Interaction logging export to BigQuery

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You can export interaction logging to BigQuery. Once configured, all live interaction logging is written to your BigQuery table. This provides you with advanced analysis tools that can help you debug and improve your agent and to discover patterns in conversation data.

Limitations

The following limitations apply:

  • The Dialogflow agent and the BigQuery dataset must be in the same project.

Table description

Each row of the table contains one conversational turn with the following columns:

Column Type Description
project_id STRING The project ID.
agent_id STRING The agent ID.
conversation_name STRING The fully qualified resource name for the session.
turn_position INTEGER The conversational turn number.
request_time TIMESTAMP The time of the conversational turn.
language_code STRING The language tag.
request JSON The detect intent request.
response JSON The detect intent response.
partial_responses JSON Partial responses if applicable.
derived_data JSON Additional metadata for this conversational turn.
conversation_signals JSON NLU related analytics data. See ConversationSignals for the JSON schema.

Configuration

To configure interaction logging export:

  1. Ensure that interaction logging is enabled.
  2. Follow the BigQuery dataset creation guide to create a dataset. Note the dataset name, as you will need this in the next step.
  3. Follow the BigQuery table creation guide to create a table with a SQL schema definition. Use the following SQL statement for creation:

    CREATE TABLE <your_dataset_name>.dialogflow_bigquery_export_data(
      project_id STRING,
      agent_id STRING,
      conversation_name STRING,
      turn_position INTEGER,
      request_time TIMESTAMP,
      language_code STRING,
      request JSON,
      response JSON,
      partial_responses JSON,
      derived_data JSON,
      conversation_signals JSON
    );
    
  4. Configure your agent settings to enable BigQuery export, and to provide the dataset and table names created above.