无需设置环境变量即可追踪

正如其他指南中提到的，以下环境变量允许你配置启用追踪、API 端点、API 密钥和追踪项目

• LANGSMITH_TRACING
• LANGSMITH_API_KEY
• LANGSMITH_ENDPOINT
• LANGSMITH_PROJECT

在某些环境中，无法设置环境变量。在这些情况下，你可以通过编程方式设置追踪配置。

最近更改的行为

由于许多用户要求使用 `trace` 上下文管理器对追踪进行更细粒度的控制，我们更改了 `with trace` 的行为，以在 Python SDK 的 0.1.95 版本中遵循 `LANGSMITH_TRACING` 环境变量。你可以在发行说明中找到更多详细信息。在不设置环境变量的情况下禁用/启用追踪的推荐方法是使用 `with tracing_context` 上下文管理器，如下例所示。

Python

在 Python 中执行此操作的推荐方法是使用 `tracing_context` 上下文管理器。这适用于使用 `traceable` 注解的代码和 `trace` 上下文管理器中的代码。

import openai
from langsmith import Client, tracing_context, traceable
from langsmith.wrappers import wrap_openai

langsmith_client = Client(
  api_key="YOUR_LANGSMITH_API_KEY",  # This can be retrieved from a secrets manager
  api_url="https://api.smith.langchain.com",  # Update appropriately for self-hosted installations or the EU region
)

client = wrap_openai(openai.Client())

@traceable(run_type="tool", name="Retrieve Context")
def my_tool(question: str) -> str:
  return "During this morning's meeting, we solved all world conflict."

@traceable
def chat_pipeline(question: str):
  context = my_tool(question)
  messages = [
      { "role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
      { "role": "user", "content": f"Question: {question}
Context: {context}"}
  ]
  chat_completion = client.chat.completions.create(
      model="gpt-4o-mini", messages=messages
  )
  return chat_completion.choices[0].message.content

# Can set to False to disable tracing here without changing code structure
with tracing_context(enabled=True):
  # Use langsmith_extra to pass in a custom client
  chat_pipeline("Can you summarize this morning's meetings?", langsmith_extra={"client": langsmith_client})

TypeScript

在 TypeScript 中，你可以将客户端和 `tracingEnabled` 标志都传递给 `traceable` 装饰器。

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

const client = new Client({
    apiKey: "YOUR_API_KEY",  // This can be retrieved from a secrets manager
    apiUrl: "https://api.smith.langchain.com",  // Update appropriately for self-hosted installations or the EU region
});

const openai = wrapOpenAI(new OpenAI());

const tool = traceable((question: string) => {
    return "During this morning's meeting, we solved all world conflict.";
}, { name: "Retrieve Context", runType: "tool" });

const pipeline = traceable(
    async (question: string) => {
        const context = await tool(question);
        
        const completion = await openai.chat.completions.create({
            model: "gpt-4o-mini",
            messages: [
                { role: "system" as const, content: "You are a helpful assistant. Please respond to the user's request only based on the given context." },
                { role: "user" as const, content: `Question: ${question}\nContext: ${context}`}
            ]
        });
        
        return completion.choices[0].message.content;
    
    }, 
    { name: "Chat", client, tracingEnabled: true }
);

await pipeline("Can you summarize this morning's meetings?");

如果你更喜欢视频教程，请查看 LangSmith 课程简介中的“追踪的替代方法”视频。