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An AI-powered tool that leverages Retrieval-Augmented Generation (RAG) to match employees to roles based on their experience, skills, and resumes. It streamlines team building by efficiently aligning talent with job requirements.

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Harshita100/Skillsync

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Skillsync

This project represents a collaborative initiative focused on advancing the field of Artificial Intelligence and Machine Learning. Our team is committed to developing robust solutions that address pressing business challenges and enhance operational efficiency. By leveraging cutting-edge technologies and data-driven insights, we aim to deliver innovative applications that drive value and foster competitive advantage in the marketplace.

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About

This project uses Retrieval-Augmented Generation (RAG) model, aimed at enhancing the capabilities of natural language processing. Our team combines expertise in machine learning, data retrieval, and generative models to create a solution that effectively retrieves relevant information and generates coherent, contextually appropriate responses. By integrating state-of-the-art techniques, we strive to improve information accuracy and user interaction, demonstrating the potential of RAG models in various applications, from customer support to content creation.

Technologies Used

  • Streamlit

Contributing

  • I welcome contributions to this project! If you'd like to contribute, please fork the repository and create a pull request with your changes.

License

  • This project is licensed under the MIT License. See the LICENSE file for details.

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An AI-powered tool that leverages Retrieval-Augmented Generation (RAG) to match employees to roles based on their experience, skills, and resumes. It streamlines team building by efficiently aligning talent with job requirements.

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