קבלת תשובות ופריטים להמשך טיפול

REST

כדי לחפש ולקבל תוצאות עם תשובה שנוצרה על ידי AI:

1. מריצים את פקודת ה-curl הבאה:

   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"}
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש. לדוגמה, "השוואה בין מסדי הנתונים של BigQuery ו-Spanner".

   דוגמה לפקודה ולתוצאה חלקית

   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"
   }
   

   בדוגמה הזו, השאילתה מפורקת לחלקים: 'מהם הביצועים של Spanner?' ו'מהם הביצועים של BigQuery?'.

Python

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי לחפש ולקבל תוצאות עם תשובה שנוצרה בלי לשנות את ניסוח השאילתה:

1. מריצים את פקודת ה-curl הבאה:

   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
           }
       }
         }'
   
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • ‫APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש. לדוגמה, "השוואה בין מסדי הנתונים של BigQuery ו-Spanner".

   דוגמה לפקודה ולתוצאה חלקית

   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

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי לחפש ולקבל תוצאות עם תשובה שנוצרה על ידי AI, עם אפשרות לנסח מחדש עד חמש פעמים, פועלים לפי השלבים הבאים:

1. מריצים את פקודת ה-curl הבאה:

   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
                }
            }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש. לדוגמה, "השוואה בין מסדי הנתונים של BigQuery ו-Spanner".
   • ‫MAX_REPHRASE: המספר המקסימלי של שלבי הניסוח מחדש. הערך המקסימלי המותר הוא 5. אם לא מגדירים ערך או אם מגדירים ערך נמוך מ-1, הערך יהיה ברירת המחדל, 1.

   פקודה לדוגמה

   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

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי לקבוע אם שאילתה היא עוינת או לא מכוונת לקבלת תשובה, מבצעים את הפעולות הבאות:

1. מריצים את פקודת ה-curl הבאה:

   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"]
                }
            }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש. לדוגמה, "שלום".
   • ‫QUERY_CLASSIFICATION_TYPE: סוגי השאילתות שרוצים לזהות: ADVERSARIAL_QUERY, ‏NON_ANSWER_SEEKING_QUERY או שניהם.

   דוגמה לפקודה ולתוצאה חלקית

   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"
   }
   

   בדוגמה הזו, השאילתה 'שלום' לא נחשבת לעוינת, אבל היא מסווגת כשאילתה שלא מחפשת תשובה.

Python

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי להגדיר אפשרויות שונות שקשורות לתוצאות החיפוש שמוחזרות ולאופן שבו הן מוצגות, מבצעים את הפעולות הבאות:

1. מריצים את פקודת ה-curl הבאה:

   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
              }
            }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש. לדוגמה, "השוואה בין מסדי הנתונים של BigQuery ו-Spanner".
   • ‫MAX_RETURN_RESULTS: מספר תוצאות החיפוש שיוחזרו. ערך ברירת המחדל הוא 10. הערך המקסימלי הוא 25.
   • ‫FILTER: המסנן מציין אילו מסמכים נכללים בשאילתה. אם המטא-נתונים של מסמך מסוים עומדים בדרישות של מפרט המסנן, המסמך ייכלל בשאילתה. מידע נוסף, כולל תחביר של מסננים, זמין במאמר סינון נתונים מובְנים או לא מובְנים בחיפוש מותאם אישית.
   • ‫BOOST_SPEC: הגדרת ההגברה מאפשרת לכם להגביר את ההדגשה של מסמכים מסוימים בתוצאות החיפוש, מה שיכול להשפיע על התשובה. מידע נוסף, כולל התחביר של הגדרת ההדגשה, זמין במאמר בנושא הדגשת תוצאות חיפוש.
   • ORDER_BY: סדר החזרת המסמכים. אפשר להזמין מסמכים לפי שדה באובייקט Document. הביטוי orderBy הוא תלוי אותיות רישיות. אם השדה הזה לא מזוהה, מוחזר INVALID_ARGUMENT.
   • ‫SEARCH_RESULT_MODE: מציין את מצב תוצאות החיפוש: DOCUMENTS או CHUNKS. מידע נוסף זמין במאמרים ניתוח מסמכים וחלוקה שלהם לחלקים ו-ContentSearchSpec. השדה הזה זמין רק בגרסה v1alpha של ה-API.

   דוגמה לפקודה ולתוצאה

   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"
   }
   
   
   

   בדוגמה הזו, מספר המסמכים שמוחזרים מוגבל לשלושה.

Python

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי לדלג על מענה לשאילתות שהן עוינות או לא מכוונות למציאת תשובה, צריך לבצע את הפעולות הבאות:

1. מריצים את פקודת ה-curl הבאה:

   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
           }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש.

   דוגמה לפקודה ולתוצאה חלקית

   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"
   }
   

   בדוגמה הזו, המערכת קובעת שהשאילתה לא נועדה לקבל תשובה, ולכן לא נוצרת תשובה.

Python

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי להחזיר תשובה חלופית אם לא נמצאו תוצאות רלוונטיות, צריך לבצע את הפעולות הבאות:

1. מריצים את פקודת ה-curl הבאה:

   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
           }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש.

   דוגמה לפקודה ולתוצאה

   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"
   }
   

   בדוגמה הזו, התוצאות לא נחשבו לרלוונטיות מספיק לשאילתה, ולכן במקום תשובה ותוצאות שנוצרו, הוחזרה תשובת ברירת המחדל.

Python

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי לקבל ציון תמיכה לכל טענה (משפט בתשובה) וציון תמיכה מצטבר לתשובה, מבצעים את הפעולות הבאות:

1. מריצים את פקודת ה-curl הבאה:

   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"},
           "groundingSpec": {
              "includeGroundingSupports": true,
           }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש.

   דוגמה לפקודה ולתוצאה חלקית

   curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)"
   -H "Content-Type: application/json"
   "https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer"
   -d '{
       "query": { "text": "What is SQL?"},
       "groundingSpec": {
          "includeGroundingSupports": true,
       }
     }'
               
   
   {
   "answer": {
   "state": "SUCCEEDED",
   "answerText": "SQL stands for Structured Query Language. It is a database management programming language that is used to access and manage data in a database. SQL is used to create, update, and delete data in a database. It can also be used to query data and retrieve information. SQL is a standard language that is used by many different database systems.",
   "groundingScore" 0.9
   "groundingSupports": [
     {
       "endIndex": "41",
       "sources": [
         {
           "referenceId": "1"
         }
       ]
       "groundingScore": 0.9
       "groundingCheckRequired": true
     },
     {
       "startIndex": "42",
       "endIndex": "144",
       "sources": [
         {
           "referenceId": "1"
         }
       ]
       "groundingScore": 0.8
       "groundingCheckRequired": true
     },
     {
       "startIndex": "267",
       "endIndex": "342",
       "sources": [
         {
           "referenceId": "2"
         }
       ]
       "groundingScore": 0.6
       "groundingCheckRequired": true
     }
   ],
   "references": [
     {
       "chunkInfo": {
         "content": "There are a lot of Databases available in the market such as MS Access, Oracle and many others.For you to write programs that interact with these databases easily, there has to be a way where you could get information from all these databases using the same method.For this purpose SQL was developed.It is a kind of language (simple when compared to the likes of C or C++) which enables you to ask all your queries to a database without bothering about the exact type of database.When you use this Query the database engine would first find the table called people.Then it would find a column called firstname.Next it would compare all the values in that column with 'Reena'.Finally it would return all the details wherever it finds a match for the firstname.When you write a database program in VC++ or Java or any other language for that matter, you would make a database connection to your database and then you would query the database using SQL queries.When you query the database with any SQL query the database returns a recordset.A recordset is basically a set of records (all the entries that your query returns).This recordset is received in your program and all languages have a data structure which represents a recordset.",
         "documentMetadata": {
           "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/d993d922043374f5ef7ba297c158b106",
           "uri": "gs://my-bucket-123/documents/058dee0ec23a3e92f9bfd7cd29840e8f.txt"
           "structData": {
             "fields": [
               {
                 "key": "cdoc_url"
                 "value": { "stringValue": "058dee0ec23a3e92f9bfd7cd29840e8f" }
               },
               {
                 "key": "doc_id"
                 "value": { "stringValue": "d993d922043374f5ef7ba297c158b106" }
               }
             ]
           }
         }
       }
     },
     {
       "chunkInfo": {
         "content": "The Structured Query Language (SQL) is a database management programming language.SQL is a tool for accessing databases, and more specifically, relational databases, and can be used with different database products.This chapter will prepare you to learn basic database management using this language.SQLite – To implement SQL as a library, you need SQLite.SQLite is intended to provide users and programs a way to store data using a SQL interface within the program.SQLite3 can be used to manipulate SQLite databases for major Linux distros.SQL is used to access relational databases.Each database contains more or less tables which in turn contain more or less rows and columns.Hereby a single row is seen as a separate object with features represented by the tables' columns.To access a table's data you first have to connect to its database.With the same table, the query SELECT * FROM T WHERE C1 = 1 will result in all the elements of all the rows where the value of column C1 is '1' being shown.A WHERE clause specifies that a SQL statement should only affect rows that meet specified criteria.The criteria are expressed in the form of predicates.WHERE clauses are not mandatory clauses of SQL statements, but should be used to limit the number of rows affected by a SQL DML statement or returned by a query.",
         "documentMetadata": {
           "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3825eac51ef9e934bbc558faa42f4c71",
           "uri": "gs://my-bucket-123/documents/26f5872b0719790cb966a697bfa1ea27.txt"
           "structData": {
             "fields": [
               {
                 "key": "cdoc_url"
                 "value": { "stringValue": "26f5872b0719790cb966a697bfa1ea27" }
               },
               {
                 "key": "doc_id"
                 "value": { "stringValue": "3825eac51ef9e934bbc558faa42f4c71" }
               }
             ]
           }
         }
       }
     },
     {
       "chunkInfo": {
         "content": "This chapter focuses on using Paradox as a client/server development tool.It does not talk about connecting; it is assumed you have already connected.If you are having trouble connecting to a particular SQL server, then refer to the Connection Guide for that particular server.This chapter does review what a user can do interactively with Paradox and how to use ObjectPAL with SQL servers.Structured Query Language (SQL) was developed to create a standard for accessing database information.The ANSI standard for SQL allows a user to become familiar with the commands needed to query many different types of data.After you learn ANSI SQL, you then can query many different databases.Is SQL a solid standard?Yes and no.Yes, the core ANSI SQL commands are solid and consistent from vendor to vendor.Every vendor, however, adds capability to its version of SQL.These improvements are expected because ANSI SQL does not go far enough to cover every feature of every high-end DBMS.The SQL standard is used by many companies for their high-end products.They include Oracle, Sybase, Microsoft SQL, Informix, and Interbase.Paradox also provides the capability to use standard ANSI SQL commands on local Paradox and dBASE tables.Although SQL by definition is a standard, various flavors are on the market.",
         "documentMetadata": {
           "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b3e88db8676b87b99af1e6ecc7d8757f",
           "uri": "gs://my-bucket-123/documents/073c21335d37d8d14982cb3437a721c0.txt"
           "structData": {
             "fields": [
               {
                 "key": "cdoc_url"
                 "value": { "stringValue": "073c21335d37d8d14982cb3437a721c0" }
               },
               {
                 "key": "doc_id"
                 "value": { "stringValue": "b3e88db8676b87b99af1e6ecc7d8757f" }
               }
             ]
           }
         }
       }
     }
   ],
   ...
   "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/d993d922043374f5ef7ba297c158b106",
                 "uri": "gs://my-bucket-123/documents/058dee0ec23a3e92f9bfd7cd29840e8f.txt",
                 "chunkInfo": [
                   {
                     "content": "There are a lot of Databases available in the market such as MS Access, Oracle and many others.For you to write programs that interact with these databases easily, there has to be a way where you could get information from all these databases using the same method.For this purpose SQL was developed.It is a kind of language (simple when compared to the likes of C or C++) which enables you to ask all your queries to a database without bothering about the exact type of database.When you use this Query the database engine would first find the table called people.Then it would find a column called firstname.Next it would compare all the values in that column with 'Reena'.Finally it would return all the details wherever it finds a match for the firstname.When you write a database program in VC++ or Java or any other language for that matter, you would make a database connection to your database and then you would query the database using SQL queries.When you query the database with any SQL query the database returns a recordset.A recordset is basically a set of records (all the entries that your query returns).This recordset is received in your program and all languages have a data structure which represents a recordset."
                   }
                 ]
               },
               {
                 "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3825eac51ef9e934bbc558faa42f4c71",
                 "uri": "gs://my-bucket-123/documents/26f5872b0719790cb966a697bfa1ea27.txt",
                 "chunkInfo": [
                   {
                     "content": "The Structured Query Language (SQL) is a database management programming language.SQL is a tool for accessing databases, and more specifically, relational databases, and can be used with different database products.This chapter will prepare you to learn basic database management using this language.SQLite – To implement SQL as a library, you need SQLite.SQLite is intended to provide users and programs a way to store data using a SQL interface within the program.SQLite3 can be used to manipulate SQLite databases for major Linux distros.SQL is used to access relational databases.Each database contains more or less tables which in turn contain more or less rows and columns.Hereby a single row is seen as a separate object with features represented by the tables' columns.To access a table's data you first have to connect to its database.With the same table, the query SELECT * FROM T WHERE C1 = 1 will result in all the elements of all the rows where the value of column C1 is '1' being shown.A WHERE clause specifies that a SQL statement should only affect rows that meet specified criteria.The criteria are expressed in the form of predicates.WHERE clauses are not mandatory clauses of SQL statements, but should be used to limit the number of rows affected by a SQL DML statement or returned by a query."
                   }
                 ]
               },
               {
                 "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/b3e88db8676b87b99af1e6ecc7d8757f",
                 "uri": "gs://my-bucket-123/documents/073c21335d37d8d14982cb3437a721c0.txt",
                 "chunkInfo": [
                   {
                     "content": "This chapter focuses on using Paradox as a client/server development tool.It does not talk about connecting; it is assumed you have already connected.If you are having trouble connecting to a particular SQL server, then refer to the Connection Guide for that particular server.This chapter does review what a user can do interactively with Paradox and how to use ObjectPAL with SQL servers.Structured Query Language (SQL) was developed to create a standard for accessing database information.The ANSI standard for SQL allows a user to become familiar with the commands needed to query many different types of data.After you learn ANSI SQL, you then can query many different databases.Is SQL a solid standard?Yes and no.Yes, the core ANSI SQL commands are solid and consistent from vendor to vendor.Every vendor, however, adds capability to its version of SQL.These improvements are expected because ANSI SQL does not go far enough to cover every feature of every high-end DBMS.The SQL standard is used by many companies for their high-end products.They include Oracle, Sybase, Microsoft SQL, Informix, and Interbase.Paradox also provides the capability to use standard ANSI SQL commands on local Paradox and dBASE tables.Although SQL by definition is a standard, various flavors are on the market."
                   }
                 ]
               },
   ...
               {
                 "document": "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/3dd4014e41044c5dd6a0fe380847f369",
                 "uri": "gs://my-bucket-123/documents/76245cb33a66f4fbd9030a2a11eea00d.txt",
                 "chunkInfo": [
                   {
                     "content": "SQL injection is a code injection technique that might destroy your database.You can read more here OWASP sql injection testing sheet.Description: SQL injection ( second order) SQL injection vulnerabilities arise when user- controllable data is incorporated sheet into database SQL queries in an unsafe manner.This sheet cheat wiki assumes you have a basic understanding of SQL injection, please go here for an introduction if you are unfamiliar.Bypass login page with sql SQL injection [ closed].Gone are the days when knowledge sheet of just sql SQL Injection or XSS could help you land a lucrative high- paying InfoSec job.There is many sheet differnet variations you would login have probably have to try to make this exploit work ( sql especially if it is sql a blind SQL exploit).SQL injection usually occurs when you ask a user for input, like their.ゲストブック/ コメントの例.Submit Text Post.Get an ad- free experience with special benefits, and directly support Reddit.get reddit premium.SQL Injection Cheat.Many web applications have an authentication system: a user provides a user name and password, the web application checks them and stores the corresponding user id in the session hash.Login # 1 Login # 2 Login # 3 Login # 4."
                   }
                 ]
               }
             ]
           }
         }
       ]
     }
   ]
   }
   }
   

   בדוגמה הזו, ציון התמיכה (groundingScore) מוחזר לכל ציטוט.

REST

כדי להחזיר תשובה רק אם היא עומדת בסף של ציון התמיכה, צריך לבצע את הפעולות הבאות:

1. מריצים את פקודת ה-curl הבאה:

   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"},
           "groundingSpec": {
              "filteringLevel": "FILTER_LEVEL"
           }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש.
   • ‫FILTER_LEVEL: ספירה לסינון תשובות על סמך ניקוד התמיכה בהצגת מקורות. האפשרויות הן: FILTERING_LEVEL_LOW ו-FILTERING_LEVEL_HIGH. אם לא מציינים את filteringLevel, לא מוחל על התשובה סינון לפי ציון התמיכה.

   דוגמה לפקודה ולתוצאה חלקית

   curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
   -d '{
       "query": { "text": "When can an NCD be made?"},
       "groundingSpec": {
          "filtering_level": "FILTERING_LEVEL_HIGH"
       }
     }'
     
   
     {
       answer {
         state: SUCCEEDED
         answer_text: "We do not have a summary for your query."
         steps {
           state: SUCCEEDED
           description: "Rephrase the query and search."
           actions {
             search_action {
               query: "test?"
             }
             observation {
               search_results {
                 document: "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7f5cfde02"
                 uri: "gs://my-bucket-123/data/CoverageDocumentation.pdf"
                 title: "ABC345_0101"
                 chunk_info {
                   content: "This notice implements part of section 731 of the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 by describing a method of developing, and making available to the public, guidance documents under the Medicare program… "
                 }
       ...
               search_results {
                 document: "projects/123456/locations/global/collections/default_collection/dataStores/my-data-store/branches/0/documents/f7f5cfde02"
                 uri: "gs://my-bucket-123/data/CoverageDocumentation.pdf"
                 title: "ABC345_0101"
                 chunk_info {
                   content: "For the purposes of this notice, the term guidance documents means documents prepared for our staff, potential requestors of National Coverage Determinations, and other interested parties explaining the NCD process… "
                 }
               }
             }
           }
         }
         answer_skipped_reasons: LOW_GROUNDED_CONTENT
       }
   

   בדוגמה הזו, לא מוחזרת תשובה כי לא הושג ערך גבוה מסף ההגדרה.

REST

כדי ליצור תשובה באמצעות מודל ששונה ממודל ברירת המחדל:

1. מריצים את פקודת ה-curl הבאה:

   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",
              }
            }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש.
   • ‫MODEL_VERSION: גרסת המודל שרוצים להשתמש בה כדי ליצור את התשובה. מידע נוסף זמין במאמר גרסאות של מודלים ליצירת תשובות ומחזור החיים שלהן.

   דוגמה לפקודה ולתוצאה חלקית

   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"
   }
   

   בדוגמה הזו, נעשה שימוש בגרסת התצוגה המקדימה של המודל במקום במודל ברירת המחדל.

Python

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי ליצור תשובה באמצעות מודל ששונה ממודל ברירת המחדל:

1. מריצים את פקודת ה-curl הבאה:

   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",
              }
           }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש.
   • PREAMBLE: הוראה בשפה טבעית להתאמה אישית של התשובה. לדוגמה, אפשר לנסות show the answer format in an ordered list או give a very detailed answer.

   דוגמה לפקודה ולתוצאה

   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"
   }
   

   בדוגמה הזו, ההקדמה מבקשת תשובה פשוטה יותר מזו שמוגדרת כברירת מחדל.

Python

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(response)

    return response

REST

כדי לקבל שאלות קשורות עם תשובה שנוצרה:

1. מריצים את פקודת ה-curl הבאה:

   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"},
           "relatedQuestionsSpec": { "enable": true }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש. לדוגמה, "אילו סוגי נתונים אפשר לייבא לחיפוש מבוסס סוכנים?".

   דוגמה לפקודה ולתוצאה חלקית

   curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json" \
     "https://discoveryengine.googleapis.com/v1/projects/123456/locations/global/collections/default_collection/engines/my-app/servingConfigs/default_search:answer" \
     -d '{
           "query": { "text": "what kinds of data can I import?"},
           "session": "projects/123456/locations/global/collections/default_collection/engines/my-app/sessions/10291252835232308789",
           "searchSpec":{ "searchParams": {"filter": ""} },
           "relatedQuestionsSpec": { "enable": true }
   }'
   
   {
     "answer": {
       "state": "SUCCEEDED",
       "answerText": "You can import various kinds of data, depending on the type of data store you create. Each data store can contain one type of data. The types of data stores include website data, structured data, structured data for media, unstructured data, and healthcare data. ... You can import FHIR R4 data into a data store in the following ways: Batch import (a one-time import).\n",
       "relatedQuestions": [
         "Can you provide examples of structured data formats?",
         "What file types are supported for unstructured data?",
         "How does website data ingestion work?",
         "What are the limitations of healthcare data import?",
         "Is there a size limit for importing data?"
       ],
       ...
     }
   }
   

   בדוגמה הזו, השדה enabled באובייקט relatedQuestionsSpec מוגדר ל-true, והתגובה מכילה שאלות קשורות במערך relatedQuestions.

REST

כדי ליצור תשובה באמצעות מודל ששונה ממודל ברירת המחדל:

1. מריצים את פקודת ה-curl הבאה:

   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
           }
         }'
   

   מחליפים את מה שכתוב בשדות הבאים:

   • ‫PROJECT_ID: מזהה הפרויקט ב- Google Cloud .
   • APP_ID: המזהה של אפליקציית חיפוש מבוסס סוכנים שרוצים לשלוח אליה שאילתה.
   • ‫QUERY: מחרוזת טקסט חופשי שמכילה את השאלה או את שאילתת החיפוש.
   • ‫INCLUDE_CITATIONS: מציין אם לכלול במענה מטא-נתונים של ציטוט. ערך ברירת המחדל הוא false.

   דוגמה לפקודה ולתוצאה חלקית

   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,
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               "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. ",
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               "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. ",
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         }
         ],
       "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"
                       }
                     ]
                   },
                   ...
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                     "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

מידע נוסף מופיע בתיעוד העזר של ה-API של חיפוש מבוסס סוכנים Python.

כדי לבצע אימות ב-חיפוש מבוסס סוכנים, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

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
    # The `query_understanding_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/QueryUnderstandingSpec
    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
    # The `answer_generation_spec` below includes all available query phase options.
    # For more details, refer to https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1/AnswerGenerationSpec
    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(
        # Use the 2026 stable production model for answer generation
        model_version="gemini-2.5-flash/answer_gen/stable",
        ),
        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,
        user_pseudo_id="user-pseudo-id",  # Optional: Add user pseudo-identifier for queries.
    )

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

    # Handle the response
    print(