monitoring_db#

class baf.db.monitoring_db.MonitoringDB[source]#

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()[source]#: Close the connection to the monitoring database

connect_to_db(agent)[source]#

Connect to the monitoring database.

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

delete_session(session)[source]#

Deletes the session information, chat messages, and transitions related to the given session from the monitoring database.

Parameters:: session (Session) – The session to delete.

get_last_state_of_session(agent_name, platform_name, session_id)[source]#

Retrieves the last dest_state for a given session from the transition table.

Parameters:

agent_name (str) – The agent name.
platform_name (str) – The platform name.
session_id (str) – The session ID.

Returns:

The last dest_state value, or None if not found.

Return type:

str | None

get_table(table_name)[source]#

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()[source]#: Initialize the monitoring database, creating the tables if necessary.

insert_chat(session, message)[source]#

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)[source]#

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)[source]#

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)[source]#

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)[source]#

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

load_session_variables(session)[source]#

Loads the session variables from the monitoring database, transforms the JSON string into a dictionary, and sets each key-value pair in the session using session.set(key, value).

Parameters:: session (Session) – The session whose variables should be loaded.

run_statement(stmt)[source]#

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=None, until_timestamp=None)[source]#

Retrieves chat records from the chat table of the database for a given session.

Parameters:

session (Session) – the session to get chat records from the database
n (Optional[int]) – the number of latest chat records to retrieve. If None, retrieves all records.
until_timestamp (Optional[datetime]) – if provided, retrieves only chat records up to this timestamp.

Returns:

the chat records for the given session

Return type:

pandas.DataFrame

select_session(session)[source]#

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

session_exists(agent_name, platform_name, session_id)[source]#

Checks whether there is an entry with the given agent_name, platform_name, and session_id in the sessions table.

Parameters:

agent_name (str) – The agent name to check.
platform_name (str) – The platform name to check.
session_id (str) – The session ID to check.

Returns:

True if the session exists, False otherwise.

Return type:

bool

store_session_variables(session)[source]#

Stores the current session variables (dictionary) as a JSON string in the monitoring database, replacing the old value for the given session.

Parameters:: session (Session) – The session whose variables should be stored.

baf.db.monitoring_db.TABLE_CHAT = 'chat'#: The name of the database table that contains the chat records

baf.db.monitoring_db.TABLE_EVENT = 'event'#: The name of the database table that contains the event records

baf.db.monitoring_db.TABLE_INTENT_PREDICTION = 'intent_prediction'#: The name of the database table that contains the intent prediction records

baf.db.monitoring_db.TABLE_PARAMETER = 'parameter'#: The name of the database table that contains the parameter records

baf.db.monitoring_db.TABLE_SESSION = 'session'#: The name of the database table that contains the session records

baf.db.monitoring_db.TABLE_TRANSITION = 'transition'#: The name of the database table that contains the transition records