Course Description:
This course equips learners with the skills to store, retrieve, and optimize data for AI applications using vector databases and knowledge graph systems. Students will master fundamentals, explore platform-specific implementations, and apply advanced retrieval-augmented generation (RAG) techniques for high-performance AI workflows.

Lecture 1: Vector Databases Fundamentals
Learn the core principles of vector databases, including how data is represented numerically, indexed, and searched for similarity in AI-driven systems.

Key Objectives:

• Numerical representation of data
• Indexing strategies
• Similarity search concepts

Lecture 2: Platform Implementations
Explore leading vector database platforms and related backend technologies. Understand how to use APIs, data fabrics, and frameworks for scalable AI data management.

Key Objectives:

• Pinecone: Large-scale ML workloads
• Supabase: Open-source backend with PostgreSQL
• GraphQL
• Data fabric concepts
• REST API
• FAST API

Lecture 3: Advanced RAG Implementation
Implement high-performance RAG systems with multi-source integration and optimized vector database usage. Learn advanced strategies for chunking, scaling, and tuning.

Key Objectives:

• Complex document processing
• Multi-source data integration (Multi-document context handling)
• Vector database optimization (Pinecone, Supabase)
• Chunk size and overlap strategies
• Performance tuning and scaling

Lecture 4: Knowledge Graph Integration
Understand how to design and manage knowledge graphs for structured, interconnected data. Learn querying, updating, and visualizing graph-based systems.

Key Objectives:

• Structured data representation
• Relationship mapping and queries
• Dynamic knowledge updates
• Graph visualization and analysis