阿里云 OpenSearch

阿里云 OpenSearch 是一个一站式平台，用于开发智能搜索服务。OpenSearch 建立在阿里巴巴开发的大规模分布式搜索引擎之上。OpenSearch 为阿里巴巴集团的 500 多个业务案例和数千个阿里云客户提供服务。OpenSearch 帮助在不同的搜索场景中开发搜索服务，包括电子商务、O2O、多媒体、内容行业、社区和论坛以及企业中的大数据查询。

OpenSearch 帮助您开发高质量、免维护、高性能的智能搜索服务，为您的用户提供高搜索效率和准确性。

OpenSearch 提供向量搜索功能。在特定场景下，尤其是在测试题搜索和图像搜索场景中，您可以将向量搜索功能与多模态搜索功能结合使用，以提高搜索结果的准确性。

此笔记本展示了如何使用与阿里云 OpenSearch 向量搜索版相关的功能。

设置

购买实例并进行配置

从 阿里云 购买 OpenSearch 向量搜索版并根据帮助 文档 配置实例。

要运行，您应该有一个正在运行的 OpenSearch 向量搜索版 实例。

阿里云 OpenSearch 向量存储类

AlibabaCloudOpenSearch 类支持以下功能

• add_texts
• add_documents
• from_texts
• from_documents
• similarity_search
• asimilarity_search
• similarity_search_by_vector
• asimilarity_search_by_vector
• similarity_search_with_relevance_scores
• delete_doc_by_texts

阅读 帮助文档 以快速熟悉和配置 OpenSearch 向量搜索版实例。

如果您在使用过程中遇到任何问题，请随时联系 [email protected]，我们将尽力为您提供帮助和支持。

实例运行后，请按照以下步骤拆分文档、获取嵌入、连接到阿里云 OpenSearch 实例、索引文档并执行向量检索。

我们首先需要安装以下 Python 包。

%pip install --upgrade --quiet  langchain-community alibabacloud_ha3engine_vector

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

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

示例

from langchain_community.vectorstores import (
    AlibabaCloudOpenSearch,
    AlibabaCloudOpenSearchSettings,
)
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

API 参考：AlibabaCloudOpenSearch | AlibabaCloudOpenSearchSettings | OpenAIEmbeddings | CharacterTextSplitter

拆分文档并获取嵌入。

from langchain_community.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 参考：TextLoader

创建 OpenSearch 设置。

settings = AlibabaCloudOpenSearchSettings(
    endpoint=" The endpoint of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch.",
    instance_id="The identify of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch.",
    protocol="Communication Protocol between SDK and Server, default is http.",
    username="The username specified when purchasing the instance.",
    password="The password specified when purchasing the instance.",
    namespace="The instance data will be partitioned based on the namespace field. If the namespace is enabled, you need to specify the namespace field name during initialization. Otherwise, the queries cannot be executed correctly.",
    tablename="The table name specified during instance configuration.",
    embedding_field_separator="Delimiter specified for writing vector field data, default is comma.",
    output_fields="Specify the field list returned when invoking OpenSearch, by default it is the value list of the field mapping field.",
    field_name_mapping={
        "id": "id",  # The id field name mapping of index document.
        "document": "document",  # The text field name mapping of index document.
        "embedding": "embedding",  # The embedding field name mapping of index document.
        "name_of_the_metadata_specified_during_search": "opensearch_metadata_field_name,=",
        # The metadata field name mapping of index document, could specify multiple, The value field contains mapping name and operator, the operator would be used when executing metadata filter query,
        # Currently supported logical operators are: > (greater than), < (less than), = (equal to), <= (less than or equal to), >= (greater than or equal to), != (not equal to).
        # Refer to this link: https://help.aliyun.com/zh/open-search/vector-search-edition/filter-expression
    },
)

# for example

# settings = AlibabaCloudOpenSearchSettings(
#     endpoint='ha-cn-5yd3fhdm102.public.ha.aliyuncs.com',
#     instance_id='ha-cn-5yd3fhdm102',
#     username='instance user name',
#     password='instance password',
#     table_name='test_table',
#     field_name_mapping={
#         "id": "id",
#         "document": "document",
#         "embedding": "embedding",
#         "string_field": "string_filed,=",
#         "int_field": "int_filed,=",
#         "float_field": "float_field,=",
#         "double_field": "double_field,="
#
#     },
# )

通过设置创建 OpenSearch 访问实例。

# Create an opensearch instance and index docs.
opensearch = AlibabaCloudOpenSearch.from_texts(
    texts=docs, embedding=embeddings, config=settings
)

或

# Create an opensearch instance.
opensearch = AlibabaCloudOpenSearch(embedding=embeddings, config=settings)

添加文本并构建索引。

metadatas = [
    {"string_field": "value1", "int_field": 1, "float_field": 1.0, "double_field": 2.0},
    {"string_field": "value2", "int_field": 2, "float_field": 3.0, "double_field": 4.0},
    {"string_field": "value3", "int_field": 3, "float_field": 5.0, "double_field": 6.0},
]
# the key of metadatas must match field_name_mapping in settings.
opensearch.add_texts(texts=docs, ids=[], metadatas=metadatas)

查询并检索数据。

query = "What did the president say about Ketanji Brown Jackson"
docs = opensearch.similarity_search(query)
print(docs[0].page_content)

查询并检索包含元数据的数据。

query = "What did the president say about Ketanji Brown Jackson"
metadata = {
    "string_field": "value1",
    "int_field": 1,
    "float_field": 1.0,
    "double_field": 2.0,
}
docs = opensearch.similarity_search(query, filter=metadata)
print(docs[0].page_content)

如果您在使用过程中遇到任何问题，请随时联系 [email protected]，我们将尽力为您提供帮助和支持。

相关

• 向量存储 概念指南
• 向量存储 操作指南