Venika - RAG App

Project information

Developed a PDF-based RAG (Retrieval-Augmented Generation) chatbot designed to answer college-related queries efficiently using document-based semantic search. Used college.pdf as the primary knowledge source containing attendance policies, semester examination details, placement eligibility, academic guidelines, and extracurricular club information. Selected PDF documents because educational institutions commonly store notices, rules, and handbooks in PDF format, making the chatbot practical for real-world applications. Implemented PDF loading using PyPDFLoader to extract textual content and convert it into readable document objects for further processing. Applied text chunking using RecursiveCharacterTextSplitter to divide large document content into smaller manageable sections for better processing. Configured chunking with chunk_size=500 and chunk_overlap=50 to improve retrieval performance and maintain contextual continuity between chunks. Used overlapping chunks to prevent context loss when important information is split across multiple sections. Improved semantic retrieval accuracy by enabling the chatbot to understand meaning rather than relying on exact keyword matching. Integrated the Hugging Face embedding model sentence-transformers/all-MiniLM-L6-v2 for generating high-quality vector embeddings. Chose the embedding model because it is lightweight, fast, beginner-friendly, and efficient for semantic search tasks. Converted textual content into vector representations, allowing similar meanings to be matched effectively during user queries. Enabled intelligent query handling where questions with different wording could still retrieve relevant answers from the document.