morpheus_llm.service.vdb.faiss_vdb_service.FaissVectorDBService
- class FaissVectorDBService(local_dir, embeddings)[source]
Bases:
morpheus_llm.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_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.