Improve Your Search with Vector Capabilities in Opensearch Service Ant210

Title

AWS re:Invent 2023 - Improve your search with vector capabilities in OpenSearch Service (ANT210)

Summary

  • John Handler, Achal Kumar from Intuit, and Aruna Guvindaraju from OpenSearch Service presented on the integration of vector capabilities in OpenSearch Service.
  • OpenSearch Service is an open-source Apache 2 licensed project that includes a search engine and a UI for visualizations and dashboards.
  • The session focused on the vector component of OpenSearch, which improves search relevance by using semantic search and vectorization of natural language text.
  • OpenSearch supports various AI/ML techniques, including segmentation, user preferences, collaborative filtering, and the K-Nearest Neighbors (KNN) plugin.
  • The Neural plugin for OpenSearch simplifies the process of vectorizing data and running vector queries.
  • Amazon OpenSearch Service now has zero ETL connectors to DynamoDB and S3, and supports serverless deployment options.
  • The session also covered the integration of OpenSearch with third-party model hosting services like SageMaker, Bedrock, OpenAI, and Cohere.
  • Achal Kumar shared Intuit's use of vector search in various applications, including generative Q&A, fraud detection, and document similarity.
  • Aruna Guvindaraju demonstrated the creation and use of AI/ML connectors in OpenSearch to generate vectors and perform searches.
  • The session concluded with a call to action to explore more content on OpenSearch's vector capabilities and to provide feedback through surveys.

Insights

  • Vector capabilities in OpenSearch Service enable semantic search, which goes beyond text-to-text matching by understanding the context and intent behind queries.
  • The integration of OpenSearch with large language models (LLMs) and AI/ML techniques can significantly improve the relevance of search results.
  • OpenSearch's vector search is becoming a core component of generative AI platforms, as seen in Intuit's use cases.
  • The ability to connect OpenSearch to external model hosting services simplifies the process of vector generation and allows for more dynamic and up-to-date search capabilities.
  • The session highlighted the importance of cost optimization and efficient use of resources, as vector storage and processing can be resource-intensive.
  • The presenters emphasized the need for continued development and support for higher dimensions and compression techniques to manage the growing scale and complexity of vector search applications.
  • The session showcased the practical application of OpenSearch's vector capabilities in real-world scenarios, providing attendees with actionable insights and tools to enhance their own search applications.