Class OpenSearchEmbeddingStore
java.lang.Object
dev.langchain4j.store.embedding.opensearch.OpenSearchEmbeddingStore
- All Implemented Interfaces:
EmbeddingStore<TextSegment>
Represents an OpenSearch index as an
embedding store. This implementation uses K-NN and the cosinesimil space type.
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Nested Class Summary
Nested Classes -
Constructor Summary
ConstructorsConstructorDescriptionOpenSearchEmbeddingStore
(String serverUrl, String apiKey, String userName, String password, String indexName) Creates an instance of OpenSearchEmbeddingStore to connect with OpenSearch clusters running locally and network reachable.OpenSearchEmbeddingStore
(String serverUrl, String serviceName, String region, org.opensearch.client.transport.aws.AwsSdk2TransportOptions options, String indexName) Creates an instance of OpenSearchEmbeddingStore to connect with OpenSearch clusters running as a fully managed service at AWS.OpenSearchEmbeddingStore
(org.opensearch.client.opensearch.OpenSearchClient openSearchClient, String indexName) Creates an instance of OpenSearchEmbeddingStore using provided OpenSearchClient -
Method Summary
Modifier and TypeMethodDescriptionAdds a given embedding to the store.add
(Embedding embedding, TextSegment textSegment) Adds a given embedding and the corresponding content that has been embedded to the store.void
Adds a given embedding to the store.Adds multiple embeddings to the store.addAll
(List<Embedding> embeddings, List<TextSegment> embedded) Adds multiple embeddings and their corresponding contents that have been embedded to the store.builder()
findRelevant
(Embedding referenceEmbedding, int maxResults, double minScore) This implementation uses the exact k-NN with scoring script to calculate See https://opensearch.org/docs/latest/search-plugins/knn/knn-score-script/Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface dev.langchain4j.store.embedding.EmbeddingStore
findRelevant, findRelevant, findRelevant, remove, removeAll, removeAll, removeAll, search
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Constructor Details
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OpenSearchEmbeddingStore
public OpenSearchEmbeddingStore(String serverUrl, String apiKey, String userName, String password, String indexName) Creates an instance of OpenSearchEmbeddingStore to connect with OpenSearch clusters running locally and network reachable.- Parameters:
serverUrl
- OpenSearch Server URL.apiKey
- OpenSearch API key (optional)userName
- OpenSearch username (optional)password
- OpenSearch password (optional)indexName
- OpenSearch index name.
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OpenSearchEmbeddingStore
public OpenSearchEmbeddingStore(String serverUrl, String serviceName, String region, org.opensearch.client.transport.aws.AwsSdk2TransportOptions options, String indexName) Creates an instance of OpenSearchEmbeddingStore to connect with OpenSearch clusters running as a fully managed service at AWS.- Parameters:
serverUrl
- OpenSearch Server URL.serviceName
- The AWS signing service name, one of `es` (Amazon OpenSearch) or `aoss` (Amazon OpenSearch Serverless).region
- The AWS region for which requests will be signed. This should typically match the region in `serverUrl`.options
- The options to establish connection with the service. It must include which credentials should be used.indexName
- OpenSearch index name.
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OpenSearchEmbeddingStore
public OpenSearchEmbeddingStore(org.opensearch.client.opensearch.OpenSearchClient openSearchClient, String indexName) Creates an instance of OpenSearchEmbeddingStore using provided OpenSearchClient- Parameters:
openSearchClient
- OpenSearch client providedindexName
- OpenSearch index name.
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Method Details
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builder
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add
Description copied from interface:EmbeddingStore
Adds a given embedding to the store.- Specified by:
add
in interfaceEmbeddingStore<TextSegment>
- Parameters:
embedding
- The embedding to be added to the store.- Returns:
- The auto-generated ID associated with the added embedding.
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add
Description copied from interface:EmbeddingStore
Adds a given embedding to the store.- Specified by:
add
in interfaceEmbeddingStore<TextSegment>
- Parameters:
id
- The unique identifier for the embedding to be added.embedding
- The embedding to be added to the store.
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add
Description copied from interface:EmbeddingStore
Adds a given embedding and the corresponding content that has been embedded to the store.- Specified by:
add
in interfaceEmbeddingStore<TextSegment>
- Parameters:
embedding
- The embedding to be added to the store.textSegment
- Original content that was embedded.- Returns:
- The auto-generated ID associated with the added embedding.
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addAll
Description copied from interface:EmbeddingStore
Adds multiple embeddings to the store.- Specified by:
addAll
in interfaceEmbeddingStore<TextSegment>
- Parameters:
embeddings
- A list of embeddings to be added to the store.- Returns:
- A list of auto-generated IDs associated with the added embeddings.
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addAll
Description copied from interface:EmbeddingStore
Adds multiple embeddings and their corresponding contents that have been embedded to the store.- Specified by:
addAll
in interfaceEmbeddingStore<TextSegment>
- Parameters:
embeddings
- A list of embeddings to be added to the store.embedded
- A list of original contents that were embedded.- Returns:
- A list of auto-generated IDs associated with the added embeddings.
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findRelevant
public List<EmbeddingMatch<TextSegment>> findRelevant(Embedding referenceEmbedding, int maxResults, double minScore) This implementation uses the exact k-NN with scoring script to calculate See https://opensearch.org/docs/latest/search-plugins/knn/knn-score-script/- Specified by:
findRelevant
in interfaceEmbeddingStore<TextSegment>
- Parameters:
referenceEmbedding
- The embedding used as a reference. Returned embeddings should be relevant (closest) to this one.maxResults
- The maximum number of embeddings to be returned.minScore
- The minimum relevance score, ranging from 0 to 1 (inclusive). Only embeddings with a score of this value or higher will be returned.- Returns:
- A list of embedding matches. Each embedding match includes a relevance score (derivative of cosine distance), ranging from 0 (not relevant) to 1 (highly relevant).
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