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Local Retrieval-Augmented Generation (RAG) system built with FastAPI, integrating vector search, Elasticsearch, and optional web search to power LLM-based intelligent question answering using models like Mistral or GPT-4.
Document intelligence framework for Python - Extract text, metadata, and structured data from PDFs, images, Office documents, and more. Built on Pandoc, PDFium, and Tesseract.
🤖 Build AI systems to enhance education with a Virtual Teaching Assistant and voice agents that ensure secure and accurate support for students and faculty.
A Flask-based chatbot for customer service FAQs. This project includes a web interface for user interaction, basic intent recognition using NLTK, and a JSON-based knowledge base for storing frequently asked questions and their responses
Specialised AI-powered Query Assistant for Agriculture and Horticulture using Retrieval-Augmented Generation (RAG) architecture, which combines the strengths of a fine-tuned model with external, verifiable data.
An opinionated development framework for building production-ready AI agents with LangGraph. It grounds AI coding assistants (Cursor, Windsurf, Cline) and guides them to use local, official documentation, ensuring reliable, secure, and observable agentic workflows.
AI-driven medical assistant prototype leveraging Retrieval-Augmented Generation (RAG) and large language models to provide trusted, evidence-based clinical information from medical manuals.
End-to-end financial text-analysis using Bigdata API and the Bigdata-Research-Tools library. Ready-to-use notebooks with RAG & GenAI enabling thematic and risk screening, trend tracking, and automated report generation, extracting insights at scale.