Case Study
AI Study Assistant
RAG-Powered Conversational PDF Chatbot
The Problem
Challenge & Background
Students need to quickly extract specific answers from dense academic PDFs without reading entire documents. Traditional keyword search fails on conceptual and contextual queries.
The Solution
System Approach
A RAG-powered chatbot that enables conversational question-answering over uploaded PDFs using semantic retrieval over FAISS vector stores with HuggingFace sentence embeddings and LLaMA 3.1 generation.
Architecture
System Architecture & Data Flow
PDF Upload
Document input
Chunking
Text splitting
Embeddings
HuggingFace
FAISS Index
Vector store
History-Aware Retriever
Context-aware
LLaMA 3.1
Via Groq LPU
Response
Generated answer
Engineering Decisions
Trade-offs & Technical Choices
Why FAISS over hosted vector databases?
→ Zero-cost local vector search, optimal for single-user document sessions, with no external network latency or API dependencies.
Why Groq API for LLaMA 3.1 inference?
→ LPU-accelerated inference delivers near-instant response times compared to standard CPU/GPU endpoints, crucial for interactive multi-turn chat UX.
Why History-Aware Retrieval?
→ LangChain's create_history_aware_retriever preserves context across conversational follow-ups by reformulating queries based on prior turns.
Capabilities
Key Features & Engineering Scope
- ▹Semantic retrieval over FAISS vector stores
- ▹History-aware multi-turn conversations
- ▹HuggingFace sentence embeddings
- ▹Groq LPU-accelerated LLaMA 3.1 inference
- ▹Docker containerized deployment
- ▹Streamlit user interface
- ▹Zero-setup PDF question answering
Outcomes
Results & Impact
LinkedIn Impressions
Members Reached
Deployed on HuggingFace Spaces. Organically reached 3,900+ members and 6,800+ impressions within 24 hours of launch.