本页介绍了流式回答方法。
流式回答方法具有与 answer 方法相同的许多功能，此外还提供一项额外的功能：流式传输。当您以流式传输方式获取回答时，生成的回答会被分成多个部分，并按顺序发送。
如果生成的回答很长，一次性发送整个回答会导致延迟，因此流式回答特别有用。流式传输回答可减少延迟感。
限制
流式回答方法与回答方法具有相同的功能，但有以下例外情况：
重新措辞的步骤数为 1。您无法停用重述功能，也无法更改步骤数上限。
只有 Gemini 模型才能与流式回答方法搭配使用。 如需查看模型列表，请参阅可用模型。
逐字逐句给出回答
以下命令展示了如何调用 streaming answer 方法，并以一系列 JSON 响应的形式返回生成的答案。通常，每个回答都包含一句答案。
此基本命令仅显示必需的输入内容。选项保留为默认值。
如需查看其他选项的示例，请参阅获取回答和后续问题。部分回答选项不适用于回答流式传输；请参阅本页面中的限制。
REST
如需搜索并获取包含流式生成的答案的结果，请执行以下操作：
运行以下 curl 命令：
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json" \ "https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID/locations/global/collections/default_collection/engines/APP_ID/servingConfigs/default_search:streamAnswer" \ -d '{ "query": { "text": "QUERY"} }'替换以下内容：
PROJECT_ID：您的 Google Cloud 项目的 ID。APP_ID：要查询的 Vertex AI Search 应用的 ID。QUERY：包含问题或搜索查询的自由文本字符串。例如，“哪个数据库更快，是 BigQuery 还是 Spanner？”。
命令和部分结果示例
curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json" \ "https://discoveryengine.googleapis.com/v1/projects/my-project-123/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:streamAnswer" \ -d '{ "query":{"text":"Which database is faster, bigquery or spanner?"} }'
[{ "answer": { "state": "STREAMING", "steps": [ { "description": "Rephrase the query and search.", "actions": [ { "searchAction": { "query": " What is the performance of Spanner?" }, "observation": { "searchResults": [ { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/1a9f55e00c42c06ca97bf5a5868dbcdc", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/answer", "title": "Get answers and follow-ups", "snippetInfo": [ { "snippet": "QUERY : a free-text string that contains the question or search query. For example, "Compare the BigQuery and \u003cb\u003eSpanner\u003c/b\u003e databases?". Example command and result.", "snippetStatus": "SUCCESS" } ] }, ... { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b95bb201a0adb24f769627f56cf34405", "uri": "https://abc.xyz/assets/investor/static/pdf/2017_Q1_Earnings_Transcript.pdf", "title": "\u200b \u200b", "snippetInfo": [ { "snippet": "well as Hardware related costs, reflecting the continued strong \u003cb\u003eperformance\u003c/b\u003e of our new Made by ... We introduced dozens of new products, including \u003cb\u003eSpanner\u003c/b\u003e, a ...", "snippetStatus": "SUCCESS" } ] } ] } }, { "searchAction": { "query": " What is the performance of BigQuery?" }, "observation": { "searchResults": [ { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/18bcc727bfd6a3d1be0aa4bd49fe2c50", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/evaluate-search-quality", "title": "Evaluate search quality", "snippetInfo": [ { "snippet": "You can evaluate the \u003cb\u003eperformance\u003c/b\u003e of generic search apps that contain structured, unstructured, and website data. ... Import from \u003cb\u003eBigQuery\u003c/b\u003e: import \u003cb\u003eBigQuery\u003c/b\u003e data ...", "snippetStatus": "SUCCESS" } ] }, ... { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/1a9f55e00c42c06ca97bf5a5868dbcdc", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/answer", "title": "Get answers and follow-ups", "snippetInfo": [ { "snippet": "QUERY : a free-text string that contains the question or search query. For example, "Compare the \u003cb\u003eBigQuery\u003c/b\u003e and Spanner databases?". Example command and result.", "snippetStatus": "SUCCESS" } ] } ] } } ] } ] } } , { "answer": { "state": "STREAMING", "references": [ { "chunkInfo": { "content": "Example command and partial result curl -X POST -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"Content-Type: application/json\" \"https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/servingConfigs/default_search:answer\" -d '{ \"query\": { \"text\": \"Compare bigquery with spanner database?\"} \"queryUnderstandingSpec\": { \"queryRephraserSpec\": { \"disable\": true } } }' { \"answer\": { \"state\": \"SUCCEEDED\", \"answerText\": \"You can compare BigQuery and Spanner databases using the following criteria:\\n\\n* **Pricing:** BigQuery is priced per GB of data processed, while Spanner is priced per hour of compute time.\\n* **Performance:** BigQuery is designed for fast analytics, while Spanner is designed for high availability and scalability.\\n* **Features:** BigQuery supports a wide range of features, including SQL, machine learning, and streaming. ", "documentMetadata": { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/1a9f55e00c42c06ca97bf5a5868dbcdc", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/answer", "title": "Get answers and follow-ups" } } }, ... { "chunkInfo": { "content": "Here is an example of a summary, with citations and citation metadata, returned at the end of a search response: See more code actions. Dismiss View Light code theme Dark code theme \"summary\": { \"summaryText\": \"BigQuery is Google Cloud's fully managed and completely serverless enterprise data warehouse [1]. BigQuery supports all data types, works across clouds, and has built-in machine learning and business intelligence, all within a unified platform [2, 3].\", \"summaryWithMetadata\": { \"summary\": \"BigQuery is Google Cloud's fully managed and completely serverless enterprise data warehouse. ", "documentMetadata": { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7ba2e8666f5514b5bc14f5e300d7678", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/get-search-summaries", "title": "Get search summaries" } } } ] } } , { "answer": { "state": "STREAMING", "answerText": "Span" } } , { "answer": { "state": "STREAMING", "answerText": "ner is Google's large-scale database that scales 20 times better than" } } , ... { "answer": { "state": "STREAMING", "answerText": " Web Services, and on-premises data sources. " } } , { "answer": { "state": "STREAMING", "answerText": "Spanner is a distributed, strongly consistent, SQL database designed to scale to 10 million servers. \n" } } , { "answer": { "state": "SUCCEEDED", "answerText": "Spanner is Google's large-scale database that scales 20 times better than any competitor. Spanner is designed for high availability and scalability, while BigQuery is designed for fast analytics. BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse that enables businesses to analyze all their data very quickly. BigQuery is a very powerful tool that can be used to analyze data from many different sources, including Google Cloud Platform, Amazon Web Services, and on-premises data sources. Spanner is a distributed, strongly consistent, SQL database designed to scale to 10 million servers. \n", "references": [ { "chunkInfo": { "content": "Example command and partial result curl -X POST -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"Content-Type: application/json\" \"https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/servingConfigs/default_search:answer\" -d '{ \"query\": { \"text\": \"Compare bigquery with spanner database?\"} \"queryUnderstandingSpec\": { \"queryRephraserSpec\": { \"disable\": true } } }' { \"answer\": { \"state\": \"SUCCEEDED\", \"answerText\": \"You can compare BigQuery and Spanner databases using the following criteria:\\n\\n* **Pricing:** BigQuery is priced per GB of data processed, while Spanner is priced per hour of compute time.\\n* **Performance:** BigQuery is designed for fast analytics, while Spanner is designed for high availability and scalability.\\n* **Features:** BigQuery supports a wide range of features, including SQL, machine learning, and streaming. ", "documentMetadata": { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/1a9f55e00c42c06ca97bf5a5868dbcdc", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/answer", "title": "Get answers and follow-ups" } } }, { "chunkInfo": { "content": "Second, we also give them the ability to build applications using a set of technology that can run on any environment that they have. When we say on any environment - at their premise, on our cloud or on any other cloud. So, in other words, they can learn once, write once, deploy anywhere; and we make money no matter where they deploy. An example of that is a recent product we introduced called AlloyDB. It's the fastest-performing relational database in the market. We run it in all four environments: Our cloud, on-premise and on other clouds. And it's the only relational database that can run in any of those configurations. You see that in our adoption, both at the top end of the market where a system - for example, like Spanner, which is our large-scale database - scales 20 times better than the largest scalable system of any competitor. So for high-end, we work extremely well. And, also, because we made it so easy to use, startups and small businesses are growing very quickly in their adoption of our platform. When we introduced our AI systems, we introduced a platform called Vertex AI. ", "documentMetadata": { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/8ad08d0844d601733e135381512e2a16", "uri": "http://abc.xyz/thomas-kurian-ceo-google-cloud-at-the-goldman-sachs-2023-communacopia-technology-conference-on-september-7th-2023", "title": "Thomas Kurian, CEO, Google Cloud at the Goldman Sachs 2023 Communacopia + Technology Conference on September 7th, 2023 - Alphabet Investor Relations" } } }, ... { "chunkInfo": { "content": "BigQuery is also integrated with other Google Cloud services, such as Google Kubernetes Engine, Cloud Data Fusion, and Cloud Dataproc, making it easy to build and deploy data pipelines. Here are some of the benefits of using BigQuery: * **Fast and scalable:** BigQuery can process petabytes of data very quickly, and it can scale to handle even the most demanding workloads. * **Cost-effective:** BigQuery is a very cost-effective way to store and analyze data. You only pay for the data that you use, and there are no upfront costs or commitments. * **Secure:** BigQuery is a secure platform that meets the needs of even the most security-conscious organizations. * **Easy to use:** BigQuery is easy to use, even for non-technical users. It has a simple and intuitive user interface, and it supports a variety of data sources. * **Integrated with other Google Cloud services:** BigQuery is integrated with other Google Cloud services, making it easy to build and deploy data pipelines. If you are looking for a fast, scalable, and cost-effective way to analyze your data, then BigQuery is a great option. ", "documentMetadata": { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7ba2e8666f5514b5bc14f5e300d7678", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/get-search-summaries", "title": "Get search summaries" } } }, { "chunkInfo": { "content": "Here is an example of a summary, with citations and citation metadata, returned at the end of a search response: See more code actions. Dismiss View Light code theme Dark code theme \"summary\": { \"summaryText\": \"BigQuery is Google Cloud's fully managed and completely serverless enterprise data warehouse [1]. BigQuery supports all data types, works across clouds, and has built-in machine learning and business intelligence, all within a unified platform [2, 3].\", \"summaryWithMetadata\": { \"summary\": \"BigQuery is Google Cloud's fully managed and completely serverless enterprise data warehouse. ", "documentMetadata": { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7ba2e8666f5514b5bc14f5e300d7678", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/get-search-summaries", "title": "Get search summaries" } } } ], "steps": [ { "description": "Rephrase the query and search.", "actions": [ { "searchAction": { "query": " What is the performance of Spanner?" }, "observation": { "searchResults": [ { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/1a9f55e00c42c06ca97bf5a5868dbcdc", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/answer", "title": "Get answers and follow-ups", "snippetInfo": [ { "snippet": "QUERY : a free-text string that contains the question or search query. For example, "Compare the BigQuery and \u003cb\u003eSpanner\u003c/b\u003e databases?". Example command and result.", "snippetStatus": "SUCCESS" } ] }, { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/9d022f7bdf24bac6714a9cf61a5458c7", "uri": "https://abc.xyz/assets/87/4c/162ca71d4178a3f4d39002467439/thomas-kurian-goldman-sachs-090723.pdf", "title": "Thomas Kurian Goldman Sachs 090723", "snippetInfo": [ { "snippet": "2X better training \u003cb\u003eperformance\u003c/b\u003e per dollar1 compared to a leading cloud alternative. More than 70% of gen AI unicorns are Google Cloud customers. Best ...", "snippetStatus": "SUCCESS" } ] }, ... { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/20641e370fa86c78f1c81f3dab22efe1", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/release-notes", "title": "AI Applications release notes | Google Cloud", "snippetInfo": [ { "snippet": "Generative answers have been updated with \u003cb\u003eperformance\u003c/b\u003e improvements. ... This lets you assess your search engine's \u003cb\u003eperformance\u003c/b\u003e ... Importing data from \u003cb\u003eSpanner\u003c/b\u003e, Cloud ...", "snippetStatus": "SUCCESS" } ] }, { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b95bb201a0adb24f769627f56cf34405", "uri": "https://abc.xyz/assets/investor/static/pdf/2017_Q1_Earnings_Transcript.pdf", "title": "\u200b \u200b", "snippetInfo": [ { "snippet": "well as Hardware related costs, reflecting the continued strong \u003cb\u003eperformance\u003c/b\u003e of our new Made by ... We introduced dozens of new products, including \u003cb\u003eSpanner\u003c/b\u003e, a ...", "snippetStatus": "SUCCESS" } ] } ] } }, { "searchAction": { "query": " What is the performance of BigQuery?" }, "observation": { "searchResults": [ { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/18bcc727bfd6a3d1be0aa4bd49fe2c50", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/evaluate-search-quality", "title": "Evaluate search quality", "snippetInfo": [ { "snippet": "You can evaluate the \u003cb\u003eperformance\u003c/b\u003e of generic search apps that contain structured, unstructured, and website data. ... Import from \u003cb\u003eBigQuery\u003c/b\u003e: import \u003cb\u003eBigQuery\u003c/b\u003e data ...", "snippetStatus": "SUCCESS" } ] }, { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/2a3221d40533a4bdaf35778962a2a079", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/check-media-data-quality", "title": "Check data quality for media recommendations", "snippetInfo": [ { "snippet": "... model that will result in \u003cb\u003eperformance\u003c/b\u003e issue if not met for all media recommendations models and all business objectives.", "condition": { "expression ...", "snippetStatus": "SUCCESS" } ] }, ... { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/18c258b9c770f4d762e6233d1a1bc81c", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/user-events", "title": "About user events", "snippetInfo": [ { "snippet": "This section provides the data formats for each event type supported by media recommendations. Examples for JavaScript Pixel are provided. For \u003cb\u003eBigQuery\u003c/b\u003e, the ...", "snippetStatus": "SUCCESS" } ] }, { "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/1a9f55e00c42c06ca97bf5a5868dbcdc", "uri": "https://cloud.google.com/generative-ai-app-builder/docs/answer", "title": "Get answers and follow-ups", "snippetInfo": [ { "snippet": "QUERY : a free-text string that contains the question or search query. For example, "Compare the \u003cb\u003eBigQuery\u003c/b\u003e and Spanner databases?". Example command and result.", "snippetStatus": "SUCCESS" } ] } ] } } ] } ] } }在此示例中，对“哪个数据库更快，是 BigQuery 还是 Spanner？”这一问题的回答以一系列 JSON 输出的形式呈现。最终输出的状态为
SUCCEEDED，并包含完整答案。在此示例中，
steps和references流式传输响应出现在AnswerText流式传输响应之前。但实际情况可能并非总是如此。如果您要解析输出，请勿假设steps和references响应会先于AnswerText响应出现。
其他示例
以流式传输方式回答问题中显示的基本命令是最简单的命令，未指定任何选项。不过，您可以应用与 answer 方法相同的选项，但需遵守此页面上列出的限制。
流式回答还可以与后续会话搭配使用。