向跟踪添加元数据和标签

LangSmith 支持随跟踪发送任意元数据和标签。

标签是可用于分类或标记跟踪的字符串。元数据是键值对的字典，可用于存储有关跟踪的附加信息。

两者都可用于将额外信息与跟踪关联起来，例如执行跟踪的环境、启动跟踪的用户或内部关联 ID。有关标签和元数据的更多信息，请参阅概念页面。有关如何通过元数据和标签查询跟踪和运行的信息，请参阅在应用程序中筛选跟踪页面。

Python

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!"}
]

# You can set metadata & tags **statically** when decorating a function
# Use the @traceable decorator with tags and metadata
# Ensure that the LANGSMITH_TRACING environment variables are set for @traceable to work
@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-4o-mini"
) -> str:
  # You can also dynamically set metadata on the parent run:
  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,
  # To add at **invocation time**, when calling the function.
  # via the langsmith_extra parameter
  langsmith_extra={"tags": ["my-other-tag"], "metadata": {"my-other-key": "my-value"}}
)

# Alternatively, you can use the context manager
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-4o-mini",
          messages=messages,
      )
      rt.metadata["some-conditional-key"] = "some-val"
      rt.end(outputs={"output": chat_completion})


# You can use the same techniques with the wrapped client
patched_client = wrap_openai(
  client, tracing_extra={"metadata": {"my-key": "my-value"}, "tags": ["a-tag"]}
)
chat_completion = patched_client.chat.completions.create(
  model="gpt-4o-mini",
  messages=messages,
  langsmith_extra={
      "tags": ["my-other-tag"],
      "metadata": {"my-other-key": "my-value"},
  },
)

TypeScript

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-4o-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" },
}
);
// Call the traceable function
await traceableCallOpenAI(messages);