from_texts(
cls: Type[InMemoryVectorStore],
texts: List[str],| Name | Type | Description |
|---|---|---|
texts* | List[str] | List of texts to add to the vectorstore. |
embedding* | Embeddings | Embedding model class (i.e. OpenAIEmbeddings) for embedding queries. |
metadatas | Optional[List[dict]] | Default: None |
index_name | Optional[str] | Default: None |
vector_schema | Optional[Dict[str, Union[str, int]]] | Default: None |
**kwargs | Any | Default: {} |
Create a InMemoryVectorStore vectorstore from a list of texts.
This is a user-friendly interface that:
This method will generate schema based on the metadata passed in
if the index_schema is not defined. If the index_schema is defined,
it will compare against the generated schema and warn if there are
differences. If you are purposefully defining the schema for the
metadata, then you can ignore that warning.
To examine the schema options, initialize an instance of this class and print out the schema using the `InMemoryVectorStore.schema`` property. This will include the content and content_vector classes which are always present in the langchain schema.
Example:
.. code-block:: python
from langchain_aws.vectorstores import InMemoryVectorStore embeddings = OpenAIEmbeddings()
Optional list of metadata dicts to add to the vectorstore. Defaults to None.
Optional name of the index to create or add to. Defaults to None.
Optional vector schema to use. Defaults to None.
Additional keyword arguments to pass to the