# AI Document Assistant

An AI-powered Document Assistant built using RAG (Retrieval-Augmented Generation), FAISS, MCP (Model Context Protocol), Ollama, and Streamlit.

Upload PDF documents, ask questions about their content, generate summaries, extract keywords, and answer general knowledge questions using Wikipedia integration.

Features

Document Question Answering

• Ask questions about uploaded PDF documents.

• Retrieves relevant document chunks using FAISS vector search.

• Generates natural language answers using Ollama.

Document Summarization

• Generate concise summaries of uploaded documents.

Keyword Extraction

• Extract important keywords and topics from documents.

General Knowledge Questions

• Wikipedia integration for questions outside the uploaded document.

MCP Integration

• Exposes tools through MCP.

• Allows tool discovery and execution through MCP clients.

PDF Upload Support

• Upload PDF files directly from the Streamlit interface.

• Automatically creates embeddings and indexes documents for retrieval.

Streamlit Interface

• Simple and user-friendly chat interface.

• Upload PDFs and interact with documents in real time.

Screenshots

Streamlit Interface

MCP Server Connection

MCP Tools

Architecture

PDF
 │
 ▼
PDF Loader
 │
 ▼
Text Chunking
 │
 ▼
Embeddings
 │
 ▼
FAISS Vector Store
 │
 ▼
Retrieval
 │
 ▼
LLM (Ollama)
 │
 ▼
Answer Generation

Tech Stack

Backend

• Python

LLM

• Ollama

• Qwen 2.5 Coder 7B

Vector Database

• FAISS

Embeddings

• Sentence Transformers

Protocol

• MCP (Model Context Protocol)

Frontend

• Streamlit

External Knowledge

• Wikipedia API

Project Structure

AI-Document-Assistant/
│
├── datas/
│
├── screenshots/
│   ├── streamlit-ui.png
│   ├── mcp-server.png
│   └── mcp-tools.png
│
├── src/
│   ├── pdf_loader.py
│   ├── chunker.py
│   ├── embeddings.py
│   ├── vector_store.py
│   └── rag_store.py
│
├── tools/
│   ├── search_tool.py
│   ├── summary_tool.py
│   ├── keyword_tool.py
│   ├── qa_tool.py
│   └── wiki_tool.py
│
├── app.py
├── agent.py
├── build_rag.py
├── mcp_client.py
├── mcp_server.py
├── requirements.txt
├── README.md
└── .gitignore

Installation

Clone Repository

git clone https://github.com/yourusername/AI-Document-Assistant.git

cd AI-Document-Assistant

Create Virtual Environment

python -m venv .venv

Activate Environment

Windows:

.venv\Scripts\activate

Linux/macOS:

source .venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Install Ollama

Download and install Ollama:

https://ollama.com

Pull the model:

ollama pull qwen2.5-coder:7b

Start Ollama:

ollama serve

Run the Application

streamlit run app.py

Open:

http://localhost:8501

How It Works

Document Questions

Example:

What is MySQL Workbench?

The assistant:

1. Searches relevant document chunks.

2. Retrieves matching context using FAISS.

3. Sends context to Ollama.

4. Generates a final answer.

General Knowledge Questions

Example:

Who is Elon Musk?

The assistant:

1. Detects the question is not document-specific.

2. Uses Wikipedia.

3. Returns a concise answer.

MCP Tools

document_search

Search relevant document chunks.

document_summary

Generate document summaries.

document_keywords

Extract important keywords.

ask_document

Question answering over uploaded documents.

wiki_search

General knowledge lookup using Wikipedia.

Future Improvements

• Multi-PDF support

• Chat history memory

• Conversation context

• Source citations

• Hybrid Search (BM25 + Vector Search)

• Persistent Vector Database

• Docker deployment

• Authentication and user management

Author

Yadu