text: youâ€™d like to look up for some similar documents, but youâ€™d also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10, lambda_param=0.5)Using a preexisting OpenSearch instanceâ€‹It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present.# this is just an example, you would need to change these values to point to another opensearch instancedocsearch = OpenSearchVectorSearch(    index_name="index-*",    embedding_function=embeddings,    opensearch_url="http://localhost:9200",)# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadatadocs = docsearch.similarity_search(    "Who was asking about getting lunch today?",    search_type="script_scoring",    space_type="cosinesimil",    vector_field="message_embedding",    text_field="message",    metadata_field="message_metadata",)PreviousMyScaleNextpg_embeddingInstallationsimilarity_search using Approximate k-NNsimilarity_search using Script Scoringsimilarity_search using Painless ScriptingMaximum marginal relevance search (MMR)Using a preexisting OpenSearch instanceCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.