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阿里云 OpenSearch

阿里云 Opensearch 是开发智能搜索服务的一站式平台。OpenSearch构建在Alibaba.OpenSearch服务于阿里巴巴集团的 500 多个商业案例和数千家阿里云客户。OpenSearch帮助开发不同搜索场景下的搜索服务，包括电子商务、O2O、多媒体、内容行业、社区和论坛、企业大数据查询。

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，尤其是试题搜索和图片搜索场景下，你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Alibaba Cloud OpenSearch Vector Search Edition.

建立

购买实例并配置

从阿里云购买 OpenSearch Vector Search Edition，并参考帮助文档配置实例。

要运行，您应该启动并运行一个 OpenSearch Vector Search Edition 实例。

阿里云 OpenSearch Vector Store 类

AlibabaCloudOpenSearch类支持函数：

  • 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 Vector Search Edition 实例。

如果您在使用过程中遇到任何问题，请随时联系 xingshaomin.xsm@alibaba-inc.com，我们将尽最大努力为您提供帮助和支持。

实例启动并运行后，请按照以下步骤拆分文档、获取嵌入、连接到阿里云 OpenSearch 实例、为文档编制索引并执行向量检索。

我们需要先安装以下 Python 包。

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

我们想使用OpenAIEmbeddings所以我们必须获取 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:")

例

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

查询和检索数据。

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

查询和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 xingshaomin.xsm@alibaba-inc.com，我们将尽最大努力为您提供帮助和支持。