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OpenSearch 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. 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 for more details.

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Installation

Install the Python client. 
%pip install -qU opensearch-py langchain-community
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. 
import getpass import os  if "OPENAI_API_KEY" not in os.environ:  os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") 
from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import OpenSearchVectorSearch from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import CharacterTextSplitter 
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()

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similarity_search using Approximate k-NN

similarity_search using Approximate k-NN Search with Custom Parameters 
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, # ) 
query = "What did the president say about Ketanji Brown Jackson" docs = docsearch.similarity_search(query, k=10) 
print(docs[0].page_content) 
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) 
print(docs[0].page_content)

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similarity_search using Script Scoring

similarity_search using Script Scoring with Custom Parameters 
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", ) 
print(docs[0].page_content)

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similarity_search using Painless Scripting

similarity_search using Painless Scripting with Custom Parameters 
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, ) 
print(docs[0].page_content)

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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. 
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)

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Using a preexisting OpenSearch instance

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 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", )

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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. 
%pip install -qU boto3 requests requests-aws4auth 
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, )

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Using AOS (Amazon OpenSearch Service)

%pip install -qU boto3 
# 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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