Generating Data Dictionary for Excel Files Using OpenPyxl and AI Agents

Introduction

Each firm I labored for till as we speak, there it was: the resilient MS Excel.

Excel was first launched in 1985 and has remained robust till as we speak. It has survived the rise of relational databases, the evolution of many programming languages, the Web with its infinite variety of on-line functions, and at last, additionally it is surviving the period of the AI.

Phew!

Do you’ve gotten any doubts about how resilient Excel is? I don’t.

I feel the rationale for that’s its practicality to begin and manipulate a doc shortly. Take into consideration this case: we’re at work, in a gathering, and all of the sudden the management shares a CSV file and asks for a fast calculation or a number of calculated numbers. Now, the choices are:

1. Open an IDE (or a pocket book) and begin coding like loopy to generate a easy matplotlib graphic;

2. Open Energy BI, import the info, and begin making a report with dynamic graphics.

3. Open the CSV in Excel, write a few formulation, and create a graphic.

I can’t communicate for you, however many occasions I’m going for choice 3. Particularly as a result of Excel recordsdata are suitable with all the things, simply shareable, and beginner-friendly.

I’m saying all of this as an Introduction to make my level that I don’t suppose that Excel recordsdata are going away anytime quickly, even with the quick improvement of AI. Many will love that, many will hate that.

So, my motion right here was to leverage AI to make Excel recordsdata higher documented. One of many foremost complaints of information groups about Excel is the dearth of greatest practices and reproducibility, provided that the names of the columns can have any names and knowledge varieties, however zero documentation.

So, I’ve created an AI Agent that reads the Excel file and creates this small documentation. Right here is the way it works:

The Excel file is transformed to CSV and fed into the Giant Language Mannequin (LLM).
The AI Agent generates the info dictionary with column data (variable identify, knowledge kind, description).
The info dictionary will get added as feedback to the Excel file’s header.
Output file saved with feedback.

Okay. Palms-on now. Let’s get that executed on this tutorial.

Code

Let’s code! | Picture generated by AI. Meta Llama, 2025. https://meta.ai

We’ll start by organising a digital atmosphere. Create a venv with the instrument of your selection, akin to Poetry, Python Venv, Anaconda, or UV. I actually like UV, as it’s the quickest and the only, for my part. When you’ve got UV put in [5], open a terminal and create your venv.

uv init data-docs
cd data-docs
uv venv
uv add streamlit openpyxl pandas agno mcp google-genai

Now, allow us to import the mandatory modules. This venture was created with Python 3.12.1, however I imagine Python 3.9 or larger would possibly do the trick already. We’ll use:

Agno: for the AI Agent administration
OpenPyxl: for the manipulation of Excel recordsdata
Streamlit: for the front-end interface.
Pandas, OS, JSON, Dedent and Google Genai as help modules.

# Imports
import os
import json
import streamlit as st
from textwrap import dedent

from agno.agent import Agent
from agno.fashions.google import Gemini
from agno.instruments.file import FileTools

from openpyxl import load_workbook
from openpyxl.feedback import Remark
import pandas as pd

Nice. The following step is creating the capabilities we’ll have to deal with the Excel recordsdata and to create the AI Agent.

Discover that each one the capabilities have detailed docstrings. That is intentional as a result of LLMs use docstrings to know what a given perform does and determine whether or not to make use of it or not as a instrument.

So, when you’re utilizing Python capabilities as Instruments for an AI Agent, be certain to make use of detailed docstrings. These days, with free copilots akin to Windsurf [6] it’s even simpler to create them.

Changing the file to CSV

This perform will:

Take the Excel file and browse solely the primary 10 rows. That is sufficient for us to ship to the LLM. Doing that, we’re additionally stopping sending too many tokens as enter and making this agent too costly.
Save the file as CSV to make use of as enter for the AI Agent. The CSV format is simpler for the mannequin to soak up, as it’s a bunch of textual content separated by commas. And we all know LLMs shine working with textual content.

Right here is the perform.

def convert_to_csv(file_path:str):
   """
    Use this instrument to transform the excel file to CSV.

    * file_path: Path to the Excel file to be transformed
    """
   # Load the file  
   df = pd.read_excel(file_path).head(10)

   # Convert to CSV
   st.write("Changing to CSV... :leftwards_arrow_with_hook:")
   return df.to_csv('temp.csv', index=False)

Let’s transfer on.

Creating the Agent

The following perform creates the AI agent. I’m utilizing Agno [1], as it is rather versatile and simple to make use of. I additionally selected the mannequin Gemini 2.0 Flash. Throughout the check part, this was the best-performing mannequin producing the info docs. To make use of it, you’ll need an API Key from Google. Don’t overlook to get one right here [7].

The perform:

Receives the CSV output from the earlier perform.
Passes by means of the AI Agent, which generates the info dictionary with column identify, description, and knowledge kind.
Discover that the description argument is the immediate for the agent. Make it detailed and exact.
The info dictionary shall be saved as a JSON file utilizing a instrument referred to as FileTools that may learn and write recordsdata.
I’ve arrange retries=2 so we are able to work round any error on a primary strive.

def create_agent(apy_key):
    agent = Agent(
        mannequin=Gemini(id="gemini-2.0-flash", api_key=apy_key),
        description= dedent("""
                            You might be an agent that reads the temp.csv dataset offered to you and 
                            based mostly on the identify and knowledge kind of every column header, decide the next data:
                            - The info kinds of every column
                            - The outline of every column
                            - The primary column numer is 0

                            Utilizing the FileTools offered, create a knowledge dictionary in JSON format that features the beneath data:
                            {<ColNumber>: {ColName: <ColName>, DataType: <DataType>, Description: <Description>}}

                            In case you are unable to find out the info kind or description of a column, return 'N/A' for that column for the lacking values.
                            
                            """),
        instruments=[ FileTools(read_files=True, save_files=True) ],
        retries=2,
        show_tool_calls=True
        )

    return agent

Okay. Now we’d like one other perform to save lots of the info dictionary to the file.

Including Information Dictionary to the File’s Header

That is the final perform to be created. It would:

Get the info dictionary json from the earlier step and the unique Excel file.
Add the info dictionary to the file’s header as feedback.
Save the output file.
As soon as the file is saved, it shows a obtain button for the consumer to get the modified file.

def add_comments_to_header(file_path:str, data_dict:dict="data_dict.json"):
    """
    Use this instrument so as to add the info dictionary {data_dict.json} as feedback to the header of an Excel file and save the output file.

    The perform takes the Excel file path as argument and provides the {data_dict.json} as feedback to every cell
    Begin counting from column 0
    within the first row of the Excel file, utilizing the next format:    
        * Column Quantity: <column_number>
        * Column Identify: <column_name>
        * Information Kind: <data_type>
        * Description: <description>

    Parameters
    ----------
    * file_path : str
        The trail to the Excel file to be processed
    * data_dict : dict
        The info dictionary containing the column quantity, column identify, knowledge kind, description, and variety of null values

    """
    
    # Load the info dictionary
    data_dict = json.load(open(data_dict))

    # Load the workbook
    wb = load_workbook(file_path)

    # Get the energetic worksheet
    ws = wb.energetic

    # Iterate over every column within the first row (header)
    for n, col in enumerate(ws.iter_cols(min_row=1, max_row=1)):
        for header_cell in col:
            header_cell.remark = Remark(dedent(f"""
                              ColName: {data_dict[str(n)]['ColName']}, 
                              DataType: {data_dict[str(n)]['DataType']},
                              Description: {data_dict[str(n)]['Description']}
    """),'AI Agent')

    # Save the workbook
    st.write("Saving File... :floppy_disk:")
    wb.save('output.xlsx')

    # Create a obtain button
    with open('output.xlsx', 'rb') as f:
        st.download_button(
            label="Obtain output.xlsx",
            knowledge=f,
            file_name='output.xlsx',
            mime='utility/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
        )

Okay. The following step is to attach all of this collectively on a Streamlit front-end script.

Streamlit Entrance-Finish

On this step, I might have created a special file for the front-end and imported the capabilities in there. However I made a decision to make use of the identical file, so let’s begin with the well-known:

if __name__ == "__main__":

First, a few traces to configure the web page and messages displayed within the Internet Software. We’ll use the content material centered on the web page, and there may be some details about how the App works.

# Config web page Streamlit
    st.set_page_config(structure="centered", 
                       page_title="Information Docs", 
                       page_icon=":paperclip:",
                       initial_sidebar_state="expanded")
    
    # Title
    st.title("Information Docs :paperclip:")
    st.subheader("Generate a knowledge dictionary in your Excel file.")
    st.caption("1. Enter your Gemini API key and the trail of the Excel file on the sidebar.")
    st.caption("2. Run the agent.")
    st.caption("3. The agent will generate a knowledge dictionary and add it as feedback to the header of the Excel file.")
    st.caption("ColName: <ColName> | DataType: <DataType> | Description: <Description>")
    
    st.divider()

Subsequent, we’ll arrange the sidebar, the place the consumer can enter their API Key from Google and choose a .xlsx file to be modified.

There’s a button to run the appliance, one other to reset the app state, and a progress bar. Nothing too fancy.

with st.sidebar:
        # Enter your API key
        st.caption("Enter your API key and the trail of the Excel file.")
        api_key = st.text_input("API key: ", placeholder="Google Gemini API key", kind="password")
        
        # Add file
        input_file = st.file_uploader("File add", 
                                       kind='xlsx')
        

        # Run the agent
        agent_run = st.button("Run")

        # progress bar
        progress_bar = st.empty()
        progress_bar.progress(0, textual content="Initializing...")

        st.divider()

        # Reset session state
        if st.button("Reset Session"):
            st.session_state.clear()
            st.rerun()

As soon as the run button is clicked, it triggers the remainder of the code to run the Agent. Right here is the sequence of steps carried out:

The primary perform known as to rework the file to CSV
The progress is registered on the progress bar.
The Agent is created.
Progress bar up to date.
A immediate is fed into the agent to learn the temp.csv file, create the info dictionary, and save the output to data_dictionary.json.
The info dictionary is printed on the display, so the consumer can see what was generated whereas it’s being saved to the Excel file.
The Excel file is modified and saved.

# Create the agent
    if agent_run:
        # Convert Excel file to CSV
        convert_to_csv(input_file)

        # Register progress
        progress_bar.progress(15, textual content="Processing CSV...")

        # Create the agent
        agent = create_agent(api_key)

        # Begin the script
        st.write("Operating Agent... :runner:")

        # Register progress
        progress_bar.progress(50, textual content="AI Agent is operating...")

        # Run the agent    
        agent.print_response(dedent(f"""
                                1. Use FileTools to learn the temp.csv as enter to create the info dictionary for the columns within the dataset. 
                                2. Utilizing the FileTools instrument, save the info dictionary to a file named 'data_dict.json'.
                                
                                """),
                        markdown=True)

        # Print the info dictionary
        st.write("Producing Information Dictionary... :page_facing_up:")
        with open('data_dict.json', 'r') as f:
            data_dict = json.load(f)
            st.json(data_dict, expanded=False)

        # Add feedback to header
        add_comments_to_header(input_file, 'data_dict.json')

        # Take away short-term recordsdata
        st.write("Eradicating short-term recordsdata... :wastebasket:")
        os.take away('temp.csv')
        os.take away('data_dict.json')    
    
    # If file exists, present success message
    if os.path.exists('output.xlsx'):
        st.success("Accomplished! :white_check_mark:")
        os.take away('output.xlsx')

    # Progress bar finish
    progress_bar.progress(100, textual content="Accomplished!")

That’s it. Here’s a demonstration of the agent in motion.

Information Docs added to your Excel File. Picture by the writer.

Stunning consequence!

Strive It

You possibly can strive the deployed app right here: https://excel-datadocs.streamlit.app/

Earlier than You Go

In my humble opinion, Excel recordsdata will not be going away anytime quickly. Loving or hating them, we’ll have to stay with them for some time.

Excel recordsdata are versatile, simple to deal with and share, thus they’re nonetheless very helpful for the routine ad-hoc duties at work.

Nonetheless, now we are able to leverage AI to assist us deal with these recordsdata and make them higher. Artificial Intelligence is touching so many factors of our lives. The routine and instruments at work are solely one other one.

Let’s benefit from AI and work smarter daily!

When you appreciated this content material, discover extra of my work in my web site and GitHub, shared beneath.