rds = Redis.from_texts(
    texts, # a list of strings
    metadata, # a list of metadata dicts
    embeddings, # an Embeddings object
    index_schema="path/to/index_schema.yaml", # can also be a dictionary
    redis_url="redis://localhost:6379",
)
When connecting to an existing index where a custom schema has been applied, it’s
important to pass in the same schema to the from_existing_index method.
Otherwise, the schema for newly added samples will be incorrect and metadata
will not be returned.
Initialize Redis vector store with necessary components.
Attributes
DEFAULT_VECTOR_SCHEMA
embeddings
Access the query embedding object if available.
schema
Return the schema of the index.
Methods
__init__(redis_url, index_name, embedding[, ...])
Initialize Redis vector store with necessary components.
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, embeddings, ...])
Add more texts 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.