OpenSearch는 Apache 2.0 라이선스 하에 제공되는 검색, 분석 및 관찰성 애플리케이션을 위한 확장 가능하고 유연하며 확장 가능한 오픈 소스 소프트웨어 제품군입니다. OpenSearch는 Apache Lucene 기반의 분산 검색 및 분석 엔진입니다.
이 노트북은 OpenSearch 데이터베이스와 관련된 기능을 사용하는 방법을 보여줍니다. 실행하려면 OpenSearch 인스턴스가 실행 중이어야 합니다: 간편한 Docker 설치는 여기를 참조하세요. similarity_search는 기본적으로 Approximate k-NN Search를 수행하며, 대규모 데이터셋에 권장되는 lucene, nmslib, faiss와 같은 여러 알고리즘 중 하나를 사용합니다. 무차별 대입 검색을 수행하기 위해 Script Scoring 및 Painless Scripting으로 알려진 다른 검색 방법이 있습니다. 자세한 내용은 여기를 확인하세요.

​
설치

Python 클라이언트를 설치합니다. 
pip install -qU  opensearch-py langchain-community
OpenAIEmbeddings를 사용하려면 OpenAI API Key를 가져와야 합니다. 
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()
사용자 정의 매개변수를 사용한 Approximate k-NN Search를 통한 similarity_search 
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)
사용자 정의 매개변수를 사용한 Script Scoring을 통한 similarity_search 
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)
사용자 정의 매개변수를 사용한 Painless Scripting을 통한 similarity_search 
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)

유사한 문서를 찾고 싶지만 다양한 결과도 받고 싶다면 MMR이 고려해야 할 방법입니다. Maximal marginal relevance는 쿼리와의 유사성과 선택된 문서 간의 다양성을 모두 최적화합니다. 
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)

​
기존 OpenSearch 인스턴스 사용하기

이미 벡터가 있는 문서가 포함된 기존 OpenSearch 인스턴스를 사용하는 것도 가능합니다. 
# 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",
)

​
AOSS (Amazon OpenSearch Service Serverless) 사용하기

faiss 엔진과 efficient_filter를 사용하는 AOSS의 예제입니다. 여러 python 패키지를 설치해야 합니다. 
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,
)

​
AOS (Amazon OpenSearch Service) 사용하기

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,
)
Edit the source of this page on GitHub.
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.
⌘I