Vector Databases in Production for Busy Engineers

By Roie Schwaber-Cohen, Bear Douglas & Zachary Proser

Vector databases are core components of production systems for RAG, semantic search, and classification. This series gives brief, clear advice for dealing with common production issues: handling multitenancy, data pipelines, fine tuning, evaluation, and managing the software development lifecycle.

Introduction

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Companies are all in various states of transformation as they figure out how AI can help them operate better and more efficiently. And the landscape of research, tools, and best practice techniques changes constantly. Even the most sophisticated teams who have large-scale applications in production have questions, conduct experiments, and experience failures.

We’ve compiled the most common questions, issues, pitfalls, and lessons that we’ve learned working hand in hand with our customers and partners in recent months. We'll touch on: data ingestion, data modeling for multi-tenancy, security, and operational pieces like managing your software development cycle.

What follows is a mix of materials that are all geared to help you be successful more quickly: informational blog posts, reference architecture diagrams, code snippets, and live workshops where you’ll have a chance to connect with Pinecone customers and partners who are navigating similar challenges.

You may already be on firm ground with some of these topics; on others you might be seeking advice. Every week that we post new content in this series, we’ll also be opening up a related discussion thread on our forum. We invite you to contribute ideas wherever you have lessons to share, and we hope you’ll ask questions fearlessly about the parts you’re still working through.

Chapter 1

Handling multi-tenancy

Using namespaces for data isolation and scale

Chapter 2

CI/CD for cloud-based vector databases

Learn to integrate Pinecone with your CI/CD workflow

Chapter 3

Streamlining CI/CD with Pinecone Local

Chapter 4

RAG Evaluation: Don't let customers tell you first

Using information retrieval metrics we can quantify and improve the performance of RAG pipelines

Chapter 5