from_documents

Example:

from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document

documents = [
    Document(
        page_content="The quick brown fox",
        metadata={"animal": "fox"}
    ),
    Document(
        page_content="jumps over the lazy dog",
        metadata={"animal": "dog"}
    )
]

embeddings = OpenAIEmbeddings()

vector_store = RedisVectorStore.from_documents(
    documents=documents,
    embedding=embeddings,
    index_name="animal-docs",
    redis_url="redis://localhost:6379"
)

# Now you can use the vector_store for similarity search
results = vector_store.similarity_search("quick animal", k=1)
print(results[0].page_content)

Note:

• This method creates a new RedisVectorStore instance and adds the provided documents to it.
• The method extracts the text content and metadata from each Document object.
• If a RedisConfig object is not provided, one will be created using the additional kwargs passed to this method.
• The embedding function is used to convert the document text into vector representations for efficient similarity search.