AI Document Assistant MCP Server

# 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.

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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.

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Screenshots

Streamlit Interface

MCP Server Connection

MCP Tools

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Architecture

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

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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

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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

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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

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Install Ollama

Download and install Ollama:

https://ollama.com

Pull the model:

ollama pull qwen2.5-coder:7b

Start Ollama:

ollama serve

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Run the Application

streamlit run app.py

Open:

http://localhost:8501

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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.

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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.

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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.

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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

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Author

Yadu

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