Vector

Milvus implementation of vector similarity search capability.

This module provides a Milvus implementation of the VectorCapability protocol for vector-based semantic similarity search operations. Encryption and batching are handled by the base class.

MilvusVectorCapability(collection_name, client, uri, em_invoker, dimension, distance_metric='L2', vector_field='dense_vector', id_max_length=100, content_max_length=65535, encryption=None, default_batch_size=None)

Bases: BaseVectorCapability

Milvus implementation of VectorCapability protocol.

This class provides vector similarity search operations using Milvus. Encryption and batching are handled by the base class.

Attributes:

Name	Type	Description
collection_name	str	The name of the Milvus collection.
client	AsyncMilvusClient	Async Milvus client instance.
dimension	int	Vector dimension.
distance_metric	str	Distance metric ("L2", "IP", "COSINE").
vector_field	str	Field name for dense vectors.
id_max_length	int	Maximum length for ID field.
content_max_length	int	Maximum length for content field.

Initialize the Milvus vector capability.

Parameters:

Name	Type	Description	Default
collection_name	str	The name of the Milvus collection.	required
client	AsyncMilvusClient	The async Milvus client instance.	required
uri	str	Milvus URI for creating sync client to prepare index params.	required
em_invoker	BaseEMInvoker	The embedding model invoker.	required
dimension	int	Vector dimension.	required
distance_metric	str	Distance metric. Defaults to "L2". Supported: "L2", "IP", "COSINE".	'L2'
vector_field	str	Field name for dense vectors. Defaults to "dense_vector".	'dense_vector'
id_max_length	int	Maximum length for ID field. Defaults to 100.	100
content_max_length	int	Maximum length for content field. Defaults to 65535.	65535
encryption	EncryptionCapability | None	Encryption capability. Defaults to None.	None
default_batch_size	int | None	Default batch size for create/update. Defaults to None.	None

ensure_index(index_type='IVF_FLAT', index_params=None, query_field='content', **kwargs) async

Ensure collection and vector index exist, creating them if necessary.

This method is idempotent - if the collection and index already exist, it will skip creation and return early. Uses a lock to prevent race conditions when called concurrently from multiple coroutines.

Parameters:

Name	Type	Description	Default
index_type	str	Index type. Defaults to "IVF_FLAT". Supported: "IVF_FLAT", "HNSW".	'IVF_FLAT'
index_params	dict[str, Any] | None	Index-specific parameters. Defaults to None, in which case default parameters are used.	None
query_field	str	Field name for text content. Defaults to "content".	'content'
**kwargs	Any	Additional parameters (e.g. from base capability interface); ignored.	{}

Raises:

Type	Description
RuntimeError	If collection or index creation fails.