--- 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**: Configuration missing in update.ini for ghostai1/internalRAGCX: Expected sections InternalragcxUpdate and InternalragcxEmojis - May 28, 2025 📝 - - January 14, 2026 📝 - - January 12, 2026 📝 - - January 09, 2026 📝 - - January 07, 2026 📝 - - January 04, 2026 📝 - - January 02, 2026 📝 - - December 30, 2025 📝 - - December 28, 2025 📝 - - December 25, 2025 📝 - - December 23, 2025 📝 - - December 20, 2025 📝 - - December 18, 2025 📝 - - December 15, 2025 📝 - - December 13, 2025 📝 - - December 10, 2025 📝 - - December 08, 2025 📝 - - December 06, 2025 📝 - - December 05, 2025 📝 - - December 03, 2025 📝 - - December 01, 2025 📝 - - November 30, 2025 📝 - - November 28, 2025 📝 - - November 26, 2025 📝 - - November 25, 2025 📝 - - November 23, 2025 📝 - - November 21, 2025 📝 - - November 20, 2025 📝 - - November 18, 2025 📝 - - November 16, 2025 📝 - - November 14, 2025 📝 - - November 10, 2025 📝 - - November 07, 2025 📝 - - November 02, 2025 📝 - - October 31, 2025 📝 - - October 29, 2025 📝 - - October 28, 2025 📝 - - October 26, 2025 📝 - - October 24, 2025 📝 - - October 23, 2025 📝 - - October 21, 2025 📝 - - October 19, 2025 📝 - - October 18, 2025 📝 - - October 16, 2025 📝 - - October 14, 2025 📝 - - October 13, 2025 📝 - - October 11, 2025 📝 - - October 10, 2025 📝 - - October 07, 2025 📝 - - October 05, 2025 📝 - - October 02, 2025 📝 - - September 30, 2025 📝 - - September 28, 2025 📝 - - September 27, 2025 📝 - - September 25, 2025 📝 - - September 22, 2025 📝 - - September 20, 2025 📝 - - September 17, 2025 📝 - - September 15, 2025 📝 - - September 12, 2025 📝 - - September 10, 2025 📝 - - September 07, 2025 📝 - - September 05, 2025 📝 - - September 03, 2025 📝 - - September 02, 2025 📝 - - August 31, 2025 📝 - - August 28, 2025 📝 - - August 26, 2025 📝 - - August 23, 2025 📝 - - August 21, 2025 📝 - - August 19, 2025 📝 - - August 18, 2025 📝 - - August 16, 2025 📝 - - August 15, 2025 📝 - - August 14, 2025 📝 - - August 13, 2025 📝 - - August 12, 2025 📝 - - August 11, 2025 📝 - - August 10, 2025 📝 - - August 09, 2025 📝 - - August 08, 2025 📝 - - August 07, 2025 📝 - - August 06, 2025 📝 - - August 05, 2025 📝 - - August 04, 2025 📝 - - August 03, 2025 📝 - - August 02, 2025 📝 - - August 01, 2025 📝 - - July 31, 2025 📝 - - July 30, 2025 📝 - - July 29, 2025 📝 - - July 28, 2025 📝 - - July 27, 2025 📝 - - July 26, 2025 📝 - - July 25, 2025 📝 - - July 24, 2025 📝 - - July 23, 2025 📝 - - July 22, 2025 📝 - - July 21, 2025 📝 - - July 20, 2025 📝 - - July 19, 2025 📝 - - July 18, 2025 📝 - - July 17, 2025 📝 - - July 16, 2025 📝 - - July 15, 2025 📝 - - July 14, 2025 📝 - - July 11, 2025 📝 - - July 10, 2025 📝 - - July 09, 2025 📝 - - July 08, 2025 📝 - - July 07, 2025 📝 - - July 06, 2025 📝 - - July 05, 2025 📝 - - July 04, 2025 📝 - - July 03, 2025 📝 - - July 02, 2025 📝 - - July 01, 2025 📝 - - June 30, 2025 📝 - - June 29, 2025 📝 - - June 28, 2025 📝 - - June 27, 2025 📝 - - June 26, 2025 📝 - - June 25, 2025 📝 - - June 24, 2025 📝 - - June 23, 2025 📝 - - June 22, 2025 📝 - - June 21, 2025 📝 - - June 20, 2025 📝 - - June 19, 2025 📝 - - June 18, 2025 📝 - - June 17, 2025 📝 - - June 16, 2025 📝 - - June 15, 2025 📝 - - June 14, 2025 📝 - - June 13, 2025 📝 - - June 12, 2025 📝 - - June 11, 2025 📝 - - June 10, 2025 📝 - - June 09, 2025 📝 - - June 08, 2025 📝 - - June 07, 2025 📝 - - June 06, 2025 📝 - - June 05, 2025 📝 - - June 04, 2025 📝 - - June 03, 2025 📝 - - June 02, 2025 📝 - - June 01, 2025 📝 - - May 31, 2025 📝 - - May 30, 2025 📝 - - May 29, 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