AI Study Assistant with RAG Pipeline: Transform PDFs Into Interactive Learning

AI Study Assistant with RAG Pipeline: Transform PDFs Into Interactive LearningAI Study Assistant with RAG Pipeline: Transform PDFs Into Interactive Learning

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David Ayo

• 1d

RAG Study Buddy Built a full-stack, AI-powered study assistant that allows users to upload PDF textbooks and documents, instantly transforming them into interactive learning environments.

The application leverages an advanced Retrieval-Augmented Generation (RAG) pipeline orchestrated with LangChain. Uploaded documents are parsed, chunked, and embedded into a Pinecone vector database. Upon querying, the system retrieves the most semantically relevant sections and passes them to OpenAI's API, ensuring that all answers are strictly grounded in the source text to drastically reduce hallucinations.

Beyond standard Q&A, the system automatically generates study flashcards and maps out document relationships using knowledge graph visualization.

Tech Stack: Python · FastAPI · Next.js · LangChain · Pinecone · OpenAI API

This project demonstrates my ability to build production-ready AI microservices, manage complex backend RAG logic (including semantic chunking and embedding strategies), and integrate them seamlessly with a modern web frontend.

Backend Development Machine Learning Engineering FastAPI LangChain Python

Ritvik Tiwari

• Apr 29

:Designed and developed a responsive school website with a clean UI and smooth user experience using HTML & CSS.

Web Development Website CSS HTML5 Python Visual Studio Code

Muyideenaliyah Ayomide

• Apr 30

nice

Bryan Munuve

• Apr 28

Built an interactive Streamlit dashboard that models revenue recovery scenarios for e-commerce stores. Allows filtering by customer state, product category, and payment type. Three interactive sliders model recovery assumptions — late delivery reduction, customer churn rate, and cancellation recovery rate — and instantly recalculate dollar outcomes. Identified $333,874 in structural revenue risk across 99,441 orders with recovery potential of $40K–$137K depending on execution.