---
title: "ElasticsearchEmbeddingRetriever"
id: elasticsearchembeddingretriever
slug: "/elasticsearchembeddingretriever"
description: "An embedding-based Retriever compatible with the Elasticsearch Document Store."
---

# ElasticsearchEmbeddingRetriever

An embedding-based Retriever compatible with the Elasticsearch Document Store.

<div className="key-value-table">

|                                        |                                                                                                                                                                                                                                                                         |
| :------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | 1. After a Text Embedder and before a [`PromptBuilder`](../builders/promptbuilder.mdx)  in a RAG pipeline 2. The last component in the semantic search pipeline 3. After a Text Embedder and before an [`ExtractiveReader`](../readers/extractivereader.mdx)  in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of [ElasticsearchDocumentStore](../../document-stores/elasticsearch-document-store.mdx)                                                                                                                                                                       |
| **Mandatory run variables**            | `query_embedding`: A list of floats                                                                                                                                                                                                                                     |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                                                        |
| **API reference**                      | [Elasticsearch](/reference/integrations-elasticsearch)                                                                                                                                                                                                                         |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/elasticsearch                                                                                                                                                                         |

</div>

## Overview

The `ElasticsearchEmbeddingRetriever` is an embedding-based Retriever compatible with the `ElasticsearchDocumentStore`. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the `ElasticsearchDocumentStore` based on the outcome.

When using the `ElasticsearchEmbeddingRetriever` in your NLP system, ensure it has the query and Document embeddings available. You can do so by adding a Document Embedder to your indexing pipeline and a Text Embedder to your query pipeline.

In addition to the `query_embedding`, the `ElasticsearchEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.

When initializing Retriever, you can also set `num_candidates`: the number of approximate nearest neighbor candidates on each shard. It's an advanced setting you can read more about in the [Elasticsearch documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html#tune-approximate-knn-for-speed-accuracy).

The `embedding_similarity_function` to use for embedding retrieval must be defined when the corresponding `ElasticsearchDocumentStore` is initialized.

## Installation

[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.

If you have Docker set up, we recommend pulling the Docker image and running it.

```shell
docker pull docker.elastic.co/elasticsearch/elasticsearch:8.11.1
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
```

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`:

```shell
docker compose up
```

Once you have a running Elasticsearch instance, install the `elasticsearch-haystack` integration:

```shell
pip install elasticsearch-haystack
```

## Usage

### In a pipeline

Use this Retriever in a query Pipeline like this:

```python
from haystack_integrations.components.retrievers.elasticsearch import (
    ElasticsearchEmbeddingRetriever,
)
from haystack_integrations.document_stores.elasticsearch import (
    ElasticsearchDocumentStore,
)

from haystack.document_stores.types import DuplicatePolicy
from haystack import Document, Pipeline
from haystack.components.embedders import (
    SentenceTransformersTextEmbedder,
    SentenceTransformersDocumentEmbedder,
)

document_store = ElasticsearchDocumentStore(hosts="http://localhost:9200/")

model = "BAAI/bge-large-en-v1.5"

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors.",
    ),
    Document(
        content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves.",
    ),
]

document_embedder = SentenceTransformersDocumentEmbedder(model=model)
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)

document_store.write_documents(
    documents_with_embeddings.get("documents"),
    policy=DuplicatePolicy.SKIP,
)

query_pipeline = Pipeline()
query_pipeline.add_component(
    "text_embedder",
    SentenceTransformersTextEmbedder(model=model),
)
query_pipeline.add_component(
    "retriever",
    ElasticsearchEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "How many languages are there?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])
```

The example output would be:

```python
Document(id=cfe93bc1c274908801e6670440bf2bbba54fad792770d57421f85ffa2a4fcc94, content: 'There are over 7,000 languages spoken around the world today.', score: 0.87717235, embedding: vector of size 1024)
```
