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Elasticsearch

https://www.elastic.co/

Maven Dependency​

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-elasticsearch</artifactId>
    <version>1.10.0-beta18</version>
</dependency>

Overview​

The langchain4j-elasticsearch module provides integration with Elasticsearch as an embedding store and content retriever.

It comes with two main classes:

  • ElasticsearchEmbeddingStore: an implementation of the EmbeddingStore interface that uses Elasticsearch to store and retrieve embeddings.
  • ElasticsearchContentRetriever: an implementation of the ContentRetriever interface that uses Elasticsearch to retrieve relevant documents based on vector similarity search.

Both classes need an Elasticsearch RestClient to connect to the Elasticsearch server.

String apiKey = "VnVhQ2ZHY0JDZGJrU...";
RestClient restClient = RestClient
    .builder(HttpHost.create("https://localhost:9200"))
    .setDefaultHeaders(new Header[]{
        new BasicHeader("Authorization", "ApiKey " + apiKey)
    })
    .build();

Note:

See the Elasticsearch documentation on how to create a RestClient instance.

ElasticsearchEmbeddingStore​

To create the ElasticsearchEmbeddingStore instance, you need to provide an Elasticsearch RestClient:

ElasticsearchEmbeddingStore store = ElasticsearchEmbeddingStore.builder()
    .restClient(restClient)
    .build();

It comes with the following options:

  • indexName: the name of the Elasticsearch index to use. Default is default.
  • configuration: the ElasticsearchConfiguration to use. Default is ElasticsearchConfigurationKnn.

The previous code is equivalent to:

ElasticsearchEmbeddingStore store = ElasticsearchEmbeddingStore.builder()
    .restClient(restClient)
    .configuration(ElasticsearchConfigurationKnn.builder().build())
    .indexName("default")
    .build();

ElasticsearchContentRetriever​

A ContentRetriever needs an embedding model:

EmbeddingModel embeddingModel = new AllMiniLmL6V2QuantizedEmbeddingModel();

To create an ElasticsearchContentRetriever instance, you need to provide the Elasticsearch RestClient and the EmbeddingModel:

ElasticsearchContentRetriever contentRetriever = ElasticsearchContentRetriever.builder()
    .restClient(restClient)
    .embeddingModel(embeddingModel)
    .build();

It comes with the following options:

  • configuration: the ElasticsearchConfiguration to use (see below). Default is ElasticsearchConfigurationKnn.
  • indexName: the name of the Elasticsearch index to use. Default is default. Index will be created automatically if not exists.
  • maxResults: the maximum number of results to retrieve. Default is 3.
  • minScore: the minimum score threshold for retrieved results. Default is 0.0.
  • filter: a Filter to apply during retrieval if any. Default is null.

The previous code is equivalent to:

ElasticsearchContentRetriever contentRetriever = ElasticsearchContentRetriever.builder()
    .restClient(restClient)
    .embeddingModel(embeddingModel)
    .configuration(ElasticsearchConfigurationKnn.builder().build())
    .indexName("default")
    .maxResults(3)
    .minScore(0.0)
    .filter(null)
    .build();

ElasticsearchConfiguration​

An ElasticsearchConfiguration defines how the embedding store or content retriever will interact with the Elasticsearch server. You can create your own configuration by implementing the ElasticsearchConfiguration interface, or use one of the provided implementations:

To create a configuration instance, you can use the builder provided by each implementation. For example:

ElasticsearchConfiguration configuration = ElasticsearchConfigurationKnn.builder().build();

ElasticsearchConfigurationKnn​

The ElasticsearchConfigurationKnn uses approximate kNN queries to perform vector similarity search.

It is the default configuration used by both ElasticsearchEmbeddingStore and ElasticsearchContentRetriever.

To create an instance, you can use the builder:

ElasticsearchConfiguration configuration = ElasticsearchConfigurationKnn.builder().build();

It comes with the following options:

  • numCandidates: the number of candidate neighbors to consider during the search. Default is null, meaning using the default Elasticsearch value.
  • includeVectorResponse: whether to include vector fields in the search response. Default is false.

Note: From version 9.2 of the elasticsearch server, vector fields are excluded from the response by default. To include vector fields in the responses, set the includeVectorResponse in the builder:

ElasticsearchConfigurationKnn configuration = ElasticsearchConfigurationKnn.builder()
    .includeVectorResponse(true)
    .build();

ElasticsearchConfigurationScript​

The ElasticsearchConfigurationScript uses scriptScore queries to perform vector similarity search. Note that this implementation is using cosine similarity.

It is available for both ElasticsearchEmbeddingStore and ElasticsearchContentRetriever.

To create an instance, you can use the builder:

ElasticsearchConfiguration configuration = ElasticsearchConfigurationScript.builder().build();

It comes with the following options:

  • includeVectorResponse: whether to include vector fields in the search response. Default is false.

Note: From version 9.2 of the elasticsearch server, vector fields are excluded from the response by default. To include vector fields in the responses, set the includeVectorResponse in the builder:

ElasticsearchConfiguration configuration = ElasticsearchConfigurationScript.builder()
    .includeVectorResponse(true)
    .build();

ElasticsearchConfigurationFullText​

The ElasticsearchConfigurationFullText uses full text search to retrieve relevant documents.

It is available ElasticsearchContentRetriever only.

To create an instance, you can use the builder:

ElasticsearchConfiguration configuration = ElasticsearchConfigurationFullText.builder().build();

ElasticsearchConfigurationHybrid​

The ElasticsearchConfigurationHybrid uses hybrid search to combine a kNN vector query with a full text query. Note that hybrid search requires an elasticsearch enterprise license or a trial.

It is available ElasticsearchContentRetriever only.

To create an instance, you can use the builder:

ElasticsearchConfiguration configuration = ElasticsearchConfigurationHybrid.builder().build();

It comes with the following options:

  • numCandidates: the number of candidate neighbors to consider during the search. Default is null, meaning using the default Elasticsearch value.
  • includeVectorResponse: whether to include vector fields in the search response. Default is false.

Note: From version 9.2 of the elasticsearch server, vector fields are excluded from the response by default. To include vector fields in the responses, set the includeVectorResponse in the builder:

ElasticsearchConfiguration configuration = ElasticsearchConfigurationHybrid.builder()
    .includeVectorResponse(true)
    .build();

Creating Custom Configurations​

You can create your own Elasticsearch configuration by implementing the ElasticsearchConfiguration interface. For example:

public class MyElasticsearchConfiguration implements ElasticsearchConfiguration {
    @Override
    SearchResponse<Document> vectorSearch(
            ElasticsearchClient client,
            String indexName,
            EmbeddingSearchRequest embeddingSearchRequest) {
        // Your custom vector search implementation here
    }

    @Override
    SearchResponse<Document> fullTextSearch(
            ElasticsearchClient client, 
            String indexName, 
            String textQuery) {
        // Your custom full text search implementation here
    }

    @Override
    SearchResponse<Document> hybridSearch(
            ElasticsearchClient client,
            String indexName,
            EmbeddingSearchRequest embeddingSearchRequest,
            String textQuery) {
        // Your custom hybrid search implementation here
    }
}

Examples​