🧠 IntelliDoc AI – Local RAG Document Assistant

IntelliDoc AI is a fully local Retrieval-Augmented Generation (RAG) application that lets you upload PDF documents and ask intelligent questions about them using a locally running LLM.

Built with Streamlit + LangChain + Ollama + ChromaDB, it runs entirely on your machine — no external API calls required.

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🚀 Features

• 📄 Upload and chat with multiple PDF documents
• 🔎 Semantic search using vector embeddings
• 🤖 Local LLM inference via Ollama
• 🧠 Retrieval-Augmented Generation (RAG) pipeline
• 💬 Clean Streamlit chat interface
• 💾 Persistent vector store (ChromaDB)

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🏗️ Tech Stack

• Frontend: Streamlit
• LLM: Ollama (Qwen2.5 / Mistral supported)
• Embeddings: FastEmbed
• Vector Database: ChromaDB
• Framework: LangChain

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⚙️ Setup Instructions

1️⃣ Clone the Repository

git clone https://github.com/your-username/intellidoc-ai.git
cd intellidoc-ai

2️⃣ Create Virtual Environment

python -m venv venv

Activate:

Mac/Linux

source venv/bin/activate

Windows

venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Install & Setup Ollama

Download from:

https://ollama.com

Pull a model:

ollama pull qwen2.5

(Optional alternative)

ollama pull mistral

Ensure Ollama is running in the background.

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▶️ Run the Application

streamlit run app.py

Open in browser:

http://localhost:8501

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📂 How It Works

1. Upload a PDF
2. Text is split into chunks
3. Chunks are embedded using FastEmbed
4. Stored in ChromaDB
5. User query retrieves relevant chunks
6. Context + Question → LLM → Final Answer

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📁 Project Structure

.
├── app.py
├── rag.py
├── requirements.txt
├── chroma_db/
└── README.md

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🔐 Why Local RAG?

• No API costs
• Full data privacy
• Works offline
• Fully customizable
• Ideal for experimentation & research

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📌 Future Improvements

• DOCX / TXT support
• Hybrid retrieval (BM25 + embeddings)
• Streaming responses
• Cloud deployment
• Authentication support

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👨‍💻 Author

Ashutosh Zawar
MS Computer Science
AI/ML • Backend • Cloud • RAG Systems

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