bulk_insert_batch_concurrency = 80
    bulk_insert_overwrite_concurrency = 10
A bit of experimentation is required to nail the best results here,
depending on both the machine/network specs and the expected workload
(specifically, how often a write is an update of an existing id).
Remember you can pass concurrency settings to individual calls to
add_texts and add_documents as well.
Create an AstraDB vector store object. See class docstring for help.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(*, embedding, collection_name[, ...])
Create an AstraDB vector store object.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, ...])
Run texts through the embeddings and add them to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)