GuardianPDF - Audit-First PDF Assistant

A high-performance, security-focused PDF Q&A system combining C++ performance, modern RAG/LLM, and AI integrity verification.

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🎯 What Makes GuardianPDF Different?

Unlike simple "Chat-with-PDF" wrappers, GuardianPDF is an Audit-First tool that:

• ✅ Verifies PDF integrity before AI processing
• ✅ Detects AI-generated content using perplexity analysis
• ✅ Optimizes performance with C++ for large documents (1000+ pages)
• ✅ Provides grounded answers using RAG (Retrieval-Augmented Generation)

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                     GuardianPDF System                       │
├───────────────────┬──────────────────┬──────────────────────┤
│  Module 1: C++    │  Module 2: RAG   │  Module 3: Security  │
│  Parsing Engine   │  Intelligence    │  Auditor             │
├───────────────────┼──────────────────┼──────────────────────┤
│ • PDFShredder     │ • FastAPI API    │ • AI Detection       │
│ • TextChunker     │ • Embeddings     │ • Perplexity Check   │
│ • Rabin-Karp      │ • ChromaDB       │ • Signature Verify   │
│ • PyBind11        │ • Ollama LLM     │ • ReDoS Protection   │
└───────────────────┴──────────────────┴──────────────────────┘

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🚀 Quick Start

Prerequisites

# macOS dependencies
brew install poppler pybind11 catch2 cmake ollama

# Pull Ollama model
ollama pull llama3.2:latest

Installation

# 1. Clone repository
git clone https://github.com/yourusername/guardian_pdf
cd guardian_pdf

# 2. Build C++ module
cd cpp_engine
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j4
cd ../..

# 3. Set up Python environment
python3 -m venv venv
source venv/bin/activate
pip install -r rag_engine/requirements.txt

# 4. Start server
./start_server.sh

Server will be available at http://localhost:8000

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📖 Usage

Command Line

import sys
sys.path.insert(0, 'cpp_engine/build')
import pdf_shredder

# Quick processing
chunks = pdf_shredder.process_pdf("document.pdf")
print(f"Extracted {len(chunks)} chunks")

REST API

# Upload PDF
curl -X POST http://localhost:8000/upload_pdf \
  -F "file=@document.pdf"

# Ask question
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"question": "What is this document about?"}'

Python Client

import requests

# Upload
with open("document.pdf", "rb") as f:
    requests.post(
        "http://localhost:8000/upload_pdf",
        files={"file": f}
    )

# Query
response = requests.post(
    "http://localhost:8000/query",
    json={"question": "Summarize the main points"}
)
print(response.json()["answer"])

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🧪 Testing

C++ Unit Tests

cd cpp_engine/build
./test_pdfshredder
# Output: All tests passed (11 assertions in 3 test cases)

Python Integration Tests

source venv/bin/activate
pytest rag_engine/tests/ -v

API Testing

python test_api.py path/to/document.pdf

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🎓 Technical Highlights

Module 1 Module 1: High-Performance Parsing (C++)

Technologies: C++17, poppler-cpp, PyBind11, Catch2

Key Features:

• PDFShredder: Memory-efficient streaming PDF parser
• TextChunker: Intelligent 500-word chunking with overlap
• Rabin-Karp Deduplication: DAA showcase (rolling hash + Jaccard similarity)
• 5-10x faster than pure Python for large PDFs

Performance:

C++ Parser:    200ms  (1000-page PDF)
Python Parser: 1.5s   (same document)
Speedup:       7.5x

Module 2: RAG Intelligence (Python)

Technologies: FastAPI, sentence-transformers, ChromaDB, Ollama

Pipeline:

1. Embedding: Convert text → 384D vectors (all-MiniLM-L6-v2)
2. Storage: ChromaDB vector database
3. Retrieval: Top-3 semantic search
4. Generation: Ollama LLM (llama3.2) with context

Endpoints:

• POST /upload_pdf: Process and store PDF
• POST /query: Ask questions with RAG
• GET /stats: System statistics
• DELETE /clear: Clear database

Module 3: Security Auditor (In Progress)

Planned Features:

• Perplexity analysis for AI-generated text detection
• Digital signature verification
• Metadata integrity checks
• ReDoS vulnerability protection

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📁 Project Structure

guardian_pdf/
├── cpp_engine/                 # Module 1: C++ parsing
│   ├── src/
│   │   ├── PDFShredder.{h,cpp}
│   │   ├── TextChunker.{h,cpp}
│   │   ├── RabinKarpDedup.{h,cpp}
│   │   └── bindings.cpp        # PyBind11 interface
│   ├── tests/
│   │   └── test_pdfshredder.cpp
│   └── CMakeLists.txt
├── rag_engine/                 # Module 2: RAG intelligence
│   ├── app.py                  # FastAPI backend
│   ├── embeddings.py
│   ├── vector_store.py
│   ├── rag_pipeline.py
│   └── requirements.txt
├── security_auditor/           # Module 3: Integrity checks
│   └── (in progress)
├── test_api.py                 # API test client
└── start_server.sh             # Startup script

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🎯 Use Cases

1. Academic Research: Verify source integrity before citing
2. Legal Documents: Detect AI-generated clauses
3. Technical Documentation: Fast Q&A over large manuals
4. Compliance Audits: Ensure document authenticity

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🧠 For Recruiters

This project demonstrates:

• Systems Programming: C++ memory management, Pimpl idiom, library integration
• Algorithm Design: Rabin-Karp rolling hash, Jaccard similarity, semantic search
• Modern AI Stack: RAG, vector embeddings, LLM integration
• API Development: RESTful design with FastAPI, async/await patterns
• DevOps: CMake build systems, virtual environments, containerization-ready
• Security Mindset: Input validation, integrity verification, vulnerability prevention

Resume Keywords: C++, Python, FastAPI, RAG, LLM, Vector Databases, ChromaDB, PyBind11, Ollama, CMake, pytest, REST API

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🤝 Contributing

# 1. Fork the repository
# 2. Create feature branch
git checkout -b feature/amazing-feature

# 3. Make changes and test
pytest rag_engine/tests/ -v
cd cpp_engine/build && ./test_pdfshredder

# 4. Commit and push
git commit -m "Add amazing feature"
git push origin feature/amazing-feature

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📄 License

MIT License - see LICENSE file

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🙏 Acknowledgments

• poppler-cpp: PDF parsing library
• sentence-transformers: Embedding models
• ChromaDB: Vector database
• Ollama: Local LLM inference
• Catch2: C++ testing framework

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Made with 💙 for secure, intelligent PDF processing

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