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OpenSearch

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.

Installation​

Install the Python client.

pip install opensearch-py

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import os
import getpass

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import OpenSearchVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader

loader = TextLoader("../../../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()

API Reference:

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)

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)

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)

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)

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",
)

Using AOSS (Amazon OpenSearch Service Serverless)​

# This is just an example to show how to use AOSS with faiss engine and efficient_filter, you need to set proper values.

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)​

# This is just an example to show how to use AOS , you need to set proper values.

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,
)