from_texts

Example:

from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings

texts = [
    "The quick brown fox jumps over the lazy dog",
    "Hello world",
    "Machine learning is fascinating"
]
metadatas = [
    {"source": "book", "page": 1},
    {"source": "greeting", "language": "english"},
    {"source": "article", "topic": "AI"}
]

embeddings = OpenAIEmbeddings()

vector_store = RedisVectorStore.from_texts(
    texts=texts,
    embedding=embeddings,
    metadatas=metadatas,
    index_name="langchain-demo",
    redis_url="redis://localhost:6379",
    distance_metric="COSINE"
)

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

Note:

• This method creates a new RedisVectorStore instance and adds the provided texts to it.
• If metadatas is provided, it must have the same length as texts.
• If keys is provided, it must have the same length as texts.
• The return_keys parameter determines whether the method returns just the RedisVectorStore instance or a tuple of (RedisVectorStore, List[str]) where the second element is the list of keys for the added documents.