1. Audit Logging Large Language Model Inferences in BigQuery


    To audit log large language model inferences in BigQuery, you'll want to capture data related to each inference request and store it. The data could include details such as the timestamp of the request, the input provided, the output generated, and possibly other metadata like user identification or session information.

    In Pulumi, you could achieve this by creating a BigQuery dataset and a table within that dataset where logs will be stored. Additionally, you could set up IAM policies to control access to the dataset and table if necessary. You'll typically want to work with high-level constructs wherever they are available and default to Google Cloud provider (pulumi_gcp) classes for resources.

    Here's how you can set up the BigQuery resources using Pulumi with Python:

    1. Import the necessary Google Cloud provider classes.
    2. Create a BigQuery dataset.
    3. Create a BigQuery table with a schema that matches the log data structure you want to track.
    4. (Optional) Set up IAM permissions to control access to the table.

    The following Pulumi program accomplishes this task:

    import pulumi import pulumi_gcp as gcp # Define the BigQuery dataset # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/bigquery/dataset/ dataset = gcp.bigquery.Dataset("logging_dataset", dataset_id="language_model_logging", location="US", description="Dataset to store logs of large language model inferences") # Define the schema of the logging table LOGGING_TABLE_SCHEMA = [ {"name": "timestamp", "type": "TIMESTAMP", "mode": "REQUIRED"}, {"name": "input_text", "type": "STRING", "mode": "REQUIRED"}, {"name": "output_text", "type": "STRING", "mode": "REQUIRED"}, {"name": "user_id", "type": "STRING", "mode": "NULLABLE"}, {"name": "session_id", "type": "STRING", "mode": "NULLABLE"}, # Add more fields as necessary ] # Define the BigQuery table where inference logs will be stored # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/bigquery/table/ logging_table = gcp.bigquery.Table("logging_table", dataset_id=dataset.dataset_id, table_id="inference_logs", schema=LOGGING_TABLE_SCHEMA, deletion_protection=False) # Export the dataset and table ID pulumi.export("dataset_id", dataset.dataset_id) pulumi.export("table_id", logging_table.table_id)

    What we have done in the code above:

    • We've created a dataset with the gcp.bigquery.Dataset class which is the container for our tables.
    • We've created a table with the gcp.bigquery.Table class with a specific schema that matches our logging needs.
      • The schema for our table is defined using a list of dictionaries, where each dictionary contains a field name, type, and mode (whether the field is required or nullable).
    • Finally, we exported the IDs for the dataset and the table which can be used as references in other parts of our infrastructure if needed.

    Keep in mind that in a real-world scenario, you might need to set up more configurations, such as IAM policies for access management, streaming settings for incoming logs, or partitioning and clustering settings for optimizing query performance.

    This is a fundamental setup for audit logging, and you can expand upon this template to include more sophisticated features like data retention policies, more complex IAM configurations, or separate tables for different types of logs.