Buscar en un almacén de datos con peticiones de seguimiento

Realizar una búsqueda multirrespuesta o conversacional en un almacén de datos.

Investigar más

Para obtener documentación detallada que incluya este código de muestra, consulta lo siguiente:

• Buscar con tareas pendientes o comentarios adicionales

Código de ejemplo

Python

Para obtener más información, consulta la documentación de referencia de la API AI Applications Python.

Para autenticarte en las aplicaciones de IA, configura las credenciales predeterminadas de la aplicación. Para obtener más información, consulta el artículo Configurar la autenticación en un entorno de desarrollo local.

from typing import List

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"
# data_store_id = "YOUR_DATA_STORE_ID"
# search_queries = ["YOUR_FIRST_SEARCH_QUERY", "YOUR_SECOND_SEARCH_QUERY"]


def multi_turn_search_sample(
    project_id: str,
    location: str,
    data_store_id: str,
    search_queries: List[str],
) -> List[discoveryengine.ConverseConversationResponse]:
    #  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
    )

    # Initialize Multi-Turn Session
    conversation = client.create_conversation(
        # The full resource name of the data store
        # e.g. projects/{project_id}/locations/{location}/dataStores/{data_store_id}
        parent=client.data_store_path(
            project=project_id, location=location, data_store=data_store_id
        ),
        conversation=discoveryengine.Conversation(),
    )


    for search_query in search_queries:
        # Add new message to session
        request = discoveryengine.ConverseConversationRequest(
            name=conversation.name,
            query=discoveryengine.TextInput(input=search_query),
            serving_config=client.serving_config_path(
                project=project_id,
                location=location,
                data_store=data_store_id,
                serving_config="default_config",
            ),
            # Options for the returned summary
            summary_spec=discoveryengine.SearchRequest.ContentSearchSpec.SummarySpec(
                # Number of results to include in summary
                summary_result_count=3,
                include_citations=True,
            ),
        )
        response = client.converse_conversation(request)

        print(f"Reply: {response.reply.summary.summary_text}\n")

        for i, result in enumerate(response.search_results, 1):
            result_data = result.document.derived_struct_data
            print(f"[{i}]")
            print(f"Link: {result_data['link']}")
            print(f"First Snippet: {result_data['snippets'][0]['snippet']}")
            print(
                "First Extractive Answer: \n"
                f"\tPage: {result_data['extractive_answers'][0]['pageNumber']}\n"
                f"\tContent: {result_data['extractive_answers'][0]['content']}\n\n"
            )
        print("\n\n")

Siguientes pasos

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