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PolicyGPT: Intelligent PDF Q&A System using RAG, OpenAI & ChromaDB

pdf-rag-assistant

Build a Production-Ready RAG system for intelligent Q&A over PDFs (policies, contracts, resumes). Powered by OpenAI Structured Outputs, Pydantic for schema enforcement, ChromaDB for local vector storage, and a Streamlit UI. Ideal for enterprise AI in legal, banking, and HR. Scalable, deterministic, and easy to deploy locally.

Architecture Overview

PDFs → Text Extraction → Chunking → Embeddings → ChromaDB User Query → Retrieval → Prompt → OpenAI → JSON Output → UI

Project Strature:

pdf-rag-assistant/ │── app.py │── main.py │── rag/ │ ├── ingest.py │ ├── retrieve.py │ ├── prompt.py │ ├── pdf_loader.py │── models/ │ ├── schema.py │── utils/ │ ├── openai_client.py │── data/ │ ├── sample.pdf │── chroma_db/ │── requirements.txt │── README.md │── .env

🔥 Key Features

  • 📄 Upload and process PDF documents
  • 🔍 Semantic search using embeddings (vector database)
  • 🧠 RAG pipeline (Retrieval + Generation)
  • 📊 Structured JSON output (Pydantic schema)
  • ⚡ Fast Streamlit UI for interaction
  • 🏗️ Modular and production-ready architecture

⚙️ Tech Stack

  • Python 3
  • OpenAI API (LLM + Embeddings)
  • ChromaDB (Vector Database)
  • Pydantic (Structured Output Validation)
  • Streamlit (Frontend UI)
  • PyPDF (PDF Parsing)

🚀 Installation & Setup

git clone https://github.com/NxtGenCodeBase/pdf-rag-assistant.git
cd policygpt

python -m venv venv
venv\Scripts\activate

pip install -r requirements.txt