from_existing_index

Example:

from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
from redis import Redis

embeddings = OpenAIEmbeddings()

# Connect to an existing index
vector_store = RedisVectorStore.from_existing_index(
    index_name="my-existing-index",
    embedding=embeddings,
    redis_url="redis://localhost:6379",
    vector_query_field="embedding",
    content_field="text"
)

# 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 assumes that the index already exists in Redis.
• The embedding function provided should be compatible with the embeddings stored in the existing index.
• If you're using custom field names for vectors or content in your existing index, make sure to specify them using vector_query_field and content_field respectively.
• This method is useful for scenarios where you want to reuse an existing index, such as when the index was created by another process or when you want to use the same index across different sessions or applications.