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* add missing headers * external integrations header row * implement headerless tables * more tables with key-value pairs
74 lines
3.0 KiB
Plaintext
74 lines
3.0 KiB
Plaintext
---
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title: "OpenSearchDocumentStore"
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id: opensearch-document-store
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slug: "/opensearch-document-store"
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description: "A Document Store for storing and retrieval from OpenSearch."
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---
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# OpenSearchDocumentStore
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A Document Store for storing and retrieval from OpenSearch.
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<div className="key-value-table">
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| | |
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| --- | --- |
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| API reference | [OpenSearch](/reference/integrations-opensearch) |
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| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch |
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</div>
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OpenSearch is a fully open source search and analytics engine for use cases such as log analytics, real-time application monitoring, and clickstream analysis. For more information, see the [OpenSearch documentation](https://opensearch.org/docs/).
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This Document Store is great if you want to evaluate the performance of different retrieval options (dense vs. sparse). It’s compatible with the Amazon OpenSearch Service.
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OpenSearch provides support for vector similarity comparisons and approximate nearest neighbors algorithms.
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### Initialization
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[Install](https://opensearch.org/docs/latest/install-and-configure/install-opensearch/index/) and run an OpenSearch instance.
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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 opensearchproject/opensearch:2.11.0
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docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms1024m -Xmx1024m" opensearchproject/opensearch:2.11.0
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```
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As an alternative, you can go to [OpenSearch integration GitHub](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch) and start a Docker container running OpenSearch 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 OpenSearch instance, install the `opensearch-haystack` integration:
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```shell
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pip install opensearch-haystack
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```
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Then, initialize an `OpenSearchDocumentStore` object that’s connected to the OpenSearch instance and writes documents to it:
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```python
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from haystack_integrations.document_stores.opensearch import OpenSearchDocumentStore
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from haystack import Document
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document_store = OpenSearchDocumentStore(hosts="http://localhost:9200", use_ssl=True,
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verify_certs=False, http_auth=("admin", "admin"))
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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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[`OpenSearchBM25Retriever`](../pipeline-components/retrievers/opensearchbm25retriever.mdx): A keyword-based Retriever that fetches documents matching a query from the Document Store.
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[`OpenSearchEmbeddingRetriever`](../pipeline-components/retrievers/opensearchembeddingretriever.mdx): Compares the query and document embeddings and fetches the documents most relevant to the query.
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## Additional References
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🧑🍳 Cookbook: [PDF-Based Question Answering with Amazon Bedrock and Haystack](https://haystack.deepset.ai/cookbook/amazon_bedrock_for_documentation_qa)
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