> ## Documentation Index > Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt > Use this file to discover all available pages before exploring further. # OpenSearch 集成 > 使用 LangChain Python 与 OpenSearch 向量存储库集成。 > [OpenSearch](https://opensearch.org/) 是一个可扩展、灵活且可拓展的开源软件套件，用于搜索、分析和可观测性应用程序，采用 Apache 2.0 许可。`OpenSearch` 是基于 `Apache Lucene` 的分布式搜索和分析引擎。 本笔记本展示了如何使用与 `OpenSearch` 数据库相关的功能。 要运行，您应该有一个正在运行的 OpenSearch 实例：[查看简单的 Docker 安装指南](https://hub.docker.com/r/opensearchproject/opensearch)。 `similarity_search` 默认执行近似 k-NN 搜索，该搜索使用多种算法中的一种，如 lucene、nmslib、faiss，适用于大型数据集。为了执行暴力搜索，我们有其他称为脚本评分和 Painless 脚本的搜索方法。请查看 [此链接](https://opensearch.org/docs/latest/search-plugins/knn/index/) 了解更多详情。 ## 安装 安装 Python 客户端。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU opensearch-py langchain-community ``` 我们要使用 [`OpenAIEmbeddings`](https://reference.langchain.com/python/langchain-openai/embeddings/base/OpenAIEmbeddings)，因此需要获取 OpenAI API 密钥。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import getpass import os if "OPENAI_API_KEY" not in os.environ: os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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() ``` ## 使用近似 k-NN 进行 similarity\_search 使用自定义参数进行 `Approximate k-NN` 搜索的 `similarity_search` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} query = "What did the president say about Ketanji Brown Jackson" docs = docsearch.similarity_search(query, k=10) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} print(docs[0].page_content) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} print(docs[0].page_content) ``` ## 使用脚本评分进行 similarity\_search 使用自定义参数进行 `Script Scoring` 的 `similarity_search` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} print(docs[0].page_content) ``` ## 使用 Painless 脚本进行 similarity\_search 使用自定义参数进行 `Painless Scripting` 的 `similarity_search` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} print(docs[0].page_content) ``` ## 最大边际相关性搜索 (MMR) 如果您想查找一些相似文档，但也希望获得多样化的结果，那么 MMR 是您应该考虑的方法。最大边际相关性优化了与查询的相似度以及所选文档之间的多样性。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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) ``` ## 使用现有的 OpenSearch 实例 也可以使用具有现成向量的现有 OpenSearch 实例。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # 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", ) ``` ## 使用 AOSS (Amazon OpenSearch Service serverless) 这是使用 `faiss` 引擎和 `efficient_filter` 的 `AOSS` 示例。 我们需要安装几个 `python` 包。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU boto3 requests requests-aws4auth ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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, ) ``` ## 使用 AOS (Amazon OpenSearch Service) ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU boto3 ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # 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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