OpenSearch

OpenSearch 是一个可扩展、灵活且可扩展的开源软件套件，用于在 Apache 2.0 许可下授权的搜索、分析和可观察性应用程序。OpenSearch 是一个基于Apache Lucene的分布式搜索和分析引擎。

此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：请参阅此处的简单 Docker 安装。

similarity_search 默认执行近似 k-NN 搜索，该搜索使用几种算法（如 lucene、nmslib、faiss）之一，推荐用于大型数据集。为了执行蛮力搜索，我们有其他搜索方法，称为脚本评分和无痛脚本。请查看此内容了解更多详情。

安装

安装 Python 客户端。

%pip install --upgrade --quiet  opensearch-py langchain-community

我们想使用 OpenAIEmbeddings，因此我们必须获取 OpenAI API 密钥。

import getpass
import os

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

API 参考：TextLoader | OpenSearchVectorSearch | OpenAIEmbeddings | 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()

API 参考：TextLoader

使用近似 k-NN 进行相似性搜索

使用Approximate k-NN 搜索和自定义参数进行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)

使用脚本评分进行相似性搜索

使用Script Scoring 和自定义参数进行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)

使用无痛脚本进行相似性搜索

使用Painless Scripting 和自定义参数进行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)

最大边际相关性搜索（MMR）

如果您想查找一些类似的文档，但还想收到多样化的结果，则应考虑使用 MMR 方法。最大边际相关性针对与查询的相似性和所选文档之间的多样性进行优化。

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)

使用预先存在的 OpenSearch 实例

也可以使用预先存在的 OpenSearch 实例，其中包含已经存在向量的文档。

# 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 无服务器）

这是AOSS 与faiss 引擎和efficient_filter 的示例。

我们需要安装几个python 包。

%pip install --upgrade --quiet  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,
)

使用 AOS（Amazon OpenSearch Service）

%pip install --upgrade --quiet  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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• 向量存储操作指南