Initial README for Internal RAG CX Data Preprocessing Demo

README.md

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+---
+tags: [model]
+---
+# 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**: Placeholder update - January 01, 2025 📝  
+- 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