This session explores the fundamentals of Embeddings and Vectorization — essential concepts for modern AI-driven applications. Attendees will gain a clear understanding of why embeddings matter, how vectorization works, and what real-world problems they solve.

We’ll compare multiple embedding models and delve into the use of various vector databases such as ChromaDB, FAISS, Pinecone, Azure AI Search, and Azure PostgreSQL. Through practical scenarios, we’ll demonstrate how these tools perform in standard retrieval workflows — and highlight the limitations that arise when working with large-scale document corpora.

As data volume increases, traditional approaches can fall short. We'll explain why this is often referred to as "Naive RAG", and how it can lead to irrelevant or incomplete answers.

To address these challenges, the session will introduce Knowledge Graph-based Vectorization — a more structured approach that leverages nodes, relationships, and semantic context. Technologies like GraphRAG, LightRAG, and Neo4j Knowledge Graph will be showcased to demonstrate how they enhance accuracy, scalability, and relevance in large knowledge retrieval systems.

Whether you're a developer, data scientist, or architect, you'll leave with actionable insights on choosing the right vectorization strategy for your use case.

You will learn:

• About Naive RAG
• About GraphRAG
• When to use which technology