TonyDain

Introduction

Handling large Excel files can be overwhelming, especially when searching for specific insights. The Excel Analyzer is a powerful Streamlit application designed to make data querying seamless. Using Retrieval-Augmented Generation (RAG), this tool allows users to ask questions about their Excel data and receive AI-generated responses.

With technologies like LangChain, Hugging Face Embeddings, FAISS, and Google Gemini API, the Excel Analyzer ensures fast, accurate, and context-aware answers. Let’s explore how it works, its setup, and how you can customize it.

🔧 Technologies Used

• Streamlit – Creates an interactive web application.
• Pandas – Loads and manipulates Excel data.
• LangChain – Orchestrates the RAG pipeline.
• Hugging Face Embeddings – Generates high-quality vector representations.
• FAISS – Manages an efficient vector database.
• Google Gemini API – Provides intelligent, AI-powered answers.

⚙️ How to Set Up the Excel Analyzer

Step 1: Install Dependencies

To get started, install the required Python libraries:

pip install streamlit pandas sentence-transformers faiss-cpu langchain google-generativeai

Step 2: Configure the Google Gemini API Key

To use the AI-powered response generation, you need a Google Gemini API key. Enter the key in the sidebar of the application after launching it.

Step 3: Upload an Excel File

Once the application is running, simply upload an Excel file using the file uploader.

Step 4: Ask Questions

Use the text input field to ask questions about your dataset. The AI model will retrieve relevant data and generate insightful answers.

📁 Code Structure

The project is structured as follows:

/excel-analyzer
│── app.py               # Main application logic
│── run_app.bat          # Batch file to start the Streamlit app
│── documentation.txt    # Technical documentation

app.py

This file contains the core logic of the Excel Analyzer, including:

• User Interface (UI) – Uses Streamlit components for file upload and question input.
• Data Processing – Loads and chunks the data for efficient retrieval.
• Embedding Generation – Converts chunks into vector embeddings.
• Query Processing – Finds the most relevant data chunks using FAISS.
• Answer Generation – Sends the query and relevant data to the Google Gemini API for response generation.

run_app.bat

A simple batch file to launch the application:

@echo off
echo Starting Streamlit app...
streamlit run app.py
pause

• @echo off – Hides unnecessary command line output.
• echo – Displays a message indicating the app is starting.
• streamlit run app.py – Runs the Streamlit application.
• pause – Keeps the terminal open after execution.

🧠 How the RAG Pipeline Works

1️⃣ Data Loading

The Excel file is loaded into a Pandas DataFrame.

2️⃣ Data Chunking

Since large datasets are difficult to process, the data is divided into smaller, manageable chunks using CharacterTextSplitter.

3️⃣ Embedding Generation

Each chunk is transformed into a numerical embedding using Hugging Face Embeddings.

4️⃣ Vector Storage (FAISS)

The embeddings are stored in FAISS, a vector database that enables fast retrieval.

5️⃣ Retrieval Process

When a question is asked, its embedding is generated and matched against stored chunks to find the most relevant data.

6️⃣ Response Generation

The retrieved data is sent to Google Gemini API, which generates an accurate and context-aware answer.

🎨 Customization Options

You can modify various aspects of the Excel Analyzer:

• Adjust chunk size – Optimize performance by changing the chunk size in app.py.
• Change language model – Modify the model_name parameter to use a different AI model.
• Enhance UI – Streamlit offers multiple ways to customize the UI.

🔍 Troubleshooting Guide

If you encounter any issues, here are some common solutions:

Issue	Solution
App fails to start	Ensure all required libraries are installed (pip install -r requirements.txt)
Google Gemini API key error	Verify that the API key is valid and correctly entered
Incorrect answers	Try adjusting chunking parameters or modifying the AI prompt

🚀 Conclusion

The Excel Analyzer brings the power of AI to Excel file analysis, making it easier to extract insights from large datasets. Whether you’re a data analyst, researcher, or business professional, this tool enhances efficiency and provides quick, AI-driven answers.