Vector

Elasticsearch implementation of vector search and CRUD capability.

ElasticsearchVectorCapability(index_name, client, em_invoker, query_field='text', vector_query_field='vector', retrieval_strategy=None, distance_strategy=None, encryption=None, default_batch_size=None)

Bases: BaseVectorCapability, ElasticLikeCore

Elasticsearch implementation of VectorCapability protocol.

This class provides document CRUD operations and vector search using Elasticsearch.

Attributes:

Name	Type	Description
index_name	str	The name of the Elasticsearch index.
vector_store	AsyncElasticsearchStore	The vector store instance.
em_invoker	BaseEMInvoker	The embedding model to perform vectorization.

Initialize the Elasticsearch vector capability.

Parameters:

Name	Type	Description	Default
index_name	str	The name of the Elasticsearch index.	required
client	AsyncElasticsearch	The Elasticsearch client.	required
em_invoker	BaseEMInvoker	The embedding model to perform vectorization.	required
query_field	str	The field name for text queries. Defaults to "text".	'text'
vector_query_field	str	The field name for vector queries. Defaults to "vector".	'vector'
retrieval_strategy	AsyncRetrievalStrategy | None	The retrieval strategy for retrieval. Defaults to None, in which case DenseVectorStrategy() is used.	None
distance_strategy	str | None	The distance strategy for retrieval. Defaults to None.	None
encryption	EncryptionCapability | None	Encryption capability for field-level encryption. Defaults to None.	None
default_batch_size	int | None	Default batch size for operations. Defaults to None.	None

delete_by_id(id_, **kwargs) async

Deletes records from the data store based on IDs.

Parameters:

Name	Type	Description	Default
id_	str | list[str]	ID or list of IDs to delete.	required
**kwargs	Any	Additional arguments.	{}

ensure_index(mapping=None, index_settings=None, dimension=None, distance_strategy=None, **kwargs) async

Ensure Elasticsearch index exists, creating it if necessary.

This method is idempotent - if the index already exists, it will skip creation and return early.

Parameters:

Name	Type	Description	Default
mapping	dict[str, Any] | None	Custom mapping dictionary to use for index creation. If provided, this mapping will be used directly. The mapping should follow Elasticsearch mapping format. Defaults to None, in which default mapping will be used.	None
index_settings	dict[str, Any] | None	Custom index settings. These settings will be merged with any default settings. Defaults to None.	None
dimension	int | None	Vector dimension. If not provided and mapping is not provided, will be inferred from em_invoker by generating a test embedding.	None
distance_strategy	str | None	Distance strategy for vector similarity. Supported values: "cosine", "l2_norm", "dot_product", etc. Only used when building default mapping. Defaults to "cosine" if not specified.	None
**kwargs	Any	Additional arguments.	{}

Raises:

Type	Description
ValueError	If mapping is invalid or required parameters are missing.
RuntimeError	If index creation fails due to backend errors.