> ## Documentation Index > Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-lsapi-1779323176-451247e.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # OpenSearch integration > Integrate with the OpenSearch vector store using LangChain Python. > [OpenSearch](https://opensearch.org/) is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. `OpenSearch` is a distributed search and analytics engine based on `Apache Lucene`. This notebook shows how to use functionality related to the `OpenSearch` database. To run, you should have an OpenSearch instance up and running: [see here for an easy Docker installation](https://hub.docker.com/r/opensearchproject/opensearch). `similarity_search` by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting. Check [this](https://opensearch.org/docs/latest/search-plugins/knn/index/) for more details. ## Installation Install the Python client. ```python theme={null} pip install -qU opensearch-py langchain-community ``` We want to use [`OpenAIEmbeddings`](https://reference.langchain.com/python/langchain-openai/embeddings/base/OpenAIEmbeddings) so we have to get the OpenAI API Key. ```python theme={null} import getpass import os if "OPENAI_API_KEY" not in os.environ: os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") ``` ```python theme={null} from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import OpenSearchVectorSearch from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import CharacterTextSplitter ``` ```python theme={null} from langchain_community.document_loaders import TextLoader loader = TextLoader("../../how_to/state_of_the_union.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() ``` ## similarity\_search using approximate k-NN `similarity_search` using `Approximate k-NN` Search with Custom Parameters ```python theme={null} docsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200" ) # If using the default Docker installation, use this instantiation instead: # docsearch = OpenSearchVectorSearch.from_documents( # docs, # embeddings, # opensearch_url="https://localhost:9200", # http_auth=("admin", "admin"), # use_ssl = False, # verify_certs = False, # ssl_assert_hostname = False, # ssl_show_warn = False, # ) ``` ```python theme={null} query = "What did the president say about Ketanji Brown Jackson" docs = docsearch.similarity_search(query, k=10) ``` ```python theme={null} print(docs[0].page_content) ``` ```python theme={null} docsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200", engine="faiss", space_type="innerproduct", ef_construction=256, m=48, ) query = "What did the president say about Ketanji Brown Jackson" docs = docsearch.similarity_search(query) ``` ```python theme={null} print(docs[0].page_content) ``` ## similarity\_search using script scoring `similarity_search` using `Script Scoring` with Custom Parameters ```python theme={null} docsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False ) query = "What did the president say about Ketanji Brown Jackson" docs = docsearch.similarity_search( "What did the president say about Ketanji Brown Jackson", k=1, search_type="script_scoring", ) ``` ```python theme={null} print(docs[0].page_content) ``` ## similarity\_search using painless scripting `similarity_search` using `Painless Scripting` with Custom Parameters ```python theme={null} docsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False ) filter = {"bool": {"filter": {"term": {"text": "smuggling"}}}} query = "What did the president say about Ketanji Brown Jackson" docs = docsearch.similarity_search( "What did the president say about Ketanji Brown Jackson", search_type="painless_scripting", space_type="cosineSimilarity", pre_filter=filter, ) ``` ```python theme={null} print(docs[0].page_content) ``` ## Maximum marginal relevance search (MMR) If you’d like to look up for some similar documents, but you’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. ```python theme={null} 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 instance It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present. ```python theme={null} # this is just an example, you would need to change these values to point to another opensearch instance docsearch = 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 metadata docs = 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", ) ``` ## Using AOSS (Amazon OpenSearch Service serverless) It is an example of the `AOSS` with `faiss` engine and `efficient_filter`. We need to install several `python` packages. ```python theme={null} pip install -qU boto3 requests requests-aws4auth ``` ```python theme={null} import boto3 from opensearchpy import RequestsHttpConnection from requests_aws4auth import AWS4Auth service = "aoss" # must set the service as 'aoss' region = "us-east-2" credentials = boto3.Session( aws_access_key_id="xxxxxx", aws_secret_access_key="xxxxx" ).get_credentials() awsauth = AWS4Auth("xxxxx", "xxxxxx", region, service, session_token=credentials.token) docsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="host url", http_auth=awsauth, timeout=300, use_ssl=True, verify_certs=True, connection_class=RequestsHttpConnection, index_name="test-index-using-aoss", engine="faiss", ) docs = docsearch.similarity_search( "What is feature selection", efficient_filter=filter, k=200, ) ``` ## Using AOS (Amazon OpenSearch Service) ```python theme={null} pip install -qU boto3 ``` ```python theme={null} # This is just an example to show how to use Amazon OpenSearch Service, you need to set proper values. import boto3 from opensearchpy import RequestsHttpConnection service = "es" # must set the service as 'es' region = "us-east-2" credentials = boto3.Session( aws_access_key_id="xxxxxx", aws_secret_access_key="xxxxx" ).get_credentials() awsauth = AWS4Auth("xxxxx", "xxxxxx", region, service, session_token=credentials.token) docsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="host url", http_auth=awsauth, timeout=300, use_ssl=True, verify_certs=True, connection_class=RequestsHttpConnection, index_name="test-index", ) docs = docsearch.similarity_search( "What is feature selection", k=200, ) ``` ***
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