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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:
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