🐪
Vertex AI Searchを試す
Cloud FunctionsでVertex AI Searchの動作確認を行いました。
Vertex AI Search
ベクトルデータストアには、サンプルでGCSに公開されているGoogleの決算書を登録します。
cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs
Cloud Functions
requirements.txt
functions-framework==3.*
google-api-core==2.14.0
google-cloud-discoveryengine==0.11.3
search.py
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_query = "YOUR_SEARCH_QUERY"
def search_sample(
project_id: str,
location: str,
data_store_id: str,
search_query: str,
) -> List[discoveryengine.SearchResponse]:
# 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.SearchServiceClient(client_options=client_options)
# The full resource name of the search engine serving config
# e.g. projects/{project_id}/locations/{location}/dataStores/{data_store_id}/servingConfigs/{serving_config_id}
serving_config = client.serving_config_path(
project=project_id,
location=location,
data_store=data_store_id,
serving_config="default_config",
)
# Optional: Configuration options for search
# Refer to the `ContentSearchSpec` reference for all supported fields:
# https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.types.SearchRequest.ContentSearchSpec
content_search_spec = discoveryengine.SearchRequest.ContentSearchSpec(
# For information about snippets, refer to:
# https://cloud.google.com/generative-ai-app-builder/docs/snippets
snippet_spec=discoveryengine.SearchRequest.ContentSearchSpec.SnippetSpec(
return_snippet=True
),
# For information about search summaries, refer to:
# https://cloud.google.com/generative-ai-app-builder/docs/get-search-summaries
summary_spec=discoveryengine.SearchRequest.ContentSearchSpec.SummarySpec(
summary_result_count=5,
include_citations=True,
ignore_adversarial_query=True,
ignore_non_summary_seeking_query=True,
),
)
# Refer to the `SearchRequest` reference for all supported fields:
# https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1.types.SearchRequest
request = discoveryengine.SearchRequest(
serving_config=serving_config,
query=search_query,
page_size=10,
content_search_spec=content_search_spec,
query_expansion_spec=discoveryengine.SearchRequest.QueryExpansionSpec(
condition=discoveryengine.SearchRequest.QueryExpansionSpec.Condition.AUTO,
),
spell_correction_spec=discoveryengine.SearchRequest.SpellCorrectionSpec(
mode=discoveryengine.SearchRequest.SpellCorrectionSpec.Mode.AUTO
),
)
response = client.search(request)
print(response)
return response
main.py
import functions_framework
from search import search_sample
@functions_framework.http
def hello_http(request):
search_sample(
project_id='xxxxx',
location='global',
data_store_id='test-data_xxxxxx',
search_query='google 2013'
)
return 'Hello !'
結果
search_query:'google 2013'からデータストア内の、2013Q4_google_earnings_releaseというタイトルのpdfがベクトル値が近いと判定されて、結果が返ってきました。
# responseの結果(長いので一部のみ)
SearchPager<results {
string_value: "2013Q4_google_earnings_release"
}
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