from_texts_return_keys

This is a user-friendly interface that:

1. Embeds documents.
2. Creates a new InMemoryVectorStore index if it doesn't already exist
3. Adds the documents to the newly created InMemoryVectorStore index.
4. Returns the keys of the newly created documents once stored.

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:

from langchain_aws.vectorstores import InMemoryVectorStore
                embeddings = OpenAIEmbeddings()
redis, keys = InMemoryVectorStore.from_texts_return_keys(
    texts,
    embeddings,
    redis_url="redis://cluster_endpoint:6379"
)