无需设置环境变量即可跟踪

如其他指南中所述，以下环境变量可用于配置是否启用追踪、API 端点、API 密钥以及追踪项目：

• LANGSMITH_TRACING
• LANGSMITH_API_KEY
• LANGSMITH_ENDPOINT
• LANGSMITH_PROJECT

在某些环境中，无法设置环境变量。在这种情况下，您可以通过编程方式设置跟踪配置。

最近更改的行为

由于许多用户要求对使用 trace 上下文管理器的追踪功能进行更精细的控制， 我们更改了其行为：自 Python SDK 版本 0.1.95 起，with trace 将遵循 LANGSMITH_TRACING 环境变量。更多详情请参阅 发布说明。 推荐的在不设置环境变量的情况下启用/禁用追踪的方法是使用 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式是使用 tracing_context 上下文管理器。该方法既适用于使用 traceable 注解的代码，也适用于位于 trace 上下文管理器内的代码。

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，您可以将客户端和 tracingEnabled 标志一同传递给 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?");

如果您更喜欢视频教程，请观看《LangSmith 入门课程》中的追踪的其他方法视频。