morpheus.service.vdb.faiss_vdb_service.FaissVectorDBService
- class FaissVectorDBService(local_dir, embeddings)[source]
Bases:
morpheus.service.vdb.vector_db_service.VectorDBServiceService class for FAISS Vector Database implementation. This class provides functions for interacting with a FAISS vector database.
- Parameters
- local_dir
- embeddings
The local directory where the FAISS index files are stored.
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
- name
- **kwargs
Name of the collection.
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
- name
- overwrite
- **kwargs
Name of the collection to be created.
If True, the collection will be overwritten if it already exists, by default False.
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
- name
- df
- overwrite
- **kwargs
Name of the collection.
The dataframe to create the collection from.
Whether to overwrite the collection if it already exists. Default is False.
Extra keyword arguments specific to the vector database implementation.
- delete(name, expr, **kwargs)[source]
Delete vectors from the collection using expressions.
- Parameters
- name
- expr
- **kwargs
Name of the collection.
Delete expression.
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
- name
- keys
- **kwargs
Name of the collection.
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 delete from the collection.
- describe(name, **kwargs)[source]
Describe the collection in the vector database.
- Parameters
- name
- **kwargs
Name of the collection.
Additional keyword arguments specific to the vector database.
- Returns
- dict
Returns collection information.
- drop(name, **kwargs)[source]
Drop a collection.
- Parameters
- name
- **kwargs
Name of the collection, index, or partition to be dropped.
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
- name
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
- name
- data
- **kwargs
Name of the collection to be inserted.
Data to be inserted in the collection.
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
- name
- df
- **kwargs
Name of the collection to be inserted.
Dataframe to be inserted in the collection.
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
- name
- **kwargs
The VDB resource to load. For FAISS, this corresponds to the index name, by default “index”
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
- name
- query
- **kwargs
Name of the collection to search within.
The search query, which can be a filter expression.
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
- name
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
- name
- keys
- **kwargs
Name of the collection.
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(name, **kwargs)[source]
Perform a similarity search within the collection.
- Parameters
- name
- **kwargs
Name of the collection.
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
- data
- **kwargs
Data to be updated in the resource.
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
- name
- data
- **kwargs
Name of the collection.
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.