Internal RAG CX Data Preprocessing Demo

A robust data preprocessing pipeline for Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG) systems, deployed on Hugging Face as a Model repository (free tier). Built with over 5 years of AI expertise since 2020, this demo focuses on cleaning and preparing call center datasets for enterprise-grade CX applications in SaaS, HealthTech, FinTech, and eCommerce. It integrates advanced data wrangling with Pandas, ensuring high-quality FAQs for downstream RAG/CAG pipelines, and is compatible with Amazon SageMaker and Azure AI for scalable modeling.

Technical Architecture

Data Preprocessing Pipeline

The core of this demo is a comprehensive data preprocessing pipeline designed to clean raw call center datasets:

Data Ingestion:
- Parses CSVs with pd.read_csv, using io.StringIO for embedded data, with explicit quotechar and escapechar to handle complex strings.
- Handles datasets with columns: call_id, question, answer, language.
Junk Data Cleanup:
- Null Handling: Drops rows with missing question or answer using df.dropna().
- Duplicate Removal: Eliminates redundant FAQs via df[~df['question'].duplicated()].
- Short Entry Filtering: Excludes questions <10 chars or answers <20 chars with df[(df['question'].str.len() >= 10) & (df['answer'].str.len() >= 20)].
- Malformed Detection: Uses regex ([!?]{2,}|(Invalid|N/A)) to filter invalid questions.
- Standardization: Normalizes text (e.g., "mo" to "month") and fills missing language with "en".
Output:
- Generates cleaned_call_center_faqs.csv for downstream modeling.
- Provides cleanup stats: nulls removed, duplicates removed, short entries filtered, malformed entries detected.

Enterprise-Grade Modeling Compatibility

The cleaned dataset is optimized for:

Amazon SageMaker: Ready for training BERT-based models (e.g., bert-base-uncased) for intent classification or FAQ retrieval, deployable via SageMaker JumpStart.
Azure AI: Compatible with Azure Machine Learning pipelines for fine-tuning models like DistilBERT in Azure Blob Storage, enabling scalable CX automation.
LLM Integration: Supports fine-tuning LLMs (e.g., distilgpt2) for generative tasks, leveraging your FastAPI experience for API-driven inference.

Performance Monitoring and Visualization

The demo includes a performance monitoring suite:

Processing Time Tracking: Measures data ingestion, cleaning, and output times using time.perf_counter(), reported in milliseconds.
Cleanup Metrics: Tracks the number of nulls, duplicates, short entries, and malformed entries removed.
Visualization: Uses Matplotlib to plot a bar chart (cleanup_stats.png):
- Bars: Number of entries removed per category (Nulls, Duplicates, Short, Malformed).
- Palette: Professional muted colors for enterprise aesthetics.

Gradio Interface for Interactive Demo

The demo is accessible via Gradio, providing an interactive data preprocessing experience:

Input: Upload a sample call center CSV or use the embedded demo dataset.
Outputs:
- Cleaned Dataset: Download cleaned_call_center_faqs.csv.
- Cleanup Stats: Detailed breakdown (e.g., “Cleaned FAQs: 6; removed 4 junk entries: 2 nulls, 1 duplicates, 1 short, 0 malformed”).
- Performance Plot: Visual metrics for processing time and cleanup stats.
Styling: Custom dark theme CSS (#2a2a2a background, blue buttons) for a sleek, enterprise-ready UI.

Setup

Clone this repository to a Hugging Face Model repository (free tier, public).
Add requirements.txt with dependencies (gradio==4.44.0, pandas==2.2.3, matplotlib==3.9.2, etc.).
Upload app.py (includes embedded demo dataset for seamless deployment).
Configure to run with Python 3.9+, CPU hardware (no GPU).

Usage

Upload CSV: Provide a call center CSV in the Gradio UI, or use the default demo dataset.
Output:
- Cleaned Dataset: Download the processed cleaned_call_center_faqs.csv.
- Cleanup Stats: “Cleaned FAQs: 6; removed 4 junk entries: 2 nulls, 1 duplicates, 1 short, 0 malformed”.
- Performance Plot: Visual metrics for processing time and cleanup stats.

Example:

Input CSV: Sample dataset with 10 FAQs, including 2 nulls, 1 duplicate, 1 short entry.
Output:
- Cleaned Dataset: 6 FAQs in cleaned_call_center_faqs.csv.
- Cleanup Stats: “Cleaned FAQs: 6; removed 4 junk entries: 2 nulls, 1 duplicates, 1 short, 0 malformed”.
- Plot: Processing Time (Ingestion: 50ms, Cleaning: 30ms, Output: 10ms), Cleanup Stats (Nulls: 2, Duplicates: 1, Short: 1, Malformed: 0).

Technical Details

Stack:

Pandas: Data wrangling and preprocessing for call center CSVs.
Gradio: Interactive UI for real-time data preprocessing demos.
Matplotlib: Performance visualization with bar charts.
FastAPI Compatibility: Designed with API-driven preprocessing in mind, leveraging your experience with FastAPI for scalable deployments.

Free Tier Optimization: Lightweight with CPU-only dependencies, no GPU required.

Extensibility: Ready for integration with RAG/CAG pipelines, and cloud deployments on AWS Lambda or Azure Functions.

Purpose

This demo showcases expertise in data preprocessing for AI-driven CX automation, focusing on call center data quality. Built on over 5 years of experience in AI, data engineering, and enterprise-grade deployments, it demonstrates the power of Pandas-based data cleaning for RAG/CAG pipelines, making it ideal for advanced CX solutions in call center environments.

Latest Update

Status Update: Configuration missing in update.ini for ghostai1/internalRAGCX: Expected sections InternalragcxUpdate and InternalragcxEmojis - May 09, 2025 📝

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Placeholder update text.

Future Enhancements

Real-Time Streaming: Add support for real-time data streaming from Kafka for live preprocessing.
FastAPI Deployment: Expose preprocessing pipeline via FastAPI endpoints for production-grade use.
Advanced Validation: Implement stricter data validation rules using machine learning-based outlier detection.
Cloud Integration: Enhance compatibility with AWS Glue or Azure Data Factory for enterprise data pipelines.

Website: https://ghostainews.com/
Discord: https://discord.gg/BfA23aYz