🧠 RAG PDF Assistant | Production-Grade AI Document Q&A

Upload any PDF. Ask anything. Get grounded, accurate answers — backed by a production-ready pipeline.

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🚀 What is this?

Most AI projects are great for learning but fall apart in production. This project bridges that gap — a fully production-grade RAG (Retrieval-Augmented Generation) application that lets users upload PDF documents and ask natural language questions about their content, with all the reliability features a real-world app demands.

Built with FastAPI + Inngest + LlamaIndex + Qdrant + Google Gemini + Streamlit.

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✨ Features

• 📄 PDF Ingestion — Upload and parse any PDF via LlamaIndex
• 🔍 Vector Search — Embeddings stored and queried from Qdrant vector database
• 🤖 Grounded Answers — Google Gemini LLM answers only from your document context, no hallucination
• ⚙️ Production-Ready Backend — Async job processing via Inngest with full observability
• 🔁 Automatic Retries — Failed jobs retry automatically, no silent failures
• 🚦 Rate Limiting & Throttling — Concurrency controls to prevent abuse
• 📊 Full Observability — Real-time job monitoring via Inngest dashboard
• 🖥️ Streamlit Frontend — Clean, interactive UI for uploading and querying

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🏗️ Architecture

User uploads PDF
      ↓
FastAPI endpoint → Inngest job queue
      ↓
LlamaIndex parses & chunks PDF
      ↓
Embeddings stored in Qdrant
      ↓
User asks question → Qdrant vector search → Gemini generates answer

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🛠️ Tech Stack

Layer	Technology
Backend API	FastAPI
Job Queue	Inngest
Document Parsing	LlamaIndex
Vector Database	Qdrant
LLM	Google Gemini
Frontend	Streamlit
Language	Python 3.12+

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📁 Project Structure

├── main.py              # FastAPI app & Inngest functions
├── data_loader.py       # PDF ingestion & chunking (LlamaIndex)
├── vector_db.py         # Qdrant vector store operations
├── custom_types.py      # Pydantic models
├── streamlit_app.py     # Frontend UI
├── test_embed.py        # Embedding tests
└── .env.example         # Environment variables template

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🚀 Getting Started

Prerequisites

• Python 3.12+
• Qdrant instance (local or cloud)
• Google Gemini API key
• Inngest account

Installation

git clone https://github.com/GANGOUCH1/rag-pdf-assistant.git
cd rag-pdf-assistant
pip install uv
uv sync

Configure environment

cp .env.example .env
# Fill in your API keys in .env

Run

# Terminal 1 — Start FastAPI
uvicorn main:app --reload

# Terminal 2 — Start Inngest dev server
npx inngest-cli@latest dev

# Terminal 3 — Start Streamlit
streamlit run streamlit_app.py

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💡 Why this project stands out

Unlike typical RAG tutorials, this app is built for the real world:

• No silent failures — every job is tracked, retried, and observable
• Scalable by design — rate limiting and concurrency controls built in
• Grounded responses — Gemini only answers from your document context
• Production patterns — async background processing with full observability

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📜 License

MIT