Benjamin Flast, Director, Product Management at MongoDB discusses vector search capabilities, integration with AWS Bedrock, and its transformative role in enabling scalable, efficient, and AI-powered solutions.

Topics Include:

Introduction to MongoDB's vector search and AWS Bedrock
Core concepts of vectors and embeddings explained
High-dimensional space and vector similarity overview
Embedding model use in vector creation
Importance of distance functions in vector relations
Vector search uses k-nearest neighbor algorithm
Euclidean, Cosine, and Dot Product similarity functions
Applications for different similarity functions discussed
Large language models and vector search explained
Introduction to retrieval-augmented generation (RAG)
Combining external data with LLMs in RAG
MongoDB's document model for flexible data storage
MongoDB Atlas platform capabilities overview
Unified interface for MongoDB document model
Approximate nearest neighbor search for efficiency
Vector indexing in MongoDB for fast querying
Search nodes for scalable vector search processing
MongoDB AI integrations with third-party libraries
Semantic caching for efficient response retrieval
MongoDB's private link support on AWS Bedrock
Future potential of vector search and RAG applications
Example use case: Metaphor Data's data catalog
Example use case: Okta's conversational interface
Example use case: Delivery Hero product recommendations
Final takeaways on MongoDB Atlas vector search

Participants:

Benjamin Flast - Director, Product Management, MongoDB

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Ep064: Agentic Gen AI Experiences with Atlas Vector Search and Amazon Bedrock