Vector Stores and Embeddings in AWS
Explore how embeddings transform data into semantic vectors and how vector stores like Amazon OpenSearch, Aurora pgvector, and S3 Vectors support scalable, accurate retrieval in GenAI systems. Understand AWS options for managing vector data within RAG architectures to optimize search relevance and application performance.
Vector stores and embeddings form the backbone of modern generative AI and retrieval-augmented generation architectures. Retrieval systems to supply relevant and accurate context to FMs at inference time. Traditional keyword-based search cannot capture semantic meaning, which makes it insufficient for GenAI workloads that depend on nuance, similarity, and intent.
This lesson introduces embeddings and vector stores as the core mechanisms that enable semantic retrieval in GenAI systems. Specifically, this lesson covers the following areas in detail:
Embeddings and semantic representation: Understanding how text and other content are transformed into numerical vectors that capture meaning, similarity, and intent rather than exact keywords.
Vector stores for semantic retrieval: Exploring how vector databases enable similarity search at scale and why they are essential for grounding foundation model responses in relevant context.
AWS vector store implementation choices: Learning when to use Amazon OpenSearch Service, Amazon Aurora with pgvector, or Amazon S3 Vectors based on scale, latency, and operational requirements.
Architectural decision making for RAG systems: Recognizing exam-relevant cues that indicate the correct vector storage and retrieval ...