adk-python/contributing/samples/bigtable

Bigtable Tools Sample

Introduction

This sample agent demonstrates the Bigtable first-party tools in ADK, distributed via the google.adk.tools.bigtable module. These tools include:

1. bigtable_list_instances

Fetches Bigtable instance ids in a Google Cloud project.

1. bigtable_get_instance_info

Fetches metadata information about a Bigtable instance.

1. bigtable_list_tables

Fetches table ids in a Bigtable instance.

1. bigtable_get_table_info

Fetches metadata information about a Bigtable table.

1. bigtable_execute_sql

Runs a DQL SQL query in Bigtable database.

How to use

Set up environment variables in your .env file for using Google AI Studio or Google Cloud Vertex AI for the LLM service for your agent. For example, for using Google AI Studio you would set:

• GOOGLE_GENAI_USE_VERTEXAI=FALSE
• GOOGLE_API_KEY={your api key}

With Application Default Credentials

This mode is useful for quick development when the agent builder is the only user interacting with the agent. The tools are run with these credentials.

1. Create application default credentials on the machine where the agent would be running by following https://cloud.google.com/docs/authentication/provide-credentials-adc.

2. Set CREDENTIALS_TYPE=None in agent.py

3. Run the agent

With Service Account Keys

This mode is useful for quick development when the agent builder wants to run the agent with service account credentials. The tools are run with these credentials.

1. Create service account key by following https://cloud.google.com/iam/docs/service-account-creds#user-managed-keys.

2. Set CREDENTIALS_TYPE=AuthCredentialTypes.SERVICE_ACCOUNT in agent.py

3. Download the key file and replace "service_account_key.json" with the path

4. Run the agent

With Interactive OAuth

1. Follow https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name. to get your client id and client secret. Be sure to choose "web" as your client type.

2. Follow https://developers.google.com/workspace/guides/configure-oauth-consent to add scope "https://www.googleapis.com/auth/bigtable.admin" and "https://www.googleapis.com/auth/bigtable.data" as a declaration, this is used for review purpose.

3. Follow https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred to add http://localhost/dev-ui/ to "Authorized redirect URIs".

   Note: localhost here is just a hostname that you use to access the dev ui, replace it with the actual hostname you use to access the dev ui.

4. For 1st run, allow popup for localhost in Chrome.

5. Configure your .env file to add two more variables before running the agent:

   • OAUTH_CLIENT_ID={your client id}
   • OAUTH_CLIENT_SECRET={your client secret}

   Note: don't create a separate .env, instead put it to the same .env file that stores your Vertex AI or Dev ML credentials

6. Set CREDENTIALS_TYPE=AuthCredentialTypes.OAUTH2 in agent.py and run the agent

Sample prompts

• Show me all instances in the my-project.
• Show me all tables in the my-instance instance in my-project.
• Describe the schema of the my-table table in the my-instance instance in my-project.
• Show me the first 10 rows of data from the my-table table in the my-instance instance in my-project.