My Projects

Project Overview:

The Bio-RAG Foundation Agent is an advanced Retrieval-Augmented Generation system designed for biomedical document analysis. It combines state-of-the-art embedding models with a robust vector database to provide accurate, context-aware responses to complex scientific queries.

Key Features:

• Intelligent document ingestion with automatic chunking strategies
• Gemma2 embedding generation for semantic understanding
• Weaviate vector database for efficient similarity search
• Advanced retrieval with hybrid search (keyword + semantic)
• Context-aware answer generation with source attribution
• FastAPI-based REST API with comprehensive documentation
• Performance monitoring and evaluation dashboards

Technical Implementation:

Built using Python with FastAPI framework, the system leverages Ollama for local LLM deployment, Weaviate as the vector database, and custom embedding models. The architecture follows microservices principles with Docker containerization for easy deployment and scaling.

Challenges & Solutions:

• Challenge: Handling diverse document formats and structures
• Solution: Implemented flexible parsing pipeline with format-specific handlers
• Challenge: Optimizing retrieval accuracy while maintaining speed
• Solution: Hybrid search combining keyword matching with semantic similarity
• Challenge: Ensuring response quality and relevance
• Solution: Multi-stage filtering and ranking with confidence scoring

Technologies Used: Python, FastAPI, Ollama, Gemma2, Weaviate, Docker, Redis, PostgreSQL, Prometheus, Grafana

Project Overview:

A comprehensive restaurant recommendation platform that combines multiple recommendation approaches to provide personalized dining suggestions. The system analyzes user preferences, dining history, and restaurant characteristics to deliver accurate recommendations.

Key Features:

• Hybrid recommendation engine (collaborative + content-based)
• User profiling and preference learning
• Real-time recommendation generation
• Location-based filtering and suggestions
• Social features and friend recommendations
• Advanced search and filtering options
• User reviews and rating system

Technical Implementation:

The backend is built with Django REST API, utilizing TensorFlow for machine learning models and Scikit-learn for traditional ML algorithms. The system employs TF-IDF vectorization for content-based filtering and matrix factorization for collaborative filtering.

Algorithm Details:

• Collaborative Filtering: Matrix factorization with regularization
• Content-Based: TF-IDF with cosine similarity
• Hybrid Approach: Weighted combination of both methods
• Cold Start Problem: Content-based for new users

Technologies Used: Python, Django, TensorFlow, Scikit-learn, PostgreSQL, Docker, Redis, Nginx