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# `langchain_elasticsearch` 0.1.3

## `langchain_elasticsearch.cache`

### Classes

`cache.ElasticsearchCache`(index_name[, ...]) | An Elasticsearch cache integration for LLMs.

## `langchain_elasticsearch.chat_history`

### Classes

`chat_history.ElasticsearchChatMessageHistory`(...) | Chat message history that stores history in Elasticsearch.

## `langchain_elasticsearch.client`

### Functions

`client.create_elasticsearch_client`([url, ...]) |

## `langchain_elasticsearch.embeddings`

### Classes

`embeddings.ElasticsearchEmbeddings`(client, ...) | Elasticsearch embedding models.

## `langchain_elasticsearch.retrievers`

### Classes

`retrievers.ElasticsearchRetriever` | Elasticsearch retriever

## `langchain_elasticsearch.vectorstores`

### Classes

`vectorstores.ApproxRetrievalStrategy`([...]) | Approximate retrieval strategy using the HNSW algorithm.
`vectorstores.BM25RetrievalStrategy`([k1, b]) | Retrieval strategy using the native BM25 algorithm of Elasticsearch.
`vectorstores.BaseRetrievalStrategy`() | Base class for Elasticsearch retrieval strategies.
`vectorstores.ElasticsearchStore`(index_name, ...) | Elasticsearch vector store.
`vectorstores.ExactRetrievalStrategy`() | Exact retrieval strategy using the script_score query.
`vectorstores.SparseRetrievalStrategy`([model_id]) | Sparse retrieval strategy using the text_expansion processor.