답변 및 후속 질문 확인하기

REST

생성된 답변으로 검색하고 결과를 얻으려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"}
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다. 예: 'BigQuery와 Spanner 데이터베이스를 비교해 주세요.'

   명령어 및 일부 결과 예시

   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:answer" \
   -d '{"query": { "text": "Which database is faster, bigquery or spanner?"}}'
           
   
   {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "The provided sources do not directly compare the speed of BigQuery and Spanner.  However, they do highlight the performance capabilities of each database. BigQuery is described as having strong query performance, particularly for short and complex queries.  It also offers a serverless architecture that provides consistent performance regardless of query complexity.  Spanner is described as having high performance at virtually unlimited scale, with single-digit millisecond latency for strongly-consistent reads and writes.  It also offers a five-nines availability SLA.  Ultimately, the best database for a particular use case will depend on the specific requirements of the application. \n",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "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/9ab3ef91bcfde1fcd091efe9df7c699c",
                     "uri": "https://cloud.google.com/bigquery/docs/best-practices-performance-overview",
                     "title": "Introduction to optimizing query performance | BigQuery | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "After a query begins execution, \u003cb\u003eBigQuery\u003c/b\u003e calculates how many slots each query stage uses based on the stage size and complexity and the number of slots ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4e545c5cb69b06b251265114d9099cb4",
                     "uri": "https://cloud.google.com/bigquery/docs/query-insights",
                     "title": "Get query performance insights | BigQuery | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "This document describes how to use the query execution graph to diagnose query \u003cb\u003eperformance\u003c/b\u003e issues, and to see query \u003cb\u003eperformance\u003c/b\u003e insights. \u003cb\u003eBigQuery\u003c/b\u003e offers ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d34672d877eefe596f9c7d1a3d7076b1",
                     "uri": "https://cloud.google.com/bigquery/docs/best-practices-performance-compute",
                     "title": "Optimize query computation | BigQuery | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "After addressing the query \u003cb\u003eperformance\u003c/b\u003e insights, you can further optimize your query by performing the following tasks: Reduce data that is to be processed ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/75ce2f05833683e60ddc21a11ce0466f",
                     "uri": "https://cloud.google.com/blog/products/data-analytics/troubleshoot-and-optimize-your-bigquery-analytics-queries-with-query-execution-graph/",
                     "title": "Troubleshoot and optimize your BigQuery analytics queries with query execution graph | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "Since query \u003cb\u003eperformance\u003c/b\u003e is multi-faceted, \u003cb\u003eperformance\u003c/b\u003e insights might only provide a partial picture of the overall query \u003cb\u003eperformance\u003c/b\u003e. Execution graph. When ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             },
             {
               "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/f3d036b60379873acf7c73081c5e5b5c",
                     "uri": "https://cloud.google.com/spanner/docs/performance",
                     "title": "Performance overview | Spanner | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "These \u003cb\u003eperformance\u003c/b\u003e improvements should result in higher throughput and better latency in \u003cb\u003eSpanner\u003c/b\u003e nodes in both regional and multi-region instance configurations.",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/422496248ade354c73b4c906b8eb9b5f",
                     "uri": "https://cloud.google.com/blog/products/databases/announcing-cloud-spanner-price-performance-updates",
                     "title": "Announcing Cloud Spanner price-performance updates | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "Alongside lower costs, Cloud \u003cb\u003eSpanner\u003c/b\u003e provides single-digit ms latencies and strong consistency across multiple availability zones in the same region.",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/53c2a1a6990480ba4aa05cc6b4404562",
                     "uri": "https://cloud.google.com/blog/topics/developers-practitioners/understanding-cloud-spanner-performance-metrics-scale-key-visualizer",
                     "title": "Understanding Cloud Spanner performance metrics at scale with Key Visualizer | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "Designed for \u003cb\u003eperformance\u003c/b\u003e tuning and instance sizing, you can use Key Visualizer today in the web-based Cloud Console for all \u003cb\u003eSpanner\u003c/b\u003e databases at no additional ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a6501ecd5d6391e3ade49097bab0ad3a",
                     "uri": "https://cloud.google.com/blog/products/databases/a-technical-overview-of-cloud-spanners-query-optimizer",
                     "title": "A technical overview of Cloud Spanner's query optimizer | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "... performance. Typically, a join will ... Google is continuously improving out-of-the-box \u003cb\u003eperformance of Spanner\u003c/b\u003e and reducing the need for manual tuning.",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ]
     },
     "answerQueryToken": "NMwKDAj1_d62BhC72_X_AhIkNjZkN2I4YWEtMDAwMC0yYTdiLWIxMmMtMDg5ZTA4MjhlNzY0"
   }
   

   이 예시에서 쿼리는 'Spanner의 성능은 무엇인가요?' 및 'BigQuery의 성능은 무엇인가요?'로 분리됩니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

쿼리 문구 수정을 적용하지 않은 생성된 답변으로 검색하고 결과를 얻으려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "queryUnderstandingSpec": {
              "queryRephraserSpec": {
                 "disable": true
           }
       }
         }'
   
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다. 예: 'BigQuery와 Spanner 데이터베이스를 비교해 주세요.'

   명령어 및 일부 결과 예시

   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:answer" \
   -d '{
         "query": { "text": "Which database is faster, bigquery or spanner?"},
         "queryUnderstandingSpec": { "queryRephraserSpec": { "disable": true } }
       }'
       
   
       {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "The sources provided do not directly compare the speed of BigQuery and Spanner. They do mention that Spanner is optimized for transactional workloads and BigQuery is optimized for analytical workloads. Spanner is a fully managed relational database that provides seamless replication across regions in Google Cloud. BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse. Spanner is designed to scale horizontally across multiple regions and continents. BigQuery is designed for business agility. \n",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "Which database is faster, bigquery or spanner?"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/ecc0e7547253f4ca3ff3328ce89995af",
                     "uri": "https://cloud.google.com/blog/topics/developers-practitioners/how-spanner-and-bigquery-work-together-handle-transactional-and-analytical-workloads",
                     "title": "How Spanner and BigQuery work together to handle transactional and analytical workloads | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "A federated \u003cb\u003equery\u003c/b\u003e might not be as \u003cb\u003efast\u003c/b\u003e as querying local \u003cb\u003eBigQuery tables\u003c/b\u003e. There may be higher latency because of the small wait time for the source \u003cb\u003edatabase\u003c/b\u003e to ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d7e238f73608a860e00b752ef80e2941",
                     "uri": "https://cloud.google.com/blog/products/databases/cloud-spanner-gets-stronger-with-bigquery-federated-queries",
                     "title": "Cloud Spanner gets stronger with BigQuery-federated queries | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "... \u003cb\u003equick\u003c/b\u003e lookup on \u003cb\u003edata\u003c/b\u003e that's in \u003cb\u003eSpanner\u003c/b\u003e -- you can ... Set up an external \u003cb\u003edata\u003c/b\u003e source for the \u003cb\u003eSpanner\u003c/b\u003e shopping \u003cb\u003edatabase\u003c/b\u003e in \u003cb\u003eBigQuery\u003c/b\u003e. ... The \u003cb\u003equery\u003c/b\u003e is executed in ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f3d036b60379873acf7c73081c5e5b5c",
                     "uri": "https://cloud.google.com/spanner/docs/performance",
                     "title": "Performance overview | Spanner | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "The information on this page applies to both GoogleSQL and PostgreSQL \u003cb\u003edatabases\u003c/b\u003e. Note: We are in the process of rolling out \u003cb\u003eperformance\u003c/b\u003e and storage changes that ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/47b09cb5ad5e3ab3b1eb93d99ecb0896",
                     "uri": "https://cloud.google.com/blog/products/databases/rewe-uses-cloud-spanner-to-optimize-for-speed-and-performance",
                     "title": "REWE uses Cloud Spanner to optimize for speed and performance | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "As a fully managed relational \u003cb\u003edatabase\u003c/b\u003e, \u003cb\u003eSpanner\u003c/b\u003e provides unlimited scale, strong consistency, and up to 99.999% availability. By choosing this approach to ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ]
     },
     "answerQueryToken": "M8gKCwjp_t62BhC7wOFMEiQ2NmQ3YjhhZS0wMDAwLTJhN2ItYjEyYy0wODllMDgyOGU3NjQ"
   }
   

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

최대 5단계의 문구 수정을 허용하는 생성된 답변으로 검색하고 결과를 얻으려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "queryUnderstandingSpec": {
               "queryRephraserSpec": {
                   "maxRephraseSteps": MAX_REPHRASE
                }
            }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다. 예: 'BigQuery와 Spanner 데이터베이스를 비교해 주세요.'
   • MAX_REPHRASE: 최대 문구 수정 단계 수입니다. 허용되는 최댓값은 5입니다. 설정하지 않거나 1보다 작은 값으로 설정하면 기본값인 1이 됩니다.

   명령어 예

   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:answer" \
     -d '{
           "query": { "text": "How much longer does it take to train a recommendations model than a search model"},
           "queryUnderstandingSpec": {
               "queryRephraserSpec": {
               "maxRephraseSteps": 5
                }
            }
         }'
         
   
   

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

쿼리가 적대적인 쿼리인지, 답변을 구하지 않는 쿼리인지 확인하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "queryUnderstandingSpec": {
               "queryClassificationSpec": {
                   "types": ["QUERY_CLASSIFICATION_TYPE"]
                }
            }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다. 예: 'hello'
   • QUERY_CLASSIFICATION_TYPE: 식별하려는 쿼리 유형입니다(ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY 또는 둘 다).

   명령어 및 일부 결과 예시

   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:answer" \
   -d '{
         "query": {
             "text": "Hello!"},
             "queryUnderstandingSpec": {
                "queryClassificationSpec": {
                "types": ["ADVERSARIAL_QUERY", "NON_ANSWER_SEEKING_QUERY"]
                 }
             }
   }'
   
   {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "A user reported that their Google Voice account was randomly sending \"Hello!\" replies to incoming texts. The user was frustrated because they did not want to send these replies and found the behavior random. The user was unable to find any linked accounts, Google extensions, or other settings that could be causing the issue. The user confirmed that Google Voice does not have auto-reply functions. The user was seeking help to stop the automatic replies. \n",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "Hello!"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/69e92e5b1de5b1e583fbe95f94dd4cbf",
                     "uri": "https://support.google.com/voice/thread/152245405/google-voice-is-randomly-automatically-sending-hello-replies-to-incoming-texts?hl=en",
                     "title": "Google voice is randomly/automatically sending \"Hello!\" replies to incoming texts",
                     "snippetInfo": [
                       {
                         "snippet": "There IS a new "Smart reply" feature on the Android or iOS client apps, but you'd have to a) receive a SMS/MMS, b) open it up, c) look at the three suggested ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/44fb313bcc09877e7239f3810ddb132b",
                     "uri": "https://support.google.com/mail/thread/58174131/gmail-sends-random-email-saying-hello-to-my-emails-without-me-touching-it?hl=en",
                     "title": "Gmail sends random email saying \"Hello!!\" to my emails without me touching it",
                     "snippetInfo": [
                       {
                         "snippet": "Gmail sends random email saying "\u003cb\u003eHello\u003c/b\u003e!!" to my emails without me touching it. Whenever I email somebody and they reply, a random email from my Gmail is sent to ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/16d65e2af7fa854d1a00995525646dc3",
                     "uri": "https://support.google.com/voice/thread/112990484/google-voice-sending-hello-in-response-to-text-messages?hl=en",
                     "title": "Google Voice sending \"Hello,\" in response to text messages",
                     "snippetInfo": [
                       {
                         "snippet": "When I receive text messages, a reply is instantly sent out reading "\u003cb\u003eHello\u003c/b\u003e," and I cannot figure out how this is happening. I have no linked accounts, ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a828eb8f442f1dfbdda06dbeb52841b0",
                     "uri": "https://support.google.com/a/thread/161821861/hello-hello-the-lost-phone?hl=en",
                     "title": "Hello.Hello the lost phone - Google Workspace Admin Community",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eHello\u003c/b\u003e the lost phone. My wife lost her phone but she cannot remember her emails pasward to help track .",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ],
       "queryUnderstandingInfo": {
         "queryClassificationInfo": [
           {
             "type": "ADVERSARIAL_QUERY"
           },
           {
           "type": "NON_ANSWER_SEEKING_QUERY",
             "positive": true
           }
         ]
       }
     },
     "answerQueryToken": "NMwKDAjVloK3BhCdt8u9AhIkNjZkYmFhNWItMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
   }
   

   이 예시에서 쿼리 'hello'는 적대적이지는 않지만 답변을 구하지 않는 것으로 분류됩니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

반환되는 검색 결과와 반환 방식과 관련된 다양한 옵션을 설정하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
             "searchSpec": {
             "searchParams": {
               "maxReturnResults": MAX_RETURN_RESULTS,
               "filter": "FILTER",
               "boostSpec": BOOST_SPEC,
               "orderBy": "ORDER_BY",
               "searchResultMode": SEARCH_RESULT_MODE
              }
            }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다. 예: 'BigQuery와 Spanner 데이터베이스를 비교해 주세요.'
   • MAX_RETURN_RESULTS: 반환할 검색 결과 수입니다. 기본값은 10입니다.
   • FILTER: 필터는 쿼리할 문서를 지정합니다. 문서의 메타데이터가 필터 사양을 충족하면 문서가 쿼리됩니다. 필터 구문 등에 대한 자세한 내용은 정형 데이터 또는 비정형 데이터의 일반 검색 필터링을 참조하세요.
   • BOOST_SPEC: 부스트 사양을 사용하면 검색 결과에서 특정 문서를 부스팅할 수 있으며, 이는 대답에 영향을 줄 수 있습니다. 부스트 사양 구문 등에 대한 자세한 내용은 검색 결과 부스트를 참조하세요.
   • ORDER_BY: 문서가 반환되는 순서입니다. 문서는 문서 객체의 필드별로 정렬할 수 있습니다. orderBy 표현식은 대소문자를 구분합니다. 이 필드를 인식할 수 없으면 INVALID_ARGUMENT가 반환됩니다.
   • SEARCH_RESULT_MODE: 검색 결과 모드를 지정합니다(DOCUMENTS 또는 CHUNKS). 자세한 내용은 문서 파싱 및 청크 처리 및 ContentSearchSpec을 참조하세요. 이 필드는 API의 v1alpha 버전에서만 사용할 수 있습니다.

   명령어 및 결과 예시

   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:answer" \
   -d '{
         "query": {
             "text": "Does spanner database have an API?"},
             "searchSpec": {
             "searchParams": { "maxReturnResults": 3 }
             }
       }'
       
   
   {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "Spanner database has an API that provides programmatic access to the database. The API is available through client libraries, RPC, and REST. The client libraries allow you to interact with Spanner in your preferred language. The RPC API and REST API provide programmatic access to Spanner. The Cloud Spanner API is a managed, mission-critical, globally consistent and scalable relational database service. \n",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "Does spanner database have an API?"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d135b46c4a44d0cc6b652538c1887f4d",
                     "uri": "https://cloud.google.com/spanner/docs/apis",
                     "title": "APIs & reference | Spanner | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "The client libraries, the RPC \u003cb\u003eAPI\u003c/b\u003e, and the REST \u003cb\u003eAPI\u003c/b\u003e provide programmatic access to \u003cb\u003eSpanner\u003c/b\u003e. \u003cb\u003eSpanner\u003c/b\u003e client libraries. \u003cb\u003eGet\u003c/b\u003e started with \u003cb\u003eSpanner\u003c/b\u003e in your language ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7a744d43e61ccd33539de74d5c1f6313",
                     "uri": "https://cloud.google.com/spanner/docs/reference/rest",
                     "title": "Cloud Spanner API",
                     "snippetInfo": [
                       {
                         "snippet": "Returns permissions that the caller \u003cb\u003ehas\u003c/b\u003e on the specified \u003cb\u003edatabase\u003c/b\u003e or backup resource. updateDdl, PATCH /v1/{\u003cb\u003edatabase\u003c/b\u003e=projects/*/instances/*/\u003cb\u003edatabases\u003c/b\u003e/*}/ddl",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/70834ebf4b72b6dc69e06c44ee80f90b",
                     "uri": "https://cloud.google.com/spanner/docs/reference/rpc",
                     "title": "Cloud Spanner API",
                     "snippetInfo": [
                       {
                         "snippet": "ChangeQuorum \u003cb\u003eis\u003c/b\u003e strictly restricted to \u003cb\u003edatabases\u003c/b\u003e ... Returns the schema of a Cloud \u003cb\u003eSpanner database\u003c/b\u003e ... Returns permissions that the caller \u003cb\u003ehas\u003c/b\u003e on the specified ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ]
     },
     "answerQueryToken": "NMwKDAj2l4K3BhCqiv66ARIkNjZkYmFhNmMtMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
   }
   
   
   

   이 예시에서는 반환되는 문서 수가 3개로 제한됩니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

적대적이거나 답변을 구하지 않는 쿼리에 대한 답변을 건너뛰려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "answerGenerationSpec": {
              "ignoreAdversarialQuery": true,
              "ignoreNonAnswerSeekingQuery": true
           }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다.

   명령어 및 일부 결과 예시

   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:answer" \
       -d '{
         "query": { "text": "Hello"},
         "answerGenerationSpec": {
             "ignoreAdversarialQuery": true,
             "ignoreNonAnswerSeekingQuery": true }
           }'
           
   
           {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "A summary could not be generated for your search query. Here are some search results.",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "Hello"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/69e92e5b1de5b1e583fbe95f94dd4cbf",
                     "uri": "https://support.google.com/voice/thread/152245405/google-voice-is-randomly-automatically-sending-hello-replies-to-incoming-texts?hl=en",
                     "title": "Google voice is randomly/automatically sending \"Hello!\" replies to incoming texts",
                     "snippetInfo": [
                       {
                         "snippet": "There IS a new "Smart reply" feature on the Android or iOS client apps, but you'd have to a) receive a SMS/MMS, b) open it up, c) look at the three suggested ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/16d65e2af7fa854d1a00995525646dc3",
                     "uri": "https://support.google.com/voice/thread/112990484/google-voice-sending-hello-in-response-to-text-messages?hl=en",
                     "title": "Google Voice sending \"Hello,\" in response to text messages",
                     "snippetInfo": [
                       {
                         "snippet": "When I receive text messages, a reply is instantly sent out reading "\u003cb\u003eHello\u003c/b\u003e," and I cannot figure out how this is happening. I have no linked accounts, ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b3bdde4957f588a1458c533269626d09",
                     "uri": "https://support.google.com/voice/thread/4307458/lately-an-automatic-text-response-saying-hello-is-going-out-how-do-i-stop-this?hl=en",
                     "title": "Lately an automatic text response saying, \"Hello\" is going out. How do I stop this? - Google Voice Community",
                     "snippetInfo": [
                       {
                         "snippet": "You need to find out what app is causing it and deactivate or delete it. Last edited Apr 16, 2019.",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/a828eb8f442f1dfbdda06dbeb52841b0",
                     "uri": "https://support.google.com/a/thread/161821861/hello-hello-the-lost-phone?hl=en",
                     "title": "Hello.Hello the lost phone - Google Workspace Admin Community",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eHello\u003c/b\u003e the lost phone. My wife lost her phone but she cannot remember her emails pasward to help track .",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ],
       "answerSkippedReasons": [
         "NON_ANSWER_SEEKING_QUERY_IGNORED"
       ]
     },
     "answerQueryToken": "NMwKDAjFgN-2BhDlsKaZARIkNjZkN2I0NmItMDAwMC0yZmQ5LTkwMDktZjQwMzA0M2E5YTg4"
   }
   

   이 예시에서는 쿼리가 답변을 구하지 않는 확인되어 답변이 생성되지 않습니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

관련성 있는 결과가 없을 때 대체 답변을 반환하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "answerGenerationSpec": {
              "ignoreLowRelevantContent": true
           }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다.

   명령어 및 결과 예시

   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:answer" \
   -d '{"query": { "text": "foobar"}, "answerGenerationSpec": {
          "ignoreLowRelevantContent": true
       } }'
       
   
    {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "We do not have a summary for your query.",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "foobar"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/719b79786f0c143717c569eade5305d9",
                     "uri": "https://support.google.com/websearch/thread/261714267/google-foobar-bug-console-disappeared?hl=en",
                     "title": "Google Foobar Bug - Console Disappeared",
                     "snippetInfo": [
                       {
                         "snippet": "Google \u003cb\u003eFoobar\u003c/b\u003e Bug - Console Disappeared. After I logged in today the top bar says "The \u003cb\u003eFoobar\u003c/b\u003e Challenge will be turned down on 1 April 2024. If you run out of ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/932369826585ff45f6ab3eba01ba6933",
                     "uri": "https://support.google.com/websearch/thread/95251114/unable-to-contact-foobar-recruiter?hl=en",
                     "title": "Unable to contact Foobar Recruiter - Google Search Community",
                     "snippetInfo": [
                       {
                         "snippet": "Access is by invitation only so you will need to have the proper credentials to login. You can always reach out using the contact us button, but there is no ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/fb736a30ff90d058be755f0a04a522a8",
                     "uri": "https://support.google.com/websearch/thread/121151780/foobar-challenge-appeared-to-me-then-disappeared?hl=en",
                     "title": "Foobar challenge appeared to me then disappeared - Google Search Community",
                     "snippetInfo": [
                       {
                         "snippet": "Hi. I got the \u003cb\u003efoobar\u003c/b\u003e challenge some months ago. But then it disappeared immediately, maybe by misclick (though I don't think I misclicked).",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f56f2656b0d02b839509d0e67e60c1c9",
                     "uri": "https://support.google.com/chrome/thread/159931759/cannot-access-google-foobar-challenge?hl=en",
                     "title": "Cannot Access Google FooBar Challenge",
                     "snippetInfo": [
                       {
                         "snippet": "I knew I wouldn't have time for it today, so I just kept the tab in the background. Tonight, I went to go close all my tabs, but the page had changed. It said " ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ],
       "answerSkippedReasons": [
         "NO_RELEVANT_CONTENT"
       ]
     },
     "answerQueryToken": "M8gKCwiokvy2BhDtv8EDEiQ2NmQ5NDQxZC0wMDAwLTIxMGQtOWU2Yi1mNDAzMDQ1ZGJkMzA"
   }
   

   이 예시에서는 결과가 쿼리와 충분히 관련이 없는 것으로 판단되어 생성된 답변 및 결과 대신 대세 답변이 반환되었습니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

기본 모델과 다른 모델을 사용하여 답변을 생성하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "answerGenerationSpec": {
              "modelSpec": {
                 "modelVersion": "MODEL_VERSION",
              }
            }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다.
   • MODEL_VERSION: 답변을 생성하는 데 사용할 모델 버전입니다. 자세한 내용은 답변 생성 모델 버전 및 수명 주기를 참조하세요.

   명령어 및 일부 결과 예시

   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:answer" \
   -d '{"query": { "text": "Compare bigquery with spanner database?"},   "answerGenerationSpec": {
        "modelSpec": {
        "modelVersion": "preview",
        }
      } }'
      
   
      {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "Cloud Spanner is a fully managed relational database optimized for transactional workloads. BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility. BigQuery is optimized for ad-hoc analysis and reporting. Both Spanner and BigQuery are built on Google's distributed storage system, Colossus, and their internal cluster management system, Borg. They are also built on Jupiter, Google's in-house custom network hardware and software.\n\nBigQuery can query data stored in Spanner in real time without moving or copying the data. This is possible with BigQuery's query federation support. To run a federated query, you need to configure an external data source in BigQuery that points to the intended Spanner instance. You can then write queries that can be used to populate a BigQuery table on demand or scheduled to run as needed. You can also join the query with another BigQuery result set dynamically.\n\nYou can also use Dataflow to copy data from Spanner to BigQuery. Dataflow is a service that can be used to ingest Spanner data into BigQuery. This is useful for more complex transformations or external dependencies. For example, an online gaming company might use Spanner to store game data and BigQuery to perform analytics on player behavior. They can replicate data from Spanner into BigQuery and perform analytics against local data, or they can use federated queries to retrieve data from Spanner on-demand.\n",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "Compare bigquery with spanner database?"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/ecc0e7547253f4ca3ff3328ce89995af",
                     "uri": "https://cloud.google.com/blog/topics/developers-practitioners/how-spanner-and-bigquery-work-together-handle-transactional-and-analytical-workloads",
                     "title": "How Spanner and BigQuery work together to handle transactional and analytical workloads | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "Using Cloud \u003cb\u003eSpanner\u003c/b\u003e and \u003cb\u003eBigQuery\u003c/b\u003e also allows customers to build their \u003cb\u003edata\u003c/b\u003e clouds using Google Cloud, a unified, open approach to \u003cb\u003edata\u003c/b\u003e-driven transformation ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d7e238f73608a860e00b752ef80e2941",
                     "uri": "https://cloud.google.com/blog/products/databases/cloud-spanner-gets-stronger-with-bigquery-federated-queries",
                     "title": "Cloud Spanner gets stronger with BigQuery-federated queries | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "As enterprises compete for market share, their need for real-time insights has given rise to increased demand for transactional \u003cb\u003edatabases\u003c/b\u003e to support \u003cb\u003edata\u003c/b\u003e ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/e10a5a3c267dc61579e7c00fefe656eb",
                     "uri": "https://cloud.google.com/blog/topics/developers-practitioners/replicating-cloud-spanner-bigquery-scale",
                     "title": "Replicating from Cloud Spanner to BigQuery at scale | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "... \u003cb\u003eSpanner data\u003c/b\u003e into \u003cb\u003eBigQuery\u003c/b\u003e for analytics. In this post, you will learn how to efficiently use this feature to replicate large tables with high throughput ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/648c220055c1d2ac369165007d9f6650",
                     "uri": "https://cloud.google.com/blog/products/databases/choosing-cloud-spanner-for-game-development",
                     "title": "Choosing Cloud Spanner for game development | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "To get started with \u003cb\u003eSpanner\u003c/b\u003e, create a \u003cb\u003edatabase\u003c/b\u003e, or try it out with a \u003cb\u003eSpanner\u003c/b\u003e Qwiklab. ... AlloyDB \u003cb\u003evs\u003c/b\u003e. ... SQL for SQL Server to \u003cb\u003eBigQuery\u003c/b\u003e. By Alexander ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ]
     },
     "answerQueryToken": "NMwKDAivmvy2BhCxnsqdARIkNjZkOTQ0NWEtMDAwMC0yMTBkLTllNmItZjQwMzA0NWRiZDMw"
   }
   

   이 예시에서는 기본 모델 대신 모델의 미리보기 버전이 사용됩니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

기본 모델과 다른 모델을 사용하여 답변을 생성하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "answerGenerationSpec": {
              "promptSpec": {
                  "preamble": "PREAMBLE",
              }
           }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다.
   • PREAMBLE: 답변을 맞춤설정하기 위한 자연어 안내입니다. 예를 들어 show the answer format in an ordered list 또는 give a very detailed answer을 사용해 보세요.

   명령어 및 결과 예시

   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:answer" \
       -d '{
             "query": { "text": "what is bigquery?"},
             "answerGenerationSpec": {
                "promptSpec": {
                   "preamble": "Explain like you are a ten years old
                   kid",
        }
     }
   }'
   
   {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "BigQuery is like a super-powered storage space for your data, but it's in the cloud, not on your computer. It's like a giant warehouse for all your information, but you don't have to build or manage it yourself. You can use BigQuery to find patterns and insights in your data, like figuring out what people like to buy or how many people visited your website. It's like having a super-smart assistant that can help you understand your data better. BigQuery is really good at working with lots of data, even billions of pieces of information. \n",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "What is BigQuery?"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/2d032dc582689e8c0ecea7fc7bfa3189",
                     "uri": "https://cloud.google.com/bigquery",
                     "title": "BigQuery enterprise data warehouse | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4474f4a5a18ecd611dedfe323dfe55d9",
                     "uri": "https://cloud.google.com/bigquery/docs/introduction",
                     "title": "BigQuery overview | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is a fully managed, AI-ready data platform that helps you manage and analyze your data with built-in features like machine learning, search, ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/c840fdef90d86328f13bbedbdbf0ac10",
                     "uri": "https://cloud.google.com/bigquery/docs/query-overview",
                     "title": "Overview of BigQuery analytics | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eBigQuery\u003c/b\u003e lets you save queries and share queries with others. When you save a query, it can be private (visible only to you), shared at the project level ( ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/5cbfab8ce1d5f6ffe45fef22900c9a00",
                     "uri": "https://cloud.google.com/curated-resources/bigquery",
                     "title": "Guides BigQuery | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eBigQuery\u003c/b\u003e is Google Cloud's fully managed and serverless enterprise data warehouse solution, designed to help you make informed decisions quickly, so you can ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ]
     },
     "answerQueryToken": "NMwKDAi8hN-2BhC0jMCPARIkNjZkN2I4MzItMDAwMC0yMTliLTkxN2EtMDg5ZTA4MjA0YjFj"
   }
   

   이 예시에서 프리앰블은 기본값이 제공할 수 있는 것보다 더 간단한 답변을 요청합니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

기본 모델과 다른 모델을 사용하여 답변을 생성하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "answerGenerationSpec": {
              "includeCitations": INCLUDE_CITATIONS
           }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다.
   • INCLUDE_CITATIONS: 답변에 인용 메타데이터를 포함할지 여부를 지정합니다. 기본값은 false입니다.

   명령어 및 일부 결과 예시

   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:answer" \
   -d '{"query": { "text": "What is SQL"},   "answerGenerationSpec": {
       "includeCitations": true
   }
   }'
   
   {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "SQL stands for Structured Query Language and is a programming language used to manage, query, and retrieve data in a relational database. It is the standard language used by relational database management systems (RDBMS) such as PostgreSQL, SQL Server, MySQL, and Oracle Database. SQL statements are written in a statement format for queries and other database operations, allowing users to manipulate data in relational database tables. SQL is used to create and update the structure of tables, read and write data, manage user permissions, and perform administrative tasks. While originally created for relational databases, SQL is now a foundation for many technology capabilities, making SQL knowledge essential for many technology roles. \n",
       "citations": [
         {
           "endIndex": "137",
           "sources": [
             {
               "referenceId": "0"
             },
             {
               "referenceId": "1"
             }
           ]
         },
         {
           "startIndex": "138",
           "endIndex": "437",
           "sources": [
             {
               "referenceId": "3"
             }
           ]
         },
         {
           "startIndex": "438",
           "endIndex": "575",
           "sources": [
             {
               "referenceId": "2"
             }
           ]
         },
         {
           "startIndex": "576",
           "endIndex": "742",
           "sources": [
             {
               "referenceId": "3"
             }
           ]
         }
       ],
       "references": [
         {
           "chunkInfo": {
             "content": "There may be a second table that stores visit information. A relational database uses a unique ID for each row to maintain the linked patient information across the two tables. This way, you can quickly look up the visits of each patient. Sign up for a free trial for any of Google Cloud's SQL databases, which include AlloyDB, Cloud SQL, and Spanner. Get started for free What is SQL? SQL (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. SQL statements are English-like, making the language accessible to software developers, data analysts, and other practitioners. Benefits of SQL databases Enterprises choose SQL databases for being: Efficient. Relational databases are incredibly efficient in managing complex queries. Fast. SQL databases can retrieve large amounts of data, quickly. This makes them highly desirable for real-time transactional data. Reliable. SQL databases provide a high degree of data integrity and are ACID-compliant. SQL database engines There are numerous SQL database engines (products) used to build software applications. Some of the most popular include PostgreSQL, MySQL, SQL Server, and Oracle. Some database engines are open source while others are commercial offerings. ",
             "relevanceScore": 0.9,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
               "uri": "https://cloud.google.com/discover/what-are-sql-databases",
               "title": "SQL Databases | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud Page Contents Topics PostgreSQL vs. SQL PostgreSQL vs SQL Server: What are the key differences? Trying to find the right database for your applications? When it comes to choosing a database technology, the most common SQL options to consider are PostgreSQL vs. SQL Server. While both systems share many core features, there are some key differences—the major one being that PostgreSQL is open source and SQL Server is owned by Microsoft. Today, it is more vital than ever for companies to be able to manage, store, and activate data for modern business operations. With the growing assortment of databases available to choose from, it can be overwhelming to pick the right one for your applications. The most important thing to remember is that no single database will be a good match for every project requirement, so it's critical to understand the option that will work best for your specific use case. So, what is PostgreSQL vs. SQL Server? In this short guide, we'll discuss the basic differences between PostgreSQL and SQL Server. Get started for freeStay informed What is SQL? Structured Query Language or SQL, as it's more commonly known, is a programming language used to manage, query, and retrieve data in a relational database. ",
             "relevanceScore": 0.8,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
               "uri": "https://cloud.google.com/learn/postgresql-vs-sql",
               "title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "SQL Databases | Google Cloud Page Contents Topics What are SQL databases? What are SQL databases? A SQL database, also known as a relational database, is a system that stores and organizes data into highly structured tables of rows and columns. These databases offer Structured Query Language (SQL) to read and write the data, and are categorized as relational database management systems (RDBMS). SQL statements are used to create and update the structure of tables, read and write data, manage user permissions, and perform administrative tasks. For example, a CREATE statement is used to create a table, an INSERT statement adds a new row to a table, and a SELECT statement performs a database query. Statements that make structural or administrative changes are usually reserved for software developers and administrators, while read and write operations are performed by end-user applications. A relational database maintains the ability to link information across multiple tables. This format makes it easy to quickly gain insights about the relationships between various columns or data points in these tables. A relational database can create indexes for particular columns for faster lookups. For example, a healthcare facility might maintain a table containing rows of patient information, where each row is one patient and the columns contain data points, such as the patient's name, insurance information, and contact details. ",
             "relevanceScore": 0.8,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
               "uri": "https://cloud.google.com/discover/what-are-sql-databases",
               "title": "SQL Databases | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "It is the standard language used by relational database management systems (RDBMS), including PostgreSQL, SQL Server, MySQL, and Oracle Database. SQL typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database tables. While originally created for relational databases, SQL acts as a foundation for many of today's technology capabilities, making SQL knowledge an essential skill for many technology roles today, including data analysts, database engineers, and even backend programming. However, you will find that there are different variants of SQL depending on the database or database management system that you choose. What is Microsoft SQL Server? SQL Server is a leading RDBMS that is built on top of SQL and developed by Microsoft. It is used to manage and store data to support numerous enterprise use cases for business intelligence, transaction processing, data analytics, and machine learning services. SQL Server has a row-based table structure that allows you to connect related data elements from different tables without having to store data multiple times in a database. In general, Microsoft SQL Server is known for its high availability, fast performance when handling large workloads, and easy integration with other applications to gain business intelligence across your entire data estate. For more information, we recommend reviewing the official SQL Server documentation. ",
             "relevanceScore": 0.8,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
               "uri": "https://cloud.google.com/learn/postgresql-vs-sql",
               "title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "Send feedback The GoogleSQL language in Spanner bookmark_borderbookmark Stay organized with collections Save and categorize content based on your preferences. Dismiss Got it GoogleSQL is the new name for Google Standard SQL! New name, same great SQL dialect. This page provides an overview of supported statements in GoogleSQL. GoogleSQL is an ANSI compliant Structured Query Language (SQL) which includes the following types of supported statements: Query statements, also known as Data Query Language (DQL) statements, are the primary method to analyze data in Spanner. They scan one or more tables or expressions and return the computed result rows. Data Definition Language (DDL) statements let you create and modify database objects such as tables, views, and database roles. Data Manipulation Language (DML) statements enable you to update, insert, and delete data from your Spanner tables. Data Access Control statements let you grant and revoke access privileges at the table and column level. Transaction Control statements allow you to manage transactions for data modifications. Was this helpful? Send feedback Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. ",
             "relevanceScore": 0.7,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/0c5c094170756eeb6bdfec6eb5c7d081",
               "uri": "https://cloud.google.com/spanner/docs/reference/standard-sql/overview",
               "title": "The GoogleSQL language in Spanner | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "FAQ Expand all What is Cloud SQL? Cloud SQL is a service that delivers fully managed relational databases in the cloud. It offers MySQL, PostgreSQL, and SQL Server database engines. How is Cloud SQL different from other cloud databases? Cloud SQL is valued for its openness, ease of use, security, cost-efficiency, and Google Cloud integration—in fact, more than 95% of Google Cloud's top 100 customers use it. If you're comparing PostgreSQL options on Google Cloud, view our comparison chart. What's the difference between the Enterprise and Enterprise Plus editions? For PostgreSQL, the Enterprise Plus edition brings enhanced availability, performance, and data protection capabilities. Specifically, it provides a 99.99% availability SLA with near-zero downtime maintenance, optimized hardware and software configurations, intelligent data caching for read-intensive transactional workloads, a configurable data cache option and 35 days of log retention. For MySQL, the Enterprise Plus edition brings enhanced availability, performance, and data protection capabilities. Specifically, it provides a 99.99% availability SLA with near-zero downtime maintenance, optimized hardware and software configurations, intelligent data caching for read-intensive transactional workloads, a configurable data cache option, 35 days of log retention and advanced disaster recovery capabilities like orchestrated failover and switchback. ",
             "relevanceScore": 0.7,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/931f2c8e19ed54a407857f1cad3b5aaa",
               "uri": "https://cloud.google.com/sql",
               "title": "Cloud SQL for MySQL, PostgreSQL, and SQL Server | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "PostgreSQL versus SQL PostgreSQL is an open-source, object-relational database (ORDBMS) designed for enterprise-level performance and is valued for its reliability and robust features. Its long history of development and its use of SQL makes it one of the most popular open source databases worldwide. Its default procedural language is an extension of pgSQL (PL/pgSQL), with procedural language extensions of Tcl, Perl, and Python included in the standard distribution (written as PL/Tcl, PL/Perl, and PL/Python). Many more languages are supported through extensions, including Java, Ruby, C, C++, Delphi, and JavaScript. For a more in-depth comparison, visit our PostgreSQL versus SQL guide. MySQL versus SQL MySQL is a popular open source relational database created in 1995 and currently sponsored by Oracle. It supports SQL queries and can be administered either through a graphical user interface (GUI) or a command line. MySQL can be deployed manually on a physical machine or through a cloud service provider. Enterprises are increasingly choosing fully managed services to reduce the maintenance burden of their databases. What is SQL Server? SQL Server is a Microsoft-owned database that runs SQL queries. Dive into the differences between PostgreSQL and SQL Server. ",
             "relevanceScore": 0.6,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
               "uri": "https://cloud.google.com/discover/what-are-sql-databases",
               "title": "SQL Databases | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "Send feedback On this page BigQuery SQL dialects Changing from the default dialect What's next Introduction to SQL in BigQuery bookmark_borderbookmark Stay organized with collections Save and categorize content based on your preferences. Dismiss Got it GoogleSQL is the new name for Google Standard SQL! New name, same great SQL dialect. This document provides an overview of supported statements and SQL dialects in BigQuery. GoogleSQL is an ANSI compliant Structured Query Language (SQL) which includes the following types of supported statements: Query statements, also known as Data Query Language (DQL) statements, are the primary method to analyze data in BigQuery. They scan one or more tables or expressions and return the computed result rows. Procedural language statements are procedural extensions to GoogleSQL that allow you to execute multiple SQL statements in one request. Procedural statements can use variables and control-flow statements, and can have side effects. Data Definition Language (DDL) statements let you create and modify database objects such as tables, views, functions, and row-level access policies. Data Manipulation Language (DML) statements enable you to update, insert, and delete data from your BigQuery tables. Data Control Language (DCL) statements let you control BigQuery system resources such as access and capacity. Transaction Control Language (TCL) statements allow you to manage transactions for data modifications. ",
             "relevanceScore": 0.6,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/2f6fc3e29873518196cb50195d7ded45",
               "uri": "https://cloud.google.com/bigquery/docs/introduction-sql",
               "title": "Introduction to SQL in BigQuery | Google Cloud"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "Database administration Cloud SQL pricing Connect to a Cloud SQL managed database Cloud SQL updates Configuration updates System updates What's next Home Cloud SQL Documentation Guides Was this helpful? Send feedback Cloud SQL overview bookmark_borderbookmark Stay organized with collections Save and categorize content based on your preferences. Dismiss Got it On this page Database configurations with Cloud SQL Use cases for Cloud SQL What Cloud SQL provides What is a Cloud SQL instance? Database administration Cloud SQL pricing Connect to a Cloud SQL managed database Cloud SQL updates Configuration updates System updates What's next Cloud SQL is a fully managed relational database service for MySQL, PostgreSQL, and SQL Server. This frees you from database administration tasks so that you have more time to manage your data. This page discusses basic concepts and terminology for Cloud SQL, which provides SQL data storage for Google Cloud. For a more in-depth explanation of key concepts, see the key terms and features pages. For information about how Cloud SQL databases compare with one another, see Cloud SQL feature support by database engine. Database configurations with Cloud SQL The following video shows you the benefits of using Cloud SQL. ",
             "relevanceScore": 0.6,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/4098ae11bfa400e8f1b8e9ba59d2b71b",
               "uri": "https://cloud.google.com/sql/docs/introduction",
               "title": "Cloud SQL overview"
             }
           }
         },
         {
           "chunkInfo": {
             "content": "Cloud SQL documentation View all product documentation Cloud SQL is a fully-managed database service that helps you set up, maintain, manage, and administer your relational databases on Google Cloud Platform. You can use Cloud SQL with MySQL, PostgreSQL, or SQL Server. Not sure what database option is right for you? Learn more about our database services. Learn more about Cloud SQL. Documentation resources Find quickstarts and guides, review key references, and get help with common issues. format_list_numbered Guides Cloud SQL overview Database engine feature support MySQL PostgreSQL SQL Server find_in_page Reference gcloud commands REST API Client libraries info Resources Pricing Release notes Resources Try Cloud SQL for yourself Create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads. Try Cloud SQL free Was this helpful? Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Last updated 2024-08-29 UTC. ",
             "relevanceScore": 0.5,
             "documentMetadata": {
               "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/37935181d99a6ad3b4897e673a7a7986",
               "uri": "https://cloud.google.com/sql/docs",
               "title": "Cloud SQL documentation"
             }
           }
         }
         ],
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "What is SQL?"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
                     "uri": "https://cloud.google.com/discover/what-are-sql-databases",
                     "title": "SQL Databases | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eSQL\u003c/b\u003e (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. \u003cb\u003eSQL\u003c/b\u003e statements are English-like, ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
                     "uri": "https://cloud.google.com/learn/postgresql-vs-sql",
                     "title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eSQL\u003c/b\u003e typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3afdede140d0906c2146a2f2b3a7821e",
                     "uri": "https://cloud.google.com/blog/topics/developers-practitioners/what-cloud-sql",
                     "title": "What is Cloud SQL? | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "It is a fully managed relational database for MySQL, PostgreSQL and \u003cb\u003eSQL\u003c/b\u003e Server. It reduces maintenance cost and automates database provisioning, storage ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/0c5c094170756eeb6bdfec6eb5c7d081",
                     "uri": "https://cloud.google.com/spanner/docs/reference/standard-sql/overview",
                     "title": "The GoogleSQL language in Spanner | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eGoogleSQL\u003c/b\u003e is the new name for Google Standard \u003cb\u003eSQL\u003c/b\u003e! New name, same great \u003cb\u003eSQL\u003c/b\u003e dialect. This page provides an overview of supported statements in \u003cb\u003eGoogleSQL\u003c/b\u003e.",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ]
     },
     "answerQueryToken": "NMwKDAiFm_y2BhC_nfrYAxIkNjZkYjg3NjItMDAwMC0yZTBkLTg0ZDAtMDg5ZTA4MmRjYjg0"
   }
   

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

기본 모델과 다른 모델을 사용하여 답변을 생성하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   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:answer" \
     -d '{
           "query": { "text": "QUERY"},
           "answerGenerationSpec": {
              "answerLanguageCode": "ANSWER_LANGUAGE_CODE"
              }
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: 쿼리할 Vertex AI Search 앱의 ID입니다.
   • QUERY: 질문 또는 검색어를 포함하는 자유 텍스트 문자열입니다.
   • ANSWER_LANGUAGE_CODE: 답변의 언어 코드입니다. BCP47: 언어 식별을 위한 태그에 정의된 언어 태그를 사용하세요.

   명령어 및 결과 예시

   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:answer" \
   -d '{"query": { "text": "What is SQL"},   "answerGenerationSpec": {
     "answerLanguageCode": "es"
        }
   }'
   
   {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "SQL, que significa Structured Query Language, es un lenguaje de programación utilizado para almacenar, recuperar y administrar datos en una base de datos relacional. Las instrucciones de SQL son similares al inglés, lo que hace que el lenguaje sea accesible para desarrolladores de software, analistas de datos y otros profesionales. Las bases de datos SQL se utilizan para administrar y almacenar datos para apoyar numerosos casos de uso empresariales, como la inteligencia empresarial, el procesamiento de transacciones, el análisis de datos y los servicios de aprendizaje automático. SQL es el lenguaje estándar utilizado por los sistemas de gestión de bases de datos relacionales (RDBMS), incluidos PostgreSQL, SQL Server, MySQL y Oracle Database. SQL se utiliza para crear y actualizar la estructura de las tablas, leer y escribir datos, administrar los permisos de los usuarios y realizar tareas administrativas. \n",
       "steps": [
         {
           "state": "SUCCEEDED",
           "description": "Rephrase the query and search.",
           "actions": [
             {
               "searchAction": {
                 "query": "What is SQL?"
               },
               "observation": {
                 "searchResults": [
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/7218ff4f57328d86059246d4af3a9953",
                     "uri": "https://cloud.google.com/discover/what-are-sql-databases",
                     "title": "SQL Databases | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eSQL\u003c/b\u003e (Structured Query Language) is a programming language used to store, retrieve, and manage data in a relational database. \u003cb\u003eSQL\u003c/b\u003e statements are English-like, ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7cd9afab1282a9f57cdcee1885bb4c6",
                     "uri": "https://cloud.google.com/learn/postgresql-vs-sql",
                     "title": "PostgreSQL vs. SQL Server: What's the difference? | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eSQL\u003c/b\u003e typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3afdede140d0906c2146a2f2b3a7821e",
                     "uri": "https://cloud.google.com/blog/topics/developers-practitioners/what-cloud-sql",
                     "title": "What is Cloud SQL? | Google Cloud Blog",
                     "snippetInfo": [
                       {
                         "snippet": "It is a fully managed relational database for MySQL, PostgreSQL and \u003cb\u003eSQL\u003c/b\u003e Server. It reduces maintenance cost and automates database provisioning, storage ...",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   },
                   ...
                                   {
                     "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/0c5c094170756eeb6bdfec6eb5c7d081",
                     "uri": "https://cloud.google.com/spanner/docs/reference/standard-sql/overview",
                     "title": "The GoogleSQL language in Spanner | Google Cloud",
                     "snippetInfo": [
                       {
                         "snippet": "\u003cb\u003eGoogleSQL\u003c/b\u003e is the new name for Google Standard \u003cb\u003eSQL\u003c/b\u003e! New name, same great \u003cb\u003eSQL\u003c/b\u003e dialect. This page provides an overview of supported statements in \u003cb\u003eGoogleSQL\u003c/b\u003e.",
                         "snippetStatus": "SUCCESS"
                       }
                     ]
                   }
                 ]
               }
             }
           ]
         }
       ]
     },
     "answerQueryToken": "NMwKDAjim_y2BhDftIjEAhIkNjZkOTQ0NWQtMDAwMC0yMTBkLTllNmItZjQwMzA0NWRiZDMw"
   }
   

   이 예시에서 원본 문서는 영어로 작성되었지만 답변은 스페인어로 제공됩니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"                    # Values: "global", "us", "eu"
# engine_id = "YOUR_APP_ID"


def answer_query_sample(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.AnswerQueryResponse:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.ConversationalSearchServiceClient(
        client_options=client_options
    )

    # The full resource name of the Search serving config
    serving_config = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_serving_config"

    # Optional: Options for query phase
    query_understanding_spec = discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec(
        query_rephraser_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryRephraserSpec(
            disable=False,  # Optional: Disable query rephraser
            max_rephrase_steps=1,  # Optional: Number of rephrase steps
        ),
        # Optional: Classify query types
        query_classification_spec=discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec(
            types=[
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.ADVERSARIAL_QUERY,
                discoveryengine.AnswerQueryRequest.QueryUnderstandingSpec.QueryClassificationSpec.Type.NON_ANSWER_SEEKING_QUERY,
            ]  # Options: ADVERSARIAL_QUERY, NON_ANSWER_SEEKING_QUERY or both
        ),
    )

    # Optional: Options for answer phase
    answer_generation_spec = discoveryengine.AnswerQueryRequest.AnswerGenerationSpec(
        ignore_adversarial_query=False,  # Optional: Ignore adversarial query
        ignore_non_answer_seeking_query=False,  # Optional: Ignore non-answer seeking query
        ignore_low_relevant_content=False,  # Optional: Return fallback answer when content is not relevant
        model_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.ModelSpec(
            model_version="gemini-1.5-flash-001/answer_gen/v2",  # Optional: Model to use for answer generation
        ),
        prompt_spec=discoveryengine.AnswerQueryRequest.AnswerGenerationSpec.PromptSpec(
            preamble="Give a detailed answer.",  # Optional: Natural language instructions for customizing the answer.
        ),
        include_citations=True,  # Optional: Include citations in the response
        answer_language_code="en",  # Optional: Language code of the answer
    )

    # Initialize request argument(s)
    request = discoveryengine.AnswerQueryRequest(
        serving_config=serving_config,
        query=discoveryengine.Query(text="What is Vertex AI Search?"),
        session=None,  # Optional: include previous session ID to continue a conversation
        query_understanding_spec=query_understanding_spec,
        answer_generation_spec=answer_generation_spec,
    )

    # Make the request
    response = client.answer_query(request)

    # Handle the response
    print(response)

    return response

REST

데이터 스토어에서 세션을 가져오려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   curl -X GET -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/sessions/SESSION_ID"
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: Vertex AI Search 앱의 ID입니다.
   • SESSION_ID: 가져올 세션의 ID입니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import discoveryengine_v1 as discoveryengine


def get_session(
    project_id: str,
    location: str,
    engine_id: str,
    session_id: str,
) -> discoveryengine.Session:
    """Retrieves a session.

    Args:
        project_id: The ID of your Google Cloud project.
        location: The location of the app.
        engine_id: The ID of the app.
        session_id: The ID of the session.
    """

    client = discoveryengine.ConversationalSearchServiceClient()

    # The full resource name of the session
    name = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/sessions/{session_id}"

    session = client.get_session(name=name)

    print(f"Session details: {session}")
    return session

REST

앱에서 세션을 삭제하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   curl -X DELETE -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/sessions/SESSION_ID"
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: Vertex AI Search 앱의 ID입니다.
   • SESSION_ID: 삭제할 세션의 ID입니다.

   명령어 및 결과 예시

   curl -X DELETE -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/engines/my-app/sessions/16002628354770206943"
       
   
   {}
   

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import discoveryengine_v1 as discoveryengine


def delete_session(
    project_id: str,
    location: str,
    engine_id: str,
    session_id: str,
) -> None:
    """Deletes a session.

    Args:
        project_id: The ID of your Google Cloud project.
        location: The location of the app.
        engine_id: The ID of the app.
        session_id: The ID of the session.
    """

    client = discoveryengine.ConversationalSearchServiceClient()

    # The full resource name of the session
    name = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/sessions/{session_id}"

    client.delete_session(name=name)

    print(f"Session {name} deleted.")

REST

앱에서 세션을 업데이트하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   curl -X PATCH \
     -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/sessions/SESSION_ID?updateMask=state" \
     -d '{
           "state": "NEW_STATE"
         }'
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: Vertex AI Search 앱의 ID입니다.
   • SESSION_ID: 업데이트할 세션의 ID입니다.
   • NEW_STATE: 상태의 새 값입니다(예: IN_PROGRESS).

   명령어 및 결과 예시

   curl -X PATCH -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/engines/my-app/sessions/16002628354770206943?updateMask=state"
   -d '{
   "state": "IN_PROGRESS"
   }'
       
   
   {
     "name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943",
     "state": "IN_PROGRESS",
     "userPseudoId": "test_user",
     "turns": [
       {
         "query": {
           "queryId": "projects/123456/locations/global/questions/741830",
           "text": "Compare bigquery with spanner database?"
         },
         "answer": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943/answers/4861507376861383072"
       }
     ],
     "startTime": "2024-09-13T18:47:10.465311Z",
     "endTime": "2024-09-13T18:49:41.579151Z"
   }
   

   이 예시에서는 세션 상태를 열림(진행 중)으로 변경합니다. 유사한 패턴을 따라 userPseudoId를 업데이트합니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import discoveryengine_v1 as discoveryengine
from google.protobuf import field_mask_pb2


def update_session(
    project_id: str,
    location: str,
    engine_id: str,
    session_id: str,
) -> discoveryengine.Session:
    """Updates a session.

    Args:
        project_id: The ID of your Google Cloud project.
        location: The location of the app.
        engine_id: The ID of the app.
        session_id: The ID of the session.
    Returns:
        discoveryengine.Session: The updated Session.
    """
    client = discoveryengine.ConversationalSearchServiceClient()

    # The full resource name of the session
    name = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/sessions/{session_id}"

    session = discoveryengine.Session(
        name=name,
        state=discoveryengine.Session.State.IN_PROGRESS,  # Options: IN_PROGRESS, STATE_UNSPECIFIED
    )

    # Fields to Update
    update_mask = field_mask_pb2.FieldMask(paths=["state"])

    session = client.update_session(session=session, update_mask=update_mask)
    print(f"Updated session: {session.name}")
    return session

REST

앱의 세션을 나열하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   curl -X GET \
     -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/sessions"
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: Vertex AI Search 앱의 ID입니다.

   명령어 및 결과 예시

   curl -X GET -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/engines/my-app/sessions"
       
   
   {
     "sessions": [
       {
         "name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10000135306311111817",
         "state": "IN_PROGRESS",
         "turns": [
           {
             "query": {
               "queryId": "projects/123456/locations/global/questions/10000135306311114276",
               "text": "bugs reported by tiktok on grounding"
             }
           }
         ],
         "startTime": "2024-09-03T00:38:40.338623Z",
         "endTime": "2024-09-03T00:38:40.338623Z"
       },
       {
         "name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10000827040519035859",
         "state": "IN_PROGRESS",
         "turns": [
           {
             "query": {
               "queryId": "projects/123456/locations/global/questions/10000827040519033518",
               "text": "GDM models"
             }
           }
         ],
         "startTime": "2024-07-19T15:53:06.521775Z"
       },
       {
         "name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10003910515245149877",
         "state": "IN_PROGRESS",
         "turns": [
           {
             "query": {
               "queryId": "projects/123456/locations/global/questions/10003910515245148378",
               "text": "gyorgyattila"
             },
             "answer": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10003910515245149877/answers/17036357111873257990"
           }
         ],
         "startTime": "2024-08-08T11:40:04.632463Z",
         "endTime": "2024-08-08T11:40:04.632463Z"
       },
   ...
       {
         "name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10198752942940073431",
         "state": "IN_PROGRESS",
         "turns": [
           {
             "query": {
               "queryId": "projects/123456/locations/global/questions/10198752942940071818",
               "text": "hello"
             },
             "answer": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10198752942940073431/answers/13411441797796265380"
           }
         ],
         "startTime": "2024-08-14T17:30:21.203439Z",
         "endTime": "2024-08-14T17:30:21.203439Z"
       }
     ],
     "nextPageToken": "IDEDgIwL_vuieLC"
   }
   

   응답에는 세션 목록과 nextPageToken이 포함됩니다. nextPageToken이 반환되지 않으면 더 이상 나열할 세션이 없습니다. 기본 페이지 크기는 50입니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import discoveryengine_v1 as discoveryengine


def list_sessions(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.ListSessionsResponse:
    """Lists all sessions associated with a data store.

    Args:
        project_id: The ID of your Google Cloud project.
        location: The location of the app.
        engine_id: The ID of the app.
    Returns:
        discoveryengine.ListSessionsResponse: The list of sessions.
    """

    client = discoveryengine.ConversationalSearchServiceClient()

    # The full resource name of the engine
    parent = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}"

    response = client.list_sessions(
        request=discoveryengine.ListSessionsRequest(
            parent=parent,
            filter='state="IN_PROGRESS"',  # Optional: Filter requests by userPseudoId or state
            order_by="update_time",  # Optional: Sort results
        )
    )

    print("Sessions:")
    for session in response.sessions:
        print(session)

    return response

REST

사용자 또는 방문자와 연결된 세션을 나열하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   curl -X GET \
     -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/sessions?filter=userPseudoId=USER_PSEUDO_ID"
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: Vertex AI Search 앱의 ID입니다.
   • USER_PSEUDO_ID: 세션을 나열할 사용자의 가명 ID입니다.

   명령어 및 결과 예시

   curl -X GET -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/engines/my-app/sessions?filter=userPseudoId=test_user"
       
   
   {
     "sessions": [
       {
         "name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943",
         "state": "IN_PROGRESS",
         "userPseudoId": "test_user",
         "turns": [
           {
             "query": {
               "queryId": "projects/123456/locations/global/questions/741830",
               "text": "Compare bigquery with spanner database?"
             },
             "answer": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943/answers/4861507376861383072"
           }
         ],
         "startTime": "2024-09-13T18:47:10.465311Z",
         "endTime": "2024-09-13T18:49:41.579151Z"
       }
     ]
   }
   

   이 예시에서는 test_user와 연결된 세션이 하나 있습니다. 세션의 쿼리와 답변이 표시됩니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import discoveryengine_v1 as discoveryengine


def list_sessions(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.ListSessionsResponse:
    """Lists all sessions associated with a data store.

    Args:
        project_id: The ID of your Google Cloud project.
        location: The location of the app.
        engine_id: The ID of the app.
    Returns:
        discoveryengine.ListSessionsResponse: The list of sessions.
    """

    client = discoveryengine.ConversationalSearchServiceClient()

    # The full resource name of the engine
    parent = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}"

    response = client.list_sessions(
        request=discoveryengine.ListSessionsRequest(
            parent=parent,
            filter='state="IN_PROGRESS"',  # Optional: Filter requests by userPseudoId or state
            order_by="update_time",  # Optional: Sort results
        )
    )

    print("Sessions:")
    for session in response.sessions:
        print(session)

    return response

REST

열리거나 닫혀 있고 특정 사용자 또는 방문자와 연결된 사용자의 세션을 나열하려면 다음 단계를 따르세요.

1. 다음 curl 명령어를 실행합니다.

   curl -X GET -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/sessions?filter=userPseudoId=USER_PSEUDO_ID%20AND%20state=STATE"
   

   다음을 바꿉니다.

   • PROJECT_ID: Google Cloud 프로젝트의 ID입니다.
   • APP_ID: Vertex AI Search 앱의 ID입니다.
   • USER_PSEUDO_ID: 세션을 나열할 사용자의 가명 ID입니다.
   • STATE: 세션의 상태입니다(STATE_UNSPECIFIED(닫힘 또는 알 수 없음) 또는 IN_PROGRESS(열림)).

   명령어 및 결과 예시

   
   curl -X GET -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/engines/my-app/sessions?filter=userPseudoId=test_user%20AND%20state=IN_PROGRESS"
       
   
   {
     "sessions": [
       {
         "name": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943",
         "state": "IN_PROGRESS",
         "userPseudoId": "test_user",
         "turns": [
           {
             "query": {
               "queryId": "projects/123456/locations/global/questions/741830",
               "text": "Compare bigquery with spanner database?"
             },
             "answer": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/16002628354770206943/answers/4861507376861383072"
           }
         ],
         "startTime": "2024-09-13T18:47:10.465311Z",
         "endTime": "2024-09-13T18:49:41.579151Z"
       }
     ]
   }
   

   예상 결과는 빈 응답입니다.

Python

자세한 내용은 Vertex AI Agent Builder Python API 참고 문서를 확인하세요.

Vertex AI Agent Builder에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import discoveryengine_v1 as discoveryengine


def list_sessions(
    project_id: str,
    location: str,
    engine_id: str,
) -> discoveryengine.ListSessionsResponse:
    """Lists all sessions associated with a data store.

    Args:
        project_id: The ID of your Google Cloud project.
        location: The location of the app.
        engine_id: The ID of the app.
    Returns:
        discoveryengine.ListSessionsResponse: The list of sessions.
    """

    client = discoveryengine.ConversationalSearchServiceClient()

    # The full resource name of the engine
    parent = f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}"

    response = client.list_sessions(
        request=discoveryengine.ListSessionsRequest(
            parent=parent,
            filter='state="IN_PROGRESS"',  # Optional: Filter requests by userPseudoId or state
            order_by="update_time",  # Optional: Sort results
        )
    )

    print("Sessions:")
    for session in response.sessions:
        print(session)

    return response