get_current_run_tree/getCurrentRunTree 函数来访问当前运行。
有关 RunTree 对象可用属性的完整列表，请参阅此参考文档。
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 Run Id: {run.id}")
print(f"format_prompt Trace Id: {run.trace_id}")
print(f"format_prompt Parent Run 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 Run Id: {run.id}")
print(f"invoke_llm Trace Id: {run.trace_id}")
print(f"invoke_llm Parent Run Id: {run.parent_run.id}")
return openai.chat.completions.create(
messages=messages, model="gpt-4.1-mini", temperature=0
)
@traceable
def parse_output(response):
run = get_current_run_tree()
print(f"parse_output Run Id: {run.id}")
print(f"parse_output Trace Id: {run.trace_id}")
print(f"parse_output Parent Run Id: {run.parent_run.id}")
return response.choices[0].message.content
@traceable
def run_pipeline():
run = get_current_run_tree()
print(f"run_pipeline Run Id: {run.id}")
print(f"run_pipeline Trace Id: {run.trace_id}")
messages = format_prompt("colorful socks")
response = invoke_llm(messages)
return parse_output(response)
run_pipeline()
import { traceable, getCurrentRunTree } from "langsmith/traceable";
import OpenAI from "openai";
const openai = new OpenAI();
const formatPrompt = traceable((subject: string) => {
const run = getCurrentRunTree();
console.log("formatPrompt Run ID", run.id)
console.log("formatPrompt Trace ID", run.trace_id)
console.log("formatPrompt Parent Run ID", run.parent_run.id)
return [
{
role: "system" as const,
content: "You are a helpful assistant.",
},
{
role: "user" as const,
content: `What's a good name for a store that sells ${subject}?`,
},
];
}, { name: "formatPrompt" });
const invokeLLM = traceable(
async (messages: { role: string; content: string }[]) => {
const run = getCurrentRunTree();
console.log("invokeLLM Run ID", run.id)
console.log("invokeLLM Trace ID", run.trace_id)
console.log("invokeLLM Parent Run ID", run.parent_run.id)
return openai.chat.completions.create({
model: "gpt-4.1-mini",
messages: messages,
temperature: 0,
});
},
{ run_type: "llm", name: "invokeLLM" }
);
const parseOutput = traceable(
(response: any) => {
const run = getCurrentRunTree();
console.log("parseOutput Run ID", run.id)
console.log("parseOutput Trace ID", run.trace_id)
console.log("parseOutput Parent Run ID", run.parent_run.id)
return response.choices[0].message.content;
},
{ name: "parseOutput" }
);
const runPipeline = traceable(
async () => {
const run = getCurrentRunTree();
console.log("runPipline Run ID", run.id)
console.log("runPipline Trace ID", run.trace_id)
console.log("runPipline Parent Run ID", run.parent_run?.id)
const messages = await formatPrompt("colorful socks");
const response = await invokeLLM(messages);
return parseOutput(response);
},
{ name: "runPipeline" }
);
await runPipeline();
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