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+---
+title: "Elasticsearch"
+id: integrations-elasticsearch
+description: "Elasticsearch integration for Haystack"
+slug: "/integrations-elasticsearch"
+---
+
+
+## haystack_integrations.components.retrievers.elasticsearch.bm25_retriever
+
+### ElasticsearchBM25Retriever
+
+Retrieves documents from ElasticsearchDocumentStore using the BM25 algorithm.
+
+Finds the most similar documents to a user's query.
+
+This retriever is only compatible with ElasticsearchDocumentStore.
+
+Usage example:
+
+```python
+from haystack import Document
+from haystack_integrations.document_stores.elasticsearch import ElasticsearchDocumentStore
+from haystack_integrations.components.retrievers.elasticsearch import ElasticsearchBM25Retriever
+
+document_store = ElasticsearchDocumentStore(hosts="http://localhost:9200")
+retriever = ElasticsearchBM25Retriever(document_store=document_store)
+
+# Add documents to DocumentStore
+documents = [
+    Document(text="My name is Carla and I live in Berlin"),
+    Document(text="My name is Paul and I live in New York"),
+    Document(text="My name is Silvano and I live in Matera"),
+    Document(text="My name is Usagi Tsukino and I live in Tokyo"),
+]
+document_store.write_documents(documents)
+
+result = retriever.run(query="Who lives in Berlin?")
+for doc in result["documents"]:
+    print(doc.content)
+```
+
+#### __init__
+
+```python
+__init__(
+    *,
+    document_store: ElasticsearchDocumentStore,
+    filters: dict[str, Any] | None = None,
+    fuzziness: str = "AUTO",
+    top_k: int = 10,
+    scale_score: bool = False,
+    filter_policy: str | FilterPolicy = FilterPolicy.REPLACE
+) -> None
+```
+
+Initialize ElasticsearchBM25Retriever with an instance ElasticsearchDocumentStore.
+
+**Parameters:**
+
+- **document_store** (<code>ElasticsearchDocumentStore</code>) – An instance of ElasticsearchDocumentStore.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents, for more info
+  see `ElasticsearchDocumentStore.filter_documents`.
+- **fuzziness** (<code>str</code>) – Fuzziness parameter passed to Elasticsearch. See the official
+  [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/common-options.html#fuzziness)
+  for more details.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **scale_score** (<code>bool</code>) – If `True` scales the Document\`s scores between 0 and 1.
+- **filter_policy** (<code>str | FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>ValueError</code> – If `document_store` is not an instance of `ElasticsearchDocumentStore`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> ElasticsearchBM25Retriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>ElasticsearchBM25Retriever</code> – Deserialized component.
+
+#### close
+
+```python
+close() -> None
+```
+
+Release the synchronous resources of the underlying Document Store.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Release the asynchronous resources of the underlying Document Store.
+
+#### run
+
+```python
+run(
+    query: str, filters: dict[str, Any] | None = None, top_k: int | None = None
+) -> dict[str, list[Document]]
+```
+
+Retrieve documents using the BM25 keyword-based algorithm.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – String to search in the `Document`s text.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents. The way runtime filters are applied depends on
+  the `filter_policy` chosen at retriever initialization. See init method docstring for more
+  details.
+- **top_k** (<code>int | None</code>) – Maximum number of `Document` to return.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: List of `Document`s that match the query.
+
+#### run_async
+
+```python
+run_async(
+    query: str, filters: dict[str, Any] | None = None, top_k: int | None = None
+) -> dict[str, list[Document]]
+```
+
+Asynchronously retrieve documents using the BM25 keyword-based algorithm.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – String to search in the `Document` text.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents. The way runtime filters are applied depends on
+  the `filter_policy` chosen at retriever initialization. See init method docstring for more
+  details.
+- **top_k** (<code>int | None</code>) – Maximum number of `Document` to return.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: List of `Document`s that match the query.
+
+## haystack_integrations.components.retrievers.elasticsearch.embedding_retriever
+
+### ElasticsearchEmbeddingRetriever
+
+ElasticsearchEmbeddingRetriever retrieves documents from the ElasticsearchDocumentStore using vector similarity.
+
+Usage example:
+
+```python
+from haystack import Document
+
+# Requires: pip install sentence-transformers-haystack
+from haystack_integrations.components.embedders.sentence_transformers import (
+    SentenceTransformersTextEmbedder,
+)
+from haystack_integrations.document_stores.elasticsearch import (
+    ElasticsearchDocumentStore,
+)
+from haystack_integrations.components.retrievers.elasticsearch import (
+    ElasticsearchEmbeddingRetriever,
+)
+
+document_store = ElasticsearchDocumentStore(hosts="http://localhost:9200")
+retriever = ElasticsearchEmbeddingRetriever(document_store=document_store)
+
+# Add documents to DocumentStore
+documents = [
+    Document(text="My name is Carla and I live in Berlin"),
+    Document(text="My name is Paul and I live in New York"),
+    Document(text="My name is Silvano and I live in Matera"),
+    Document(text="My name is Usagi Tsukino and I live in Tokyo"),
+]
+document_store.write_documents(documents)
+
+te = SentenceTransformersTextEmbedder()
+query_embeddings = te.run("Who lives in Berlin?")["embedding"]
+
+result = retriever.run(query=query_embeddings)
+for doc in result["documents"]:
+    print(doc.content)
+```
+
+#### __init__
+
+```python
+__init__(
+    *,
+    document_store: ElasticsearchDocumentStore,
+    filters: dict[str, Any] | None = None,
+    top_k: int = 10,
+    num_candidates: int | None = None,
+    filter_policy: str | FilterPolicy = FilterPolicy.REPLACE
+) -> None
+```
+
+Create the ElasticsearchEmbeddingRetriever component.
+
+**Parameters:**
+
+- **document_store** (<code>ElasticsearchDocumentStore</code>) – An instance of ElasticsearchDocumentStore.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied to the retrieved Documents.
+  Filters are applied during the approximate KNN search to ensure that top_k matching documents are returned.
+- **top_k** (<code>int</code>) – Maximum number of Documents to return.
+- **num_candidates** (<code>int | None</code>) – Number of approximate nearest neighbor candidates on each shard. Defaults to top_k * 10.
+  Increasing this value will improve search accuracy at the cost of slower search speeds.
+  You can read more about it in the Elasticsearch
+  [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html#tune-approximate-knn-for-speed-accuracy)
+- **filter_policy** (<code>str | FilterPolicy</code>) – Policy to determine how filters are applied.
+
+**Raises:**
+
+- <code>ValueError</code> – If `document_store` is not an instance of ElasticsearchDocumentStore.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> ElasticsearchEmbeddingRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>ElasticsearchEmbeddingRetriever</code> – Deserialized component.
+
+#### close
+
+```python
+close() -> None
+```
+
+Release the synchronous resources of the underlying Document Store.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Release the asynchronous resources of the underlying Document Store.
+
+#### run
+
+```python
+run(
+    query_embedding: list[float],
+    filters: dict[str, Any] | None = None,
+    top_k: int | None = None,
+) -> dict[str, list[Document]]
+```
+
+Retrieve documents using a vector similarity metric.
+
+**Parameters:**
+
+- **query_embedding** (<code>list\[float\]</code>) – Embedding of the query.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied when fetching documents from the Document Store.
+  Filters are applied during the approximate kNN search to ensure the Retriever returns
+  `top_k` matching documents.
+  The way runtime filters are applied depends on the `filter_policy` selected when initializing the Retriever.
+- **top_k** (<code>int | None</code>) – Maximum number of documents to return.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: List of `Document`s most similar to the given `query_embedding`
+
+#### run_async
+
+```python
+run_async(
+    query_embedding: list[float],
+    filters: dict[str, Any] | None = None,
+    top_k: int | None = None,
+) -> dict[str, list[Document]]
+```
+
+Asynchronously retrieve documents using a vector similarity metric.
+
+**Parameters:**
+
+- **query_embedding** (<code>list\[float\]</code>) – Embedding of the query.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Filters applied when fetching documents from the Document Store.
+  Filters are applied during the approximate kNN search to ensure the Retriever returns
+  `top_k` matching documents.
+  The way runtime filters are applied depends on the `filter_policy` selected when initializing the Retriever.
+- **top_k** (<code>int | None</code>) – Maximum number of documents to return.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with the following keys:
+- `documents`: List of `Document`s that match the query.
+
+## haystack_integrations.components.retrievers.elasticsearch.sql_retriever
+
+### ElasticsearchSQLRetriever
+
+Executes raw Elasticsearch SQL queries against an ElasticsearchDocumentStore.
+
+This component allows you to execute SQL queries directly against the Elasticsearch index,
+which is useful for fetching metadata, aggregations, and other structured data at runtime.
+
+Returns the raw JSON response from the Elasticsearch SQL API.
+
+Usage example:
+
+```python
+from haystack_integrations.document_stores.elasticsearch import ElasticsearchDocumentStore
+from haystack_integrations.components.retrievers.elasticsearch import ElasticsearchSQLRetriever
+
+document_store = ElasticsearchDocumentStore(hosts="http://localhost:9200")
+retriever = ElasticsearchSQLRetriever(document_store=document_store)
+
+result = retriever.run(
+    query="SELECT content, category FROM \"my_index\" WHERE category = 'A'"
+)
+# result["result"] contains the raw Elasticsearch JSON response
+```
+
+#### __init__
+
+```python
+__init__(
+    *,
+    document_store: ElasticsearchDocumentStore,
+    raise_on_failure: bool = True,
+    fetch_size: int | None = None
+) -> None
+```
+
+Creates the ElasticsearchSQLRetriever component.
+
+**Parameters:**
+
+- **document_store** (<code>ElasticsearchDocumentStore</code>) – An instance of ElasticsearchDocumentStore to use with the Retriever.
+- **raise_on_failure** (<code>bool</code>) – Whether to raise an exception if the API call fails. Otherwise, log a warning and return an empty dict.
+- **fetch_size** (<code>int | None</code>) – Optional number of results to fetch per page. If not provided, the default
+  fetch size set in Elasticsearch is used.
+
+**Raises:**
+
+- <code>ValueError</code> – If `document_store` is not an instance of ElasticsearchDocumentStore.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> ElasticsearchSQLRetriever
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>ElasticsearchSQLRetriever</code> – Deserialized component.
+
+#### close
+
+```python
+close() -> None
+```
+
+Release the synchronous resources of the underlying Document Store.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Release the asynchronous resources of the underlying Document Store.
+
+#### run
+
+```python
+run(
+    query: str,
+    document_store: ElasticsearchDocumentStore | None = None,
+    fetch_size: int | None = None,
+) -> dict[str, dict[str, Any]]
+```
+
+Execute a raw Elasticsearch SQL query against the index.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – The Elasticsearch SQL query to execute.
+- **document_store** (<code>ElasticsearchDocumentStore | None</code>) – Optionally, an instance of ElasticsearchDocumentStore to use with the Retriever.
+- **fetch_size** (<code>int | None</code>) – Optional number of results to fetch per page. If not provided, uses the value
+  specified during initialization, or the default fetch size set in Elasticsearch.
+
+**Returns:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – A dictionary containing the raw JSON response from Elasticsearch SQL API:
+  - result: The raw JSON response from Elasticsearch (dict) or empty dict on error.
+
+Example:
+`python     retriever = ElasticsearchSQLRetriever(document_store=document_store)     result = retriever.run(         query="SELECT content, category FROM \"my_index\" WHERE category = 'A'"     )     # result["result"] contains the raw Elasticsearch JSON response     # result["result"]["columns"] contains column metadata     # result["result"]["rows"] contains the data rows     `
+
+#### run_async
+
+```python
+run_async(
+    query: str,
+    document_store: ElasticsearchDocumentStore | None = None,
+    fetch_size: int | None = None,
+) -> dict[str, dict[str, Any]]
+```
+
+Asynchronously execute a raw Elasticsearch SQL query against the index.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – The Elasticsearch SQL query to execute.
+- **document_store** (<code>ElasticsearchDocumentStore | None</code>) – Optionally, an instance of ElasticsearchDocumentStore to use with the Retriever.
+- **fetch_size** (<code>int | None</code>) – Optional number of results to fetch per page. If not provided, uses the value
+  specified during initialization, or the default fetch size set in Elasticsearch.
+
+**Returns:**
+
+- <code>dict\[str, dict\[str, Any\]\]</code> – A dictionary containing the raw JSON response from Elasticsearch SQL API:
+  - result: The raw JSON response from Elasticsearch (dict) or empty dict on error.
+
+Example:
+`python     retriever = ElasticsearchSQLRetriever(document_store=document_store)     result = await retriever.run_async(         query="SELECT content, category FROM \"my_index\" WHERE category = 'A'"     )     # result["result"] contains the raw Elasticsearch JSON response     # result["result"]["columns"] contains column metadata     # result["result"]["rows"] contains the data rows     `
+
+## haystack_integrations.document_stores.elasticsearch.document_store
+
+### ElasticsearchDocumentStore
+
+An ElasticsearchDocumentStore instance that works with Elastic Cloud or your own Elasticsearch cluster.
+
+Usage example (Elastic Cloud):
+
+```python
+from haystack_integrations.document_stores.elasticsearch import ElasticsearchDocumentStore
+document_store = ElasticsearchDocumentStore(
+    api_key_id=Secret.from_env_var("ELASTIC_API_KEY_ID", strict=False),
+    api_key=Secret.from_env_var("ELASTIC_API_KEY", strict=False),
+)
+```
+
+Usage example (self-hosted Elasticsearch instance):
+
+```python
+from haystack_integrations.document_stores.elasticsearch import ElasticsearchDocumentStore
+document_store = ElasticsearchDocumentStore(hosts="http://localhost:9200")
+```
+
+In the above example we connect with security disabled just to show the basic usage.
+We strongly recommend to enable security so that only authorized users can access your data.
+
+For more details on how to connect to Elasticsearch and configure security,
+see the official Elasticsearch
+[documentation](https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/connecting.html)
+
+All extra keyword arguments will be passed to the Elasticsearch client.
+
+#### __init__
+
+```python
+__init__(
+    *,
+    hosts: Hosts | None = None,
+    custom_mapping: dict[str, Any] | None = None,
+    index: str = "default",
+    api_key: Secret | str | None = Secret.from_env_var(
+        "ELASTIC_API_KEY", strict=False
+    ),
+    api_key_id: Secret | str | None = Secret.from_env_var(
+        "ELASTIC_API_KEY_ID", strict=False
+    ),
+    embedding_similarity_function: Literal[
+        "cosine", "dot_product", "l2_norm", "max_inner_product"
+    ] = "cosine",
+    sparse_vector_field: str | None = None,
+    ingest_pipeline: str | None = None,
+    **kwargs: Any
+) -> None
+```
+
+Creates a new ElasticsearchDocumentStore instance.
+
+It will also try to create that index if it doesn't exist yet. Otherwise, it will use the existing one.
+
+One can also set the similarity function used to compare Documents embeddings. This is mostly useful
+when using the `ElasticsearchDocumentStore` in a Pipeline with an `ElasticsearchEmbeddingRetriever`.
+
+For more information on connection parameters, see the official Elasticsearch
+[documentation](https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/connecting.html)
+
+For the full list of supported kwargs, see the official Elasticsearch
+[reference](https://elasticsearch-py.readthedocs.io/en/stable/api.html#module-elasticsearch)
+
+Authentication is provided via Secret objects, which by default are loaded from environment variables.
+You can either provide both `api_key_id` and `api_key`, or just `api_key` containing a base64-encoded string
+of `id:secret`. Secret instances can also be loaded from a token using the `Secret.from_token()` method.
+
+**Parameters:**
+
+- **hosts** (<code>Hosts | None</code>) – List of hosts running the Elasticsearch client.
+- **custom_mapping** (<code>dict\[str, Any\] | None</code>) – Custom mapping for the index. If not provided, a default mapping will be used.
+- **index** (<code>str</code>) – Name of index in Elasticsearch.
+- **api_key** (<code>Secret | str | None</code>) – A Secret object containing the API key for authenticating or base64-encoded with the
+  concatenated secret and id for authenticating with Elasticsearch (separated by “:”).
+- **api_key_id** (<code>Secret | str | None</code>) – A Secret object containing the API key ID for authenticating with Elasticsearch.
+- **embedding_similarity_function** (<code>Literal['cosine', 'dot_product', 'l2_norm', 'max_inner_product']</code>) – The similarity function used to compare Documents embeddings.
+  This parameter only takes effect if the index does not yet exist and is created.
+  To choose the most appropriate function, look for information about your embedding model.
+  To understand how document scores are computed, see the Elasticsearch
+  [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params)
+- **sparse_vector_field** (<code>str | None</code>) – If set, the name of the Elasticsearch field where sparse embeddings
+  will be stored using the `sparse_vector` field type. When not set, any `sparse_embedding`
+  data on Documents is silently dropped during writes.
+- **ingest_pipeline** (<code>str | None</code>) – If set, the id of an Elasticsearch ingest pipeline to run on each bulk
+  index or create. This is the recommended way to generate embeddings at index time using
+  Elasticsearch's inference processors (e.g. ELSER or a dense model) without running a
+  Haystack embedder component. Leading and trailing whitespace is stripped.
+
+Requirements when using inference processors:
+
+- Configure the processor with `input_output` so the embedding is written directly
+  to the right field: `output_field` must match `"embedding"` (for dense retrieval)
+  or the value of `sparse_vector_field` (for ELSER / sparse retrieval). The ES default
+  target `ml.inference.<tag>` will not be found by Haystack's retrievers.
+- Do **not** also run a Haystack `DocumentEmbedder` upstream. If documents arrive with
+  a pre-computed `embedding`, the pipeline will overwrite it with its own model's
+  vectors, causing a silent mismatch between stored and query embeddings at retrieval time.
+- If you supply `custom_mapping`, include the output field with the correct type
+  (`dense_vector` or `sparse_vector`).
+
+Sparse embedding note: Elasticsearch does not store `sparse_vector` data generated
+by inference pipelines in `_source`; it goes only into the inverted index. Haystack
+works around this by requesting the field via the ES `fields` API on every search so
+that `Document.sparse_embedding` is populated correctly on returned documents.
+
+- \*\***kwargs** (<code>Any</code>) – Optional arguments that `Elasticsearch` takes.
+
+#### client
+
+```python
+client: Elasticsearch
+```
+
+Returns the synchronous Elasticsearch client, initializing it if necessary.
+
+#### async_client
+
+```python
+async_client: AsyncElasticsearch
+```
+
+Returns the asynchronous Elasticsearch client, constructing it if necessary.
+
+#### close
+
+```python
+close() -> None
+```
+
+Release the associated synchronous resources.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Release the associated asynchronous resources.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> ElasticsearchDocumentStore
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>ElasticsearchDocumentStore</code> – Deserialized component.
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Returns how many documents are present in the document store.
+
+**Returns:**
+
+- <code>int</code> – Number of documents in the document store.
+
+#### count_documents_async
+
+```python
+count_documents_async() -> int
+```
+
+Asynchronously returns how many documents are present in the document store.
+
+**Returns:**
+
+- <code>int</code> – Number of documents in the document store.
+
+#### filter_documents
+
+```python
+filter_documents(filters: dict[str, Any] | None = None) -> list[Document]
+```
+
+The main query method for the document store. It retrieves all documents that match the filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\] | None</code>) – A dictionary of filters to apply. For more information on the structure of the filters,
+  see the official Elasticsearch
+  [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html)
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of `Document`s that match the filters.
+
+#### filter_documents_async
+
+```python
+filter_documents_async(filters: dict[str, Any] | None = None) -> list[Document]
+```
+
+Asynchronously retrieves all documents that match the filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\] | None</code>) – A dictionary of filters to apply. For more information on the structure of the filters,
+  see the official Elasticsearch
+  [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html)
+
+**Returns:**
+
+- <code>list\[Document\]</code> – List of `Document`s that match the filters.
+
+#### write_documents
+
+```python
+write_documents(
+    documents: list[Document],
+    policy: DuplicatePolicy = DuplicatePolicy.NONE,
+    refresh: Literal["wait_for", True, False] = "wait_for",
+) -> int
+```
+
+Writes `Document`s to Elasticsearch.
+
+**Parameters:**
+
+- **documents** (<code>list\[Document\]</code>) – List of Documents to write to the document store.
+- **policy** (<code>DuplicatePolicy</code>) – DuplicatePolicy to apply when a document with the same ID already exists in the document store.
+- **refresh** (<code>Literal['wait_for', True, False]</code>) – Controls when changes are made visible to search operations.
+- `True`: Force refresh immediately after the operation.
+- `False`: Do not refresh (better performance for bulk operations).
+- `"wait_for"`: Wait for the next refresh cycle (default, ensures read-your-writes consistency).
+  For more details, see the [Elasticsearch refresh documentation](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/refresh-parameter).
+
+**Returns:**
+
+- <code>int</code> – Number of documents written to the document store.
+
+**Raises:**
+
+- <code>ValueError</code> – If `documents` is not a list of `Document`s.
+- <code>DuplicateDocumentError</code> – If a document with the same ID already exists in the document store and
+  `policy` is set to `DuplicatePolicy.FAIL` or `DuplicatePolicy.NONE`.
+- <code>DocumentStoreError</code> – If an error occurs while writing the documents to the document store.
+
+#### write_documents_async
+
+```python
+write_documents_async(
+    documents: list[Document],
+    policy: DuplicatePolicy = DuplicatePolicy.NONE,
+    refresh: Literal["wait_for", True, False] = "wait_for",
+) -> int
+```
+
+Asynchronously writes `Document`s to Elasticsearch.
+
+**Parameters:**
+
+- **documents** (<code>list\[Document\]</code>) – List of Documents to write to the document store.
+- **policy** (<code>DuplicatePolicy</code>) – DuplicatePolicy to apply when a document with the same ID already exists in the document store.
+- **refresh** (<code>Literal['wait_for', True, False]</code>) – Controls when changes are made visible to search operations.
+- `True`: Force refresh immediately after the operation.
+- `False`: Do not refresh (better performance for bulk operations).
+- `"wait_for"`: Wait for the next refresh cycle (default, ensures read-your-writes consistency).
+  For more details, see the [Elasticsearch refresh documentation](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/refresh-parameter).
+
+**Returns:**
+
+- <code>int</code> – Number of documents written to the document store.
+
+**Raises:**
+
+- <code>ValueError</code> – If `documents` is not a list of `Document`s.
+- <code>DuplicateDocumentError</code> – If a document with the same ID already exists in the document store and
+  `policy` is set to `DuplicatePolicy.FAIL` or `DuplicatePolicy.NONE`.
+- <code>DocumentStoreError</code> – If an error occurs while writing the documents to the document store.
+
+#### delete_documents
+
+```python
+delete_documents(
+    document_ids: list[str],
+    refresh: Literal["wait_for", True, False] = "wait_for",
+) -> None
+```
+
+Deletes all documents with a matching document_ids from the document store.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – the document ids to delete
+- **refresh** (<code>Literal['wait_for', True, False]</code>) – Controls when changes are made visible to search operations.
+- `True`: Force refresh immediately after the operation.
+- `False`: Do not refresh (better performance for bulk operations).
+- `"wait_for"`: Wait for the next refresh cycle (default, ensures read-your-writes consistency).
+  For more details, see the [Elasticsearch refresh documentation](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/refresh-parameter).
+
+#### delete_documents_async
+
+```python
+delete_documents_async(
+    document_ids: list[str],
+    refresh: Literal["wait_for", True, False] = "wait_for",
+) -> None
+```
+
+Asynchronously deletes all documents with a matching document_ids from the document store.
+
+**Parameters:**
+
+- **document_ids** (<code>list\[str\]</code>) – the document ids to delete
+- **refresh** (<code>Literal['wait_for', True, False]</code>) – Controls when changes are made visible to search operations.
+- `True`: Force refresh immediately after the operation.
+- `False`: Do not refresh (better performance for bulk operations).
+- `"wait_for"`: Wait for the next refresh cycle (default, ensures read-your-writes consistency).
+  For more details, see the [Elasticsearch refresh documentation](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/refresh-parameter).
+
+#### delete_all_documents
+
+```python
+delete_all_documents(
+    recreate_index: bool = False, refresh: bool = True
+) -> None
+```
+
+Deletes all documents in the document store.
+
+A fast way to clear all documents from the document store while preserving any index settings and mappings.
+
+**Parameters:**
+
+- **recreate_index** (<code>bool</code>) – If True, the index will be deleted and recreated with the original mappings and
+  settings. If False, all documents will be deleted using the `delete_by_query` API.
+- **refresh** (<code>bool</code>) – If True, Elasticsearch refreshes all shards involved in the delete by query after the request
+  completes. If False, no refresh is performed. For more details, see the
+  [Elasticsearch delete_by_query refresh documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-delete-by-query#operation-delete-by-query-refresh).
+
+#### delete_all_documents_async
+
+```python
+delete_all_documents_async(
+    recreate_index: bool = False, refresh: bool = True
+) -> None
+```
+
+Asynchronously deletes all documents in the document store.
+
+A fast way to clear all documents from the document store while preserving any index settings and mappings.
+
+**Parameters:**
+
+- **recreate_index** (<code>bool</code>) – If True, the index will be deleted and recreated with the original mappings and
+  settings. If False, all documents will be deleted using the `delete_by_query` API.
+- **refresh** (<code>bool</code>) – If True, Elasticsearch refreshes all shards involved in the delete by query after the request
+  completes. If False, no refresh is performed. For more details, see the
+  [Elasticsearch delete_by_query refresh documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-delete-by-query#operation-delete-by-query-refresh).
+
+#### delete_by_filter
+
+```python
+delete_by_filter(filters: dict[str, Any], refresh: bool = False) -> int
+```
+
+Deletes all documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to select documents for deletion.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- **refresh** (<code>bool</code>) – If True, Elasticsearch refreshes all shards involved in the delete by query after the request
+  completes. If False, no refresh is performed. For more details, see the
+  [Elasticsearch delete_by_query refresh documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-delete-by-query#operation-delete-by-query-refresh).
+
+**Returns:**
+
+- <code>int</code> – The number of documents deleted.
+
+#### delete_by_filter_async
+
+```python
+delete_by_filter_async(filters: dict[str, Any], refresh: bool = False) -> int
+```
+
+Asynchronously deletes all documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to select documents for deletion.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- **refresh** (<code>bool</code>) – If True, Elasticsearch refreshes all shards involved in the delete by query after the request
+  completes. If False, no refresh is performed. For more details, see the
+  [Elasticsearch refresh documentation](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/refresh-parameter).
+
+**Returns:**
+
+- <code>int</code> – The number of documents deleted.
+
+#### update_by_filter
+
+```python
+update_by_filter(
+    filters: dict[str, Any], meta: dict[str, Any], refresh: bool = False
+) -> int
+```
+
+Updates the metadata of all documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to select documents for updating.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- **meta** (<code>dict\[str, Any\]</code>) – The metadata fields to update.
+- **refresh** (<code>bool</code>) – If True, Elasticsearch refreshes all shards involved in the update by query after the request
+  completes. If False, no refresh is performed. For more details, see the
+  [Elasticsearch update_by_query refresh documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-update-by-query#operation-update-by-query-refresh).
+
+**Returns:**
+
+- <code>int</code> – The number of documents updated.
+
+#### update_by_filter_async
+
+```python
+update_by_filter_async(
+    filters: dict[str, Any], meta: dict[str, Any], refresh: bool = False
+) -> int
+```
+
+Asynchronously updates the metadata of all documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to select documents for updating.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- **meta** (<code>dict\[str, Any\]</code>) – The metadata fields to update.
+- **refresh** (<code>bool</code>) – If True, Elasticsearch refreshes all shards involved in the update by query after the request
+  completes. If False, no refresh is performed. For more details, see the
+  [Elasticsearch update_by_query refresh documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-update-by-query#operation-update-by-query-refresh).
+
+**Returns:**
+
+- <code>int</code> – The number of documents updated.
+
+#### count_documents_by_filter
+
+```python
+count_documents_by_filter(filters: dict[str, Any]) -> int
+```
+
+Returns the number of documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to count documents.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+
+**Returns:**
+
+- <code>int</code> – The number of documents that match the filters.
+
+#### count_documents_by_filter_async
+
+```python
+count_documents_by_filter_async(filters: dict[str, Any]) -> int
+```
+
+Asynchronously returns the number of documents that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to count documents.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+
+**Returns:**
+
+- <code>int</code> – The number of documents that match the filters.
+
+#### count_unique_metadata_by_filter
+
+```python
+count_unique_metadata_by_filter(
+    filters: dict[str, Any], metadata_fields: list[str]
+) -> dict[str, int]
+```
+
+Returns the number of unique values for each specified metadata field that match the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to count documents.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- **metadata_fields** (<code>list\[str\]</code>) – List of field names to calculate unique values for.
+  Field names can include or omit the "meta." prefix.
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – A dictionary mapping each metadata field name to the count of its unique values among the filtered
+  documents.
+
+**Raises:**
+
+- <code>ValueError</code> – If any of the requested fields don't exist in the index mapping.
+
+#### count_unique_metadata_by_filter_async
+
+```python
+count_unique_metadata_by_filter_async(
+    filters: dict[str, Any], metadata_fields: list[str]
+) -> dict[str, int]
+```
+
+Asynchronously returns unique value counts for each specified metadata field matching the provided filters.
+
+**Parameters:**
+
+- **filters** (<code>dict\[str, Any\]</code>) – The filters to apply to count documents.
+  For filter syntax, see [Haystack metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- **metadata_fields** (<code>list\[str\]</code>) – List of field names to calculate unique values for.
+  Field names can include or omit the "meta." prefix.
+
+**Returns:**
+
+- <code>dict\[str, int\]</code> – A dictionary mapping each metadata field name to the count of its unique values among the filtered
+  documents.
+
+**Raises:**
+
+- <code>ValueError</code> – If any of the requested fields don't exist in the index mapping.
+
+#### get_metadata_fields_info
+
+```python
+get_metadata_fields_info() -> dict[str, dict[str, str]]
+```
+
+Returns the information about the fields in the index.
+
+If we populated the index with documents like:
+
+```python
+    Document(content="Doc 1", meta={"category": "A", "status": "active", "priority": 1})
+    Document(content="Doc 2", meta={"category": "B", "status": "inactive"})
+```
+
+This method would return:
+
+```python
+    {
+        'content': {'type': 'text'},
+        'category': {'type': 'keyword'},
+        'status': {'type': 'keyword'},
+        'priority': {'type': 'long'},
+    }
+```
+
+**Returns:**
+
+- <code>dict\[str, dict\[str, str\]\]</code> – The information about the fields in the index.
+
+#### get_metadata_fields_info_async
+
+```python
+get_metadata_fields_info_async() -> dict[str, dict[str, str]]
+```
+
+Asynchronously returns the information about the fields in the index.
+
+If we populated the index with documents like:
+
+```python
+    Document(content="Doc 1", meta={"category": "A", "status": "active", "priority": 1})
+    Document(content="Doc 2", meta={"category": "B", "status": "inactive"})
+```
+
+This method would return:
+
+```python
+    {
+        'content': {'type': 'text'},
+        'category': {'type': 'keyword'},
+        'status': {'type': 'keyword'},
+        'priority': {'type': 'long'},
+    }
+```
+
+**Returns:**
+
+- <code>dict\[str, dict\[str, str\]\]</code> – The information about the fields in the index.
+
+#### get_metadata_field_min_max
+
+```python
+get_metadata_field_min_max(metadata_field: str) -> dict[str, int | None]
+```
+
+Returns the minimum and maximum values for the given metadata field.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – The metadata field to get the minimum and maximum values for.
+
+**Returns:**
+
+- <code>dict\[str, int | None\]</code> – A dictionary with the keys "min" and "max", where each value is the minimum or maximum value of the
+  metadata field across all documents.
+
+#### get_metadata_field_min_max_async
+
+```python
+get_metadata_field_min_max_async(metadata_field: str) -> dict[str, int | None]
+```
+
+Asynchronously returns the minimum and maximum values for the given metadata field.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – The metadata field to get the minimum and maximum values for.
+
+**Returns:**
+
+- <code>dict\[str, int | None\]</code> – A dictionary with the keys "min" and "max", where each value is the minimum or maximum value of the
+  metadata field across all documents.
+
+#### get_metadata_field_unique_values
+
+```python
+get_metadata_field_unique_values(
+    metadata_field: str,
+    search_term: str | None = None,
+    from_: int = 0,
+    size: int = 10,
+    filters: dict[str, Any] | None = None,
+) -> tuple[list[Any], int]
+```
+
+Returns unique values for a metadata field, optionally filtered by a search term.
+
+Internally still backed by composite aggregations, which only support cursor-based iteration.
+Reaching offset `from_` therefore requires walking and discarding the first `from_` buckets -
+cost scales with `from_`, not `size`.
+
+**Note**: To keep this signature uniform across document stores, offset-based pagination is
+emulated on top of the cursor by re-fetching and discarding every bucket before `from_` on each
+call, requiring additional search round-trips proportional to `from_`.
+
+**Note**: `total_count` is computed via an approximate cardinality aggregation; for fields with
+very high cardinality it may not be exact.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – The metadata field to get unique values for. Can include or omit the
+  "meta." prefix.
+- **search_term** (<code>str | None</code>) – Optional case-insensitive substring to filter the returned values by, matched
+  against the metadata field's own value (not the document content).
+  NOTE: The matching is done with a server-side script to accomplish the substring matching on the value
+  of the field and this operation is quite expensive for a large corpus
+- **from\_** (<code>int</code>) – Offset to start returning values from. Defaults to 0.
+- **size** (<code>int</code>) – The number of unique values to return per page. Defaults to 10.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Optional filters to restrict the documents considered.
+
+**Returns:**
+
+- <code>tuple\[list\[Any\], int\]</code> – A tuple of (list of unique values in their original type, total count of distinct values
+  for the field matching `search_term`). Note that filters also narrows down the number of documents
+  against which the search term is matched.
+
+#### get_metadata_field_unique_values_async
+
+```python
+get_metadata_field_unique_values_async(
+    metadata_field: str,
+    search_term: str | None = None,
+    from_: int = 0,
+    size: int = 10,
+    filters: dict[str, Any] | None = None,
+) -> tuple[list[Any], int]
+```
+
+Asynchronous counterpart of `get_metadata_field_unique_values`.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – The metadata field to get unique values for. Can include or omit the
+  "meta." prefix.
+- **search_term** (<code>str | None</code>) – Optional case-insensitive substring to filter the returned values by, matched
+  against the metadata field's own value (not the document content).
+  NOTE: The matching is done with a server-side script to accomplish the substring matching on the value
+  of the field and this operation is quite expensive for a large corpus
+- **from\_** (<code>int</code>) – Offset to start returning values from. Defaults to 0.
+- **size** (<code>int</code>) – The number of unique values to return per page. Defaults to 10.
+- **filters** (<code>dict\[str, Any\] | None</code>) – Optional filters to restrict the documents considered.
+
+**Returns:**
+
+- <code>tuple\[list\[Any\], int\]</code> – A tuple of (list of unique values in their original type, total count of distinct values
+  for the field matching `search_term`). Note that filters also narrows down the number of documents
+  against which the search term is matched.
+
+## haystack_integrations.document_stores.elasticsearch.filters