Using vector search for Amazon DocumentDB (compatible with MongoDB) to enhance semantic search capabilities.

Folks, let’s dive into the future of search with Amazon DocumentDB’s Vector search capability. Picture this: you’ve got a database marrying documents and vectors, unlocking unparalleled search prowess. Seamlessly blend structured and unstructured data for a search experience that’s out of this world 🚀. No more juggling multiple databases or syncing headaches—just pure search magic at your fingertips! Choose DocumentDB for AIML workloads and unleash the power of vectors for a game-changing search journey. Thank you! 🎉

Introduction

In this presentation, we will delve into the world of semantic search using Amazon DocumentDB. This will be divided into four parts: an introduction to Amazon DocumentDB, deep dive into Vector search capability, a demonstration of a semantic search app, and finally, reasons to choose DocumentDB for vector store.

Amazon DocumentDB Overview

Amazon DocumentDB is a Json based document store, making it very developer-friendly and schema-less. It offers decoupled storage and compute, allowing for cost optimization. With two offerings, instance-based and elastic clusters, Amazon DocumentDB is fully managed and enterprise-ready.

Key Takeaways
– Developer-friendly
– Decoupled storage and compute
– Fully managed
– Dual pricing model
– Enterprise-ready
– High availability
– Compatible with MongoDB API

Why Vectors are Important

Before diving into Vector search, it’s important to understand the significance of vectors in understanding unstructured data. Vectors allow machines to comprehend the meaning of unstructured data such as text, audio, or video. For example, using vectors, machines can understand gender and royalty-related dimensions, enabling them to derive meaning from unstructured data.

Quotes
"Vectors allow machines to comprehend unstructured data such as text, audio, or video."

Vector Search for Amazon DocumentDB

Combining DocumentDB, a document data store, with a Vector database results in Vector search for Amazon DocumentDB. This amalgamation offers a powerful solution, especially for AI and ML workloads where unstructured data is prevalent.

Key Takeaways
– Eliminates the need to manage two databases
– Built-in query performance enhancement
– Easy to get started with
– Supports various use cases
– Supports 2,000 dimensions for the index

Building a Semantic Search App

Building a semantic search app involves loading the database and relevant vector embeddings into DocumentDB. With the right architecture and embedding model, one can effectively build a semantic search app to understand and retrieve relevant documents based on their meaning.

Demonstration

A live demonstration of a semantic search app showcases how it captures the meaning of search queries to retrieve relevant documents, even when certain keywords are not explicitly present in the text. This demonstration highlights the power of combining text search and semantic search for more intuitive results.

Reasons to Choose Amazon DocumentDB

Finally, Amazon DocumentDB offers a compelling solution for vector store in AIML workloads due to its powerful capabilities with unstructured data, rich aggregation pipeline, flexible pricing model, and seamless integration with other AWS services.

In conclusion, Vector search for Amazon DocumentDB presents a powerful solution for semantic search and AI/ML workloads, providing a comprehensive and efficient way to derive meaning from unstructured data.

FAQ
Q: What are the advantages of Vector search for Amazon DocumentDB?
A: It eliminates the need to manage two databases, enhances query performance, and is easy to get started with.

Thank you for your attention, and together, let’s build the future with Vector search for Amazon DocumentDB!

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Amazon Web Services
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