在被追踪的函数中访问当前运行（跨度）

from langsmith import traceable
from langsmith.run_helpers import get_current_run_tree
from openai import Client

    openai = Client()

    @traceable
    def format_prompt(subject):
        run = get_current_run_tree()
        print(f"format_prompt 运行 ID: {run.id}")
        print(f"format_prompt 追踪 ID: {run.trace_id}")
        print(f"format_prompt 父运行 ID: {run.parent_run.id}")
        return [
            {
                "role": "system",
                "content": "You are a helpful assistant.",
            },
            {
                "role": "user",
                "content": f"What's a good name for a store that sells {subject}?"
            }
        ]

    @traceable(run_type="llm")
    def invoke_llm(messages):
        run = get_current_run_tree()
        print(f"invoke_llm 运行 ID: {run.id}")
        print(f"invoke_llm 追踪 ID: {run.trace_id}")
        print(f"invoke_llm 父运行 ID: {run.parent_run.id}")
        return openai.chat.completions.create(
            messages=messages, model="gpt-5.4-mini", temperature=0
        )

    @traceable
    def parse_output(response):
        run = get_current_run_tree()
        print(f"parse_output 运行 ID: {run.id}")
        print(f"parse_output 追踪 ID: {run.trace_id}")
        print(f"parse_output 父运行 ID: {run.parent_run.id}")
        return response.choices[0].message.content

    @traceable
    def run_pipeline():
        run = get_current_run_tree()
        print(f"run_pipeline 运行 ID: {run.id}")
        print(f"run_pipeline 追踪 ID: {run.trace_id}")
        messages = format_prompt("colorful socks")
        response = invoke_llm(messages)
        return parse_output(response)

run_pipeline()