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阿里云 OpenSearch

阿里云开放搜索 是一站式智能搜索服务开发平台。OpenSearch 基于阿里巴巴自研的大规模分布式搜索引擎构建。OpenSearch 已服务阿里巴巴集团 500 多个业务场景，以及数千家阿里云客户。OpenSearch 助力企业开发电商、O2O、多媒体、内容行业、社区论坛、企业大数据查询等不同搜索场景下的搜索服务。

OpenSearch 帮助您开发高质量、免运维、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 提供向量检索功能。在特定场景下，尤其是在试题搜索和图像搜索场景中，您可以将向量检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。

本 Notebook 将演示如何使用 阿里云开放搜索向量检索版 的相关功能。

设置​

购买并配置实例​

从 阿里云 购买 OpenSearch 向量检索版，并根据 帮助文档 配置实例。

要运行，您需要有一个正在运行的 OpenSearch 向量检索版实例。

阿里云开放搜索向量存储类​

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 以快速熟悉并配置 OpenSearch 向量检索版实例。

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，请按照以下步骤分割文档、获取嵌入、连接到阿里云开放搜索实例、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`，因此需要获取 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

分割文档并获取嵌入。

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)

查询并检索带元数据的数据。

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，我们将尽力为您提供帮助和支持。