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RAG Flashcard Generator
A Retrieval-Augmented Generation (RAG) powered flashcard generator that turns study materials (e.g., PDFs, notes, webpages) into structured flashcards using a pipeline based on LangChain, embeddings,
Completed
Personal Project
The Challenge
Students and learners often struggle to generate effective study flashcards manually from large amounts of unstructured text (PDFs, notes, lecture material). This project aims to automate that process by building a tool that converts documents into structured, high-quality flashcards using a combination of retrieval and LLM-based generation.
The Solution
This project implements a RAG (Retrieval-Augmented Generation) pipeline:
- Document loading and chunking: Reads PDFs or text and splits them into chunks for processing.
- Vector embedding and storage: Converts chunks into embeddings (semantic vectors) and stores them in a vector database (ChromaDB).
- Retrieval: Retrieves the most relevant text chunks for each query.
- LLM flashcard generation: Uses an LLM (Google Gemini via LangChain and LCEL) to create flashcards based on the retrieved context.
- Output formatting: Structures the generated flashcards in Markdown with clear Q/A pairs.
Key Features
- Automatic document ingestion (PDFs and text).
- Vector database integration for semantic retrieval (Chroma).
- Flashcard generation using a modern LLM (Google Gemini) with LangChain/LCEL pipelines.
- Outputs flashcards in readable Markdown format.
Architecture & Implementation
This project’s high-level architecture looks like:
- Input Layer: Document loader (PDF, text)
- Preprocessing: Text chunking with character-based split.
- Embedding Store: Create and store embeddings in a vector database (Chroma).
- Retriever: Semantic search against the vector store to pick relevant chunks
- RAG Pipeline: Use LangChain/LCEL with the retriever and Google Gemini to generate flashcards.
- Output Formatter: Convert raw generation into structured, Markdown flashcards.
Technologies Used
LangChain
LCEL
Python
OpenAI GPT-4
ChromaDB
Vector Embeddings
RAG Architecture
PyPDF
Notion API
FastAPI
Challenges & Learnings
This project likely had to handle:
- Document processing complexity: PDFs and large documents vary in structure and require reliable chunking.
- Efficient semantic retrieval: Building an effective vector database pipeline for different document types
- LLM prompt design: Engineering prompts and integration with Google Gemini via LangChain to produce structured flashcards.
- Balancing context vs. cost: Retrieving enough relevant text while minimizing API usage.
Results & Impact
- Enables automatic generation of flashcards from diverse sources, saving significant time for learners
- Produces structured summaries that are useful for exam prep, revision, or knowledge retention
- Demonstrates an effective application of RAG pipelines for educational tooling.