设置线程

推荐阅读

在深入阅读本文档之前，阅读以下内容可能会有所帮助

• 为追踪添加元数据和标签

许多 LLM 应用都有类似聊天机器人的界面，用户和 LLM 应用在其中进行多轮对话。为了跟踪这些对话，你可以使用 LangSmith 中的 Threads 功能。

将追踪分组到线程中

一个 Thread 是一系列代表单个对话的追踪。每个响应都表示为自己的追踪，但这些追踪通过属于同一线程而链接在一起。

要将追踪关联在一起，你需要传入一个特殊的元数据键，其值是该线程的唯一标识符。

键值是该对话的唯一标识符。键名应为以下之一

• session_id
• thread_id
• conversation_id.

该值可以是任何你想要的字符串，但我们建议使用 UUID，例如 f47ac10b-58cc-4372-a567-0e02b2c3d479。

代码示例

此示例演示了如何从 LangSmith 记录和检索对话历史记录，以维护长时间运行的聊天。

你可以通过多种方式在 LangSmith 中为你的追踪添加元数据，此代码将展示如何动态地执行此操作，但请阅读之前链接的指南以了解所有可以为你的追踪添加线程标识符元数据的方式。

Python

import openai
from langsmith import traceable
from langsmith import Client
import langsmith as ls
from langsmith.wrappers import wrap_openai

client = wrap_openai(openai.Client())
langsmith_client = Client()

# Config used for this example

langsmith_project = "project-with-threads"


session_id = "thread-id-1"


langsmith_extra={"project_name": langsmith_project, "metadata":{"session_id": session_id}}

# gets a history of all LLM calls in the thread to construct conversation history

def get_thread_history(thread_id: str, project_name: str): # Filter runs by the specific thread and project
filter_string = f'and(in(metadata_key, ["session_id","conversation_id","thread_id"]), eq(metadata_value, "{thread_id}"))' # Only grab the LLM runs
runs = [r for r in langsmith_client.list_runs(project_name=project_name, filter=filter_string, run_type="llm")]

  # Sort by start time to get the most recent interaction
  runs = sorted(runs, key=lambda run: run.start_time, reverse=True)
  # The current state of the conversation
  return runs[0].inputs['messages'] + [runs[0].outputs['choices'][0]['message']]

# if an existing conversation is continued, this function looks up the current run’s metadata to get the session_id, calls get_thread_history, and appends the new user question before making a call to the chat model

@traceable(name="Chat Bot")
def chat_pipeline(question: str, get_chat_history: bool = False): # Whether to continue an existing thread or start a new one
if get_chat_history:
run_tree = ls.get_current_run_tree()
messages = get_thread_history(run_tree.extra["metadata"]["session_id"],run_tree.session_name) + [{"role": "user", "content": question}]
else:
messages = [{"role": "user", "content": question}]

  # Invoke the model
  chat_completion = client.chat.completions.create(
      model="gpt-4o-mini", messages=messages
  )
  return chat_completion.choices[0].message.content

# Start the conversation

chat_pipeline("Hi, my name is Bob", langsmith_extra=langsmith_extra)

TypeScript

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

// Config used for this example
const langsmithProject = "project-with-threads";
const threadId = "thread-id-1";

const client = wrapOpenAI(new OpenAI(), {
project_name: langsmithProject,
metadata: { session_id: threadId }  
});
const langsmithClient = new Client();

async function getThreadHistory(threadId: string, projectName: string) {
// Filter runs by the specific thread and project
const filterString = `and(in(metadata_key, ["session_id","conversation_id","thread_id"]), eq(metadata_value, "${threadId}"))`;

// Only grab the LLM runs
const runs = langsmithClient.listRuns({
projectName: projectName,
filter: filterString,
runType: "llm"
});

// Sort by start time to get the most recent interaction
const runsArray = [];
for await (const run of runs) {
runsArray.push(run);
}
const sortedRuns = runsArray.sort((a, b) =>
new Date(b.start_time).getTime() - new Date(a.start_time).getTime()
);

// The current state of the conversation
return [
...sortedRuns[0].inputs.messages,
sortedRuns[0].outputs.choices[0].message
];
}

const chatPipeline = traceable(
async (
question: string,
options: {
getChatHistory?: boolean;
} = {}
) => {
const {
getChatHistory = false,
} = options;

  let messages = [];
  // Whether to continue an existing thread or start a new one
  if (getChatHistory) {
    const runTree = await getCurrentRunTree();
    const historicalMessages = await getThreadHistory(
      runTree.extra.metadata.session_id,
      runTree.project_name
    );
    messages = [
      ...historicalMessages,
      { role:"user", content: question }
    ];
  } else {
    messages = [{ role:"user", content: question }];
  }

  // Invoke the model
  const chatCompletion = await client.chat.completions.create({
    model: "gpt-4o-mini",
    messages: messages
  });
  return chatCompletion.choices[0].message.content;

},
{
name: "Chat Bot",
project_name: langsmithProject,
metadata: { session_id: threadId }  
}
);

// Start the conversation
await chatPipeline("Hi, my name is Bob");

等待几秒钟后，你可以进行以下调用以继续对话。通过传递 getChatHistory: true，你可以从上次中断的地方继续对话。这意味着 LLM 将接收整个消息历史记录并对其做出响应，而不仅仅是对最新消息做出响应。

Python

# Continue the conversation (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE FRIST TRACE CAN BE INGESTED)
chat_pipeline("What is my name?", get_chat_history=True, langsmith_extra=langsmith_extra)

# Keep the conversation going (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE PREVIOUS TRACE CAN BE INGESTED)

chat_pipeline("What was the first message I sent you", get_chat_history=True, langsmith_extra=langsmith_extra)

TypeScript

// Continue the conversation (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE FRIST TRACE CAN BE INGESTED)
await chatPipeline("What is my name?", { getChatHistory: true });

// Keep the conversation going (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE PREVIOUS TRACE CAN BE INGESTED)
await chatPipeline("What was the first message I sent you", { getChatHistory: true });

查看线程

你可以通过单击任何项目详细信息页面中的 Threads 选项卡来查看线程。然后你将看到所有线程的列表，按最近的活动排序。

然后你可以单击进入特定线程。这将打开特定线程的历史记录。如果你的线程格式化为聊天消息，你将看到一个类似聊天机器人的 UI，你可以在其中查看输入和输出的历史记录。

你可以通过分别单击 Annotate 和 Open trace 在侧面板中打开追踪或注释追踪。