为跟踪添加元数据和标签

import openai
import langsmith as ls
from langsmith.wrappers import wrap_openai

client = openai.Client()
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
]

    # 您可以在装饰函数时**静态**设置元数据和标签
    # 使用带有标签和元数据的 @traceable 装饰器
    # 确保设置了 LANGSMITH_TRACING 环境变量，以便 @traceable 正常工作
    @ls.traceable(
        run_type="llm",
        name="OpenAI Call Decorator",
        tags=["my-tag"],
        metadata={"my-key": "my-value"}
    )
    def call_openai(
        messages: list[dict], model: str = "gpt-5.4-mini"
    ) -> str:
        # 您也可以在父运行上动态设置元数据：
        rt = ls.get_current_run_tree()
        rt.metadata["some-conditional-key"] = "some-val"
        rt.tags.extend(["another-tag"])
        return client.chat.completions.create(
            model=model,
            messages=messages,
        ).choices[0].message.content

    call_openai(
        messages,
        # 在**调用时**添加，当调用函数时。
        # 通过 langsmith_extra 参数
        langsmith_extra={"tags": ["my-other-tag"], "metadata": {"my-other-key": "my-value"}}
    )

    # 或者您可以为给定范围内的运行动态设置默认元数据
    # tracing_context 本身不创建 span，但它会初始化
    # 为创建的子 span 初始化上下文。
    with ls.tracing_context(metadata={"default-key": "default-value"}):
        call_openai(messages)

    # 或者，您可以使用 trace 上下文管理器
    # 这会创建一个带有给定元数据和标签的新 span
    with ls.trace(
        name="OpenAI Call Trace",
        run_type="llm",
        inputs={"messages": messages},
        tags=["my-tag"],
        metadata={"my-key": "my-value"},
    ) as rt:
        chat_completion = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
        )
        rt.metadata["some-conditional-key"] = "some-val"
        rt.end(outputs={"output": chat_completion})

# 您可以对包装后的客户端使用相同的技术
patched_client = wrap_openai(
    client, tracing_extra={"metadata": {"my-key": "my-value"}, "tags": ["a-tag"]}
)
chat_completion = patched_client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=messages,
    langsmith_extra={
        "tags": ["my-other-tag"],
        "metadata": {"my-other-key": "my-value"},
    },
)

import OpenAI from "openai";
import { traceable, getCurrentRunTree } from "langsmith/traceable";
import { wrapOpenAI } from "langsmith/wrappers";

    const client = wrapOpenAI(new OpenAI());
    const messages: OpenAI.Chat.ChatCompletionMessageParam[] = [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "Hello!" },
    ];

    const traceableCallOpenAI = traceable(
        async (messages: OpenAI.Chat.ChatCompletionMessageParam[]) => {
            const completion = await client.chat.completions.create({
                model: "gpt-5.4-mini",
                messages,
            });
            const runTree = getCurrentRunTree();
            runTree.extra.metadata = {
                ...runTree.extra.metadata,
                someKey: "someValue",
            };
            runTree.tags = [...(runTree.tags ?? []), "runtime-tag"];
            return completion.choices[0].message.content;
        },
        {
            run_type: "llm",
            name: "OpenAI Call Traceable",
            tags: ["my-tag"],
            metadata: { "my-key": "my-value" },
        }
    );

// 调用可跟踪函数
await traceableCallOpenAI(messages);