morpheus_llm.service.vdb.milvus_vector_db_service.MilvusVectorDBResourceService

class MilvusVectorDBResourceService(

name,

client,

truncate_long_strings=False,

)

Bases: VectorDBResourceService

Represents a service for managing resources in a Milvus Vector Database.

Parameters:

namestr

Name of the resource.

clientMilvusClient

An instance of the MilvusClient for interaction with the Milvus Vector Database.

truncate_long_stringsbool, optional

When true, truncate strings values that are longer than the max length of the field

Methods

count(**kwargs)	Returns number of rows/entities.
delete(expr, **kwargs)	Delete vectors from the collection using expressions.
delete_by_keys(keys, **kwargs)	Delete vectors by keys from the collection.
describe(**kwargs)	Provides a description of the collection.
drop(**kwargs)	Drop a collection, index, or partition in the Milvus vector database.
insert(data, **kwargs)	Insert data into the vector database.
insert_dataframe(df, **kwargs)	Insert a dataframe entires into the vector database.
query(query, **kwargs)	Query data in a collection in the Milvus vector database.
retrieve_by_keys(keys, **kwargs)	Retrieve the inserted vectors using their primary keys.
similarity_search(embeddings[, k])	Perform a similarity search within the collection.
update(data, **kwargs)	Update data in the collection.

count(**kwargs)

Returns number of rows/entities.

Parameters:

**kwargsdict[str, typing.Any]

Additional keyword arguments for the count operation.

Returns:

int

Returns number of entities in the collection.

delete(expr, **kwargs)

Delete vectors from the collection using expressions.

Parameters:

exprstr

Delete expression.

**kwargsdict[str, typing.Any]

Extra keyword arguments specific to the vector database implementation.

Returns:

dict[str, typing.Any]

Returns result of the given keys that are deleted from the collection.

delete_by_keys(keys, **kwargs)

Delete vectors by keys from the collection.

Parameters:

keysint | str | list

Primary keys to delete vectors.

**kwargsdict[str, typing.Any]

Extra keyword arguments specific to the vector database implementation.

Returns:

typing.Any

Returns result of the given keys that are deleted from the collection.

describe(**kwargs)

Provides a description of the collection.

Parameters:

**kwargsdict[str, typing.Any]

Extra keyword arguments specific to the vector database implementation.

Returns:

dict

Returns response content as a dictionary.

drop(**kwargs)

Drop a collection, index, or partition in the Milvus vector database.

This function allows you to drop a collection.

Parameters:

**kwargsdict

Additional keyword arguments for specifying the type and partition name (if applicable).

insert(data, **kwargs)

Insert data into the vector database.

Parameters:

datalist[list] | list[dict]

Data to be inserted into the collection.

**kwargsdict[str, typing.Any]

Extra keyword arguments specific to the vector database implementation.

Returns:

dict

Returns response content as a dictionary.

insert_dataframe(df, **kwargs)

Insert a dataframe entires into the vector database.

Parameters:

dfDataFrameType

Dataframe to be inserted into the collection.

**kwargsdict[str, typing.Any]

Extra keyword arguments specific to the vector database implementation.

Returns:

dict

Returns response content as a dictionary.

query(query, **kwargs)

Query data in a collection in the Milvus vector database.

This method performs a search operation in the specified collection/partition in the Milvus vector database.

Parameters:

querystr, optional

The search query, which can be a filter expression, by default None.

**kwargsdict

Additional keyword arguments for the search operation.

Returns:

typing.Any

The search result, which can vary depending on the query and options.

Raises:

RuntimeError

If an error occurs during the search operation. If query argument is None and data keyword argument doesn’t exist. If data keyword arguement is None.

retrieve_by_keys(keys, **kwargs)

Retrieve the inserted vectors using their primary keys.

Parameters:

keysint | str | list

Primary keys to get vectors for. Depending on pk_field type it can be int or str or a list of either.

**kwargsdict[str, typing.Any]

Additional keyword arguments for the retrieval operation.

Returns:

list[typing.Any]

Returns result rows of the given keys from the collection.

async similarity_search(

embeddings,

k=4,

**kwargs,

)

Perform a similarity search within the collection.

Parameters:

embeddingslist[list[float]]

Embeddings for which to perform the similarity search.

kint, optional

The number of nearest neighbors to return, by default 4.

**kwargsdict[str, typing.Any]

Extra keyword arguments specific to the vector database implementation.

Returns:

list[dict]

Returns a list of dictionaries representing the results of the similarity search.

update(data, **kwargs)

Update data in the collection.

Parameters:

datalist[typing.Any]

Data to be updated in the collection.

**kwargsdict[str, typing.Any]

Extra keyword arguments specific to upsert operation.

Returns:

dict[str, typing.Any]

Returns result of the updated operation stats.