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67 lines
2.7 KiB
Plaintext
67 lines
2.7 KiB
Plaintext
---
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title: "ElasticsearchDocumentStore"
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id: elasticsearch-document-store
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slug: "/elasticsearch-document-store"
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description: "Use an Elasticsearch database with Haystack."
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---
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# ElasticsearchDocumentStore
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Use an Elasticsearch database with Haystack.
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<div className="key-value-table">
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| | |
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| --- | --- |
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| API reference | [Elasticsearch](/reference/integrations-elasticsearch) |
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| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/elasticsearch |
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</div>
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ElasticsearchDocumentStore is excellent if you want to evaluate the performance of different retrieval options (dense vs. sparse) and aim for a smooth transition from PoC to production.
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It features the approximate nearest neighbours (ANN) search.
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### Initialization
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[Install](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html) Elasticsearch and then [start](https://www.elastic.co/guide/en/elasticsearch/reference/current/starting-elasticsearch.html) an instance. Haystack supports Elasticsearch 8.
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If you have Docker set up, we recommend pulling the Docker image and running it.
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```shell
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docker pull docker.elastic.co/elasticsearch/elasticsearch:8.11.1
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docker run -p 9200:9200 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms1024m -Xmx1024m" -e "xpack.security.enabled=false" elasticsearch:8.11.1
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```
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As an alternative, you can go to [Elasticsearch integration GitHub](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/elasticsearch) and start a Docker container running Elasticsearch using the provided `docker-compose.yml`:
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```shell
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docker compose up
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```
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Once you have a running Elasticsearch instance, install the `elasticsearch-haystack` integration:
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```shell
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pip install elasticsearch-haystack
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```
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Then, initialize an `ElasticsearchDocumentStore` object that’s connected to the Elasticsearch instance and writes documents to it:
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```python
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from haystack_integrations.document_stores.elasticsearch import ElasticsearchDocumentStore
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from haystack import Document
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document_store = ElasticsearchDocumentStore(hosts = "http://localhost:9200")
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document_store.write_documents([
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Document(content="This is first"),
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Document(content="This is second")
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])
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print(document_store.count_documents())
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```
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### Supported Retrievers
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[`ElasticsearchBM25Retriever`](../pipeline-components/retrievers/elasticsearchbm25retriever.mdx): A keyword-based Retriever that fetches documents matching a query from the Document Store.
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[`ElasticsearchEmbeddingRetriever`](../pipeline-components/retrievers/elasticsearchembeddingretriever.mdx): Compares the query and document embeddings and fetches the documents most relevant to the query.
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