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Semantic search tutorials

Learn how to implement semantic search in OpenSearch using embedding models to match queries by meaning rather than exact keywords.

Semantic search using the OpenAI embedding model

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: OpenAI embedding
  • Deployment: Provider API

Semantic search using Cohere Embed

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Cohere Embed
  • Deployment: Provider API

Semantic search using Cohere Embed on Amazon Bedrock

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Cohere Embed
  • Deployment: Amazon Bedrock

Semantic search using Amazon Bedrock Titan

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Amazon Titan
  • Deployment: Amazon Bedrock

Semantic search using Amazon Bedrock Titan in another account

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Amazon Titan
  • Deployment: Amazon Bedrock (in a different account than your Amazon OpenSearch Service account)

Semantic search using a model in Amazon SageMaker

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Custom
  • Deployment: Amazon SageMaker

Semantic search using AWS CloudFormation and Amazon SageMaker

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Custom
  • Deployment: Amazon SageMaker + CloudFormation

Semantic search using AWS CloudFormation and Amazon Bedrock

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Amazon Titan + Cohere
  • Deployment: Amazon Bedrock + CloudFormation

Semantic search using an asymmetric model

  • Platform: OpenSearch
  • Model: Hugging Face Multilingual-E5-small
  • Deployment: Local cluster

Semantic search using text chunking

  • Platform: OpenSearch, Amazon OpenSearch Service
  • Model: Amazon Titan Text Embeddings
  • Deployment: Amazon Bedrock
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