# projects

id: laptop-intel-engine
title: Cross-Marketplace Laptop & Review Intelligence Engine
type: AI/ML
github_url: https://github.com/tharu280
image_path: images/project_laptop.png
description:
Developed an advanced insights engine to analyse and compare laptops by integrating two distinct data sources: static technical specifications from PSREF PDFs and dynamic marketplace data (price, availability, reviews). Architected a multi-stage hybrid RAG system combining a FAISS vector index for semantic search and a SQLite database for structured SQL queries. Implemented query classification with guardrails to block or route non-RAG queries, reducing unnecessary compute and improving latency. Added contextual retrieval to enrich chunk representations, reducing top-20 retrieval failures by ~35%. Introduced a deep retrieval pipeline merging FAISS and BM25 with cross-encoder re-ranking for high-precision context selection. Added a query rewriter for improved retrieval accuracy and a query caching layer to minimize repeated embedding and LLM calls, lowering cost and enhancing response time.

technologies:
["Python", "FastAPI", "Streamlit", "Google Gemini API", "FAISS", "BM25", "Sentence Transformers", "Cross Encoders", "SQLite", "Pandas", "Pydantic", "Guardrails", "Query Caching"]


id: rag-book-recommender
title: RAG-based Advanced Book Recommendation System
type: AI/ML
github_url: https://github.com/tharu280
image_path: images/project_book.png
description:
Built a personalized book recommender using LangChain, HuggingFace Sentence Transformers, and ChromaDB, with zero-shot classification to auto-fill missing categories and enrich recommendations. Deployed the system using FastAPI, Docker, and Nginx on AWS Elastic Beanstalk for scalable, low-latency access.

technologies:
["Python", "Numpy", "Pandas", "HuggingFace Transformers", "LangChain", "ChromaDB", "FastAPI", "Docker", "Nginx", "AWS Elastic Beanstalk"]


id: eyecon
title: EyeCon – Real-Time Blink Communication System
type: AI/ML
github_url: https://github.com/tharu280
image_path: images/project_eyecon.png
description:
Developed the first system enabling fully paralysed users to communicate without any wearable devices, using only eye blinks detected via webcam. Implemented Eye Aspect Ratio (EAR)–based blink detection to differentiate short (next candidate) and long blinks (select candidate), achieving accurate, real-time interpretation of user intent. Integrated a Gemini LLM for context-aware candidate word suggestions, dynamically updating options as the sentence forms. Enabled automatic summarisation of composed sentences, providing users with coherent text output and facilitating streamlined communication.

technologies:
["Python", "OpenCV", "MediaPipe", "Kivy", "Custom Morse Decoder", "Gemini LLM", "Contextual Candidate Selection", "FastAPI"]


id: youtube-analytics
title: End-to-End YouTube Comment Analytics Pipeline
type: Data Engineering
github_url: https://github.com/tharu280
image_path: images/project_youtube.png
description:
Built an AI-powered pipeline to extract, classify, and visualize YouTube comments. Achieved 95.46% accuracy by fine-tuning BERT; also developed a BiLSTM classifier (90.75%); hosted the best model on HuggingFace Hub. Integrated Azure services (SQL Serverless, Data Factory, Power BI) for automated data flow, storage, and interactive visualisations.

technologies:
["Python", "TensorFlow", "Keras", "TF-IDF", "BiLSTM", "BERT", "HuggingFace", "FastAPI", "Docker", "YouTube API", "Azure SQL Serverless", "Azure Data Factory", "Power BI", "Azure Data Studio"]


id: mental-health-chatbot
title: Comic Character-based RAG Mental Healthcare Assistant Chatbot
type: AI/ML
github_url: https://github.com/tharu280
image_path: images/project_chatbot.png
description:
Built a RAG-based chatbot modelled after “Uncle Iroh” to provide mental health support using multi-source wisdom and personalized tone. Implemented 3 FAISS stores, with RunnableParallel & Sequential chains -cutting latency from 3.9s to 1.5s accurate and combined context retrieval. Optimised with Redis memory, LangSmith monitoring, and FastAPI; voice cloning via ElevenLabs in progress.

technologies:
["Python", "LangChain", "HuggingFace Transformers", "Gemini 1.5 Flash", "FAISS", "Redis", "LangSmith", "ElevenLabs", "FastAPI", "Pydantic"]


id: cafe-chatbot
title: AI-Powered RAG Chatbot for Cafe Business
type: Cloud AI
technologies: ["AWS Bedrock", "AWS OpenSearch Serverless", "AWS S3", "Llama 3 70B", "Titan Text G1 \u2013 Lite"]
github_url: https://github.com/tharu280
image_path: images/project_cafe.png
description:
Built a Retrieval-Augmented Generation (RAG) chatbot using Amazon Bedrock, OpenSearch Serverless, and S3 to answer customer queries based on internal café documents. Preprocessed and embedded data for semantic search; integrated foundation models with prompt engineering for contextual responses. Designed a scalable, secure deployment with proper IAM configuration and AWS-native services to ensure real-world readiness.


id: ai-coding-agent
title: AI Coding Agent – V (Open Source Project)
type: Agentic AI
technologies: ["Python", "Google Gemini API", "Gemini SDK", "Pydantic", "Agentic design patterns"]
github_url: https://github.com/tharu280
image_path: images/project_agent.png
description:
Designed and developed an AI coding agent demonstrating Agentic AI design patterns including Tool Use and Reflection Loop. Integrated Google Gemini’s function calling to perform grounded code operations such as reading, writing, and executing Python files. Implemented a 20-iteration reflection loop where the agent plans, acts, critiques, and improves results until completion.


id: food-ordering-backend
title: Event-Driven Backend for Food Ordering with Real-Time Fraud Detection
type: Backend/ML
github_url: https://github.com/tharu280
image_path: images/project_food.png
description:
Built a scalable event-driven backend using Python, Kafka, and Docker for a food ordering app with real-time processing. Modelled core events like order_placed, order_confirmed, and fraud_alert as Kafka topics across decoupled microservices. Integrated a fraud detection model into the transaction service to detect fraud in real-time and publish alerts via Kafka.

technologies:
["Python", "Apache Kafka", "kafka-python", "scikit-learn", "Docker", "Pydantic", "MLflow", "FastAPI"]


id: tour-planner-agent
title: Tour Planner AI Agent
type: Agentic AI
github_url: https://github.com/tharu280
image_path: images/project_tour.png
description:
Designed a hybrid AI system using LangGraph to orchestrate a multi-step agentic workflow for dynamic itinerary generation. Engineered a rule-based data ingestion pipeline to chain disparate APIs for gathering geocoding, route, and a broad set of location data. Implemented an “LLM as a Judge" pattern that semantically analyses the user's qualitative query to rank and filter the raw data, delivering context-aware, personalized recommendations. Solved the 'Teleportartion bias' by a dynamic API process, Reducing the API requests.

technologies:
["Python", "LangGraph", "FastAPI", "Google Gemini API", "Pydantic", "Geoapify", "OpenRouteService", "Nominatim"]


# articles

title: From PDF to API: How I Built a Hybrid RAG Insights Engine for Laptop Specs
description: A deep dive into how I created a dynamic and static data-based RAG system for a business.
type: article
url:
https://www.linkedin.com/pulse/from-pdf-api-how-i-built-hybrid-rag-insights-engine-laptop-abedheera-hwwoc


title: Parallel RAG with LangChain: Three Vector DBs, One Personality.
type: article
description:
Lessons learned building an 'Uncle Iroh' mental healthcare assistant. Covers diverse knowledge sourcing, chunking strategies, and parallel retrievers.

url:
https://www.linkedin.com/pulse/parallel-rag-langchain-three-vector-dbs-one-tharushika-abedheera-tognc


title: A Gentle Introduction to LSTMs (Long Short-Term Memory Networks)
description: Explore what LSTMs are, why they were created, and how they help machines understand sequences.
type: article
url:
https://www.linkedin.com/pulse/gentle-introduction-lstms-long-short-term-memory-tharushika-abedheera-irvzc


title: How Does L1 (Lasso) and L2 (Ridge) Regularization Work?
type: article
description:
Explore L1 (Lasso) and L2 (Ridge) regularization techniques to improve model performance by addressing overfitting.

url:
https://www.linkedin.com/pulse/how-does-l1-lasso-l2-ridge-regularization-work-tharushika-abedheera-upyvc


title: ROUGE Score: A Key Metric for Evaluating Text Summarisation Models
url: https://www.linkedin.com/pulse/rouge-score-key-metric-evaluating-text-summarisation-models-qirwc
type: article
description:
Introduction to how ROUGE scores provide valuable insights into how well a model has performed in generating summaries.


title: SQuAD Metrics: Evaluating Question-Answering Models Effectively
type: article
description:
Introduction to the SQuAD metric, an essential tool for evaluating question-answering models in NLP.

url:
https://www.linkedin.com/pulse/how-does-l1-lasso-l2-ridge-regularization-work-tharushika-abedheera-upyvc


# videos

id: 01
title: RAG and Embeddings Explained in Plain English
thumbnail_url: images/video_thumbnails/01.png
description:
Explaining RAG and embeddings in plain English. No jargon. No math. Just the core concepts, broken down so anyone can understand.

url:
https://www.linkedin.com/posts/tharushika-abedheera-3396311a4_explaining-rag-and-embeddings-in-plain-english-activity-7379239387819274240-__h6


id: 02
title: Reflection Design Pattern in AI Agents Explained Simply!
thumbnail_url: images/video_thumbnails/02.png
description:
In this short tutorial, I walk through how reflection works in AI Agents. You’ll learn how this pattern forms the foundation for self-improving AI systems.

url:
https://www.linkedin.com/posts/tharushika-abedheera-3396311a4_ai-machinelearning-agents-activity-7390137862027005952-iI2u


# research

title: AI Voice Assistant for Healthcare (ICIET 2023)
type: paper
description:
Published as a conference paper at ICIET 2023. Developed an AI voice assistant using PyTorch and NLTK to handle general and healthcare-related queries.

url:
https://www.researchgate.net/publication/376522984_A_Deep_Learning_Approach_to_Utilize_AI_Voice_Assistants_In_Healthcare


title: EL_sense: AI Solution for Human-Elephant Conflict (In Progress)
type: research
description:
Proposed a bioacoustics-based system for detecting low-frequency (20Hz) elephant rumbles using live audio streaming via Apache Kafka.


title: EyeCon – Real-Time Blink Communication System
type: paper
description:
Developed the first system enabling fully paralysed users to communicate without any wearable devices, using only eye blinks.


# skills

id: ml_dl
category: Machine Learning & Deep Learning
skill_list:
["Scikit-learn", "TensorFlow", "Keras", "PyTorch", "Hugging Face", "Transformers", "XGBoost", "Decision Trees", "Random Forest", "SVM", "Na\u00efve Bayes", "KNN", "K-Means Clustering", "PCA", "Model Evaluation", "Hyper-parameter Tuning", "Pandas", "NumPy", "LSTMs", "Transformers", "LLMs", "NLTK", "CNNs", "OpenCV", "MediaPipe", "Vision Transformers (ViTs)", "Yolo"]


id: cloud_devops
category: Cloud & CI/CD
skill_list:
["AWS", "Azure", "GCP", "Docker", "GitHub Actions", "Apache Kafka", "MLflow", "LangSmith", "CI/CD pipelines", "AWS EC2"]


