObjectBox | This project focuses on integrating Retrieval-Augmented Generation (RAG) with ObjectBox, enabling AI to run directly on devices. It emphasizes on-device AI capabilities, reducing latency, and improving privacy by keeping data processing local. This demo is particularly useful for scenarios where real-time processing is critical, and where data sensitivity requires on-device handling rather than cloud processing. |  |
Watch the Demo |
| End-to-End-Document-QA-RAG---Gemma---Groq-API | This project illustrates an end-to-end Question Answering (QA) system that utilizes RAG techniques with Gemma and Groq APIs. It demonstrates how AI can efficiently process and retrieve information from large document sets, providing precise answers to user queries. The system is designed for applications that require robust document comprehension and retrieval capabilities. |  |
Watch the Demo |
| ATS-Smart | ATS-Smart is a project that integrates AI into Applicant Tracking Systems (ATS) to enhance the recruitment process. The AI component helps in screening and shortlisting candidates by analyzing resumes and matching them with job requirements. This project showcases the potential of AI in automating and optimizing HR processes, reducing time-to-hire and improving the accuracy of candidate selection. |  |
Watch the Demo |
This project is a demonstration of a chatbot integrated with the Groc API, aimed at providing users with an AI-driven conversational experience. The chatbot is designed to interact with users, understand their queries, and provide accurate and relevant responses. The key features of this project include:
- Natural Language Processing (NLP): Utilizes advanced NLP techniques to understand and process user inputs.
- Contextual Understanding: Maintains context across the conversation for more coherent and relevant interactions.
- API Integration: Leverages the Groc API for backend processing, ensuring responses are based on up-to-date information.
This project showcases an AI system that operates entirely on-device, using Retrieval-Augmented Generation (RAG) in conjunction with ObjectBox, a high-performance database for mobile and IoT devices. The main highlights include:
- On-Device AI: All processing is done locally on the device, enhancing privacy and reducing dependency on cloud services.
- Low Latency: Ensures real-time performance with minimal delays, crucial for applications requiring instant feedback.
- ObjectBox Integration: Efficient data storage and retrieval, optimized for the constraints of on-device environments.
This project demonstrates a comprehensive system for document-based question answering using Retrieval-Augmented Generation (RAG). By integrating with Gemma and Groq APIs, the system can handle complex queries over large document sets. Key aspects include:
- Document Processing: Capable of analyzing large volumes of documents to extract relevant information.
- Accurate QA: Provides precise answers to specific queries, making it ideal for knowledge management and research applications.
- API Integration: Utilizes Gemma and Groq APIs for efficient data handling and processing.
ATS-Smart enhances traditional Applicant Tracking Systems by incorporating AI to streamline the recruitment process. This project focuses on:
- AI-Powered Screening: Automatically screens resumes and matches candidates to job descriptions.
- Efficiency: Reduces the manual effort required in the hiring process, accelerating time-to-hire.
- Accuracy: Improves the quality of shortlists by leveraging AI to identify the best candidates based on job criteria.