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* Update documentation and remove unused assets. Enhanced the 'agents' and 'components' sections with clearer descriptions and examples. Removed obsolete images and updated links for better navigation. Adjusted formatting for consistency across various documentation pages. * remove dependency * address comments * delete more empty pages * broken link * unduplicate headings * alphabetical components nav
62 lines
2.7 KiB
Plaintext
62 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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| --- | --- |
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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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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. |