maximal_marginal_relevance

Example:

from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
import numpy as np

embeddings = OpenAIEmbeddings()
vector_store = RedisVectorStore(
    index_name="langchain-demo",
    embedding=embeddings,
    redis_url="redis://localhost:6379",
)

query = "What is the capital of France?"
query_embedding = embeddings.embed_query(query)

# Assuming you have a list of document embeddings
doc_embeddings = [embeddings.embed_query(doc) for doc in documents]

selected_indices = vector_store.maximal_marginal_relevance(
    query_embedding=np.array(query_embedding),
    embedding_list=[np.array(emb) for emb in doc_embeddings],
    lambda_mult=0.5,
    k=2
)

for idx in selected_indices:
    print(f"Selected document: {documents[idx]}")