MLflow Tracing 소개

import os
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
import mlflow

assert "OPENAI_API_KEY" in os.environ, "Please set your OPENAI_API_KEY environment variable."

mlflow.set_experiment("LangChain Tracing")
mlflow.langchain.autolog(log_models=True, log_input_examples=True)

llm = OpenAI(temperature=0.7, max_tokens=1000)

prompt_template = "Imagine you are {person}, and you are answering a question: {question}"

chain = prompt_template | llm

chain.invoke({"person": "Richard Feynman", "question": "Why should we colonize Mars?"})
chain.invoke({"person": "Linus Torvalds", "question": "Can I set everyone's access to sudo?"})

import mlflow

mlflow.set_experiment("Tracing Demo")

@mlflow.trace
def some_function(x, y, z=2):
    return x + (y - z)

some_function(2, 4)

import mlflow

@mlflow.trace
def first_func(x, y=2):
    return x + y

@mlflow.trace
def second_func(a, b=3):
    return a * b

def do_math(a, x, operation="add"):
    with mlflow.start_span(name="Math") as span:
        span.set_inputs({"a": a, "x": x})
        span.set_attributes({"mode": operation})
        first = first_func(x)
        second = second_func(a)
        result = first + second if operation == "add" else first - second
        span.set_outputs({"result": result})
        return result

do_math(8, 3, "add")

from mlflow import MlflowClient

client = MlflowClient()

# 새 트레이스 시작
root_span = client.start_trace("my_trace")
request_id = root_span.request_id

# 자식 스팬 생성
child_span = client.start_span(
    name="child_span",
    request_id=request_id,
    parent_id=root_span.span_id,
    inputs={"input_key": "input_value"},
    attributes={"attribute_key": "attribute_value"},
)

# 자식 스팬 종료
client.end_span(
    request_id=child_span.request_id,
    span_id=child_span.span_id,
    outputs={"output_key": "output_value"},
    attributes={"custom_attribute": "value"},
)

# 루트 스팬(트레이스) 종료
client.end_trace(
    request_id=request_id,
    outputs={"final_output_key": "final_output_value"},
    attributes={"token_usage": "1174"},
)