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Anshul Diyewar

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CybersecuredocAI

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December 3, 2025 at 02:09 PM

Project Demo

Submission Details

hear-about-us: Other

name: Anshul Diyewar

College / University Name: VIT Bhopal

linkedIn-profile: https://www.linkedin.com/in/anshul-diyewar-04b542251/

Project Title: CybersecuredocAI

Describe what your project does?: I developed a Retrieval-Augmented Generation (RAG)–based AI chatbot that can answer questions from a given document set (PDFs, text files, etc.). The system first converts documents into embeddings, stores them in a vector database, and then retrieves the most relevant chunks for each user query. These chunks are passed to an LLM to generate accurate, context-aware responses that are grounded in the original documents. This significantly reduces hallucinations and makes the chatbot suitable for use cases like internal knowledge bases and document Q&A. (Languages: Python . Libraries: Transformers, sentence-transformers, LangChain , FAISS for vector search . Models: Embedding model for vectorization, open-source LLM for generation. )

Share a 3–5 minute HD demo video: https://drive.google.com/file/d/1WYDu5zB7CmVwVDxtt4Wod4cusDr6ISUg/view?usp=sharing

Hosted Project Link (if any)

GitHub Repository (if any): https://github.com/anshuldiyewar007/RAG-AI

artifacts/ supporting material: https://drive.google.com/drive/folders/1hX6aiaQHZnqVq3uvzVFIvAAgerAJarTy?usp=sharing

Is this project your original and proprietary work?: Yes

Does your project include any private, sensitive, or restricted code or content?: No

Do you give us (AI Demos & partners) permission to feature your work on our website and share it with our community (with full credit)?: Yes

Do you consent to us showcasing your name/profile on our social media platforms and YouTube: Yes

Are you open to freelance, internship, or collaborative opportunities based on this project?: Yes, I’m interested

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I developed a Retrieval-Augmented Generation (RAG)–based AI chatbot that can answer questions from a given document set (PDFs, text files, etc.). The system first converts documents into embeddings, stores them in a vector database, and then retrieves the most relevant chunks for each user query. These chunks are passed to an LLM to generate accurate, context-aware responses that are grounded in the original documents. This significantly reduces hallucinations and makes the chatbot suitable for use cases like internal knowledge bases and document Q&A. (Languages: Python . Libraries: Transformers, sentence-transformers, LangChain , FAISS for vector search . Models: Embedding model for vectorization, open-source LLM for generation. )