VectorIndexes

class ibm_watsonx_ai.foundation_models.utils.VectorIndexes(api_client)

Bases: WMLResource

Initiate the Vector Indexes class.

Parameters:

api_client (APIClient) – instance of APIClient with default project_id set

Example:

vector_indexes = VectorIndexes(api_client=api_client)

create(name, description=None, store=None, settings=None, tags=None, sample_questions=None, **kwargs)

Creates a new Vector Index Asset.

Parameters:

• name (str) – name for vector index asset

• description (str | None) – optional description for the vector index asset, defaults to None

• store (dict | None, optional) – store parameters, defaults to None

• settings (dict | None, optional) – settings of vector index, defaults to None

• tags (list[str] | None, optional) – tags attached to the asset, defaults to None

• sample_questions (list[str] | None, optional) – sample asked questions, defaults to None

• data_assets (list[str] | None, optional) – IDs of the associated data assets used in the vector index, defaults to None

• build (dict | None, optional) – the associated build to process the data for external vector stores, defaults to None

• status (str | None, optional) – the status of the vector index, defaults to None

Returns:

metadata of the created Vector Index Asset

Return type:

dict

Example:

params = dict(
    name="test_sdk_vector_index",
    description="Description",
    settings={
        "embedding_model_id": "<model_id>",
        "top_k": 1,
        "schema_fields": {"text": "text"},
    },
    store={
        "type": "watsonx.data",
        "connection_id": "<connection_id>",
        "index": "<index_name>",
        "new_index": False,
        "database": "default",
    },
    tags=["test_tag"],
    sample_questions=["Sample question"],
    status="ready",
)

vector_index_details = vector_indexes.create(**params)

delete(index_id)

Delete a vector index with the given id.

Parameters:

index_id (str) – Vector Index id

Returns:

“SUCCESS” if delete successfully

Return type:

str

Example:

vector_indexes.delete(index_id)

get_details(index_id)

Get details of Vector Index Asset with given index_id.

Parameters:

index_id (str) – Vector Index id

Returns:

details of Vector Index Asset with given index_id

Return type:

dict

Example:

vector_indexes.get_details(index_id)

list(*, limit=None)

List all available Vector Index Assets in the DataFrame format.

Parameters:

limit (int, optional) – limit number of fetched records, defaults to None.

Returns:

DataFrame of fundamental properties of available Vector Index Assets.

Return type:

pandas.core.frame.DataFrame

Example:

vector_indexes.list(limit=5)    # list of 5 recently created vector index assets

update(index_id, name=None, description=None, store=None, settings=None, tags=None, sample_questions=None, **kwargs)

Update a Vector Index Asset with the given id.

Parameters:

• index_id (str) – Vector Index ids

• name (str | None) – name for vector index asset, defaults to None

• description (str | None) – optional description for the vector index asset, defaults to None

• store (dict | None, optional) – store parameters, defaults to None

• settings (dict | None, optional) – settings of vector index, defaults to None

• tags (list[str] | None, optional) – tags attached to the asset, defaults to None

• sample_questions (list[str] | None, optional) – sample asked questions, defaults to None

• data_assets (list[str] | None, optional) – IDs of the associated data assets used in the vector index, defaults to None

• build (dict | None, optional) – the associated build to process the data for external vector stores, defaults to None

• status (str | None, optional) – the status of the vector index, defaults to None

Returns:

metadata of the created Vector Index Asset

Return type:

dict

Example:

vector_indexes.update(index_id, name="new_name", description="new_description")