which is more flexible and easier to use. This notebook will guide you through the process of upgrading to the new implementation.What's new?‚ÄãThe new implementation is now one class called ElasticsearchStore which can be used for approx, exact, and ELSER search retrieval, via strategies.Im using ElasticKNNSearch‚ÄãOld implementation:from langchain.vectorstores.elastic_vector_search import ElasticKNNSearchdb = ElasticKNNSearch(  elasticsearch_url="http://localhost:9200",  index_name="test_index",  embedding=embedding)New implementation:from langchain.vectorstores.elasticsearch import ElasticsearchStoredb = ElasticsearchStore(  es_url="http://localhost:9200",  index_name="test_index",  embedding=embedding,  # if you use the model_id  # strategy=ElasticsearchStore.ApproxRetrievalStrategy( query_model_id="test_model" )  # if you use hybrid search  # strategy=ElasticsearchStore.ApproxRetrievalStrategy( hybrid=True ))Im using ElasticVectorSearch‚ÄãOld implementation:from langchain.vectorstores.elastic_vector_search import ElasticVectorSearchdb = ElasticVectorSearch(  elasticsearch_url="http://localhost:9200",  index_name="test_index",  embedding=embedding)New implementation:from langchain.vectorstores.elasticsearch import ElasticsearchStoredb = ElasticsearchStore(  es_url="http://localhost:9200",  index_name="test_index",  embedding=embedding,  strategy=ElasticsearchStore.ExactRetrievalStrategy())db.client.indices.delete(index='test-metadata, test-elser, test-basic', ignore_unavailable=True, allow_no_indices=True)    ObjectApiResponse({'acknowledged': True})PreviousDocArray InMemorySearchNextEpsillaRunning and connecting to ElasticsearchRunning Elasticsearch via DockerAuthenticationElastic CloudBasic ExampleFiltering MetadataExample: Filter by keywordExample: Filter by Date RangeExample: Filter by Numeric RangeExample: Filter by Geo DistanceApproxRetrievalStrategyExample: Approx with hybridExample: Approx with Embedding Model in
Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library.
Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library. ->: which is more flexible and easier to use. This notebook will guide you through the process of upgrading to the new implementation.What's new?‚ÄãThe new implementation is now one class called ElasticsearchStore which can be used for approx, exact, and ELSER search retrieval, via strategies.Im using ElasticKNNSearch‚ÄãOld implementation:from langchain.vectorstores.elastic_vector_search import ElasticKNNSearchdb = ElasticKNNSearch(  elasticsearch_url="http://localhost:9200",  index_name="test_index",  embedding=embedding)New implementation:from langchain.vectorstores.elasticsearch import ElasticsearchStoredb = ElasticsearchStore(  es_url="http://localhost:9200",  index_name="test_index",  embedding=embedding,  # if you use the model_id  # strategy=ElasticsearchStore.ApproxRetrievalStrategy( query_model_id="test_model" )  # if you use hybrid search  # strategy=ElasticsearchStore.ApproxRetrievalStrategy( hybrid=True ))Im using ElasticVectorSearch‚ÄãOld implementation:from langchain.vectorstores.elastic_vector_search import ElasticVectorSearchdb = ElasticVectorSearch(  elasticsearch_url="http://localhost:9200",  index_name="test_index",  embedding=embedding)New implementation:from langchain.vectorstores.elasticsearch import ElasticsearchStoredb = ElasticsearchStore(  es_url="http://localhost:9200",  index_name="test_index",  embedding=embedding,  strategy=ElasticsearchStore.ExactRetrievalStrategy())db.client.indices.delete(index='test-metadata, test-elser, test-basic', ignore_unavailable=True, allow_no_indices=True)    ObjectApiResponse({'acknowledged': True})PreviousDocArray InMemorySearchNextEpsillaRunning and connecting to ElasticsearchRunning Elasticsearch via DockerAuthenticationElastic CloudBasic ExampleFiltering MetadataExample: Filter by keywordExample: Filter by Date RangeExample: Filter by Numeric RangeExample: Filter by Geo DistanceApproxRetrievalStrategyExample: Approx with hybridExample: Approx with Embedding Model in