📄 Document Intelligence
Minimal PDF Question Answering System
Document Intelligence is a clean, local-first PDF question answering system that allows users to upload documents, index their contents, and ask natural language questions to retrieve accurate, source-backed answers.
Built using LlamaIndex, HuggingFace open-source models, and Streamlit, the project emphasizes clarity, simplicity, and performance, paired with a minimal black-and-white editorial UI.
🔍 Core Topics Covered
Document Intelligence
Retrieval-Augmented Generation (RAG)
PDF Parsing & Text Extraction
Vector Embeddings & Semantic Search
LLM-powered Question Answering
Minimal UI/UX Design for AI Applications
Local-first AI Systems (No Paid APIs)
✨ Key Features
📂 Upload multiple PDF documents
🧠 Semantic indexing using vector embeddings
🔎 Ask natural language questions across documents
📄 Page-level context retrieval
⚡ Fast local inference (no external API dependency)
🎨 Minimal black & white editorial UI
🛡️ Fully offline-capable after setup
🧱 Architecture Overview User ↓ Streamlit UI ↓ Query Engine (LlamaIndex) ↓ Vector Store (Local) ↓ PDF Content + Metadata
🛠️ Tech Stack Frontend
Streamlit
Custom CSS (Minimal Black & White UI)
Backend
Python 3.10+
LlamaIndex
HuggingFace Transformers
Sentence Transformers (Embeddings)
Storage
Local Vector Store (LlamaIndex SimpleVectorStore)
Persistent Index Storage
Document Processing
PyPDF / PDF Reader utilities
📁 Project Structure document-intelligence/ │ ├── app.py # Streamlit UI ├── ingest.py # PDF loading & indexing ├── query.py # Query engine logic ├── data/ │ ├── uploads/ # Uploaded PDFs │ └── storage/ # Persisted index ├── requirements.txt └── README.md
📖 How It Works
Upload one or more PDF documents
The system extracts and chunks text
Embeddings are generated and stored locally
A semantic index is built and persisted
User questions are matched against relevant chunks
The LLM generates an answer using retrieved context
🧠 Example Questions
“Explain data mining concepts discussed in page 1.”
“What are the advantages of clustering algorithms?”
“Summarize the introduction section.”
🎨 UI Philosophy
Black & white only
No visual noise
Typography-focused layout
Content-first interaction
Editorial / studio-inspired aesthetic
The interface is designed to disappear — letting documents speak.
🔐 Privacy & Cost
❌ No OpenAI / paid APIs
✅ Runs fully on local machine
✅ Documents never leave your system
📌 Use Cases
Study notes & textbooks
Research papers
Exam preparation
Technical documentation
Personal knowledge bases