monitoring_db

class besser.agent.db.monitoring_db.MonitoringDB

Bases: object

This class is an interface to connect to a database where user interactions with the agent are stored to monitor the agent for later analysis.

conn

The connection to the monitoring database

Type:

sqlalchemy.Connection

connected

Whether there is an active connection to the monitoring database or not

Type:

bool

close_connection()

Close the connection to the monitoring database

connect_to_db(agent)

Connect to the monitoring database.

Parameters:

agent (Agent) – The agent that contains the database-related properties.

get_table(table_name)

Gets all the content of a database table (i.e., SELECT * FROM table_name).

Parameters:

table_name – the name of the table

Returns:

the table in a dataframe

Return type:

pandas.DataFrame

initialize_db()

Initialize the monitoring database, creating the tables if necessary.

insert_chat(session, message)

Insert a new record into the chat table of the monitoring database.

Parameters:

• session (Session) – the session the transition belongs to

• message (Message) – the message to insert into the database

insert_event(session, event)

Insert a new record into the event table of the monitoring database.

Parameters:

• session (Session or None) – the session the event belongs to, or None if the event is not associated to a session

• event (Event) – the event to insert into the database

insert_intent_prediction(session, state, predicted_intent)

Insert a new intent prediction record into the intent predictions table of the monitoring database.

Parameters:

• session (Session) – the session containing the predicted intent to insert into the database

• state (State) – the state where the intent prediction took place (the session’s current state may have changed since the intent prediction, so we need it as argument)

• predicted_intent (IntentClassifierPrediction) – the intent prediction

insert_session(session)

Insert a new session record into the sessions table of the monitoring database.

Parameters:

session (Session) – the session to insert into the database

insert_transition(session, transition)

Insert a new transition record into the transitions table of the monitoring database.

Parameters:

• session (Session) – the session the transition belongs to

• transition (Transition) – the transition to insert into the database

run_statement(stmt)

Executes a SQL statement.

Parameters:

stmt (sqlalchemy.Executable) – the SQL statement

Returns:

the result of the SQL statement

Return type:

sqlalchemy.CursorResult[Any] | None

select_chat(session, n)

Retrieves a conversation history from the chat table of the database.

Parameters:

• session (Session) – the session to get from the database

• n (int or None) – the number of messages to get (from the most recents). If none is provided, gets all the messages

Returns:

the session record, should be a 1 row DataFrame

Return type:

pandas.DataFrame

select_session(session)

Retrieves a session record from the sessions table of the database.

Parameters:

session (Session) – the session to get from the database

Returns:

the session record, should be a 1 row DataFrame

Return type:

pandas.DataFrame

besser.agent.db.monitoring_db.TABLE_CHAT = 'chat'

The name of the database table that contains the chat records

besser.agent.db.monitoring_db.TABLE_EVENT = 'event'

The name of the database table that contains the event records

besser.agent.db.monitoring_db.TABLE_INTENT_PREDICTION = 'intent_prediction'

The name of the database table that contains the intent prediction records

besser.agent.db.monitoring_db.TABLE_PARAMETER = 'parameter'

The name of the database table that contains the parameter records

besser.agent.db.monitoring_db.TABLE_SESSION = 'session'

The name of the database table that contains the session records

besser.agent.db.monitoring_db.TABLE_TRANSITION = 'transition'

The name of the database table that contains the transition records