AI Office Assistant

A beginner-friendly AI project using:

• MCP (Model Context Protocol)
• RAG (Retrieval-Augmented Generation)
• Groq LLM
• ChromaDB
• Weather API
• Excel Automation
• Word Automation

Features

• Ask weather questions
• Query local PDF and DOCX documents
• Automatically save weather results to Excel
• Automatically generate Word reports
• MCP tool calling

mcp-rag-office-assistant/ │ ├── app.py ├── requirements.txt ├── .env ├── .gitignore ├── README.md │ ├── data/ │ ├── pdfs/ │ ├── docs/ │ └── vector_db/ │ ├── outputs/ │ ├── excel/ │ └── word/ │ ├── config/ │ ├── init.py │ ├── settings.py │ └── logger.py │ ├── llm/ │ ├── init.py │ ├── groq_model.py │ └── prompt.py │ ├── rag/ │ ├── init.py │ ├── loader.py │ ├── splitter.py │ ├── embeddings.py │ ├── vector_store.py │ └── retriever.py │ ├── mcp_server/ │ ├── init.py │ ├── server.py │ ├── tools.py │ └── schemas.py │ ├── services/ │ ├── init.py │ ├── weather_service.py │ ├── excel_service.py │ └── word_service.py │

1. LangChain

---

Purpose

• Build LLM applications
• Prompt Templates
• Chains
• Document handling
• Retrieval

Prompt

↓

Retriever

↓

LLM

↓

Answer

2. LangChain Community

---

User │ ▼ Groq LLM │ ├──────────────┐ ▼ ▼ MCP Tools RAG │ │ Weather API ChromaDB │ │ └──────┬───────┘ ▼ Final Answer │ ┌──────┴──────┐ ▼ ▼ Excel Word

Complete Architecture

                USER
                  │
                  ▼
             Groq LLM
                  │
          Tool Calling (MCP)
                  │
 ┌──────────┬─────────────┬─────────────┐
 ▼          ▼             ▼

Weather Excel Tool Word Tool │ │ │ ▼ ▼ ▼ OpenWeather Microsoft Microsoft API Excel Word │ │ ▼ ▼ Write Cells Write Report │ │ └──────┬──────┘ ▼ Save Documents

Then We'll Upgrade Even More

• After Office automation, we'll add more desktop tools.

Desktop Agent

├── Weather Tool ├── Excel Automation ├── Word Automation ├── Open Chrome ├── Read PDF ├── Search Documents (RAG) ├── Take Screenshot ├── File Explorer ├── Calculator ├── Notepad ├── Send Email ├── Voice Input (Optional) └── OCR (Optional)

🤖 MCP RAG Office Assistant

An AI-powered Office Assistant built using LLM + RAG + Tool Calling + Office Automation.

The assistant can understand user requests, decide the required action, retrieve information from documents, call external tools, and automatically generate Microsoft Excel and Word reports.

---

🚀 Project Overview

Traditional applications require users to manually search documents, collect information, and create reports.

This project demonstrates an AI Agent workflow:

User Query
    |
    |
    v
LLM (Groq)
    |
    |
    +----------------------+
    |                      |
    v                      v
Weather Tool              RAG Pipeline
    |                      |
    |                      |
Weather API          Document Retrieval
                           |
                           |
                           v
                      ChromaDB
                           |
                           |
                           v
                       Context
                           |
                           |
                           v
                      Groq LLM
                      
                           
             |
             |
             v

       Office Automation

       +-------------+
       |             |
       v             v

    Excel        Microsoft Word

---

🎯 Project Objective

Build a beginner-level AI Agent system that demonstrates:

• Large Language Model integration
• Retrieval Augmented Generation (RAG)
• Tool execution
• MCP architecture concepts
• Document understanding
• Automated report generation
• Desktop application automation

---

🧠 Technologies Used

Artificial Intelligence

Technology	Purpose
Groq LLM	Language model
LangChain	LLM application framework
RAG	Document question answering
ChromaDB	Vector database
HuggingFace Embeddings	Text embeddings

Backend

Technology	Purpose
Python	Programming language
PyWin32	Microsoft Office automation
Requests	API calls
Logging	Application monitoring

Office Automation

Application	Usage
Microsoft Excel	Generate reports
Microsoft Word	Create documents

---

📂 Project Structure

mcp-rag-office-assistant/

│
├── app.py
│
├── config/
│   |
│   ├── settings.py
│   └── logger.py
│
│
├── llm/
│   |
│   ├── grok_model.py
│   └── prompt.py
│
│
├── rag/
│   |
│   ├── loader.py
│   ├── splitter.py
│   ├── embeddings.py
│   ├── vector_store.py
│   ├── retriever.py
│   └── rag_pipeline.py
│
│
├── services/
│   |
│   ├── weather_service.py
│   ├── excel_service.py
│   ├── word_service.py
│   └── office_agent.py
│
│
├── agent/
│   |
│   └── office_agent_executor.py
│
│
├── data/
│   |
│   ├── pdf/
│   |
│   └── docs/
│
│
├── vector_db/
│
│
├── outputs/
│   |
│   ├── excel/
│   |
│   └── word/
│
│
└── requirements.txt

---

⚙️ Installation

1. Clone Project

git clone <repository-url>

cd mcp-rag-office-assistant

---

2. Create Virtual Environment

python -m venv .venv

Activate:

Windows

.venv\Scripts\activate

---

3. Install Requirements

pip install -r requirements.txt

---

4. Environment Variables

Create:

.env

Add:

GROQ_API_KEY=your_api_key

---

🔑 Groq Configuration

The project uses:

llama-3.3-70b-versatile

Model configuration:

config/settings.py

Example:

LLM_MODEL="llama-3.3-70b-versatile"

---

📚 RAG Pipeline

The RAG system follows this architecture:

Documents

(PDF/DOCX)

     |
     v

Document Loader

     |
     v

Text Splitter

     |
     v

Embedding Model

     |
     v

Chroma Vector Database

     |
     v

Retriever

     |
     v

Groq LLM

     |
     v

Final Answer

---

📄 Supported Documents

Currently supported:

• PDF
• DOCX

Place files:

data/pdf/

data/docs/

Example:

data/pdf/company_policy.pdf

---

🔎 RAG Workflow Example

User:

What is the leave policy?

System:

1. Searches company documents
2. Retrieves relevant chunks
3. Creates context
4. Sends context + question to Groq
5. Generates answer
6. Creates Word report

Output:

outputs/word/

Company_Policy_Report.docx

---

🌦 Weather Tool

Example:

Weather Hyderabad

Workflow:

User

 |

 v

Weather Detection

 |

 v

Weather API

 |

 v

Excel Generator

 |

 v

Word Generator

Output:

outputs/

├── excel

│    └── Weather_Report.xlsx


└── word

     └── Weather_Report.docx

---

📊 Excel Automation

The project uses:

pywin32

to control Microsoft Excel.

Workflow:

Python

 |

 v

Open Excel Application

 |

 v

Create Workbook

 |

 v

Write Data

 |

 v

Save File

Example:

Generated Excel:

Weather_Report.xlsx

---

📝 Word Automation

The project automatically opens Microsoft Word.

Workflow:

Python

 |

 v

Open Word

 |

 v

Create Document

 |

 v

Insert Content

 |

 v

Save DOCX

Example:

Weather_Report.docx

---

🧩 MCP Architecture Concept

This project follows MCP principles.

AI Agent

    |

    |

    +----------------+

    |                |

    v                v

 Resources        Tools

Resources

Information sources:

Examples:

• PDF files
• DOCX files
• Vector database

Tools

Actions:

Examples:

• Weather API
• Excel Writer
• Word Writer

---

▶️ Running Project

Start:

python app.py

Example:

You : Weather Hyderabad

Output:

Weather report generated successfully

Excel:
outputs/excel/Weather_Report.xlsx

Word:
outputs/word/Weather_Report.docx

---

Example RAG:

You : Explain company leave policy

Output:

Company_Policy_Report.docx

---

🧪 Testing

Test RAG:

python test_rag.py

Expected:

RAG Pipeline Ready

Answer Generated

Documents Retrieved: 3

---

🛠 Future Enhancements

Planned improvements:

1. Streamlit Interface

Web UI:

User
 |
 v
Streamlit
 |
 v
AI Agent

---

2. Complete MCP Server

Add:

• MCP Resources
• MCP Tools
• MCP Prompts

---

3. LangGraph Integration

Future workflow:

START

 |

Agent Node

 |

Decision Node

 |

Tool Node

 |

Response Node

 |

END

---

4. More Office Actions

Future tools:

• Email generation
• PowerPoint creation
• Excel analysis
• Meeting summary
• Report generation

---

🎓 Learning Concepts Covered

This project teaches:

✅ LLM Applications
✅ Prompt Engineering
✅ LangChain
✅ RAG Architecture
✅ Embeddings
✅ Vector Databases
✅ Tool Calling
✅ AI Agents
✅ MCP Concepts
✅ Office Automation
✅ Production Project Structure

---

👨‍💻 Author

AI Engineering Learning Project

Built for understanding:

LLM + RAG + Agents + MCP + Automation