将追踪日志记录到特定项目

您可以通过环境变量静态地或在运行时动态地更改跟踪的目标项目。

设置目标项目为静态​

如追踪概念部分所述，LangSmith 使用Project的概念来对追踪进行分组。如果未指定，项目将设置为default。您可以设置LANGSMITH_PROJECT环境变量，为整个应用程序运行配置自定义项目名称。此操作应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，如果您使用的是较早版本，请改用 LANGCHAIN_PROJECT。

如果指定的项目不存在，它将在摄入第一个跟踪时自动创建。

动态设置目标项目​

您也可以根据您为追踪而标注代码的方式，在程序运行时以多种方式设置项目名称。当您希望将同一应用程序内的不同项目记录到不同的日志中时，这非常有用。

注意

使用以下任一方法动态设置项目名称将覆盖由 LANGSMITH_PROJECT 环境变量设置的名称。

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();