| Name | Type | Description |
|---|---|---|
query_embedding* | np.ndarray | Embedding of the query text. |
embedding_list* | List[np.ndarray] | List of embeddings to select from. |
lambda_mult | float | Default: 0.5Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Defaults to 0.5. |
k | int | Default: 4 |
Calculate maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Example:
.. code-block:: python
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)
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]}")
Number of results to return. Defaults to 4.
