将追踪日志记录到特定项目

你可以通过环境变量静态地以及在运行时动态地更改追踪的目标项目。

静态设置目标项目

正如 追踪概念 部分所述，LangSmith 使用 项目 的概念来分组追踪。如果未指定，项目将设置为 default。你可以设置 LANGSMITH_PROJECT 环境变量来为整个应用程序运行配置自定义项目名称。这应该在执行应用程序之前完成。

export LANGSMITH_PROJECT=my-custom-project

JS 中的 SDK 兼容性

LANGSMITH_PROJECT 标志仅在 JS SDK 版本 >= 0.2.16 中受支持，如果你使用的是旧版本，请使用 LANGCHAIN_PROJECT 代替。

如果指定的项目不存在，则在首次摄取追踪时会自动创建。

动态设置目标项目

你还可以在程序运行时以各种方式设置项目名称，具体取决于你如何 为追踪代码添加注解。当你想要将追踪日志记录到同一应用程序中的不同项目时，这非常有用。

注意

使用以下方法之一动态设置项目名称将覆盖由 LANGSMITH_PROJECT 环境变量设置的项目名称。

Python

import openai
from langsmith import traceable
from langsmith.run_trees import RunTree

client = openai.Client()

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

# Use the @traceable decorator with the 'project_name' parameter to log traces to LangSmith
# Ensure that the LANGSMITH_TRACING environment variables is set for @traceable to work
@traceable(
  run_type="llm",
  name="OpenAI Call Decorator",
  project_name="My Project"
)
def call_openai(
  messages: list[dict], model: str = "gpt-4o-mini"
) -> str:
  return client.chat.completions.create(
      model=model,
      messages=messages,
  ).choices[0].message.content

# Call the decorated function
call_openai(messages)

# You can also specify the Project via the project_name parameter
# This will override the project_name specified in the @traceable decorator
call_openai(
  messages,
  langsmith_extra={"project_name": "My Overridden Project"},
)

# The wrapped OpenAI client accepts all the same langsmith_extra parameters
# as @traceable decorated functions, and logs traces to LangSmith automatically.
# Ensure that the LANGSMITH_TRACING environment variables is set for the wrapper to work.
from langsmith import wrappers
wrapped_client = wrappers.wrap_openai(client)
wrapped_client.chat.completions.create(
  model="gpt-4o-mini",
  messages=messages,
  langsmith_extra={"project_name": "My Project"},
)


# Alternatively, create a RunTree object
# You can set the project name using the project_name parameter
rt = RunTree(
  run_type="llm",
  name="OpenAI Call RunTree",
  inputs={"messages": messages},
  project_name="My Project"
)
chat_completion = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=messages,
)
# End and submit the run
rt.end(outputs=chat_completion)
rt.post()

TypeScript

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

const client = new OpenAI();

const messages = [
  {role: "system", content: "You are a helpful assistant."},
  {role: "user", content: "Hello!"}
];

const traceableCallOpenAI = traceable(async (messages: {role: string, content: string}[], model: string) => {
  const completion = await client.chat.completions.create({
      model: model,
      messages: messages,
  });
  return completion.choices[0].message.content;
},{
  run_type: "llm",
  name: "OpenAI Call Traceable",
  project_name: "My Project"
});
// Call the traceable function
await traceableCallOpenAI(messages, "gpt-4o-mini");

// Create and use a RunTree object
const rt = new RunTree({
  runType: "llm",
  name: "OpenAI Call RunTree",
  inputs: { messages },
  project_name: "My Project"
});
await rt.postRun();

// Execute a chat completion and handle it within RunTree
rt.end({outputs: chatCompletion});
await rt.patchRun();