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
