Vectors & Vector Stores in RAG (Chapter 7)

The Memriq AI Inference Brief – Engineering Edition

Unlock the core infrastructure powering retrieval-augmented generation (RAG) systems in this technical deep dive. We explore how vector embeddings and vector stores work together to enable fast, scalable, and semantically rich retrieval for LLMs, drawing insights directly from Chapter 7 of Keith Bourne’s book.

In this episode:

- Understand the role of high-dimensional vectors and vector stores in powering RAG

- Compare embedding models like OpenAIEmbeddings, BERT, and Doc2Vec

- Explore vector store technologies including Chroma, Milvus, Pinecone, and pgvector

- Deep dive into indexing algorithms like HNSW and adaptive retrieval techniques such as Matryoshka embeddings

- Discuss architectural trade-offs for production-ready RAG systems

- Hear real-world applications and operational challenges from embedding compatibility to scaling

Key tools & technologies mentioned:

OpenAIEmbeddings, BERT, Doc2Vec, Chroma, Milvus, Pinecone, pgvector, LangChain, HNSW, Matryoshka embeddings

Timestamps:

00:00 - Introduction to vectors and vector stores in RAG

02:15 - Why vectors are the backbone of retrieval-augmented generation

05:40 - Embedding models: trade-offs and use cases

09:10 - Vector stores and indexing: Chroma, Milvus, Pinecone, pgvector

13:00 - Under the hood: indexing algorithms and adaptive retrieval

16:20 - Real-world deployments and architectural trade-offs

18:40 - Open challenges and best practices

20:30 - Final thoughts and book recommendation

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for more AI practitioner tools, resources, and deep dives