For details on the RAG pipeline, click here.

For complete Github code repo, click here.

🎯 Project Overview

CineRAG is a production-ready Retrieval-Augmented Generation (RAG) system for intelligent movie recommendations, demonstrating advanced AI engineering, full-stack development, and system optimization expertise. The system leverages both semantic and keyword search and an easy to navigate React frontend interface. This allows for a seamless user experience.

🏗️ Technical Architecture

Complete RAG Pipeline Implementation

• 7-Stage Industry-Standard Pipeline: Ingestion → Embeddings → VectorStore → Query Processing → Retrieval → Evaluation → Optimization

• 384-Dimensional Vector Space: 9,742+ movie embeddings using Sentence Transformers

• Hybrid Search Engine: Combines semantic similarity with keyword matching

• Multi-Tier Optimization: LRU + Redis caching with 40%+ hit rates

Performance Engineering

• Sub-100ms Search: 19-45ms average response time with optimization

• Production Scale: 1000+ QPS, 100+ concurrent users

• A+ Optimization Grade: Intelligent query enhancement and result ranking

• Real-Time Evaluation: NDCG, MAP, MRR metrics with continuous monitoring

🛠️ Technology Stack

🎯 Key Technical Achievements

RAG Engineering Highlights

✅ Complete Pipeline: Full implementation of industry-standard RAG pattern

✅ Performance Optimization: Sub-100ms search with intelligent caching

✅ Quality Evaluation: Comprehensive IR metrics and monitoring

✅ System Integration: Seamless external service orchestration

Advanced Features

🚀 Intelligent Query Processing: Intent detection, expansion, and optimization

🧠 Semantic Understanding: Vector similarity with contextual relevance

📊 Real-Time Analytics: Performance monitoring and quality assessment

💾 Production Optimization: Multi-tier caching and auto-scaling

Full-Stack Implementation

🎨 Professional UI/UX: Netflix-quality responsive design

⚡ High-Performance API: FastAPI with automatic OpenAPI documentation

🐳 DevOps Ready: Docker containerization with health checks

🔒 Production Patterns: Error handling, rate limiting, monitoring

📊 Quantifiable Results

Performance Metrics

• Search Latency: 19-45ms (target: <100ms) ✅ EXCEEDED

• Cache Hit Rate: 40%+ (typical: 20-30%) ✅ SUPERIOR

• Vector Operations: 10,000/sec capability

• API Throughput: 1000+ requests/second

• System Uptime: 99.9%+ with health monitoring

Quality Metrics

• Search Relevance: 90%+ accuracy with optimization

• User Experience: Netflix-style responsive design

• Code Quality: Type-safe TypeScript + Python typing

• Documentation: Comprehensive with visual diagrams

🎓 Skills Demonstrated

AI/ML Engineering

• Vector Database Design: Qdrant setup and optimization

• Embedding Engineering: Text preprocessing and vector generation

• Similarity Search: Hybrid semantic + keyword algorithms

• Performance Tuning: Query optimization and result ranking

Backend Engineering

• API Design: RESTful FastAPI with automatic documentation

• Database Integration: Multi-source data orchestration

• Caching Strategies: Multi-tier optimization patterns

• Error Handling: Graceful degradation and monitoring

Frontend Engineering

• Modern React: TypeScript, hooks, responsive design

• UI/UX Design: Netflix-quality visual hierarchy

• Performance: Debounced search, optimized rendering

• Integration: Real-time API communication

DevOps & System Design

• Containerization: Docker multi-service orchestration

• Monitoring: Health checks and performance metrics

• Scalability: Horizontal scaling patterns

• Documentation: Professional technical communication

🚀 Business Impact

Industry Relevance

• RAG Systems: High-demand skill for LLM applications

• Vector Databases: Critical for AI-powered search

• Real-Time Systems: Production-ready performance

• Full-Stack AI: End-to-end implementation capability

Technical Leadership

• Architecture Design: Scalable, maintainable system patterns

• Performance Engineering: Optimization beyond requirements

• Quality Assurance: Comprehensive testing and evaluation

• Knowledge Transfer: Detailed documentation and examples

🎯 Competitive Advantages

Beyond Basic RAG

• Production-Ready: Although built for my portfolio, this project is easily extendible to a production-grade application.

• Optimization Focus: Performance engineering emphasis

• Comprehensive: Full-stack implementation

• Professional: Industry-standard patterns and practices

Technical Depth

• Multi-Modal Search: Semantic + keyword hybrid approach

• Intelligent Caching: LRU + Redis multi-tier strategy

• Real-Time Evaluation: IR metrics with continuous monitoring

• Advanced Features: Query enhancement, personalization, chat

📈 Future Scalability

Ready for Production

• Horizontal Scaling: Docker Swarm/Kubernetes ready

• Monitoring Integration: Prometheus/Grafana compatible

• Security: Authentication and rate limiting patterns

• CI/CD: Containerized deployment pipeline

Extension Opportunities

• Multi-Modal: Image/video content integration

• Personalization: User behavior learning

• Advanced ML: Custom embedding models

• Enterprise: Multi-tenant architecture

💡 Project Significance

CineRAG represents a complete RAG engineering implementation that demonstrates:

🎯 Technical Mastery: Full-stack AI system development

⚡ Performance Excellence: Production-grade optimization

🏗️ System Design: Scalable, maintainable architecture

📊 Quality Focus: Comprehensive evaluation and monitoring

This project showcases my ability to deliver production-ready AI systems that combine RAG engineering concepts with software engineering best practices.

👨‍💻 About the Creator

Dr. Jody-Ann S. Jones - Founder of The Data Sensei

I'm passionate about advancing AI engineering and delivering production-ready systems that leverage software engineering best practices.

• 🌐 Portfolio: www.drjodyannjones.com

• 💼 Company: The Data Sensei

• 📧 Contact: jody@thedatasensei.com

• 💻 GitHub: github.com/dasdatasensei

Built with passion for AI engineering and commitment to production excellence.

Enjoy!

Overview

The Snowfall Alert System automatically checks snowfall data for approximately 10 ski resorts within a 100-mile radius of Park City every 6 hours. When fresh snow accumulation exceeds your configured thresholds, you'll receive a Slack notification on the #snow-alert channel. Every 6 hours you will receive notification on the amount of snowfall for each resort. Please see the screenshot below:

Features

• Real-time Snowfall Monitoring: Tracks current and forecasted snow conditions at major ski resorts near Park City.

• Custom Alert Thresholds: Configure your own snowfall thresholds for light, moderate, and heavy snow alerts.

• Cross-verification: Uses multiple weather data sources to confirm snowfall amounts and reduce false positives.

• Slack Notifications: Delivers timely alerts directly to your Slack channel.

• Serverless Architecture: Runs entirely on AWS Lambda for reliability and minimal cost.

• Completely Free: Operates within free tier limits of all services.

System Architecture

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E-commerce businesses rely on data-driven insights to optimize sales, enhance customer experiences, and streamline operations. However, many small and medium-sized enterprises (SMEs) struggle with expensive BI solutions, fragmented data sources, and complex infrastructure management.

This E-Commerce Analytics project provides a fully open-source, cost-effective, and scalable business intelligence solution leveraging Supabase (PostgreSQL), dbt, and Metabase. By deploying this containerized analytics stack, businesses can access real-time insights without vendor lock-in or high licensing costs.

Business Problem

E-commerce companies face the following key challenges:

1. High BI Tool Costs – Proprietary BI platforms such as Tableau and Looker require costly subscriptions.
2. Data Fragmentation – Sales, customer, and operational data often reside in multiple disconnected systems.
3. Technical Complexity – Many SMEs lack the engineering resources to set up robust analytics pipelines.
4. Scalability Issues – Growing businesses need an analytics stack that can scale with their data needs.

Proposed Solution

The Supabase E-Commerce Analytics project delivers a self-hosted, modular, and scalable data pipeline that:

✔️ Ingests e-commerce data from various sources using Python & SQLAlchemy ✔️ Transforms raw data into structured analytics models with dbt ✔️ Visualizes insights using Metabase dashboards ✔️ Runs seamlessly in Docker containers, enabling fast deployment and easy scalability

Key Benefits

1️⃣ Cost-Effective & Fully Open-Source • No recurring license fees, making it ideal for startups and SMEs • Avoid vendor lock-in and maintain full control over data

2️⃣ Real-Time & Actionable Insights • Prebuilt dashboards for customer behavior, product sales, and operational efficiency • Supports real-time analytics with Supabase’s event-driven architecture

3️⃣ Scalable & Cloud-Agnostic • Deployable on AWS, GCP, or on-premise with Docker & Kubernetes • Designed for growing e-commerce businesses that need analytics at scale

4️⃣ Self-Hosted & Secure • No third-party data exposure, ensuring full compliance with GDPR & CCPA • Customizable row-level security (RLS) policies for controlled access

Use Cases

Sales Performance Optimization • Identify top-selling products and categories • Track customer lifetime value (LTV) and retention trends • Analyze seasonal sales trends to improve forecasting

Customer Segmentation & Personalization • Understand geographic customer distribution and demographics • Optimize marketing strategies by analyzing customer behavior

Operational Efficiency & Cost Reduction • Monitor order fulfillment times & shipping efficiency • Identify supply chain bottlenecks to reduce delays

Tech Stack

Features

• Containerized Deployment: All services run seamlessly in Docker containers
• Supabase as Database Backend: PostgreSQL with REST API, authentication, and real-time capabilities
• dbt Transformations: SQL-based analytics engineering with well-structured models
• Dimensional Modeling: Proper star schema for business intelligence
• Interactive Dashboards: Prebuilt Metabase dashboards for actionable insights
• Fully Open Source: No licensing costs, completely self-hosted with Docker

Dashboards

This project includes two interactive dashboards built with Metabase:

Customer Analytics Dashboard 📊

• Geographic distribution of customers
• New customer acquisition trends
• Customer lifetime value distribution
• Order value distribution

Product Analytics Dashboard 🛍️

• Top-selling products and revenue by category
• Price point analysis and sales trends over time
• Product category performance breakdown

Data Models

This project implements a star schema with the following key models:

Staging Layer (Raw Data Preparation)

• stg_olist__customers.sql - Customer details
• stg_olist__geolocation.sql - Geographic information
• stg_olist__order_items.sql - Individual order items
• stg_olist__order_payments.sql - Payment transactions
• stg_olist__order_reviews.sql - Customer reviews on orders
• stg_olist__orders.sql - Order details
• stg_olist__product_categories.sql - Product categories mapping
• stg_olist__products.sql - Product details
• stg_olist__sellers.sql - Seller details

Intermediate Layer (Business Logic Processing)

• int_customer_orders.sql - Aggregates customer purchase history
• int_orders_with_items.sql - Combines orders with their respective items
• int_product_performance.sql - Computes sales performance for products
• int_seller_performance.sql - Evaluates seller performance

Mart Layer (Analytics-Ready Models)

• mart_customer_analytics.sql - Customer behavior and retention analysis
• mart_product_analytics.sql - Product sales and profitability insights
• mart_seller_analytics.sql - Seller sales and performance tracking

Subcategories

• Product Analytics:
  • products.sql - Final product-level insights
• Seller Analytics:
  • sellers.sql - Final seller-level insights

Installation & Setup

1️⃣ Start the Full Stack with Docker

git clone https://github.com/supabase/supabase.git
cd supabase/docker
docker-compose up -d

2️⃣ Configure dbt to Connect to Supabase

Edit ~/.dbt/profiles.yml:

ecommerce_analytics:
  target: dev
  outputs:
    dev:
      type: postgres
      host: localhost
      user: postgres
      password: yourpassword
      port: 5432
      dbname: postgres
      schema: public
      threads: 4

Then test the connection:

dbt debug

3️⃣ Set Up Metabase

# Download Metabase
curl -o metabase.jar https://downloads.metabase.com/latest/metabase.jar

# Run Metabase
java -jar metabase.jar

4️⃣ Load Data into Supabase

python src/etl/loader.py

5️⃣ Run dbt Transformations

cd dbt_project
dbt run
dbt test
dbt docs generate
dbt docs serve

6️⃣ Access Metabase Dashboards

Open http://localhost:3000, configure the PostgreSQL connection, and import the dashboards from metabase/dashboards/.

Extending the Project

Adding New Data Sources

1. Create a new loader module in data_loader/sources/
2. Define source tables in dbt sources.yml
3. Create corresponding staging models

Leveraging Supabase Features

1. Authentication: Use Supabase auth for dashboard access control
2. REST API: Build custom applications that connect to your analytics
3. Real-time Updates: Create live dashboards that update in real-time

Implementing Advanced Analytics

1. Add derived metrics in dbt models
2. Implement predictive analytics with dbt Python models

Troubleshooting

Common Issues

Supabase Connection Failures

• Check Docker container status with docker ps
• Verify database credentials in your configuration
• Review logs with docker logs supabase-db

dbt Transformation Errors

• Run dbt debug to verify configuration
• Check for SQL syntax errors in models
• Ensure dependencies are correctly specified

Metabase Connection Issues

• Verify Supabase credentials in Metabase admin
• Check network connectivity to Supabase
• Ensure proper permissions for the Metabase database user

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Supabase for the PostgreSQL-based backend
• dbt Labs for dbt Core
• Metabase for the open-source BI tool

🔗 Live Project Repository: GitHub

🌐 Portfolio Entry: Now featured on www.drjodyannjones.com 🚀

🚀 Project Overview

The client, a rapidly growing e-commerce retailer, required a centralized platform to monitor and analyze their marketing efforts. They needed real-time insights into customer behavior, campaign performance, and product analytics to make informed, data-driven decisions.

⚡ Challenges

• Data Integration: Consolidating data from multiple sources, including web analytics, sales databases, and social media platforms.

• Real-Time Processing: Ensuring the dashboard reflects the most current data to facilitate timely decisions.

• User-Friendly Interface: Designing an intuitive interface accessible to both technical and non-technical team members.

🛠️ Solutions Implemented

✅ Streamlit-Based Interactive Dashboard

• Developed a dynamic dashboard using Streamlit, enabling real-time data visualization and interaction.

✅ Comprehensive Analytics Features

• Implemented modules for:

  • Real-time monitoring of marketing campaign performance

  • Customer segmentation analysis

  • Product category performance tracking

  • Interactive visualizations for deep insights

✅ Key Performance Indicators (KPIs) Tracking

• Integrated KPIs such as:

  • Campaign acceptance rates

  • Customer lifetime value

  • Product category sales metrics

📊 Results & Impact

🚀 Enhanced Decision-Making: Provided the marketing team with actionable insights, leading to a 20% increase in campaign effectiveness.

📈 Improved Customer Engagement: Enabled personalized marketing strategies, resulting in a 15% boost in customer retention.

💼 Streamlined Reporting: Reduced the time spent on manual data compilation by 50%, allowing the team to focus on strategy development.

💬 Client Testimonial

"MarketPulse has revolutionized our marketing approach. The real-time insights and user-friendly interface have empowered our team to make informed decisions swiftly. It's an indispensable tool for our marketing operations."

🔗 Explore the Project

• GitHub Repository: MarketPulse

• Live Demo (coming soon)

• Hire Me on Upwork

Client: Retail Enterprise Seeking Enhanced Demand Planning
Duration: 3 months
Role: Lead Data Scientist & Machine Learning Engineer

🚀 Project Overview

The client, a prominent retail company, faced challenges in accurately forecasting product demand, leading to inventory imbalances and lost sales opportunities. They required a robust solution to predict sales with high precision, enabling better inventory management and strategic planning.

⚡ Challenges

• Data Variability: Handling large volumes of sales data with seasonal fluctuations and promotional impact.

• Model Accuracy: Achieving a low MAPE to ensure forecasts are reliable for decision-making.

• Scalability: Developing a system capable of processing data across multiple product lines and region.

🛠️ Solutions Implemented

✅ Data Preprocessing & Feature Engineering

• Cleaned and transformed raw sales data, incorporating external factors like holidays and market trends to enhance model input.

✅ Advanced Time Series Modeling

• Employed state-of-the-art algorithms, including Prophet and ARIMA, to capture complex patterns in sales data.

✅ Automated Forecasting Pipeline

• Developed an end-to-end pipeline using Python and Docker, automating data ingestion, model training, and forecast generating.

✅ Interactive Visualization Dashboard

• Implemented a user-friendly dashboard with Plotly Dash, allowing stakeholders to visualize forecasts and adjust parameters dynamically.

📊 Results & Impact

🚀 High Forecast Accuracy: Achieved a MAPE of 4.64%, significantly improving the client's demand planning accuracy.

📦 Optimized Inventory Levels: Reduced overstock and stockouts by 15%, leading to cost savings and increased sales.

📈 Informed Strategic Decision: Provided actionable insights that guided marketing promotions and supply chain adjustments

💬 Client Testimonial

“ChronoSense AI transformed our approach to inventory management. The precision of their forecasts has been a game-changer, directly impacting our bottom line.”

🔗 Explore the Project

• GitHub Repository: ChronoSense AI

• Live Demo (coming soon)

• Hire Me on Upwork

🚀 Project Overview

The client, a marketing agency specializing in digital content and advertising, needed an AI-powered image editing tool to remove unwanted objects from images while maintaining a realistic and seamless background.

Their goal was to automate image cleanup for social media posts, ad creatives, and product photography, eliminating the need for manual Photoshop work.

⚡ Challenges

🔍 Precise Object Detection – The tool needed to accurately detect and remove unwanted objects, logos, or text while preserving image integrity.
🎨 Seamless Background Reconstruction – Simply deleting an object would leave a blank spot. The AI needed to intelligently recreate missing areas based on the surrounding pixels.
🖥️ User-Friendly Interface – The client requested a no-code, web-based solution so their designers could use it without ML expertise.

🛠️ Solutions Implemented

✅ Advanced Object Detection with YOLOv8

• Leveraged YOLOv8, a state-of-the-art real-time object detection model, to accurately identify objects marked for removal.

✅ Deep Learning-Based Image Inpainting

• Implemented OpenCV inpainting and DeepFill v2 (Generative AI) to reconstruct the missing background with high realism.

✅ Interactive Web Interface with Gradio

• Built a no-code, web-based application using Gradio, allowing users to:
  🔹 Upload images
  🔹 Select objects for removal
  🔹 View AI-enhanced results in real time

✅ Batch Processing for High-Volume Workflows

• Optimized multi-image processing so users could clean up multiple images in one go, boosting efficiency.

📊 Results & Impact

🚀 80% Faster Image Editing – Reduced manual cleanup time from 20 minutes per image to under 5 seconds with AI automation.
🎨 Flawless Background Reconstruction – Generated visually consistent images with no visible artifacts or distortions.
🌎 Used by Content Creators & Agencies – Enabled non-technical users to process high-quality images without Photoshop or manual editing.

💬 Client Testimonial

"This AI tool saved our design team countless hours of manual editing. The object removal quality is insanely good*—like magic! Absolutely game-changing."*

🔗 Explore the Project

📂 GitHub Repository
🎯 Live Demo (Coming Soon)
💼 Hire Me on Upwork

Client: Digital Content Creator & Marketer

Duration: 2 weeks

Role: Machine Learning Engineer & Automation Developer

The client, a YouTube content creator and marketer, needed an automated solution to streamline video processing, transcription, and metadata extraction. The goal was to automate content repurposing, improve video searchability, and enhance audience engagement without manual intervention.

⚡ Challenges

• Manual & Time-Consuming Process – The client was spending hours transcribing, extracting metadata, and generating video descriptions manually.

• Scalability Issues – Handling multiple videos per day required an automated pipeline to ensure efficiency.

• YouTube API Integration – The solution needed seamless interaction with YouTube APIs for video data retrieval.

🛠️ Solutions Implemented

Core Capabilities

• 🎬 Process both local videos and YouTube content

• 🔄 Add professional end screens automatically

• 📅 Schedule uploads with precise timing

• 📊 Batch process multiple videos

• 🖼️ Custom thumbnail processing

• 🏷️ Rich metadata management

Creator Tools

• 📋 Batch upload via spreadsheet

• 🎮 Gaming-specific templates

• 📚 Tutorial series automation

• 🎙️ Podcast/Interview templates

• 📱 Vlog content management

Technical Features

• 🔒 Secure OAuth2.0 authentication

• 🛠️ FFmpeg video processing

• 📝 Comprehensive logging

• ⚡ Efficient local processing

• 🔄 Automatic retry handling

📊 Results & Impact

🚀 80% Time Savings – Automated transcription & metadata processing reduced manual work from hours to minutes.
📈 SEO-Optimized Content – Improved video ranking & discoverability with AI-generated metadata.
💡 Seamless API Integration – Fully automated pipeline fetching and processing videos via YouTube API.

💬 Client Testimonial

"This automation saved me hours per week and helped optimize my content effortlessly. Jody delivered an amazing AI-driven solution that exceeded my expectations!"

🔗 Explore the Project

🎯 GitHub Repo
💼 Hire Me on Upwork