morpheus.service.vdb.milvus_vector_db_service.MilvusVectorDBResourceService
- class MilvusVectorDBResourceService(name, client, truncate_long_strings=False)[source]
Bases:
morpheus.service.vdb.vector_db_service.VectorDBResourceServiceRepresents a service for managing resources in a Milvus Vector Database.
- Parameters
- name
- client
- truncate_long_strings
Name of the resource.
An instance of the MilvusClient for interaction with the Milvus Vector Database.
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)[source]
Returns number of rows/entities.
- Parameters
- **kwargs
Additional keyword arguments for the count operation.
- Returns
- int
Returns number of entities in the collection.
- delete(expr, **kwargs)[source]
Delete vectors from the collection using expressions.
- Parameters
- expr
- **kwargs
Delete expression.
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)[source]
Delete vectors by keys from the collection.
- Parameters
- keys
- **kwargs
Primary keys to delete vectors.
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)[source]
Provides a description of the collection.
- Parameters
- **kwargs
Extra keyword arguments specific to the vector database implementation.
- Returns
- dict
Returns response content as a dictionary.
- drop(**kwargs)[source]
Drop a collection, index, or partition in the Milvus vector database.
This function allows you to drop a collection.
- Parameters
- **kwargs
Additional keyword arguments for specifying the type and partition name (if applicable).
- insert(data, **kwargs)[source]
Insert data into the vector database.
- Parameters
- data
- **kwargs
Data to be inserted into the collection.
Extra keyword arguments specific to the vector database implementation.
- Returns
- dict
Returns response content as a dictionary.
- insert_dataframe(df, **kwargs)[source]
Insert a dataframe entires into the vector database.
- Parameters
- df
- **kwargs
Dataframe to be inserted into the collection.
Extra keyword arguments specific to the vector database implementation.
- Returns
- dict
Returns response content as a dictionary.
- query(query, **kwargs)[source]
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
- query
- **kwargs
The search query, which can be a filter expression, by default None.
Additional keyword arguments for the search operation.
- Returns
- typing.Any
The search result, which can vary depending on the query and options.
- Raises
- retrieve_by_keys(keys, **kwargs)[source]
Retrieve the inserted vectors using their primary keys.
- Parameters
- keys
- **kwargs
Primary keys to get vectors for. Depending on pk_field type it can be int or str or a list of either.
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)[source]
Perform a similarity search within the collection.
- Parameters
- embeddings
- k
- **kwargs
Embeddings for which to perform the similarity search.
The number of nearest neighbors to return, by default 4.
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)[source]
Update data in the collection.
- Parameters
- data
- **kwargs
Data to be updated in the collection.
Extra keyword arguments specific to upsert operation.
- Returns
- dict[str, typing.Any]
Returns result of the updated operation stats.