Cette session explore l'intégration de la recherche vectorielle dans Azure Cosmos DB pour MongoDB, ouvrant ainsi de nouvelles possibilités pour le développement d'applications intelligentes. Nous plongerons dans les concepts fondamentaux de la recherche vectorielle et découvrirons comment elle peut être utilisée pour enrichir les fonctionnalités de recherche dans les applications. Les développeurs peuvent ainsi créer des expériences utilisateur plus riches et plus personnalisées.

2. Azure Cosmos DB at Microsoft Build 2023
• Burst capacity
• Hierarchical partition keys
• Materialized views for Azure Cosmos DB for NoSQL,
• Vector Search in Azure Cosmos DB for MongoDB vCore

3. Burst Capacity

4. Hierarchical Partition Keys
• now generally available, to improve partitioning strategies and
performance. Hierarchical partition keys enable up to three partition
keys to be used instead of one, to better optimize data distribution
and achieve greater scale. If you use synthetic keys today or have
scenarios where partition keys can exceed 20 GB of data,
subpartitioning can help. With this feature, logical partition key
prefixes can exceed 20 GB and 10,000 RU/s, and queries by prefix are
efficiently routed to the subset of partitions with the data.

8. Materialized Views
• Reduction in cross partition queries
• Reduction in RU charges for the existing queries
• Improvement in query execution time
• Zero coding effort
• Easy fine tuning and monitoring of Materialized View Containers

10. New elasticity features for better cost and
performance

11. Vector Search
• vector search capabilities
• new opportunities for building intelligent AI-powered applications
and makes Azure Cos
• Vector search is a method that helps you find similar items based on
their data characteristics rather than exact matches on a property
field. This is especially useful in applications such as searching for
similar text, finding related images, making recommendations, or
even detecting anomalies.

12. Vector Search Benefits
• need to transfer your data to more expensive alternatives for vector
similarity search capabilities

13. Principe de la recherche vectorielle

14. Cas d’usage de la recherche vectorielle

15. Vector Search Demo – Use Case
 Meilleure expérience
utilisateur
 Meilleure pertinence des
résultats
 Meilleure compréhension des
données
o Azure OpenAI
o Azure Cosmos DB for NoSQL
o Azure Cosmos DB for MongoDB
o Azure Functions
o Azure App Service
Services cloud Azure
Use Azure Cosmos DB with Azure OpenAI to build vector search solutions with an
AI assistant user interface.
Objectifs

16. RAG

17. Vector Search & AI Assistant - Architecture