Auf dieser Seite erhalten Sie eine Einführung in die Methode zum Streamen von Antworten.
Die Methode zum Streamen von Antworten funktioniert in vielerlei Hinsicht wie die Methode für Antworten, hat aber eine zusätzliche Funktion: das Streamen. Wenn Sie eine Antwort streamen, wird die generierte Antwort in mehrere Teile unterteilt, die nacheinander gesendet werden.
Das Streamen von Antworten ist besonders nützlich, wenn die generierten Antworten lang sind, sodass das Senden der gesamten Antwort auf einmal zu einer Verzögerung führen würde. Durch das Streamen von Antworten wird die Latenz reduziert.
Beschränkungen
Die Methode zum Streamen von Antworten funktioniert in vielerlei Hinsicht wie die Methode für Antworten, aber mit den folgenden Ausnahmen:
Das Streamen von Antworten wird nur auf Englisch unterstützt.
Es gibt nur einen Umformulierungsschritt. Sie können das Umformulieren nicht deaktivieren und auch die maximale Anzahl von Schritten nicht ändern.
Für das Streamen von Antworten gibt es keine Fundierungs-Scores. Außerdem können Sie nicht festlegen, dass nur gut fundierte Antworten zurückgegeben werden.
Nur Gemini-Modelle können mit der Methode zum Streamen von Antworten verwendet werden. Eine Liste der Modelle finden Sie unter Verfügbare Modelle.
Antwort streamen
Dieser Befehl zeigt, wie Sie die Methode streamingAnswer aufrufen und eine generierte Antwort in einer Reihe von JSON-Antworten zurückgeben. Normalerweise enthält jede Antwort einen Satz der Antwort.
Dieser einfache Befehl zeigt nur die erforderliche Eingabe. Die Antwortmöglichkeiten werden mit den Standardwerten belassen.
Beispiele für andere Antwortmöglichkeiten finden Sie unter Antworten und weiterführende Fragen. Einige Antwortmöglichkeiten sind für das Streaming von Antworten nicht verfügbar. Weitere Informationen finden Sie auf dieser Seite unter Einschränkungen.
REST
So suchen Sie und erhalten Ergebnisse mit einer gestreamten generierten Antwort:
Führen Sie den folgenden curl-Befehl aus:
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"} }'Ersetzen Sie Folgendes:
PROJECT_ID: die Projekt-ID.APP_ID: die ID der App, die Sie abfragen möchten.QUERY: Ein Freitextstring mit der Frage oder Suchanfrage. Beispiel: „Welche Datenbank ist schneller: BigQuery oder Spanner?“
Beispielbefehl und Teilergebnis
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" } ] } ] } } ] } ] } }In diesem Beispiel wird die Antwort auf die Frage „Welche Datenbank ist schneller: BigQuery oder Spanner?“ in einer Reihe von JSON-Ausgaben angezeigt. Die endgültige Ausgabe erhält den Status
SUCCEEDEDund enthält die vollständige Antwort.
Weitere Beispiele
Der in Antwort streamen gezeigte einfache Befehl ist der einfachste Befehl ohne angegebene Antwortmöglichkeiten. Sie können jedoch dieselben Antwortmöglichkeiten wie bei der Methode für Antworten anwenden, mit Ausnahme der auf dieser Seite aufgeführten Einschränkungen.
Das Streamen von Antworten können auch mit Sitzungen mit weiterführenden Fragen verwendet werden.