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Google Team MemberandCopybara-Service
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This change accepts the `google.auth.credentials.Credentials` type for `BigQueryCredentialsConfig`, so any subclass of that, including `google.oauth2.credentials.Credentials` would work to integrate with BigQuery service. This opens up a whole range of possibilities, such as using service account credentials to deploy an agent using these tools. PiperOrigin-RevId: 773190440
99 lines
3.2 KiB
Markdown
99 lines
3.2 KiB
Markdown
# BigQuery Tools Sample
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## Introduction
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This sample agent demonstrates the BigQuery first-party tools in ADK,
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distributed via the `google.adk.tools.bigquery` module. These tools include:
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1. `list_dataset_ids`
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Fetches BigQuery dataset ids present in a GCP project.
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1. `get_dataset_info`
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Fetches metadata about a BigQuery dataset.
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1. `list_table_ids`
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Fetches table ids present in a BigQuery dataset.
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1. `get_table_info`
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Fetches metadata about a BigQuery table.
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1. `execute_sql`
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Runs a SQL query in BigQuery.
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## How to use
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Set up environment variables in your `.env` file for using
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[Google AI Studio](https://google.github.io/adk-docs/get-started/quickstart/#gemini---google-ai-studio)
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or
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[Google Cloud Vertex AI](https://google.github.io/adk-docs/get-started/quickstart/#gemini---google-cloud-vertex-ai)
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for the LLM service for your agent. For example, for using Google AI Studio you
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would set:
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* GOOGLE_GENAI_USE_VERTEXAI=FALSE
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* GOOGLE_API_KEY={your api key}
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### With Application Default Credentials
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This mode is useful for quick development when the agent builder is the only
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user interacting with the agent. The tools are run with these credentials.
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1. Create application default credentials on the machine where the agent would
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be running by following https://cloud.google.com/docs/authentication/provide-credentials-adc.
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1. Set `CREDENTIALS_TYPE=None` in `agent.py`
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1. Run the agent
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### With Service Account Keys
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This mode is useful for quick development when the agent builder wants to run
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the agent with service account credentials. The tools are run with these
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credentials.
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1. Create service account key by following https://cloud.google.com/iam/docs/service-account-creds#user-managed-keys.
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1. Set `CREDENTIALS_TYPE=AuthCredentialTypes.SERVICE_ACCOUNT` in `agent.py`
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1. Download the key file and replace `"service_account_key.json"` with the path
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1. Run the agent
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### With Interactive OAuth
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1. Follow
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https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name.
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to get your client id and client secret. Be sure to choose "web" as your client
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type.
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1. Follow https://developers.google.com/workspace/guides/configure-oauth-consent to add scope "https://www.googleapis.com/auth/bigquery".
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1. Follow https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred to add http://localhost/dev-ui/ to "Authorized redirect URIs".
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Note: localhost here is just a hostname that you use to access the dev ui,
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replace it with the actual hostname you use to access the dev ui.
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1. For 1st run, allow popup for localhost in Chrome.
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1. Configure your `.env` file to add two more variables before running the agent:
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* OAUTH_CLIENT_ID={your client id}
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* OAUTH_CLIENT_SECRET={your client secret}
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Note: don't create a separate .env, instead put it to the same .env file that
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stores your Vertex AI or Dev ML credentials
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1. Set `CREDENTIALS_TYPE=AuthCredentialTypes.OAUTH2` in `agent.py` and run the agent
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## Sample prompts
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* which weather datasets exist in bigquery public data?
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* tell me more about noaa_lightning
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* which tables exist in the ml_datasets dataset?
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* show more details about the penguins table
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* compute penguins population per island.
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