You've already forked adk-python
mirror of
https://github.com/encounter/adk-python.git
synced 2026-03-30 10:57:20 -07:00
George Weale
2367901ec5
Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 858763407
114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
# Copyright 2026 Google LLC
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
|
|
from google.adk.agents.llm_agent import LlmAgent
|
|
from google.adk.auth.auth_credential import AuthCredentialTypes
|
|
from google.adk.tools.spanner.settings import Capabilities
|
|
from google.adk.tools.spanner.settings import SpannerToolSettings
|
|
from google.adk.tools.spanner.settings import SpannerVectorStoreSettings
|
|
from google.adk.tools.spanner.spanner_credentials import SpannerCredentialsConfig
|
|
from google.adk.tools.spanner.spanner_toolset import SpannerToolset
|
|
import google.auth
|
|
|
|
# Define an appropriate credential type
|
|
# Set to None to use the application default credentials (ADC) for a quick
|
|
# development.
|
|
CREDENTIALS_TYPE = None
|
|
|
|
|
|
if CREDENTIALS_TYPE == AuthCredentialTypes.OAUTH2:
|
|
# Initialize the tools to do interactive OAuth
|
|
# The environment variables OAUTH_CLIENT_ID and OAUTH_CLIENT_SECRET
|
|
# must be set
|
|
credentials_config = SpannerCredentialsConfig(
|
|
client_id=os.getenv("OAUTH_CLIENT_ID"),
|
|
client_secret=os.getenv("OAUTH_CLIENT_SECRET"),
|
|
scopes=[
|
|
"https://www.googleapis.com/auth/spanner.admin",
|
|
"https://www.googleapis.com/auth/spanner.data",
|
|
],
|
|
)
|
|
elif CREDENTIALS_TYPE == AuthCredentialTypes.SERVICE_ACCOUNT:
|
|
# Initialize the tools to use the credentials in the service account key.
|
|
# If this flow is enabled, make sure to replace the file path with your own
|
|
# service account key file
|
|
# https://cloud.google.com/iam/docs/service-account-creds#user-managed-keys
|
|
creds, _ = google.auth.load_credentials_from_file("service_account_key.json")
|
|
credentials_config = SpannerCredentialsConfig(credentials=creds)
|
|
else:
|
|
# Initialize the tools to use the application default credentials.
|
|
# https://cloud.google.com/docs/authentication/provide-credentials-adc
|
|
application_default_credentials, _ = google.auth.default()
|
|
credentials_config = SpannerCredentialsConfig(
|
|
credentials=application_default_credentials
|
|
)
|
|
|
|
# Follow the instructions in README.md to set up the example Spanner database.
|
|
# Replace the following settings with your specific Spanner database.
|
|
|
|
# Define Spanner vector store settings.
|
|
vector_store_settings = SpannerVectorStoreSettings(
|
|
project_id="<PROJECT_ID>",
|
|
instance_id="<INSTANCE_ID>",
|
|
database_id="<DATABASE_ID>",
|
|
table_name="products",
|
|
content_column="productDescription",
|
|
embedding_column="productDescriptionEmbedding",
|
|
vector_length=768,
|
|
vertex_ai_embedding_model_name="text-embedding-005",
|
|
selected_columns=[
|
|
"productId",
|
|
"productName",
|
|
"productDescription",
|
|
],
|
|
nearest_neighbors_algorithm="EXACT_NEAREST_NEIGHBORS",
|
|
top_k=3,
|
|
distance_type="COSINE",
|
|
additional_filter="inventoryCount > 0",
|
|
)
|
|
|
|
# Define Spanner tool config with the vector store settings.
|
|
tool_settings = SpannerToolSettings(
|
|
capabilities=[Capabilities.DATA_READ],
|
|
vector_store_settings=vector_store_settings,
|
|
)
|
|
|
|
# Get the Spanner toolset with the Spanner tool settings and credentials config.
|
|
# Filter the tools to only include the `vector_store_similarity_search` tool.
|
|
spanner_toolset = SpannerToolset(
|
|
credentials_config=credentials_config,
|
|
spanner_tool_settings=tool_settings,
|
|
# Comment to include all allowed tools.
|
|
tool_filter=["vector_store_similarity_search"],
|
|
)
|
|
|
|
|
|
root_agent = LlmAgent(
|
|
model="gemini-2.5-flash",
|
|
name="spanner_knowledge_base_agent",
|
|
description=(
|
|
"Agent to answer questions about product-specific recommendations."
|
|
),
|
|
instruction="""
|
|
You are a helpful assistant that answers user questions about product-specific recommendations.
|
|
1. Always use the `vector_store_similarity_search` tool to find information.
|
|
2. Directly present all the information results from the `vector_store_similarity_search` tool naturally and well formatted in your response.
|
|
3. If no information result is returned by the `vector_store_similarity_search` tool, say you don't know.
|
|
""",
|
|
# Use the Spanner toolset for vector similarity search.
|
|
tools=[spanner_toolset],
|
|
)
|