Provide capabilities of runtime logging for debugging and performance analysis.
AG2 offers utilities to log data for debugging and performance analysis. This notebook demonstrates how to use them.
we log data in different modes: - SQlite Database - File
In general, users can initiate logging by calling
autogen.runtime_logging.start()
and stop logging by calling
autogen.runtime_logging.stop()
import json
import pandas as pd
import autogen
from autogen import AssistantAgent, LLMConfig, UserProxyAgent
# Setup API key. Add your own API key to config file or environment variable
llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST", temperature=0.9)
# Start logging
logging_session_id = autogen.runtime_logging.start(config={"dbname": "logs.db"})
print("Logging session ID: " + str(logging_session_id))
# Create an agent workflow and run it
assistant = AssistantAgent(name="assistant", llm_config=llm_config)
user_proxy = UserProxyAgent(
name="user_proxy",
code_execution_config=False,
human_input_mode="NEVER",
is_termination_msg=lambda msg: "TERMINATE" in msg["content"],
)
user_proxy.initiate_chat(
assistant, message="What is the height of the Eiffel Tower? Only respond with the answer and terminate"
)
autogen.runtime_logging.stop()
logs.db
should be generated, by default it’s using SQLite database.
You can view the data with GUI tool like sqlitebrowser
, using SQLite
command line shell or using python script:
def get_log(dbname="logs.db", table="chat_completions"):
import sqlite3
con = sqlite3.connect(dbname)
query = f"SELECT * from {table}"
cursor = con.execute(query)
rows = cursor.fetchall()
column_names = [description[0] for description in cursor.description]
data = [dict(zip(column_names, row)) for row in rows]
con.close()
return data
def str_to_dict(s):
return json.loads(s)
log_data = get_log()
log_data_df = pd.DataFrame(log_data)
log_data_df["total_tokens"] = log_data_df.apply(
lambda row: str_to_dict(row["response"])["usage"]["total_tokens"], axis=1
)
log_data_df["request"] = log_data_df.apply(lambda row: str_to_dict(row["request"])["messages"][0]["content"], axis=1)
log_data_df["response"] = log_data_df.apply(
lambda row: str_to_dict(row["response"])["choices"][0]["message"]["content"], axis=1
)
log_data_df
One use case of logging data is to compute the cost of a session.
# Sum totoal tokens for all sessions
total_tokens = log_data_df["total_tokens"].sum()
# Sum total cost for all sessions
total_cost = log_data_df["cost"].sum()
# Total tokens for specific session
session_tokens = log_data_df[log_data_df["session_id"] == logging_session_id]["total_tokens"].sum()
session_cost = log_data_df[log_data_df["session_id"] == logging_session_id]["cost"].sum()
print("Total tokens for all sessions: " + str(total_tokens) + ", total cost: " + str(round(total_cost, 4)))
print(
"Total tokens for session "
+ str(logging_session_id)
+ ": "
+ str(session_tokens)
+ ", cost: "
+ str(round(session_cost, 4))
)
By default, the log type is set to sqlite
as shown above, but we
introduced a new parameter for the autogen.runtime_logging.start()
the logger_type = "file"
will start to log data in the File mode.
import pandas as pd
import autogen
from autogen import AssistantAgent, LLMConfig, UserProxyAgent
# Setup API key. Add your own API key to config file or environment variable
llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST", temperature=0.9)
# Start logging with logger_type and the filename to log to
logging_session_id = autogen.runtime_logging.start(logger_type="file", config={"filename": "runtime.log"})
print("Logging session ID: " + str(logging_session_id))
# Create an agent workflow and run it
assistant = AssistantAgent(name="assistant", llm_config=llm_config)
user_proxy = UserProxyAgent(
name="user_proxy",
code_execution_config=False,
human_input_mode="NEVER",
is_termination_msg=lambda msg: "TERMINATE" in msg["content"],
)
user_proxy.initiate_chat(
assistant, message="What is the height of the Eiffel Tower? Only respond with the answer and terminate"
)
autogen.runtime_logging.stop()
This should create a runtime.log
file in your current directory.