autogen.logger.base_logger.BaseLogger

​

BaseLogger

BaseLogger()

Helper class that provides a standard way to create an ABC using inheritance.

​

Instance Methods

​

get_connection

get_connection(self) -> None | Connection

Return a connection to the logging database.

​

log_chat_completion

log_chat_completion(
    self,
    invocation_id: UUID,
    client_id: int,
    wrapper_id: int,
    source: str | Agent,
    request: dict[str, float | str | list[dict[str, str]]],
    response: str | ChatCompletion,
    is_cached: int,
    cost: float,
    start_time: str
) -> None

Log a chat completion to database.
In AG2, chat completions are somewhat complicated because they are handled by the autogen.oai.OpenAIWrapper class.
One invocation to create can lead to multiple underlying OpenAI calls, depending on the llm_config list used, and any errors or retries.

Parameters:

Name	Description
invocation_id	A unique identifier for the invocation to the OpenAIWrapper.create method call Type: uuid.UUID
client_id	A unique identifier for the underlying OpenAI client instance Type: int
wrapper_id	A unique identifier for the OpenAIWrapper instance Type: int
source	The source/creator of the event as a string name or an Agent instance Type: str | Agent
request	A dictionary representing the request or call to the OpenAI client endpoint Type: dict[str, float | str | list[dict[str, str]]]
response	The response from OpenAI Type: str | ChatCompletion
is_cached	1 if the response was a cache hit, 0 otherwise Type: int
cost	The cost for OpenAI response Type: float
start_time	A string representing the moment the request was initiated Type: str

​

log_event

log_event(
    self,
    source: str | Agent,
    name: str,
    **kwargs: dict[str, Any]
) -> None

Log an event for an agent.

Parameters:

Name	Description
source	The source/creator of the event as a string name or an Agent instance Type: str | Agent
name	The name of the event Type: str
**kwargs	Type: dict[str, Any]

​

log_function_use

log_function_use(
    self,
    source: str | Agent,
    function: F,
    args: dict[str, Any],
    returns: Any
) -> None

Log the use of a registered function (could be a tool)

Parameters:

Name	Description
source	The source/creator of the event as a string name or an Agent instance Type: str | Agent
function	The function information Type: F
args	The function args to log Type: dict[str, Any]
returns	The return Type: Any

​

log_new_agent

log_new_agent(
    self,
    agent: ConversableAgent,
    init_args: dict[str, Any]
) -> None

Log the birth of a new agent.

Parameters:

Name	Description
agent	The agent to log. Type: ConversableAgent
init_args	The arguments passed to the construct the conversable agent Type: dict[str, Any]

​

log_new_client

log_new_client(
    self,
    client: AzureOpenAI | OpenAI,
    wrapper: OpenAIWrapper,
    init_args: dict[str, Any]
) -> None

Log the birth of a new OpenAIWrapper.

Parameters:

Name	Description
client	The client to log. Type: AzureOpenAI | OpenAI
wrapper	The wrapper that created the client. Type: OpenAIWrapper
init_args	The arguments passed to the construct the client. Type: dict[str, Any]

​

log_new_wrapper

log_new_wrapper(
    self,
    wrapper: OpenAIWrapper,
    init_args: dict[str, LLMConfig | list[LLMConfig]]
) -> None

Log the birth of a new OpenAIWrapper.

Parameters:

Name	Description
wrapper	The wrapper to log. Type: OpenAIWrapper
init_args	The arguments passed to the construct the wrapper Type: dict[str, LLMConfig | list[LLMConfig]]

​

start

start(self) -> str

Open a connection to the logging database, and start recording.
Returns:
session_id (str): a unique id for the logging session

Returns:

Type	Description
str	session_id (str): a unique id for the logging session

​

stop

stop(self) -> None

Close the connection to the logging database, and stop logging.