morpheus_llm.service.vdb.faiss_vdb_service.FaissVectorDBService#

class FaissVectorDBService(local_dir, embeddings)[source]#

Bases: VectorDBService

Service class for FAISS Vector Database implementation. This class provides functions for interacting with a FAISS vector database.

Parameters:

local_dirstr: The local directory where the FAISS index files are stored.
embeddingsEmbeddings: The embeddings object to use for embedding text.

Attributes:

embeddings

Methods

`close`()	Close the vector database service and release all resources.
`count`(name, **kwargs)	Returns number of rows/entities in the given collection.
`create`(name[, overwrite])	Create a collection.
`create_from_dataframe`(name, df[, overwrite])	Create collections in the vector database.
`delete`(name, expr, **kwargs)	Delete vectors from the collection using expressions.
`delete_by_keys`(name, keys, **kwargs)	Delete vectors by keys from the collection.
`describe`(name, **kwargs)	Describe the collection in the vector database.
`drop`(name, **kwargs)	Drop a collection.
`has_store_object`(name)	Check if specific index file name exists by attempting to load FAISS index, docstore, and index_to_docstore_id from disk with the index file name.
`insert`(name, data, **kwargs)	Insert a collection specific data in the vector database.
`insert_dataframe`(name, df, **kwargs)	Converts dataframe to rows and insert to the vector database.
`list_store_objects`(**kwargs)	List the names of all resources in the vector database.
`load_resource`([name])	Loads a VDB resource into memory for use.
`query`(name[, query])	Query data in a vector database.
`release_resource`(name)	Release a loaded collection from the memory.
`retrieve_by_keys`(name, keys, **kwargs)	Retrieve the inserted vectors using their primary keys from the Collection.
`similarity_search`(name, **kwargs)	Perform a similarity search within the collection.
`transform`(data, **kwargs)	Transform data according to the specific vector database implementation.
`update`(name, data, **kwargs)	Update data in the vector database.

close()[source]#

Close the vector database service and release all resources.

count(name, **kwargs)[source]#

Returns number of rows/entities in the given collection.

Parameters:

namestr: Name of the collection.
**kwargs: Additional keyword arguments for the count operation.

Returns:

int: Returns number of entities in the collection.

create(name, overwrite=False, **kwargs)[source]#

Create a collection.

Parameters:

namestr: Name of the collection to be created.
overwritebool, optional: If True, the collection will be overwritten if it already exists, by default False.
**kwargs: Additional keyword arguments containing collection configuration.

Raises:

ValueError: If the provided schema fields configuration is empty.

create_from_dataframe(

name,

df,

overwrite=False,

**kwargs,

)[source]#

Create collections in the vector database.

Parameters:

namestr: Name of the collection.
dfDataFrameType: The dataframe to create the collection from.
overwritebool, optional: Whether to overwrite the collection if it already exists. Default is False.
**kwargs: Extra keyword arguments specific to the vector database implementation.

delete(name, expr, **kwargs)[source]#

Delete vectors from the collection using expressions.

Parameters:

namestr: Name of the collection.
exprstr: Delete expression.
**kwargs: Extra keyword arguments specific to the vector database implementation.

Returns:

dict[str, typing.Any]: Returns result of the given keys that are delete from the collection.

delete_by_keys(name, keys, **kwargs)[source]#

Delete vectors by keys from the collection.

Parameters:

namestr: Name of the collection.
keysint | str | list: Primary keys to delete vectors.
**kwargs: Extra keyword arguments specific to the vector database implementation.

Returns:

typing.Any: Returns result of the given keys that are delete from the collection.

describe(name, **kwargs)[source]#

Describe the collection in the vector database.

Parameters:

namestr: Name of the collection.
**kwargs: Additional keyword arguments specific to the vector database.

Returns:

dict: Returns collection information.

drop(name, **kwargs)[source]#

Drop a collection.

Parameters:

namestr: Name of the collection, index, or partition to be dropped.
**kwargs: Additional keyword arguments for specifying the type and partition name (if applicable).

Raises:

ValueError: If mandatory arguments are missing or if the provided ‘collection’ value is invalid.

has_store_object(name)[source]#

Check if specific index file name exists by attempting to load FAISS index, docstore, and index_to_docstore_id from disk with the index file name.

Parameters:

namestr: Name of the FAISS index file to check.

Returns:

bool: True if the file exists, False otherwise.

insert(name, data, **kwargs)[source]#

Insert a collection specific data in the vector database.

Parameters:

namestr: Name of the collection to be inserted.
datalist[list] | list[dict]: Data to be inserted in the collection.
**kwargs: Additional keyword arguments containing collection configuration.

Returns:

dict: Returns response content as a dictionary.

Raises:

RuntimeError: If the collection not exists exists.

insert_dataframe(name, df, **kwargs)[source]#

Converts dataframe to rows and insert to the vector database.

Parameters:

namestr: Name of the collection to be inserted.
dfDataFrameType: Dataframe to be inserted in the collection.
**kwargs: Additional keyword arguments containing collection configuration.

Returns:

dict: Returns response content as a dictionary.

Raises:

RuntimeError: If the collection not exists exists.

list_store_objects(**kwargs)[source]#

List the names of all resources in the vector database.

Returns:

list[str]: A list of collection names.

load_resource(name='index', **kwargs)[source]#

Loads a VDB resource into memory for use.

Parameters:

namestr, optional: The VDB resource to load. For FAISS, this corresponds to the index name, by default “index”
**kwargs: Additional keyword arguments specific to the resource service.

Returns:

FaissVectorDBResourceService: The loaded resource service.

query(name, query=None, **kwargs)[source]#

Query data in a vector database.

Parameters:

namestr: Name of the collection to search within.
querystr: The search query, which can be a filter expression.
**kwargs: Additional keyword arguments for the search operation.

Returns:

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

release_resource(name)[source]#

Release a loaded collection from the memory.

Parameters:

namestr: Name of the collection to release.

retrieve_by_keys(name, keys, **kwargs)[source]#

Retrieve the inserted vectors using their primary keys from the Collection.

Parameters:

namestr: Name of the collection.
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.
**kwargs: Additional keyword arguments for the retrieval operation.

Returns:

list[typing.Any]: Returns result rows of the given keys from the collection.

async similarity_search(name, **kwargs)[source]#

Perform a similarity search within the collection.

Parameters:

namestr: Name of the collection.
**kwargs: Extra keyword arguments specific to the vector database implementation.

Returns:

list[dict]: Returns a list of dictionaries representing the results of the similarity search.

transform(data, **kwargs)[source]#

Transform data according to the specific vector database implementation.

Parameters:

datatyping.Any: Data to be updated in the resource.
**kwargsdict[str, typing.Any]: Extra keyword arguments specific to the vector database implementation.

Returns:

typing.Any: Returns transformed data as per the implementation.

update(name, data, **kwargs)[source]#

Update data in the vector database.

Parameters:

namestr: Name of the collection.
datalist[typing.Any]: Data to be updated in the collection.
**kwargs: Extra keyword arguments specific to upsert operation.

Returns:

dict[str, typing.Any]: Returns result of the updated operation stats.