<griffe._internal.docstrings.models.DocstringAdmonition object at 0x7ff5845d0c90>
from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
client: Client = None,
namespace: str = 'test',
index_name: Optional[str] = None,
ids: Optional[List[str]] = None,
embeddings_chunk_size: int = 1000,
client_kwargs: Optional[dict] = None,
**kwargs: Any = {}
) -> AerospikeThis is a user friendly interface that:
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_community.vectorstores import Aerospike from langchain_openai import OpenAIEmbeddings from aerospike_vector_search import Client, HostPort
client = Client(seeds=HostPort(host="localhost", port=5000)) aerospike = Aerospike.from_texts( ["foo", "bar", "baz"], embedder, client, "namespace", index_name="index", vector_key="vector", distance_strategy=MODEL_DISTANCE_CALC, )