Esta página apresenta o método de resposta de streaming.
O método de resposta de streaming tem muitas das mesmas funcionalidades que o método answer, além de uma funcionalidade adicional: streaming. Quando transmite uma resposta, a resposta gerada é dividida em várias partes que são enviadas em sequência.
O streaming de respostas é particularmente útil se as respostas geradas forem longas, para que o envio da resposta completa de uma só vez cause um atraso. As respostas de streaming reduzem a aparência da latência.
Limitações
O método de resposta de streaming tem as mesmas funcionalidades que o método de resposta com as seguintes exceções:
A resposta em streaming só é suportada para inglês.
O número de passos de reformulação é um. Não pode desativar a reformulação nem alterar o número máximo de passos.
Não pode obter classificações de fundamentação para respostas de streaming, nem optar por devolver apenas respostas bem fundamentadas.
Só é possível usar modelos do Gemini com o método de resposta de streaming. Para ver uma lista de modelos, consulte o artigo Modelos disponíveis.
Fazer stream de uma resposta
O comando seguinte mostra como chamar o método streaming answer e devolver uma resposta gerada sob a forma de uma série de respostas JSON. Normalmente, cada resposta contém uma frase da resposta.
Este comando básico mostra apenas a entrada necessária. As opções são deixadas nas respetivas predefinições.
Para ver exemplos de outras opções, consulte o artigo Receba respostas e seguimentos. Algumas opções de resposta não estão disponíveis para o streaming de respostas. Consulte as limitações nesta página.
REST
Para pesquisar e receber resultados com uma resposta gerada em streaming, faça o seguinte:
Execute o seguinte comando curl:
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"} }'Substitua o seguinte:
PROJECT_ID: o ID do seu projeto.APP_ID: o ID da app que quer consultar.QUERY: uma string de texto livre que contém a pergunta ou a consulta de pesquisa. Por exemplo, "Qual base de dados é mais rápida, o BigQuery ou o Spanner?".
Comando de exemplo e resultado parcial
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/example/docs/answer", "title": "Get answers and follow-ups | Example | Google Cloud", "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/example/docs/evaluate-search-quality", "title": "Evaluate search quality | Example | Google Cloud", "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/example/docs/answer", "title": "Get answers and follow-ups | Example | Google Cloud", "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/example/docs/answer", "title": "Get answers and follow-ups | Example | Google Cloud" } } }, ... { "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/example/docs/get-search-summaries", "title": "Get search summaries | Example | Google Cloud" } } } ] } } , { "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/example/docs/answer", "title": "Get answers and follow-ups | Example | Google Cloud" } } }, { "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/example/docs/get-search-summaries", "title": "Get search summaries | Example | Google Cloud" } } }, { "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/example/docs/get-search-summaries", "title": "Get search summaries | Example | Google Cloud" } } } ], "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/example/docs/answer", "title": "Get answers and follow-ups | Example | Google Cloud", "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/example/docs/release-notes", "title": "Example 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/example/docs/evaluate-search-quality", "title": "Evaluate search quality | Example | Google Cloud", "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/example/docs/check-media-data-quality", "title": "Check data quality for media recommendations | Example | Google Cloud", "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/example/docs/user-events", "title": "About user events | Example | Google Cloud", "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/example/docs/answer", "title": "Get answers and follow-ups | Example | Google Cloud", "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" } ] } ] } } ] } ] } }Neste exemplo, a resposta à consulta "Qual base de dados é mais rápida, bigquery ou spanner?" aparece numa série de resultados JSON. O resultado final recebe o estado
SUCCEEDEDe inclui a resposta completa.
Outros exemplos
O comando básico apresentado em Transmitir uma resposta é o comando mais simples sem opções especificadas. No entanto, pode aplicar as mesmas opções disponíveis com o método answer, com exceção das limitações indicadas nesta página.
As respostas de streaming também podem ser usadas com sessões de seguimento.