Azure 认知搜索是什么？What is Azure Cognitive Search?
Azure 认知搜索（以前称为“Azure 搜索”）是一种搜索即服务云解决方案，它为开发人员提供 API 和工具，以便基于 Web、移动和企业应用程序中的专用异类内容添加丰富的搜索体验。Azure Cognitive Search (formerly known as "Azure Search") is a search-as-a-service cloud solution that gives developers APIs and tools for adding a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications. 代码或工具调用数据引入（索引）来创建和加载索引。Your code or a tool invokes data ingestion (indexing) to create and load an index. 或者，可以添加认知技能，以便在编制索引期间应用 AI 流程。Optionally, you can add cognitive skills to apply AI processes during indexing. 这样可以添加用于搜索和其他方案的新信息与结构。Doing so can add new information and structures useful for search and other scenarios.
在服务的另一端，应用程序代码发出查询请求并处理响应。On the other side of your service, your application code issues query requests and handles responses. 使用 Azure 认知搜索中的功能在客户端中定义搜索体验，通过在服务中创建，拥有并存储的持久索引执行查询。The search experience is defined in your client using functionality from Azure Cognitive Search, with query execution over a persisted index that you create, own, and store in your service.
功能通过简单的 REST API 或 .NET SDK 公开，消除了信息检索固有的复杂性。Functionality is exposed through a simple REST API or .NET SDK that masks the inherent complexity of information retrieval. 除了 API，Azure 门户还通过原型制作和查询索引工具，提供管理和内容管理支持。In addition to APIs, the Azure portal provides administration and content management support, with tools for prototyping and querying your indexes. 因为服务在云中运行，所以基础结构和可用性由 Microsoft 管理。Because the service runs in the cloud, infrastructure and availability are managed by Microsoft.
何时使用 Azure 认知搜索When to use Azure Cognitive Search
Azure 认知搜索非常适合以下应用方案：Azure Cognitive Search is well suited for the following application scenarios:
将异构内容类型整合成单个专用的可搜索索引。Consolidation of heterogeneous content types into a private, single, searchable index. 查询始终基于你创建并连同文档一起加载的索引，索引始终驻留在云中的 Azure 认知搜索服务上。Queries are always over an index that you create and load with documents, and the index always resides in the cloud on your Azure Cognitive Search service. 可以在索引中填充来自任何源或平台的 JSON 文档流。You can populate an index with streams of JSON documents from any source or platform. 或者，对于源自 Azure 的内容，可以使用索引器将数据提取到索引中。Alternatively, for content sourced on Azure, you can use an indexer to pull data into an index. 索引定义和管理/所有权是使用 Azure 认知搜索的重要原因。Index definition and management/ownership is a key reason for using Azure Cognitive Search.
原始内容是 Azure 数据源（例如 Azure Blob 存储或 Cosmos DB）中的大型无差别文本、图像文件或应用程序文件（例如 Office 内容类型）。Raw content is large undifferentiated text, image files, or application files such as Office content types on an Azure data source such as Azure Blob storage or Cosmos DB. 可以在编制索引期间应用认知技能，以添加结构，或者从图像和应用程序文件中提取含义。You can apply cognitive skills during indexing to add structure or extract meaning from image and application files.
轻松实现搜索相关的功能。Easy implementation of search-related features. Azure 认知搜索 API 简化了查询构造、分面导航、筛选器（包括地理空间搜索）、同义词映射、自动提示查询和相关性优化。Azure Cognitive Search APIs simplify query construction, faceted navigation, filters (including geo-spatial search), synonym mapping, typeahead queries, and relevance tuning. 使用内置功能可以满足最终用户对搜索体验的预期，使其觉得该体验类似于商用 Web 搜索引擎。Using built-in features, you can satisfy end-user expectations for a search experience similar to commercial web search engines.
为非结构化文本编制索引，或者从图像文件中提取文本和信息。Indexing unstructured text, or extracting text and information from image files. Azure 认知搜索的 AI 扩充功能将 AI 处理添加到索引管道。The AI enrichment feature of Azure Cognitive Search adds AI processing to an indexing pipeline. 一些常见用例包括对扫描的文档进行 OCR 识别、在大型文档中进行实体识别和关键短语提取、语言检测和文本翻译，以及情绪分析。Some common use-cases include OCR over scanned document, entity recognition and key phrase extraction over large documents, language detection and text translation, and sentiment analysis.
使用 Azure 认知搜索的自定义和语言分析器可以满足语言要求。Linguistic requirements satisfied using the custom and language analyzers of Azure Cognitive Search. 如果你使用非英语内容，Azure 认知搜索支持 Lucene 分析器和 Microsoft 的自然语言处理器。If you have non-English content, Azure Cognitive Search supports both Lucene analyzers and Microsoft's natural language processors. 还可以配置分析器以实现原始内容的专业处理，例如筛选出标注字符。You can also configure analyzers to achieve specialized processing of raw content, such as filtering out diacritics.
功能说明Feature descriptions
| 核心搜索 Core search | 功能Features |
|---|---|
| 自由格式文本搜索Free-form text search | 全文搜索是大多数基于搜索的应用的主要用例。Full-text search is a primary use case for most search-based apps. 查询可以使用支持的语法进行陈述。Queries can be formulated using a supported syntax. 简单查询语法提供逻辑运算符、短语搜索运算符、后缀运算符和优先运算符。Simple query syntax provides logical operators, phrase search operators, suffix operators, precedence operators. Lucene 查询语法包括简单语法中的所有操作，以及模糊搜索、邻近搜索、术语提升和正则表达式扩展。Lucene query syntax includes all operations in simple syntax, with extensions for fuzzy search, proximity search, term boosting, and regular expressions. |
| 相关性Relevance | 简单计分是 Azure 认知搜索的主要优势。Simple scoring is a key benefit of Azure Cognitive Search. 计分配置文件用于在文档中自行将相关性建模为值的函数。Scoring profiles are used to model relevance as a function of values in the documents themselves. 例如，你可能希望较新产品或打折产品显示在搜索结果的顶部位置。For example, you might want newer products or discounted products to appear higher in the search results. 也可以基于已跟踪和单独存储的客户搜索首选项将标记用于个性化计分，来生成计分配置文件。You can also build scoring profiles using tags for personalized scoring based on customer search preferences you've tracked and stored separately. |
| 地理搜索Geo-search | Azure 认知搜索可以处理、筛选和显示地理位置。Azure Cognitive Search processes, filters, and displays geographic locations. 它可以让用户基于搜索结果与物理位置的临近程度浏览数据。It enables users to explore data based on the proximity of a search result to a physical location. 查看此示例来了解详细信息。Review this sample to learn more. |
| 筛选器和分面导航Filters and facets | 通过单个查询参数实现分面导航。Faceted navigation is enabled through a single query parameter. Azure 认知搜索返回一个分面导航结构，可以将该结构用作类别列表背后的代码，用于自定向筛选（例如，按价格范围或品牌来筛选目录项）。Azure Cognitive Search returns a faceted navigation structure you can use as the code behind a categories list, for self-directed filtering (for example, to filter catalog items by price-range or brand). 可以使用筛选器将分面导航纳入到应用程序的 UI 中，改进查询表述，以及基于用户或开发人员指定的条件进行筛选。Filters can be used to incorporate faceted navigation into your application's UI, enhance query formulation, and filter based on user- or developer-specified criteria. 可以使用 OData 语法创建筛选器。Create filters using the OData syntax. |
| 用户体验功能User experience features | 可以为搜索栏中预先键入的查询启用自动完成。Autocomplete can be enabled for type-ahead queries in a search bar. 搜索建议也基于搜索栏中的部分文本输入开始工作，但结果是索引中的实际文档而不是查询术语。Search suggestions also works off of partial text inputs in a search bar, but the results are actual documents in your index rather than query terms. 同义词功能无需用户提供替换术语，便可关联隐式扩展查询范围的等效术语。Synonyms associates equivalent terms that implicitly expand the scope of a query, without the user having to provide the alternate terms. 命中项突出显示向搜索结果中的匹配关键字应用文本格式设置。Hit highlighting applies text formatting to a matching keyword in search results. 可以选择哪些字段返回突出显示的片段。You can choose which fields return highlighted snippets. 排序通过索引架构覆盖多个字段，可以使用一个搜索参数在查询时进行切换。Sorting is offered for multiple fields via the index schema and then toggled at query-time with a single search parameter. 通过 Azure 认知搜索所提供的对搜索结果的优化控制，分页和限制搜索结果将变得更简单。Paging and throttling your search results is straightforward with the finely tuned control that Azure Cognitive Search offers over your search results. |
| AI 扩充 AI enrichment | 功能Features |
|---|---|
| 在编制索引期间进行 AI 处理AI processing during indexing | 适用于图像和文本分析的 AI 扩充可以应用于索引管道，以从原始内容中提取文本信息。AI enrichment for image and text analysis can be applied to an indexing pipeline to extract text information from raw content. 内置技术的一些示例包括：光学字符识别（使扫描的 JPEG 变得可搜索）、实体识别（标识组织、名称或位置）、关键短语识别。A few examples of built-in skills include optical character recognition (making scanned JPEGs searchable), entity recognition (identifying an organization, name, or location), and key phrase recognition. 也可将自定义技术编码，以便将其附加到管道。You can also code custom skills to attach to the pipeline. |
| 存储丰富的内容以供在非搜索场景中分析和使用Storing enriched content for analysis and consumption in non-search scenarios | 知识存储（预览版） 是基于 AI 的索引编制的一个扩展。Knowledge store (preview) is an extension of AI-based indexing. 通过将 Azure 存储用作后端，可以保存在编制索引期间创建的扩充。With Azure storage as a backend, you can save enrichments created during indexing. 这些项目可用于帮助你设计更好的技能集，或创建不含无固定结构或不明确数据的形状和结构。These artifacts can be used to help you design better skillsets, or create shape and structure out of amorphous or ambiguous data. 可以创建定目标到特定工作负荷或用户的这些结构的投影。You can create projections of these structures that target specific workloads or users. 还可以直接分析已提取的数据，或将它加载到其他应用中。You can also directly analyze the extracted data, or load it into other apps. |
| 缓存内容Cached content | 增量扩充（预览版） 将处理限制为仅处理通过对管道进行特定编辑而更改的文档，并对未更改的管道部分使用缓存内容。Incremental enrichment (preview) limits processing to just the documents that are changed by specific edit to the pipeline, using cached content for the parts of the pipeline that do not change. |
| 数据导入/编制索引 Data import/indexing | 功能Features |
|---|---|
| 数据源Data sources | Azure 认知搜索索引接受来自任何源的数据，前提是以 JSON 数据结构提交这些数据。Azure Cognitive Search indexes accept data from any source, provided it is submitted as a JSON data structure. 索引器自动引入受支持的 Azure 数据源中的数据，并处理 JSON 序列化。Indexers automate data ingestion for supported Azure data sources and handle JSON serialization. 连接到 Azure SQL 数据库、Azure Cosmos DB 或 Azure Blob 存储，以提取主要数据存储中的可搜索内容。Connect to Azure SQL Database, Azure Cosmos DB, or Azure Blob storage to extract searchable content in primary data stores. Azure Blob 索引器可以执行“文档破解”从主要文件格式提取文本，包括 Microsoft Office、PDF 和 HTML 文档。Azure Blob indexers can perform document cracking to extract text from major file formats, including Microsoft Office, PDF, and HTML documents. |
| 分层的嵌套数据结构Hierarchical and nested data structures | 借助复杂类型和集合，可以将几乎所有类型的 JSON 结构建模为 Azure 认知搜索索引。Complex types and collections allow you to model virtually any type of JSON structure as an Azure Cognitive Search index. 可以通过集合、复杂类型和复杂类型集合，以本机方式表示一对多和多对多基数。One-to-many and many-to-many cardinality can be expressed natively through collections, complex types, and collections of complex types. |
| 语言分析Linguistic analysis | 分析器是在编制索引和搜索操作期间用于处理文本的组件。Analyzers are components used for text processing during indexing and search operations. 有两种类型。There are two types. 自定义词汇分析器用于使用拼音匹配和正则表达式的复杂搜索查询。Custom lexical analyzers are used for complex search queries using phonetic matching and regular expressions. Lucene 或 Microsoft 的语言分析器用于智能处理特定于语言的语言学，包括谓词时态、词性、不规则复数名词（例如“mouse”与“mice”）、词取消复合、词拆分（对于不带空格的语言）等。Language analyzers from Lucene or Microsoft are used to intelligently handle language-specific linguistics including verb tenses, gender, irregular plural nouns (for example, 'mouse' vs. 'mice'), word de-compounding, word-breaking (for languages with no spaces), and more. |
| 平台级别 Platform level | 功能Features |
|---|---|
| 用于原型制作和检查的工具Tools for prototyping and inspection | 在门户中，可以使用导入数据向导来配置索引器、索引设计器以建立索引，并可以使用搜索浏览器来测试查询并优化评分配置文件。In the portal, you can use the Import data wizard to configure indexers, index designer to stand up an index, and Search explorer to test queries and refine scoring profiles. 还可以打开任何索引来查看其架构。You can also open any index to view its schema. |
| 监视和诊断Monitoring and diagnostics | 启用监视功能可查看除门户中始终可见的一目了然指标外的其他指标。Enable monitoring features to go beyond the metrics-at-a-glance that are always visible in the portal. 门户页面中会捕获并报告关于每秒查询数、延迟和限制的指标，无需额外进行配置。Metrics on queries per second, latency, and throttling are captured and reported in portal pages with no additional configuration required. |
| 服务器端加密Server-side encryption | Microsoft 托管的静态加密内置在内部存储层中，它是不可撤消的。Microsoft-managed encryption-at-rest is built into the internal storage layer and is irrevocable. 可以视需要使用客户托管的加密密钥来补充默认加密。Optionally, you can supplement the default encryption with customer-managed encryption keys. 你在 Azure Key Vault 中创建和管理的密钥用于加密 Azure 认知搜索中的索引和同义词映射。Keys that you create and manage in Azure Key Vault are used to encrypt indexes and synonym maps in Azure Cognitive Search. |
| 基础结构Infrastructure | 高可用性平台确保极其可靠的搜索服务体验。The highly available platform ensures an extremely reliable search service experience. 正确缩放后，Azure 认知搜索可提供 99.9% SLA。When scaled properly, Azure Cognitive Search offers a 99.9% SLA. 作为一种完全托管且可缩放的端到端解决方案，Azure 认知搜索绝对不需要基础结构管理。Fully managed and scalable as an end-to-end solution, Azure Cognitive Search requires absolutely no infrastructure management. 通过在两个维度进行缩放以便处理更多文档存储和/或更高的查询负载，可以根据需求来定制服务。Your service can be tailored to your needs by scaling in two dimensions to handle more document storage, higher query loads, or both. |
如何使用 Azure 认知搜索How to use Azure Cognitive Search
步骤 1：预配服务Step 1: Provision service
可以通过 Azure 门户或 Azure 资源管理 API 预配 Azure 认知搜索服务。You can provision an Azure Cognitive Search service in the Azure portal or through the Azure Resource Management API. 可以选择与其他订阅者共享的免费服务，或者服务专用的资源付费层。You can choose either the free service shared with other subscribers, or a paid tier that dedicates resources used only by your service. 对于付费层，可朝两个维度缩放服务：For paid tiers, you can scale a service in two dimensions:
- 添加副本以增长容量来处理重型查询负载。Add Replicas to grow your capacity to handle heavy query loads.
- 添加分区以便为更多文档增加存储。Add Partitions to grow storage for more documents.
通过单独处理文档存储和查询吞吐量，可以根据生产要求进行资源调配。By handling document storage and query throughput separately, you can calibrate resourcing based on production requirements.
步骤 2：创建索引Step 2: Create index
上传可搜索的内容之前，必须先定义 Azure 认知搜索索引。Before you can upload searchable content, you must first define an Azure Cognitive Search index. 索引类似于用于保存数据的数据库表，可接受搜索查询。An index is like a database table that holds your data and can accept search queries. 定义要映射的索引架构，以反映要搜索的文档结构，这类似于数据库中的字段。You define the index schema to map to reflect the structure of the documents you wish to search, similar to fields in a database.
架构可在 Azure 门户中创建，也可以使用 .NET SDK 或 REST API 以编程方式创建。A schema can be created in the Azure portal, or programmatically using the .NET SDK or REST API.
步骤 3：加载数据Step 3: Load data
定义索引后，便可以上传内容。After you define an index, you're ready to upload content. 可以使用推送或提取模型。You can use either a push or pull model.
提取模型从外部数据源检索数据。The pull model retrieves data from external data sources. 支持通过索引器检索数据。索引器可以简化和自动数据引入的方方面面，例如，连接、读取和序列化数据。It's supported through indexers that streamline and automate aspects of data ingestion, such as connecting to, reading, and serializing data. 索引器适用于 Azure Cosmos DB、Azure SQL 数据库、Azure Blob 存储，以及 Azure VM 中托管的 SQL Server。Indexers are available for Azure Cosmos DB, Azure SQL Database, Azure Blob Storage, and SQL Server hosted in an Azure VM. 可以针对按需刷新或计划的数据刷新配置索引器。You can configure an indexer for on demand or scheduled data refresh.
推模型通过 SDK 或 REST API 进行提供，用于将更新的文档发送到索引。The push model is provided through the SDK or REST APIs, used for sending updated documents to an index. 可以从使用 JSON 格式的几乎任何数据集推送数据。You can push data from virtually any dataset using the JSON format. 有关加载数据的指南，请参阅添加、更新或删除文档或如何使用.NET SDK）。See Add, update, or delete Documents or How to use the .NET SDK) for guidance on loading data.
步骤 4：搜索Step 4: Search
填充索引后，可以通过将简单的 HTTP 请求与 REST API 或 .NET SDK 结合使用，向服务终结点发出搜索查询。After populating an index, you can issue search queries to your service endpoint using simple HTTP requests with REST API or the .NET SDK.
逐步完成创建第一个搜索应用以进行构建，然后扩展用于收集用户输入并处理结果的网页。Step through Create your first search app to build and then extend a web page that collects user input and handles results. 还可以使用 Postman 进行交互式 REST 调用，或使用 Azure 门户中内置的搜索浏览器来查询现有索引。You can also use Postman for interactive REST calls or the built-in Search Explorer in Azure portal to query an existing index.
它如何进行比较How it compares
客户常常询问 Azure 认知搜索与其他搜索相关解决方案有何不同。Customers often ask how Azure Cognitive Search compares with other search-related solutions. 下表总结主要区别。The following table summarizes key differences.
| 比较对象Compared to | 主要区别Key differences |
|---|---|
| 数据库搜索Database search | 许多数据库平台都包含内置的搜索体验。Many database platforms include a built-in search experience. SQL Server 具有全文搜索。SQL Server has full text search. Cosmos DB 及类似技术具有可查询的索引。Cosmos DB and similar technologies have queryable indexes. 在评估结合使用搜索和存储的产品时，确定要采用哪种方式可能颇具挑战性。When evaluating products that combine search and storage, it can be challenging to determine which way to go. 许多解决方案同时使用两种：使用 DBMS 进行存储，使用 Azure 认知搜索获取专业搜索功能。Many solutions use both: DBMS for storage, and Azure Cognitive Search for specialized search features. 与 DBMS 搜索相比，Azure 认知搜索存储来自不同来源的内容，并提供专用文本处理功能，例如 56 种语言中的语言感知文本处理（词干化、词元化、词形式）。Compared to DBMS search, Azure Cognitive Search stores content from heterogeneous sources and offers specialized text processing features such as linguistic-aware text processing (stemming, lemmatization, word forms) in 56 languages. 它还支持拼写错误单词的自动更正、同义词、建议、评分控制，Facet 和自定义词汇切分。It also supports autocorrection of misspelled words, synonyms, suggestions, scoring controls, facets, and custom tokenization. Azure 认知搜索中的全文搜索引擎基于 Apache Lucene，它是信息检索方面的行业标准。The full text search engine in Azure Cognitive Search is built on Apache Lucene, an industry standard in information retrieval. 尽管 Azure 认知搜索以倒排索引的形式持久存储数据，但它很少能替代真正的数据存储。While Azure Cognitive Search persists data in the form of an inverted index, it is rarely a replacement for true data storage. 有关详细信息，请参阅此论坛帖子。For more information, see this forum post. 资源利用是这个类别的另一个转折点。Resource utilization is another inflection point in this category. 索引和一些查询操作通常是计算密集型的。Indexing and some query operations are often computationally intensive. 将搜索从 DBMS 卸载到云中的专用解决方案可以节省用于事务处理的系统资源。Offloading search from the DBMS to a dedicated solution in the cloud preserves system resources for transaction processing. 此外，通过将搜索外部化，可以根据查询量轻松调整规模。Furthermore, by externalizing search, you can easily adjust scale to match query volume. |
| 专用搜索解决方案Dedicated search solution | 假设已决定使用全频谱功能进行专用搜索，则需要在本地解决方案或云服务之间进行最终的分类比较。Assuming you have decided on dedicated search with full spectrum functionality, a final categorical comparison is between on premises solutions or a cloud service. 许多搜索技术提供对索引和查询管道的控制、对更丰富查询和筛选语法的访问、对设置级别和相关性的控制以及自导智能搜索功能。Many search technologies offer controls over indexing and query pipelines, access to richer query and filtering syntax, control over rank and relevance, and features for self-directed and intelligent search. 如果想要获得一个开销和维护工作量极少且规模可调的统包解决方案，则云服务是适当的选择。A cloud service is the right choice if you want a turn-key solution with minimal overhead and maintenance, and adjustable scale. 在云的范式中，许多提供程序提供相当的基线功能，以及全文搜索、地理搜索，并且能够处理搜索输入中一定程度的模糊性。Within the cloud paradigm, several providers offer comparable baseline features, with full-text search, geo-search, and the ability to handle a certain level of ambiguity in search inputs. 通常，它是一项专用功能，或者是 API、工具以及用于确定最匹配项的管理功能的易化和总体简化。Typically, it's a specialized feature, or the ease and overall simplicity of APIs, tools, and management that determines the best fit. |
在所有云提供程序中，对于主要依赖于信息检索搜索和内容导航的应用，Azure 认知搜索在处理 Azure 上的内容存储和数据库的全文搜索工作负荷方面最为强大。Among cloud providers, Azure Cognitive Search is strongest for full text search workloads over content stores and databases on Azure, for apps that rely primarily on search for both information retrieval and content navigation.
主要优势包括：Key strengths include:
- 在索引层的 Azure 数据集成（爬网程序）Azure data integration (crawlers) at the indexing layer
- 用于集中管理的 Azure 门户Azure portal for central management
- Azure 可伸缩性、可靠性和世界一流的可用性Azure scale, reliability, and world-class availability
- 对原始数据进行 AI 处理，使其更易于搜索，包括识别图像中的文本，或查找非结构化内容中的模式。AI processing of raw data to make it more searchable, including text from images, or finding patterns in unstructured content.
- 语言分析和自定义分析，提供分析器，用于支持以 56 种语言进行可靠的全文搜索Linguistic and custom analysis, with analyzers for solid full text search in 56 languages
- 对以搜索为中心的应用通用的核心功能：评分、分面、建议、同义词、地理搜索，等等。Core features common to search-centric apps: scoring, faceting, suggestions, synonyms, geo-search, and more.
Note
非 Azure 数据源完全受支持，但依赖于代码密集程度更高的推送方法而不是索引器。Non-Azure data sources are fully supported, but rely on a more code-intensive push methodology rather than indexers. 使用 API 可以通过管道将任何 JSON 文档集合传输到 Azure 认知搜索索引。Using APIs, you can pipe any JSON document collection to an Azure Cognitive Search index.
在我们的所有客户中，能够利用 Azure 认知搜索中最广泛功能的客户包括在线目录、业务线程序以及文档发现应用程序。Among our customers, those able to leverage the widest range of features in Azure Cognitive Search include online catalogs, line-of-business programs, and document discovery applications.
REST API | .NET SDKREST API | .NET SDK
虽然可以在门户中执行许多任务，但 Azure 认知搜索是为希望将搜索功能集成到现有应用程序中的开发人员打造的。While many tasks can be performed in the portal, Azure Cognitive Search is intended for developers who want to integrate search functionality into existing applications. 可以使用以下编程接口。The following programming interfaces are available.
| 平台Platform | 说明Description |
|---|---|
| RESTREST | 任何编程平台和语言（包括 Xamarin、Java 和 JavaScript）支持的 HTTP 命令HTTP commands supported by any programming platform and language, including Xamarin, Java, and JavaScript |
| .NET SDK.NET SDK | REST API 的 .NET 包装器以 C# 和其他针对 .NET Framework 的托管代码语言提供了有效编码。.NET wrapper for the REST API offers efficient coding in C# and other managed-code languages targeting the .NET Framework |
免费试用Free trial
Azure 订户可以在免费层中预配服务。Azure subscribers can provision a service in the Free tier.
如果不是订户，可以免费建立一个 Azure 帐户。If you aren't a subscriber, you can open an Azure account for free. 将获得试用付费版 Azure 服务的信用额度。You get credits for trying out paid Azure services. 额度用完后，可以保留该帐户并继续使用免费的 Azure 服务。After they're used up, you can keep the account and use free Azure services. 除非你显式更改设置 www.azure.cn 并要求付费，否则不会对信用卡收取任何费用。Your credit card is never charged unless you explicitly change your settings awww.azure.cnnd ask to be charged.
如何入门How to get started
创建免费服务。Create a free service. 所有快速入门和教程都可以通过免费服务完成。All quickstarts and tutorials can be completed on the free service.
逐步学习有关使用内置工具进行索引和查询的教程。Step through the tutorial on using built-in tools for indexing and queries. 学习重要概念并熟悉门户提供的信息。Learn important concepts and gain familiarity with information the portal provides.
使用 .NET 或 REST API 继续编写代码：Move forward with code using either the .NET or REST API:
- 如何使用 .NET SDK 演示了托管代码中的主要工作流。How to use the .NET SDK demonstrates the main workflow in managed code.
- REST API 入门演示了使用 REST API 的相同步骤。Get started with the REST API shows the same steps using the REST API. 还可以使用本快速入门从 Postman 或 Fiddler 调用 REST API：探索 Azure 认知搜索 REST API。You can also use this quickstart to call REST APIs from Postman or Fiddler: Explore Azure Cognitive Search REST APIs.